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Ovarian Volume throughout Life: A Validated Normative Model


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The measurement of ovarian volume has been shown to be a useful indirect indicator of the ovarian reserve in women of reproductive age, in the diagnosis and management of a number of disorders of puberty and adult reproductive function, and is under investigation as a screening tool for ovarian cancer. To date there is no normative model of ovarian volume throughout life. By searching the published literature for ovarian volume in healthy females, and using our own data from multiple sources (combined n = 59,994) we have generated and robustly validated the first model of ovarian volume from conception to 82 years of age. This model shows that 69% of the variation in ovarian volume is due to age alone. We have shown that in the average case ovarian volume rises from 0.7 mL (95% CI 0.4-1.1 mL) at 2 years of age to a peak of 7.7 mL (95% CI 6.5-9.2 mL) at 20 years of age with a subsequent decline to about 2.8 mL (95% CI 2.7-2.9 mL) at the menopause and smaller volumes thereafter. Our model allows us to generate normal values and ranges for ovarian volume throughout life. This is the first validated normative model of ovarian volume from conception to old age; it will be of use in the diagnosis and management of a number of diverse gynaecological and reproductive conditions in females from birth to menopause and beyond.
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Ovarian Volume throughout Life: A Validated Normative
Thomas W. Kelsey
, Sarah K. Dodwell
, A. Graham Wilkinson
, Tine Greve
, Claus Y. Andersen
Richard A. Anderson
, W. Hamish B Wallace
1 School of Computer Science, University of St Andrews, St Andrews, Fife, United Kingdom, 2 School of Medicine, University of Edinburgh, Edinburgh, United Kingdom,
3 Department of Paediatric Radiology, Royal Hospital for Sick Children, Edinburgh, United Kingdom, 4 Laboratory of Reproductive Biology, Section 5712, The Juliane Marie
Centre for Women, Children and Reproduction, University Hospital of Copenhagen, University of Copenhagen, Copenhagen, Denmark, 5 MRC Centre for Reproductive
Health, Queens Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom, 6 Department of Haematology/Oncology, Royal Hospital for Sick
Children, Edinburgh, United Kingdom
The measurement of ovarian volume has been shown to be a useful indirect indicator of the ovarian reserve in women of
reproductive age, in the diagnosis and management of a number of disorders of puberty and adult reproductive function,
and is under investigation as a screening tool for ovarian cancer. To date there is no normative model of ovarian volume
throughout life. By searching the published literature for ovarian volume in healthy females, and using our own data from
multiple sources (combined n = 59,994) we have generated and robustly validated the first model of ovarian volume from
conception to 82 years of age. This model shows that 69% of the variation in ovarian volume is due to age alone. We have
shown that in the average case ovarian volume rises from 0.7 mL (95% CI 0.4–1.1 mL) at 2 years of age to a peak of 7.7 mL
(95% CI 6.5–9.2 mL) at 20 years of age with a subsequent decline to about 2.8 mL (95% CI 2.7–2.9 mL) at the menopause
and smaller volumes thereafter. Our model allows us to generate normal values and ranges for ovarian volume throughout
life. This is the first validated normative model of ovarian volume from conception to old age; it will be of use in the
diagnosis and management of a number of diverse gynaecological and reproductive conditions in females from birth to
menopause and beyond.
Citation: Kelsey TW, Dodwell SK, Wilkinson AG, Greve T, Andersen CY, et al. (2013) Ovarian Volume throughout Life: A Validated Normative Model. PLoS ONE 8(9):
e71465. doi:10.1371/journal.pone.0071465
Editor: Samuel Kim, University of Kansas Medical Center, United States of America
Received January 21, 2013; Accepted June 7, 2013; Published September 3, 2013
Copyright: ß 2013 Kelsey et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by UK EPSRC (Engineering and Physical Sciences Research Council) grant EP/H004092/1 (
NGBOViewGrant.aspx?GrantRef = EP/H004092/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
Competing Interest s: The authors have declared that no competing interests exist.
* E-mail:
The main functions of the ovary are to provide gametes and sex
steroids to allow and support the establishment of pregnancy, and
act as a repository for the non-growing follicles (NGFs) that allow
this process to take place over several decades. The main
constituents of the ovary are therefore its follicle endowment
(both growing and non-growing), and the stromal tissues that
support these functions. The human ovary establishes its complete
complement of non-growing follicles during fetal life, and after
birth there is a continuous process of recruitment until menopause
at an average age of 50–51 years, when fewer than one thousand
remain [1–3]. There is a wide variation in the age at menopause
between individuals [4,5] and it is thought that this is due in large
part to variations in the initial endowment of NGFs [1]. Currently,
clinical assessment is unable to assess reliably the number of NGFs,
or their rate of loss or activation.
Ovarian volume is one of several putative biomarkers of the
ovarian reserve, others include serum anti-Mu¨llerian Hormone
(AMH), and antral follicle count (AFC) which have have been
shown to have clinical utility in the assessment of women with
subfertility [6]. Ovarian volume is currently one of the diagnostic
criteria for the most common endocrinopathy in women
(polycystic ovary syndrome; PCOS) [7,8] and may be of value in
screening for ovarian cancer [9]. We have shown a strong and
positive correlation between ovarian volume and NGF population
in the human ovary for ages 25–51 years [10], but there is only
sparse information available on ovarian volume in healthy young
girls and women [11]. A greater understanding of the changes in
ovarian volume throughout life are likely to be helpful in the
diagnosis and treatment of many disorders in gynaecology and
reproductive medicine [12].
The data on ovarian volume in young girls is limited due to the
lack of an easy non-invasive method of imaging the ovaries
accurately. Much of the data that is published is in girls with
abnormalities in pubertal development and so does not reflect the
healthy population [13,14]. In the adult woman the advent of
transvaginal ultrasound as a routine gynaecological technique has
led to a large source of data on ovarian volume in healthy women
[15]. To date no single study has examined ovarian volume across
the lifespan in healthy females. The aim of this study is to develop
a validated model of ovarian volume in healthy females from
conception throughout life from data aggregation from multiple
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The validated model is a degree 14 polynomial of the form
z ...zc
with coefficients c
given in Table 1, and relationship to the data
given in Figure 1. The model has coefficient of determination
~0:69 indicating that around 69% of the variation in ovarian
volumes throughout life is due to age alone. The residual plot
(Figure 2) shows a distribution close to the ideal Gaussian curve
~0:993), this coefficient of determination being higher than
that for three other possible curves for these residuals. Moreover,
the proportions of residuals within one, two and three standard
deviations (respectively 71%, 96% and 99%) are close to the
expected values for data with a Gaussian distribution (respectively
68%, 95% and 99%). Figure 3 is an exemplar of the 5-fold
validation process in which a model is chosen that neither overfits
nor underfits the underlying dataset.
The log-unadjusted predictive normative model is shown in
Figure 4. This shows the mean volume per ovary in millilitres (mL)
for the healthy human population, together with prediction
intervals at +1 and +2 standard deviations (SD). Approximately
68% of ovarian volumes are expected to lie within +1 SD of the
mean; approximately 95% within +2 SD of the mean. Mean and
normative ranges for ovarian volumes are given for ages from birth
to 50 years in Table 2. Our model shows that in the average case
ovarian volume rises from 0.7 mL (95% CI 0.4–1.1 mL) at 2 years
of age to a peak of 7.7 mL (95% CI 6.5–9.2 mL) at 20 years of age
Figure 1. The validated model of log-adjusted ovarian volume throughout life. The r
coefficient of determination indicates that 69% of
the variation in human ovarian volumes is due to age alone. Colour bands indicate ranges within +1 standard deviation from mean, within +1 and
+2 standard deviations, and outside 2 standard deviations.
Table 1. Coefficients for the validated model.
Error T Value
95% Conf
95% Conf
0 8.92E-02 8.00E-03 11.2 1.24E-02 1.91E-01
1 1.10E-01 8.28E-03 13.3 5.22E-03 2.16E-01
2 23.05E-02 5.92E-03 25.2 21.06 E-01 4.47E-02
3 5.09E-03 1.63E-03 3.1 21.56E-02 2.58E-02
4 24.35E-04 2.34E-04 21.9 23.40 E-03 2.53E-03
5 2.49E-05 2.03E-05 1.2 22.33E-04 2.83E-04
6 21.23E-06 1.15E-06 21.1 21.59 E-05 1.34E-05
7 5.43E-08 4.49E-08 1.2 25.17E-07 6.25E-07
8 21.89E-09 1.23E-09 21.5 21.75 E-08 1.38E-08
9 4.66E-11 2.40E-11 1.9 22.59E-10 3.52E-10
10 27.87E-13 3.31E-13 22.4 24.99 E-12 3.42E-12
11 8.87E-15 3.15E-15 2.8 23.12E-14 4.89E-14
12 26.36E-17 1.97E-17 23.2 23.14 E-16 1.87E-16
13 2.63E-19 7.32E-20 3.6 26.67E-19 1.19E-18
14 24.77E-22 1.22E-22 23.9 22.02 E-21 1.07E-21
Coefficients for the validated normative model of human ovarian volume
throughout life. Each coefficient value is reported together with estimates of
the standard error, T-statistic and 95% confidence limits for the value.
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and declines throughout life to about 2.8 mL (95% CI 2.7–
2.9 mL) at the menopause.
The data do not support the notion of two distinct populations,
PCOS and non-PCOS, giving a bimodal distribution of ovarian
volumes at a given age. Model residual plots for ages up to
10 years are approximately normally distributed (Figure 5). Model
residual plots for ages 10 through 30 years (Figure 6) and over
30 years (Figure 7) are close to an ideal normal distribution.
When the data is censored to remove 444 values over 10 mL, in
line with the Rotterdam criteria for PCOS[7,8], the model
changes slightly both in qualitative and quantitative terms, with a
coefficient of determination r
~0:69 for both models. The
censored-data model is the same as the full-data model for young
and old ages average volume 0.7 mL (95% CI 0.4–1.1 mL) at
2 years and 2.8mL (95% CI 2.7–2.9 mL) at age 50 years. The
censored-data model has a lower peak predicted ovarian volume
in the average case, 6.4 mL (95% CI 5.4–7.6 mL), with the peak
occurring one year later at 21 years. This lower peak is outside the
95% confidence interval 6.5–9.2 mL for the full model peak,
suggesting a statistically significant difference between the two
peak values.
We have described and validated the first normative model that
describes ovarian volume in healthy females from conception to
82 years. The model has a coefficient of determination r
indicating that 69% of the variation in ovarian volumes
throughout life is due to age alone. Ovarian volume rises through
childhood and adolescence and is maximal in the average woman
at 20 years of age, declining thereafter towards the menopause
and beyond.
Transvaginal ultrasound evaluation has been used as an indirect
assessment of ovarian reserve in adult sexually active females [16].
We have previously shown a strong positive correlation (r~0:89)
between NGF numbers and ovarian volume from ages 25 to
51 years [10], i.e. during the time that both are declining. Our
normative model now adds to this by showing a steady rise in
ovarian volume from birth (Figure 2) with a modest acceleration
around the onset of puberty (age 9–10 years). The major
contribution to ovarian volume before puberty is likely to be
stromal growth; while small antral follicles are present in the
ovaries of prepubertal girls of all ages [17], larger follicles are not
found while serum gonadotrophin concentrations remain low.
After menarche and the onset of ovulation the major contribution
to changing ovarian volume is likely to be the number and size of
the antral follicles present.
Human growth in childhood is described as three additive and
partly superimposed components: infancy, childhood and puberty
[18]. Each component appears to be controlled by distinct
biological mechanisms. The infancy component is largely nutrition
dependent, the childhood component is mostly dependent on
growth hormone (GH) and the pubertal component depends on
the synergism between sex steroids and GH. The slow rise in
ovarian volume throughout mid-childhood (Figures 1 and 2)
followed by an increase in ovarian volume during the pubertal
years suggests that GH, in addition to sex steroids, may have an
important role in determining ovarian size (and possibly function)
in the early and late childhood years. A role for GH in
determining ovarian size and volume during childhood and
Figure 2. Residual distribution for the validated model. Residuals are the squared differences between data values and predicted values for
that age.
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puberty is suggested by data from Bridges et al. 1993 who studied
girls with growth disorders: GH insufficiency, skeletal dysplasia,
and tall stature [13]. Total ovarian volume of untreated GH-
insufficient girls was significantly less than that of GH-insufficient
girls on GH treatment, girls with skeletal dysplasia on GH
treatment, and girls with tall stature. They also found that tall girls
had significantly greater ovarian volume than either of the GH-
treated groups.
The measurement of ovarian volume has been found to be
useful in a wide range of disorders in children and young females.
Measurement of ovarian volume is an accurate diagnostic tool for
adolescent girls with irregular menses. In the majority of these
girls, enlarged ovaries are associated with polycystic ovary
syndrome (PCOS) [19] and ovarian volume is part of the
diagnostic criteria for that condition [7,8]. We therefore censored
our dataset to exclude all women with ovarian volume greater
than 10 mL. As the descriptions of subjects included in the original
references varies, women with PCOS or asymptomatic women
whose ovaries had polycystic ovary morphology (PCOM) may
have been included. In the largest data source, PCOS was not
actively excluded: ‘‘...Patients with a solid or cystic ovarian ovarian
tumor detected by sonography were excluded from this investi-
gation since the purpose of this study was to determine normal
ovarian volume...’’ [15]. Excluding these data points resulted in a
reduction in the peak average ovarian volume, as would be
expected, and a slight increase in the age at which the peak was
reached. Importantly, our analysis does not address the validity of
the criteria for the diagnosis of PCOS. Recent results suggest that
antral follicle counts have better discriminatory performance than
ovarian volume [20].
Girls with precocious puberty have significantly increased
ovarian volumes compared with a normal population [21] and
ovarian volume has been proposed as a useful discriminator
between central precocious puberty and premature thelarche [22].
Furthermore, measurement of ovarian volume is a useful index
with which to assess the efficacy of treatment of central precocious
puberty with GnRH analogues [23].
The role of transvaginal USS as a screening test for ovarian
cancer remains an important area of study [9,15,24] and
transvaginal USS has an established role in the assessment and
management of subfertility and in-vitro fertilization (IVF) in adult
women [12,25]. It remains difficult to assess ovarian reserve in
adolescents and young women with cancer due to the considerable
age-related changes in the various markers available. The
measurement of ovarian volume in addition to AMH may help
predict which young women are at particular risk of premature
ovarian insufficiency following cancer treatment and who may
therefore benefit from fertility preservation techniques [26,27].
Our model is derived from data from multiple sources of the
measurement of ovarian volume in otherwise healthy females.
This is both a strength and a weakness of the study. The strength is
that the measuring errors, both underestimating and overestimat-
ing ovarian volume, are likely to be negated as any bias is unlikely
to be always in the same direction for each data source. The
weakness is the heterogeneity of the values obtained from diverse
sources. We cannot be certain that the measurement of ovarian
volume by abdominal ultrasound, which is often difficult in young
Figure 3. Model validation analysis. The tradeoff between overfit and underfit for one of the five cross-validation data splits. Models with degree
less than 11 are unsuitable due to low r
; models with degree greater than 17 are unsuitable due to larger differences between test and training
mean-squared errors. The degree 14 model is optimal.
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children, is as accurate as measurement by transvaginal ultrasound
in older females [28]. Similarly measurements taken at MRI may
be different from those obtained by weighing the ovary following
oophorectomy and calculating the volume from weight. The
largest data source consists of values imputed from a very large
data source obtained by transvaginal ultrasound as part of a
screening programme for ovarian cancer [15]. This study excluded
patients with a solid or cystic ovarian tumor detected by
sonography, but not patients with polycystic ovary morphology.
Our normative model of ovarian volume using data derived from
multiple data sources and different methods of assessment
overcomes the weakness of other studies in which only one
imaging modality is used, because any potential bias in one
direction is likely to be negated.
We have shown that in the average case ovarian volume rises
from 0.7 mL (95% CI 0.4–1.1 mL) at 2 years of age to a peak of
7.7 (95% CI 6.5–9.2 mL) mL at 20 years of age and declines
throughout later life to about 2.8 mL (95% CI 2.7–2.9 mL) at the
menopause. This is the first validated normative model of ovarian
volume from conception to old age; it will be of use in the
diagnosis and management of a number of diverse gynaecological
and reproductive conditions in females from birth to menopause
and beyond.
Materials and Methods
The research methodology used both for data acquisition and
data analysis closely follows that used to derive a validated
normative model for the level of anti-Mu¨llerian hormone (AMH)
found in the blood of healthy human females for ages from
conception to menopause [29,30].
Ethics statement
Permission to perform basic science studies on the ovarian
material retrieved in Denmark was given by the Minister of Health
in Denmark and by the Committee on Biomedical Research
Ethics of the Capital Region on 21st September 2011 (protocol
number H-2-2011-044). Written informed consent for the original
human work that produced the tissue samples was obtained, and
all data were anonymised prior to analysis.
Data acquisition
The data for this study (Table 3) come from three sources: our
own measurements of ovarian volume, imputation from the large-
scale study by Pavlik et al. [15] as described in [10], and
publications in the scientific literature. Taken as a single dataset,
it approximates the healthy population in terms of ovarian
volume, for ages ranging from mid-term fetal to postmenopausal.
We included data from two unpublished sources. Firstly, a
detailed assessment of 300 MRI examinations in children without
known endocrine, chromosomal or oncological conditions that
included the pelvis, yielded 49 pairs of ovaries where both ovaries
were visualized and measurable in three dimensions (median age
13 years, range 2 to 16.7 years). Ovarian volumes were calculated
using the prolate ellipsoid approximation formula a|b|c|
Secondly, a further 384 ovaries (median age 27.5 years, range 0.5
to 39.8 years) were weighed before cryopreservation at the
University Hospital of Copenhagen. Subjects were known to have
Figure 4. The normative validated model of ovarian volume throughout life. The red line is predicted mean ovarian volume in millilitres for
any age. Colour bands indicate ranges within +1 standard deviation from mean, within +1 and +2 standard deviations, and outside 2 standard
Ovarian Volume throughout Life
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non-ovarian cancer; subjects who had received chemotherapy
were excluded. Ovarian volume was estimated using the published
conversion factor for ovarian tissue density: 1.00 g/mL [31].
Summary statistics were extracted from Pavlik et al. [15] for ages
24–85 years. Repeated (10-fold) parametric bootstrapping [32]
was used to simulate datapoints from the published distributions to
obtain a single dataset (n~58,255) that accurately reproduces the
published results.
In order to obtain data from the existing literature with
emphasis on volumes earlier in life than the 24 years minimum age
reported in Pavlik et al. [15] studies of ovarian volume in normal,
healthy girls were identified using Medline and PubMed searches
using the search terms Ovary, Child, Ovarian size/volume,
Normal, Healthy and Neonatal. The references of these identified
studies were then reviewed, and any other relevant research
papers were extracted. Papers were included if they contained
ovarian volume results for healthy, normal girls with no ovarian or
endocrinological abnormalities, so as to isolate data that approx-
imate the healthy human population. Abstracts of 37 studies were
identified via this method.
After analysis of the full papers, studies were excluded if either (i)
the results consisted purely of descriptive statistics, or (ii) subjects
were classified by pubertal stage rather than age. Of the remaining
nine studies, seven contained data measured by trans-abdominal
ultrasound and plotted in graphs [14,33–38] while two contained
tabular data (with fetal/neonatal ovaries extracted and measured/
sliced to calculate volumes) [39,40]. The data was extracted from
the graphs (n~1,151) using Plot Digitizer software [41], and
combined with the tabular data (n~64). Ovarian volumes were
standardised to the prolate ellipsoid approximation formula
since some studies used the variation a|b|c|
Data analysis
Zero volume values at conception were added to the combined
dataset (Table 3), in order to force models through the only known
volume at any age. Since variability increases with ovarian
volume, we log-adjusted the data (after adding one to each value
so that zero volume on a chart represents zero ovarian volume).
We then fitted 310 mathematical models to the training data using
TableCurve-2D (Systat Software Inc., San Jose, California, USA),
and ranked the results by coefficient of determination, r
. Each
model defines a generic type of curve and has parameters which,
when instantiated gives a specific curve of that type. For each
model we calculated values for the parameters that maximise the
coefficient. The Levenberg-Marquardt non-linear curve-fitting
algorithm was used throughout, with convergence to 6 significant
figures after a maximum of 1,500 iterations. For each candidate
Table 2. Ovarian volumes by age.
Age 3SD below 2SD below 1SD below Mean ovarian vol. 1SD above 2SD above 3SD above
0 0.0 0.0 0.0 0.2 0.5 0.8 1.3
2 0.0 0.1 0.4 0.7 1.0 1.5 2.1
4 0.0 0.3 0.6 0.9 1.3 1.8 2.5
6 0.2 0.5 0.8 1.2 1.7 2.3 3.0
8 0.5 0.8 1.2 1.7 2.3 3.0 3.9
10 0.9 1.3 1.9 2.5 3.3 4.3 5.4
12 1.5 2.1 2.8 3.7 4.7 6.0 7.5
14 2.3 3.0 3.9 5.0 6.4 8.0 10.1
16 3.0 3.9 5.0 6.4 8.0 10.0 12.5
18 3.5 4.5 5.8 7.3 9.2 11.4 14.2
20 3.7 4.8 6.1 7.7 9.6 12.0 15.0
22 3.7 4.7 6.0 7.6 9.5 11.9 14.7
24 3.5 4.5 5.7 7.2 9.0 11.2 14.0
26 3.2 4.1 5.3 6.7 8.4 10.5 13.1
28 3.0 3.9 4.9 6.3 7.9 9.9 12.4
30 2.8 3.7 4.7 6.0 7.6 9.5 11.9
32 2.8 3.6 4.6 5.9 7.5 9.4 11.7
34 2.7 3.6 4.6 5.9 7.4 9.3 11.6
36 2.7 3.6 4.6 5.8 7.4 9.2 11.5
38 2.6 3.5 4.5 5.7 7.2 9.0 11.3
40 2.5 3.3 4.2 5.4 6.8 8.6 10.7
42 2.2 3.0 3.8 4.9 6.3 7.9 9.9
44 1.9 2.6 3.4 4.4 5.6 7.1 8.9
46 1.6 2.2 2.9 3.8 4.9 6.2 7.8
48 1.3 1.8 2.5 3.3 4.2 5.4 6.8
50 1.1 1.6 2.1 2.8 3.7 4.7 6.0
Normative values for ovarian volumes in millilitres for ages from birth through 50 years at two year stages. SD below and above refer to standard deviations below and
above mean predicted volume.
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Figure 5. Model residuals for ages up to 10 years. Residuals are the squared differences between data values and predicted values for that age.
Figure 6. Model residuals for ages between 10 and 30 years. Residuals are the squared differences between data values and predicted values
for that age.
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model, the mean square error and r
were calculated after
removing the artificial zero values at conception.
The best performing family of models were high precision
polynomials. 5-fold cross validation was performed: the data were
randomly split into 5 equally sized subsets. For each subset S, the
other four subsets were used to train high precision polynomials of
degree 8 through 20, with subset S being held back as test data.
The mean square error of the test data was calculated and
compared to the mean square error of training data for the same
model. In other words, the estimated prediction error of a model
when generalized to unseen data was compared to the training
error of the model. A model was considered validated if
1. the residuals of the test data were approximately normally
distributed (Figure 3); and
2. the tradeoff between high r
(denoting possible overfitting to
the data) and low generalisation error (denoting possible
underfitting to the data) was optimal (Figure 4).
We tested for bimodal volume distributions that would suggest
distinct PCOS and non-PCOS sub-populations by analysis of
model residuals for age ranges up to 10 years, 10–30 years, and
above 30 years. Normally-distributed residuals for log-adjusted
values correspond with skew-normal population volumes (i.e. a
single population with PCOS and non-PCOS volumes forming a
smooth continuum of values). Significant variances from normality
provide evidence for a distinct PCOS sub-population.
The validated model was also assessed against the Rotterdam
criteria for PCOS [7,8] by censoring all values above the 10 mL
discriminatory cutoff volume, re-fitting the model, and comparing
peak ages and volumes.
Figure 7. Model residuals for ages over 30 years. Residuals are the squared differences between data values and predicted values for that age.
Table 3. Ovarian volume data sources.
Study Statistics
Ref. First author Year
[10] Kelsey 2012 58,227 24.0 85.0 55.0
[33] Badouraki 2008 99 1.0 11.0 7.0
[34] azzaghy-Azar 2011 480 6.1 13.6 7.0
[35] Seth 2002 92 8.0 15.0 11.5
[36] Holm 1995 165 5.9 25.4 13.9
[37] Ziereisen 2001 122 2.0 15.7 9.3
[38] Griffin 1995 153 0.0 14.9 5.8
[14] Stanhope 1985 40 0.8 13.7 7.3
* Wilkinson 2012 98 2.0 16.7 13
* Andersen 2012 384 0.5 39.8 27.5
[39] Sforza 2004 25 20.5 0.7 0.0
[40] Simkins 1932 39 20.7 14 0.3
Overall 59,954 20.7 85.0 55.0
The year column refers to the year of publication; * denotes our own
unpublished data.
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Author Contributions
Conceived and designed the experiments: TWK SKD AGW TG CYA
RAA WHBW. Performed the experiments: TWK SKD AGW TG CYA.
Analyzed the data: TWK SKD AGW TG CYA RAA WHBW. Wrote the
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Ovarian Volume throughout Life
PLOS ONE | 9 September 2013 | Volume 8 | Issue 9 | e71465
... Four sets of equations (0-2 years male, 0-2 years female, 2-20 years male, 2-20 years female) for the body weight versus age, height versus age, and organ weight versus age relationships and 2 sets of equations (0-20 years male, 0-20 years female) for organ flow rate versus age relationship were developed. In regards to the remaining literature values, please refer to the detailed description in the references (Robinow and Chumlea 1982, OznurL et al 1998, Matsuzawa et al 2001, Kelsey et al 2013, Weaver et al 2014. The sternal bone is the only literature value without a range, and the percentual difference from the reference value (19.7%) is smaller than most other tissues ranges percentage of maximum compared to minimum. ...
... Although, given the tissue's high vascularity and blood content this would have been of limited value in the current model while this could be considered in future versions. The ovaries followed a similar pattern with their volume being within the reported values by Kelsey et al (2013) and 16.7% larger than the values suggested by ICRP Publ. 89 (ICRP 2002). . ...
Full-text available
Objective: Numerical models are central in designing and testing novel medical devices and in studying how different anatomical changes may affect physiology. Despite the numerous adult models available, there are only a few whole-body pediatric numerical models with significant limitations. In addition, there is a limited representation of both male and female biological sexes in the available pediatric models despite the fact that sex significantly affects body development, especially in a highly dynamic population. As a result, we developed Athena, a realistic female whole-body pediatric numerical model with high-resolution and anatomical detail. Approach: We segmented different body tissues through Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images of a healthy 3.5-year-old female child using 3D Slicer. We validated the high anatomical accuracy segmentation through two experienced sub-specialty-certified neuro-radiologists and the inter and intra-operator variability of the segmentation results comparing sex differences in organ metrics with physiologic values. Finally, we compared Athena with Martin, a similar male model, showing differences in anatomy, organ metrics, and MRI dosimetric exposure. Main results: We segmented 267 tissue compartments, which included 50 brain tissue labels. The tissue metrics of Athena displayed no deviation from the literature value of healthy children. We show the variability of brain metrics in the male and female models. Finally, we offer an example of computing Specific Absorption Rate (SAR) and Joule heating in a toddler/preschooler at 7T MRI. Significance: This study introduces a female realistic high-resolution numerical model using MRI and CT scans of a 3.5-year-old female child, the use of which includes but is not limited to radiofrequency safety studies for medical devices (e.g., an implantable medical device safety in MRI), neurostimulation studies, and radiation dosimetry studies. This model will be open source and available on the Athinoula A. Martinos Center for Biomedical Imaging website.
... There are several studies in the literature, concerning the morphologic and histopathologic features of human ovaries, but they are highly heterogeneous in terms of methodology, studied parameters, population, ethnicity and other potential variables that may affect the results. Nevertheless, the largest and most comprehensive studies regarding ovarian volume were conducted by means of ultrasound scanning [17,18]. There are reasonable concerns about the potentially limiting role of ultrasound technology, as many studies date back several years. ...
Full-text available
Reproductive lifespan is determined by the reserve of ovarian follicles; their quality and quality determine the fertility potential at a given point in time for a particular individual. Inter-individual variations related to morphometry, laterality, medical history, demographic characteristics and ethnicity may impact ovarian histology, which however, has not been extensively studied or documented. The present cross-sectional study aims to investigate the potential association of clinical factors (age, medical and obstetric history) with ovarian morphometry and histology in females of reproductive age in the local population. The sample included 31 specimens of whole human ovaries, obtained from surgical/autopsy procedures in reproductive-aged women, processed at the Pathology Department. Morphometric characteristics were assessed, including shape, color, length, width, thickness and gross ovarian pathology. Random samples of specific dimensions were histologically examined to determine follicular counts. The results were analyzed statistically in correlation to morphometric characteristics and medical history. The majority of the patients had oval-shaped ovaries (77.8% right; 92.3% left; p = 0.368) of whitish color (38.9% right; 46.2% left; p > 0.999). Right ovaries had significantly greater length, width and volume (p-values 0.018, 0.040 and 0.050, respectively). Thickness was equivalent, as well as follicular distribution of all classes. Age correlated inversely with ovarian volume and primordial/primary follicular count on histology. Women with a caesarian-section history yielded significantly lower primordial/primary follicular counts. As estimated by ovarian histology, macroscopic and clinical factors may be significantly associated with actual ovarian reserve.
... As a result of the ovaries reaching their maximum volume and follicle count in the second decade of life, there is difficulty in interpreting and classifying ovarian morphology during this period. It has led to a need for different diagnostic criteria for PCOM during adolescence [14]. According to some reports, the prevalence of PCOM varies with age, being highest in adolescents [15][16][17]. ...
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As a complex endocrine and metabolic condition, polycystic ovarian syndrome (PCOS) affects women's reproductive health. These common symptoms include hirsutism, hyperandrogen-ism, ovulatory dysfunction, irregular menstruation, and infertility. No one knows what causes it or how to stop it yet. Alterations in gut microbiota composition and disruptions in secondary bile acid production appear to play a causative role in developing PCOS. PCOS pathophysiology and phenotypes are tightly related to both enteric and vaginal bacteria. Patients with PCOS exhibit changed microbiome compositions and decreased microbial diversity. Intestinal microorganisms also alter PCOS patient phenotypes by upregulating or downregulating hormone release, gut-brain mediators , and metabolite synthesis. The human body's gut microbiota, also known as the "second ge-nome," can interact with the environment to improve metabolic and immunological function. Inflammation is connected to PCOS and may be caused by dysbiosis in the gut microbiome. This review sheds light on the recently discovered connections between gut microbiota and insulin resistance (IR) and the potential mechanisms of PCOS. This study also describes metabolomic studies to obtain a clear view of PCOS and ways to tackle it.
... According to the International guidelines on PCOM, transabdominal ultrasound is not recommended for use as a diagnostic criterion until at least 8 years post-menarche, mainly due to the high prevalence of characteristic follicular increase in young adults and an enlarged ovarian volume during this period. 12,[70][71][72][73] In addition, the implementation of adult thresholds adjusted for the transvaginal route may lead to over-diagnosis of PCOM in adolescents; hence, PCOM in adolescents is not a reliable diagnosis of PCOS. 74 ...
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Polycystic ovary syndrome is the most common endocrine disorder in women of reproductive age, which is still incurable. However, the symptoms can be successfully managed with proper medication and lifestyle interventions. Despite its prevalence, little is known about its etiology. In this review article, the up-to-date diagnostic features and parameters recommended on the grounds of evidence-based data and different guidelines are explored. The ambiguity and insufficiency of data when diagnosing adolescent women have been put under special focus. We look at some of the most recent research done to establish relationships between different gene polymorphisms with polycystic ovary syndrome in various populations along with the underestimated impact of environmental factors like endocrine-disrupting chemicals on the reproductive health of these women. Furthermore, the article concludes with existing treatments options and the scopes for advancement in the near future. Various therapies have been considered as potential treatment through multiple randomized controlled studies, and clinical trials conducted over the years are described in this article. Standard therapies ranging from metformin to newly found alternatives based on vitamin D and gut microbiota could shine some light and guidance toward a permanent cure for this female reproductive health issue in the future.
... The follicle size and number change with age, and the greatest number of small follicles is observed during adolescence and young adulthood, with a significant decrease in follicle count with age. The ovarian volume also increases during puberty and reaches the adult volume in the years after menarche (7,8). ...
Full-text available
This study aimed to analyze the depressive and anxiety states of adolescent girls with polycystic ovary syndrome (PCOS). This was a cross-sectional, multicenter, case-control study. A total of 100 participants (PCOS group, 51; control group, 49) aged 13-18 yr were included in the study. Body mass index was higher in patients with PCOS (P = 0.002). In the PCOS group, 28.5% of the patients had moderate-to-severe depressive symptoms, whereas the incidence was lower in controls (8.3%, P = 0.021). The State-Trait Anxiety Inventory (STAI)-State, STAI-Trait, and physical, psychosocial, and total Pediatric Quality of Life Inventory PedsQL scores were higher in the PCOS group, suggesting that anxiety was more common and the quality of life was worse in patients with PCOS than in healthy participants (P = 0.01, P = 0.03, P = 0.02, P = 0.046, and P = 0.047, respectively). The serum free testosterone (fT) levels were positively correlated with the depression and anxiety scores and negatively correlated with the psychosocial PedsQL scores. In conclusion, adolescent girls diagnosed with PCOS demonstrated higher depressive and anxiety symptoms and lower psychosocial quality of life scores than their healthy counterparts. A relationship was found between the fT level and all psychological measures.
... Extended characterization for the etiology of ovarian insufficiency demonstrated a negative screen for adrenal and ovary autoantibodies, a normal 46,XX karyotype (excluding Turner syndrome), and negative FRAXA premutation analysis (excluding fragile X syndrome). Pelvic imaging revealed no ovaries or very-small-volume ovarian tissue (24). All young women progressed through puberty normally after hormone replacement therapy was commenced. ...
Full-text available
Primary ovarian insufficiency (POI) affects 1% of women and carries significant medical and psychosocial sequelae. Approximately 10% of POI has a defined genetic cause, with most implicated genes relating to biological processes involved in early fetal ovary development and function. Recently, Ythdc2, an RNA helicase and N6-methyladenosine (m6a) reader, has emerged as a novel regulator of meiosis in mice. Here, we describe homozygous pathogenic variants in YTHDC2 in three women with early-onset POI from two families: c. 2567C>G, p.P856R in the helicase-associated (HA2) domain; and c.1129G>T, p.E377*. We demonstrate that YTHDC2 is expressed in the developing human fetal ovary and is upregulated in meiotic germ cells, together with related meiosis-associated factors. The p.P856R variant results in a less flexible protein that likely disrupts downstream conformational kinetics of the HA2 domain, whereas the p.E377* variant truncates the helicase core. Taken together, our results reveal that YTHDC2 is a key new regulator of meiosis in humans and pathogenic variants within this gene are associated with POI.
... Monitoring changes in the ovary, especially follicle growth dynamics during the menstrual cycle, is crucial for the fields of Obstetrics and Gynaecology (e.g., for In-Vitro Fertilisation). On the other hand, the measurement of ovarian volume has been shown to be a useful indirect indicator of the ovarian reserve in women of reproductive age, in the diagnosis and management of a number of disorders of puberty and adult reproductive function, and is under investigation as a screening tool for ovarian cancer [3]. Clinicians today use non-invasive ultrasound devices regularly for these purposes, with which they conduct frequent examinations of patients. ...
Full-text available
Automated detection of ovarian follicles in ultrasound images is much appreciated when its effectiveness is comparable with the experts’ annotations. Today’s best methods estimate follicles notably worse than the experts. This paper describes the development of two-stage deeply-supervised 3D Convolutional Neural Networks (CNN) based on the established U-Net. Either the entire U-Net or specific parts of the U-Net decoder were replicated in order to integrate the prior knowledge into the detection. Methods were trained end-to-end by follicle detection, while transfer learning was employed for ovary detection. The USOVA3D database of annotated ultrasound volumes, with its verification protocol, was used to verify the effectiveness. In follicle detection, the proposed methods estimate follicles up to 2.9% more accurately than the compared methods. With our two-stage CNNs trained by transfer learning, the effectiveness of ovary detection surpasses the up-to-date automated detection methods by about 7.6%. The obtained results demonstrated that our methods estimate follicles only slightly worse than the experts, while the ovaries are detected almost as accurately as by the experts. Statistical analysis of 50 repetitions of CNN model training proved that the training is stable, and that the effectiveness improvements are not only due to random initialisation. Our deeply-supervised 3D CNNs can be adapted easily to other problem domains.
... Other studies revealed that the size and morphology of the uterus and ovaries are quite stable between the ages of 1-2 and 8-9 but there is a progressive increase in the size of the internal genital organs from the age of 9 onward. After 14 years of age the size of the uterus, ovaries and endometrial stripe thickness stay stable up to the age of 20 years [27][28][29][30][31][32]. A previous study by Sample et al concluded, that the uterine length in prepubertal girls should not exceed 30 mm [33]. ...
Aim: To conduct a systemic review of published data on reference values for both transabdominal and transvaginal ultrasound in gynecology. Materials and methods: Literature from 1970 to 2020 of reference values for the female pelvis in healthy subjects was reviewed. According to the determination of reference intervals for laboratory values reference values are generally determined using 95%-reference intervals and their associated 90%-confidence intervals. The list of articles was supplemented with extensive crosschecking of the reference lists of all retrieved articles. Results: A total of 33 studies were included and analyzed. The diagnostic performance of transvaginal ultrasound (TVUS) has a higher sensitivity and specificity than transabdominal ultrasound (TAUS) for high quality imaging of the uterus and the bilateral adnexa. The length of normal uterus is about 50-80 mm in fertile age. There is no consensus about the cut off value of the thickness of the endometrium in asymptomatic postmenopausal women, while a measurement of >5 mm and postmenopausal bleeding is suspect and requires further examination. The distribution of normal ovarian volumes is narrow with small volumes in postmenopausal women. Conclusion: Normal values are helpful in delimiting the pathological changes in the female pelvis. While sonomorphologic criteria are more important than the ovarian size for the assessment of ovarian masses and reference values of the uterus in adults have little impact on routine practice, normative values in pediatric patients are important for the detection of pathologies. Normative values of the internal genital organs in females are sufficiently validated; still further research is required to assess the role of normative values in routine clinical practice and in sonographic screening for endometrial and ovarian cancer.
Polycystic ovary syndrome (PCOS) is a common, complex, and chronic condition that presents many diagnostic and management challenges for managing clinicians. PCOS diagnosis in adolescents presents a particular challenge for treating clinicians due to the overlap of diagnostic features with normal physiological changes during adolescence. Adolescent diagnostic criteria include well-defined menstrual irregularity according to time postmenarche and hyperandrogenism, but does not require the use of pelvic ultrasound. Adolescents with only one criterion should be considered at risk of PCOS and be followed up around transition to adult care. While PCOS was traditionally considered to be a reproductive disorder, PCOS is now recognized to have major metabolic and cardiovascular health consequences and psychological sequelae that can be present from adolescence. Management of PCOS includes healthy lifestyle, metformin, combined oral contraceptive pill, and/or antiandrogens according to symptoms of concern even in adolescents at risk of PCOS.
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Introduction: Early puberty (EP) in girls is defined as the onset of thelarche that begins after 6 years and before 8 years and/or acceleration in the tempo of pubertal development. The stage of puberty and the ovarian volume at presentation and the effect of treatment with GnRH analogue (GnRHa) on final adult height are still debated. Patients and methods: We analyzed the data of 22 girls, who presented early and fast puberty (FEP). The clinical stage of puberty, hormonal levels and the ovarian volume (OV) (measured by ovarian ultra-sonography) at presentation were studied. We recorded the effects of 3 years treatment with GnRHa on their growth in relation to their mid parental height, pubertal progression, and bone maturation. Results and conclusion: GnRHa therapy decreased the fast progress of puberty, skeletal maturation, and GV/year. It was successful in increasing the predicted final adult height comparable to or surpassing their mid-parenteral height. A larger OV at presentation was associated with reduced Ht-SDS after 3 years of GnRHa treatment. Clearly, a definitive evaluation of the efficacy of GnRHa as treatment for EFP in girls will require expanded and concerted studies.
Full-text available
Since the 1990 NIH‐sponsored conference on polycystic ovary syndrome (PCOS), it has become appreciated that the syndrome encompasses a broader spectrum of signs and symptoms of ovarian dysfunction than those defined by the original diagnostic criteria. The 2003 Rotterdam consensus workshop concluded that PCOS is a syndrome of ovarian dysfunction along with the cardinal features hyperandrogenism and polycystic ovary (PCO) morphology. PCOS remains a syndrome and, as such, no single diagnostic criterion (such as hyperandrogenism or PCO) is sufficient for clinical diagnosis. Its clinical manifestations may include: menstrual irregularities, signs of androgen excess, and obesity. Insulin resistance and elevated serum LH levels are also common features in PCOS. PCOS is associated with an increased risk of type 2 diabetes and cardiovascular events.
Full-text available
Study question: Do the ultrasonographic criteria for polycystic ovaries supported by the 2003 Rotterdam consensus adequately discriminate between the normal and polycystic ovary syndrome (PCOS) condition in light of recent advancements in imaging technology and reliable methods for estimating follicle populations in PCOS? Study answer: Using newer ultrasound technology and a reliable grid system approach to count follicles, we concluded that a substantially higher threshold of follicle counts throughout the entire ovary (FNPO)-26 versus 12 follicles-is required to distinguish among women with PCOS and healthy women from the general population. What is known already: The Rotterdam consensus defined the polycystic ovary as having 12 or more follicles, measuring between 2 and 9 mm (FNPO), and/or an ovarian volume (OV) >10 cm(3). Since their initial proposal in 2003, a heightened prevalence of polycystic ovaries has been described in healthy women with regular menstrual cycles, which has questioned the accuracy of these criteria and marginalized the specificity of polycystic ovaries as a diagnostic criterion for PCOS. Study design, size, duration: A diagnostic test study was performed using cross-sectional data, collected from 2006 to 2011, from 168 women prospectively evaluated by transvaginal ultrasonography. Receiver operating characteristic (ROC) curve analyses were performed to determine the appropriate diagnostic thresholds for: (i) FNPO, (ii) follicle counts in a single cross section (FNPS) and (iii) OV. The levels of intra- and inter-observer reliability when five observers used the proposed criteria on 100 ultrasound cases were also determined. Participants/materials, setting, methods: Ninety-eight women diagnosed with PCOS by the National Institutes of Health criteria as having both oligo-amenorrhea and hyperandrogenism and 70 healthy female volunteers recruited from the general population. Participants were evaluated by transvaginal ultrasonography at the Royal University Hospital within the Department of Obstetrics, Gynecology and Reproductive Sciences, University of Saskatchewan (Saskatoon, SK, Canada) and in the Division of Nutritional Sciences' Human Metabolic Research Unit, Cornell University (Ithaca, NY, USA). Main results: Diagnostic potential for PCOS was highest for FNPO (0.969), followed by FNPS (0.880) and OV (0.873) as judged by the area under the ROC curve. An FNPO threshold of 26 follicles had the best compromise between sensitivity (85%) and specificity (94%) when discriminating between controls and PCOS. Similarly, an FNPS threshold of nine follicles had a 69% sensitivity and 90% specificity, and an OV of 10 cm(3) had a 81% sensitivity and 84% specificity. Levels of intra-observer reliability were 0.81, 0.80 and 0.86 when assessing FNPO, FNPS and OV, respectively. Inter-observer reliability was 0.71, 0.72 and 0.82, respectively. Limitations, reasons for caution: Thresholds proposed by this study should be limited to use in women aged between 18 and 35 years. Wider implications of the findings: Polycystic ovarian morphology has excellent diagnostic potential for detecting PCOS. FNPO have better diagnostic potential and yield greater diagnostic confidence compared with assessments of FNPS or OV. Whenever possible, images throughout the entire ovary should be collected for the ultrasonographic evaluation of PCOS. Study funding and competing interest: This study was funded by Cornell University and fellowship awards from the Saskatchewan Health Research Foundation and Canadian Institutes of Health Research. The authors have no conflict of interests to disclose.
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Cytotoxic treatment may accelerate depletion of the primordial follicle pool, leading to impaired fertility and premature menopause. Assessment of ovarian damage in prepubertal girls is not currently possible, but Anti-Müllerian Hormone (AMH) is a useful marker of ovarian reserve in adults. The objective of the study was to prospectively evaluate AMH measurement in children as a marker of ovarian toxicity during cancer treatment. This was a prospective, longitudinal study at a University Hospital. Twenty-two females (17 prepubertal), median age 4.4 yr (range 0.3-15 yr), were recruited before treatment for cancer. AMH, inhibin B, and FSH at diagnosis, after each chemotherapy course and during follow-up, were measured. Risk of gonadotoxicity was classified as low/medium (n = 13) or high (n = 9) based on chemotherapy agent, cumulative dose, and radiotherapy involving the ovaries. Pretreatment AMH was detectable across the age range studied. AMH decreased progressively during chemotherapy (P < 0.0001) in both prepubertal and pubertal girls, becoming undetectable in 50% of patients, with recovery in the low/medium risk groups after completion of treatment. In the high-risk group, AMH became undetectable in all patients and showed no recovery. Inhibin B was undetectable in most patients before treatment and, with FSH, showed no clear relationship to treatment. AMH is detectable in girls of all ages and falls rapidly during cancer treatment in both prepubertal and pubertal girls. Both the fall during treatment and recovery thereafter varied with risk of gonadotoxicity. AMH is therefore a clinically useful marker of damage to the ovarian reserve in girls receiving treatment for cancer.
Full-text available
A reliable indirect measure of ovarian reserve for the individual woman remains a challenge for reproductive specialists. Using descriptive statistics from a large-scale study of ovarian volumes, we have developed a normative model for healthy females for ages 25 through 85. For average values, this model has a strong and positive correlation (r = 0.89) with our recent model of nongrowing follicles (NGFs) in the human ovary for ages 25 through 51. When both models are log-adjusted, the correlation increases to r = 0.99, over the full range of ovarian volume. Furthermore we can deduce that an ovary of 3 cm(3) volume (or less) contains approximately 1000 NGF (or fewer). These strong correlations indicate that ovarian volume is a useful factor in the indirect estimation of human ovarian reserve for the individual woman.
Since the 1990 National Institutes of Health-sponsored conference on polycystic ovary syndrome (PCOS), it has become appreciated that the syndrome encompasses a broader spectrum of signs and symptoms of ovarian dysfunction than those defined by the original diagnostic criteria. The 2003 Rotterdam consensus workshop concluded that PCOS is a syndrome of ovarian dysfunction along with the cardinal features hyperandrogenism and polycystic ovary (PCO) morphology. PCOS remains a syndrome, and as such no single diagnostic criterion (such as hyperandrogenism or PCO) is sufficient for clinical diagnosis. Its clinical manifestations may include menstrual irregularities, signs of androgen excess, and obesity. Insulin resistance and elevated serum LH levels are also common features in PCOS. PCOS is associated with an increased risk of type 2 diabetes and cardiovascular events.
The store of primordial follicles in the ovary is fixed before birth and dwindles with age until it is unable to provide enough Graafian stages to sustain menstrual cyclicity. According to a simple bi-exponential model of ageing, the rate of follicle disappearance increases at age 37.5 years (or when 25 000 follicles remain) so that the numbers fall to approximately 1000 at 51 years, the median age of menopause in the population. This study attempts to produce a biologically more realistic model of fofficle disappearance and harmonizes follicle dynamics with the distribution of menopausal ages from an American survey. The step-change in the rate of fofficle attrition was replaced by a model which assumed that this rate changes more gradually with the size of the follicle store. This produced a distribution of predicted menopausal ages (based on an assumed threshold of 1000 follicles) which was closer to observed data. The fit further improved when the model was modified by having a threshold that varied across the population. Using such a stochastic threshold model for menopause, the number of fertile years remaining could be forecast with an acceptable margin of uncertainty if it ever becomes possible to estimate the size of the follicle store in vivo.
The changes in the relationships between circulating antimüllerian hormone, the size of the primordial follicle pool, and follicular recruitment before and through the reproductive years have now been clarified, and show dynamic changes through sexual development. The constant relationship between the number of follicles and circulating antimüllerian hormone exists only after the age of 25 years, implying that the association between follicular recruitment and follicular survival to the later stages of development is not constant across the reproductive life course. This commentary assesses the factors that may underlie these relationships and their clinical implications for reproductive health.
Pelvic ultrasound and hormonal studies were performed in 29 adolescent patients, aged 12 to 20 years, to evaluate menstrual irregularities. Patients were divided in three groups according to ultrasound ovarian volumes: group I (n = 16) both ovaries <10 cm3; group II (n = 8) one of the ovaries ≥ 10cm3; and group III (n = 5) both ovaries ≥10 cm3. Serum levels of LH, LH:FSH ratio, testosterone, and androstenedione were significantly higher (p < .05) in group III. Positive predictive value of both ovarian volumes ≥10 cm3 in terms of polycystic ovary syndrome (PCOS) was 100%, negative predictive value was 81%, sensitivity was 63%, and specificity was 100%. These data suggest that, in adolescent patients with menstrual disorders, bilateral ovarian volumes of higher than 10 cm3 are correlated with the diagnosis of PCOS. © 1996 John Wiley & Sons, Inc.