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):
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 (http://gow.epsrc.ac.uk/
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: firstname.lastname@example.org
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 . 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 . 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 . We have shown a strong and
positive correlation between ovarian volume and NGF population
in the human ovary for ages 25–51 years , but there is only
sparse information available on ovarian volume in healthy young
girls and women . 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 .
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
. 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
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
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
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
Transvaginal ultrasound evaluation has been used as an indirect
assessment of ovarian reserve in adult sexually active females .
We have previously shown a strong positive correlation (r~0:89)
between NGF numbers and ovarian volume from ages 25 to
51 years , 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 , 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
. 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
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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 . 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-
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)  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...’’ . 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 .
Girls with precocious puberty have significantly increased
ovarian volumes compared with a normal population  and
ovarian volume has been proposed as a useful discriminator
between central precocious puberty and premature thelarche .
Furthermore, measurement of ovarian volume is a useful index
with which to assess the efficacy of treatment of central precocious
puberty with GnRH analogues .
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 . 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 . 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
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].
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.
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.  as described in , 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
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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 .
Summary statistics were extracted from Pavlik et al.  for ages
24–85 years. Repeated (10-fold) parametric bootstrapping 
was used to simulate datapoints from the published distributions to
obtain a single dataset (n~58,255) that accurately reproduces the
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.  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 , 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|
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
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.
Ref. First author Year
 Kelsey 2012 58,227 24.0 85.0 55.0
 Badouraki 2008 99 1.0 11.0 7.0
 azzaghy-Azar 2011 480 6.1 13.6 7.0
 Seth 2002 92 8.0 15.0 11.5
 Holm 1995 165 5.9 25.4 13.9
 Ziereisen 2001 122 2.0 15.7 9.3
 Griffin 1995 153 0.0 14.9 5.8
 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
 Sforza 2004 25 20.5 0.7 0.0
 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
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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
paper: TWK SKD AGW TG CYA RAA WHBW.
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Ovarian Volume throughout Life
PLOS ONE | www.plosone.org 9 September 2013 | Volume 8 | Issue 9 | e71465