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Initial Evaluation of Semi-Automated Breast Cancer Screening Using Thermography



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Initial Evaluation of Human Supervised Automated Breast Cancer Screening Using
by K. Venkataramani*, H. Madhu
, S. Sharma
, H. V. Ramprakash**, A. Rajendra**,
, and G. Manjunath*
* Xerox Research Center India, Prestige Technology Park, Marathahalli, Bangalore, India,,,,,
**Central Diagnostic Research Foundation, Indiranagar, Bangalore, India,
Breast cancer has the highest incidence among cancers in women, in India and world-wide. Screening and
early detection play a large role in reducing mortality as breast cancer can be cured if it is detected in the early stages.
Mammography is considered the gold standard in screening, but it is not useful for younger women due to low sensitivity
with denser breasts and its harmful X-rays can cause an increase in the risk of cancer if used frequently. Sono-
mammography is typically used in correlation. Incidence rates are rising in younger women as compared to previous
decades, due to changes from environmental pollutants and socio-economic reasons. This is causing a relook at
thermography for low cost and non-harmful screening. In this paper, an automated thermographic screening tool is used
to classify 108 subjects from the patient database of Central Diagnostic Research Foundation, a diagnostic clinic. In
addition to classification, the location of the suspected tumor is also highlighted on the thermography images. The results
are promising with 100% sensitivity and 73% specificity. The algorithm used is novel, which combines features obtained
from the temperature distribution of the subject, in a personalized manner, to classify as well as localize the tumor.
1. Introduction
Breast cancer is the leading cause of cancer deaths for women worldwide as well as in India, with around
5,00,000 and 70,000 in the world and India, respectively, in 2012. It also has the highest incidence among cancers in
women, with 1.7 million and 145,000 diagnosed worldwide and in India, respectively, during 2012 [1]. Breast cancer is
curable, with a high survival rate of 97%, if diagnosed in the early stage [6]. This can be achieved by regular screening.
Mammography is the current gold standard for breast cancer screening. However, mammograms not only are less
sensitive at detecting tumors in young women due to denser breast tissue, but also are harmful enough to cause cancer
in young women due to the radiation exposure [6]. The sensitivity of mammography falls from 83% in less dense tissue
to 55% in the highest density tissue [6]. While no empirical studies have directly measured the risk of developing breast
cancer due to regular mammographic screening, many simulation models have been used to estimate risk depending on
the dosage of radiation, frequency of screening and age when screening started[3-5]. The most recent estimate was of
125 breast cancers and 16 deaths per 100,000 women in US screened annually from 40 years to 74 years [3]. For
women with BRCA mutations who have a high risk of developing breast cancer at a young age (<40 years) and are
recommended to undergo screening from as early as 25 years, mammography’s risk of inducing cancer negates its
benefits of detecting cancer if used for annual screening below 35 years[4]. Even for a single screening mammogram at
35 years, the lifetime risk of radiation-induced breast cancer is 11 per 100,000 women [5]. Sono-mammography is
another commonly used modality, which is typically used in correlation with mammography as its standalone approach
has too many false positives and false negatives for screening [7]. In general, there have been insufficient large scale
studies on other modalities of screening [8]. Thermography is evaluated here as an alternative imaging modality for
breast cancer screening.
Breast cancer incidence is increasing in younger women presently as compared to previous decades. Due to
excessive use of chemicals in our modern society, that causes adverse effects on our bodies, we are seeing problems
that were not heard of a hundred years ago. One such risk factor is xeno-hormones, a group of man-made laboratory
synthesized chemicals that are hormonally active agents [9,10]. Many of these xeno-hormones are proven carcinogens
[9,10]. They are also well known for their ability to damage the immune system and interrupt hormonal balance. Our cells
can’t always distinguish fully between our own hormones and xeno- hormones. The xeno-hormones that mimic the
female hormones, estrogen and progesterone, increase the risk of breast cancer. Synthetic estrogens and progestins are
found in oral contraceptives and conventional synthetic hormone replacement therapies [11]. Estrogen dominance is
probably the leading cause of breast cancer risk from hormones [10]. All American-grown, non-organic livestock are fed
estrogenic drugs to fatten them. Also, the grains they are fed are laden with chemical sprays that accumulate in animal
tissue and promote hormone disruption in the person consuming them. Petro chemically-derived pesticides, herbicides
and fungicides are also sources of xeno-hormones [9]. The chemical Bisphenol A (BPA) used in plastic bottles,
containers and almost all food-can liners is also a xeno-hormone [12]. Exposure to Bisphenol A has shown an increased
incidence of breast and prostate cancers. Solvents found in fingernail polish and polish remover, glue, have been found
to have the same cell proliferative properties and endocrine disruption. Emulsifiers found in soaps and cosmetics of the
past and present are also risk factors. Skin being the largest organ is very capable of transferring chemical through it at a
highly efficient rate.
Thermography is useful in screening for breast cancers that are affected by hormonal activity[6]. Thermography
measures the infra-red radiation emitted by the body [6]. The increased metabolic rate of malignant cells and the neo-
vascularity and angio-genesis caused by cancer, increases the temperature compared to the surrounding tissue, which is
visible in thermographic images. There was research in the 1970’s and 1980’s on thermography for breast cancer
screening [13,14] and thermography got approval by FDA since 1982 as a risk predictor for cancer [6]. However, the
lower sensitivity and specificity compared to mammography reported in a study in 1977 [15] resulted in a decline in its
usage. W ith the advent of high resolution thermal cameras, there is a relook at thermography [16]. In a study of 100
subjects with carcinoma [16], the sensitivity of thermography was 83%, and its value is in signaling abnormality in
younger subjects with carcinoma where mammograms or clinical examination did not detect malignancy. Thermography
may be able to detect malignant tumors 5 years before mammography [14]. Breast cancers that grow due to hormones
have abnormal thermograms[17]. Their progression is also faster. Thermography can help in detecting women at high
risk for cancer. The thermal cameras are also of lower cost, small and mobile, enabling non-contact and non-invasive
screening for large populations in non-hospital settings. These advantages could be useful especially in less developed
countries like India where cost and availability of hospitals play a vital role in screening.
Automatic screening algorithms can help in outreach to large populations, as doctors can focus on a fewer
number of suspicious cases for further analysis. There have been several semi-automated algorithms for breast cancer
screening with thermography [18,19] whose specificity and sensitivity is comparable with that of mammography, but have
been tested on only a small number of subjects. Ng et al [20] report a sensitivity of 86% and specificity of 91% on 25
normal subjects and 25 subjects with malignant tumors in stage 2 or stage 3 cancer. Most of these approaches use
textural features and temperature moments for classification with standard classifiers. In this paper, we use a feature
fusion based segmentation algorithm designed in our previous work [21] for high sensitivity to determine the specificity in
a dataset consisting mostly of normal subjects and benign tumor subjects.
2. Data description
Anonymized subject data has been obtained from the diagnostic clinic of Central Diagnostic Research
Foundation through a collaboration. This data has been obtained from a subset of the subjects coming to the clinic over
the past four years for breast examination. These subjects had come either for regular breast cancer screening or for
diagnosing a clinical condition of the breast. The data included thermal images, and the radiologist/thermographer’s
report having thermography and sono-mammography (ultrasound) findings and conclusions obtained from combining the
findings from both modalities. It also included subject demographic data, including age, gender, medical history of the
subject such as pregnant or lactating, clinical complaints, as well as personal history and family history of cancer.
The thermographic data has been obtained from the Meditherm camera, with a resolution of 690478 pixels.
There is a specific protocol for capturing the thermography images of the subject. The subject is asked to wear a loose
fitting gown and wait in an AC room for 15 minutes so that there is normalization of body temperature and external heat
conditions are minimized. The subject is then seated on a swivel chair at a fixed distance from the thermography camera.
The camera focus is zoomed in so that only the relevant region of the subject’s body is captured; from below the neck to
just below the infra-mammary fold. The subject’s chair is also swiveled so that the angle of capture is exactly frontal, at
45 oblique, i.e. right and left oblique, and right/left lateral. The thermography camera temperature range is also
calibrated within 8C range for each subject, with the maximum temperature of this range corresponding to the maximum
body temperature of the person. This would allow the maximum body temperature of the person to be observed at the
color corresponding to the image’s maximum temperature (in this case white). This is to assist in visual interpretation of
the image by the radiologist/ thermographer. Figure 1 shows sample images of a normal subject. In this paper, the
default view of the Meditherm camera is used, which shows the isotherm view, where pixels within every 0.5C range are
shown in a different color. The default views of the same subject are shown in figure 2. The thermographers/radiologists
find this isotherm view helpful and typically make most of their observations based on this view.
The data used in the paper consists of 108 subjects, with statistics as given in table 1. Mostly normal subjects
are presented in this data, with a significant percentage of subjects with benign tumors, such as simple cysts,
fibroadenoma, and fibrocystic disease. There are five cases of cancer. Among normal subjects, there are a few cases of
subjects who have a high hormonal response that places them at a higher risk of breast cancer, as well as faster growing
cancerous cells, if breast cancer develops. There is one case of infection in the ribs. In screening the general population
of women, it is expected that mostly normal subjects would be observed, with a significant percentage of women having
benign tumors and/or inflammatory/infectious conditions, and a few subjects with malignancy that depends on the breast
cancer incidence rates of that country/population.
3. Automated screening software features
The breast cancer screening tool (figure 3 to 7) allows the user to analyse thermal images for semi-automatic
detection and classification of breast tumours. The tool consists of three sections: 1) patient data acquisition, 2) thermal
data analysis, and 3) conclusion, data storage, and report generation.
3.1. Patient data acquisition
This section of the software tool captures relevant information needed as a pre-requisite for patient classification
and conclusion. The section allows the user to enter patient demographic information, patient cancer history, family
cancer history, patient medical history, patient complaints, and clinical examination. The information captured in this
section helps to evaluate the probability of a patient developing breast cancer. For example, history of cancer present in
patient and/or her family increases the chances of developing cancer in the future. Patient medical history plays an
important role in final diagnosis of the patient; for e.g. medical history like pregnant or lactating mothers suggests that it
might lead to temperature increase seen in the thermal images. Moreover, medical history of patient gives insight into
any benign conditions that may lead to cancer. Patient complaints and clinical/physical examination give relevant
information on the present condition of the patient, which include important data on hormonal levels, breast nodules, and
other factors. The entire acquisition section captures data to help evaluate the patient condition in a holistic manner.
3.2. Thermal Analysis
This section of the tool focuses on performing thermal analysis on the captured thermal images of the patient.
The following operations summarizes the thermal analysis section:
Load: The thermal images of the patient are captured in different views like frontal view, lateral view, oblique
view etc. The tool allows the user to load all those thermal images, browse through them, view them in infrared view or
2D contour view (contour view is created by temperature separation of 0.5 degree Celsius).
Crop: Once all the thermal images are loaded, the tool allows the user to crop the regions of interest from all the
images. These cropped regions will be input for the auto-detect tumor algorithm as well as the manual-select process.
Auto-detect: After the cropping operation is over, the tool enables the auto-detect button which when clicked by
the user will initiate the tumor auto-detection algorithm. If any suspected tumor detected, the algorithm highlights the
tumor region, displays it in the best possible view and displays a message saying “suspected malignancy detected”. If no
suspected tumor is detected, then it just displays the frontal view thermal image with a message “no tumor present”. The
tool provides the user the facility to confirm whether the output of auto-detect algorithm is correct or not. This is important
to improve the accuracy of the auto-detect tumor algorithm.
Manual-select: After the auto-detect tumor process is done, the tool enables the manual-select process. The
user chooses the desired cropped region and clicks on the manual-select button. The cropped region is displayed in 2D
contour view and allows the user to mark the suspected tumor regions. The manual-select algorithm is then processed
and the best possible thermal image view along with the highlighted suspected tumor region is displayed. The user
perform the manual-select process only when the user suspects any tumor is present.
Comments: The tool provides the facility for the user to enter the thermobiological score of the patient and
quadrant-wise comments for right breast and left breast with respect to the captured thermal images and above
mentioned processes.
3.3. Patient evaluation, data storage and report generation
This section of the tool concludes the patient evaluation, persists all data collected from other sections of the
tool, and generates report for documentation purposes. It evaluates the acquired patient and thermal information to
classify the patient as normal, benign, malignant, or bilateral. All this data is then stored in a database that contains
records of all breast cancer screening patients; the information can be captured from the database and edited and
analysed further using the tool. Also, report containing all the relevant data is generated for doctor’s reference.
4. Automated screening algorithm
In this paper, we are using human supervision through the screening software for locating the breast regions,
i.e. our Regions of Interest (ROI). Fully automated ROI location is difficult, as the region is amorphous and needs to be
located at different angles in the different images. Current approaches to automate ROI segmentation have been
generally done on just frontal images [19] through heuristic approaches and hence are very noisy, as the body shape of
subjects vary. Our approaches to ROI selection through human supervision and automated algorithms for classification &
localizing the tumor are described in the following sub-sections.
4.1. Regions of Interest selection through human supervision
The right and left breast regions from the thermograms are the Regions of Interest (ROI) to us here to determine
the presence/absence of cancer or any abnormality. The infra-mammary folds are hot normally due to friction, and hence
were not considered in the ROI. The lymph nodes in the axilla regions are also possible regions where there may be
metastasis of breast cancer and are typically examined in sono-mammograms. In thermograms, the axilla regions are
generally hot due to friction and the presence of lymph nodes. Detection of abnormalities in the axilla regions from
thermography are out of scope in this study, although it is an interesting topic for future research.
Six thermal images are captured for each cancer subject. The right/left breast region is then manually
segmented using a free-form selection in the software described in Section 3. Figure 8 shows the segmented ROIs from
a sample subject.
4.2. Automated localization and classification algorithm on ROIs
The temperature map of the ROIs is obtained from the camera’s temperature colorbar. From the ROIs, the
tumor detection and location is done automatically using a multi-feature fusion algorithm [21]. The split-and-merge
segmentation approach is used to detect/locate the tumors, although other segmentation approaches could be used. The
novelty lies in the usage of multiple features, their decision-based fusion and in using subject specific thresholds based
on their temperature distribution.
Cancerous tumors are typically “hot”, and are significantly hotter than the surrounding tissue. Small tumors in
the earlier stages of cancer growth are “warm.” As the surface body temperature is observed to vary by a few degree
centigrade across subjects, the temperature thresholds to determine “hot” areas need to be subject-dependent, and
based on the temperature distribution over the ROIs. Weak edges in the temperature map can also be obtained around
the tumors, but may not have closed boundaries. Temperature thresholds based on the temperature distribution and
edges around the tumor may be used to detect the tumor region. The following set of features are used to decide if a
particular block in the ROI belongs to a tumor or not.
1) The temperature threshold
based on the temperature distribution in the ROIs: If k
% of the block has
temperature above threshold
, decide as tumor.
2) Another temperature threshold
based on the maximum temperature in the ROIs: If k
% of the block has
temperature above threshold
, decide as tumor.
3) A temperature threshold
based on normal body surface temperature: If k
% of block has temperature
decide as normal.
4) Edges around the tumor: If the edge length is k
% of the block perimeter, decide as tumor.
These block decisions are combined using a decision fusion rule to maximize cancerous tumor detection and
minimize false positives. The optimal decision fusion is based on the inter-dependence of these features in tumor
detection. More details on selecting the optimal fusion rule is found in [22]. Figure 3 and 4 shows the tumor location on
sample subjects with cancer.
5. Results of automated thermal screening with human supervised ROI selection
The algorithm described in Section 4.2 is tuned to maximize tumor detection in cancerous subjects. In this
=max-2C, with max denoting the maximum temperature, k
=100%, and with
the global decision as tumor block if decisions 1) and 2) and 3) decide as tumor block. The results of automatic tumor
localization and classification is shown in table 2 and figure 9. There is 100% sensitivity as all cancerous cases and the
suspicious case were declared as suspicious by the automated screening, and the tumor regions were correctly
localized. There is 73% specificity. Among the errors, i.e. normal/benign tumor cases declared suspicious by the
automated algorithm are in a)12 of the 14 cases where the radiologist required a repeat study after 3 months, 6 months,
or over a year, as he felt they needed regular monitoring to rule out malignancy, b) 9 cases where external heat
conditions caused a rise in temperature in parts of the body, c) 3 high risk subjects who had a high hormonal response. It
is better to have additional stages of automated classification after this automated algorithm to re-classify the suspected
subjects into those that are at high risk for cancer and/or require regular monitoring. The imaging protocols can be
modified or additional questions to the subject can be asked to rule out external heat conditions. The imaging protocols
can include an additional cooling step, where cool air is blown on the subjects to normalize the external heat conditions.
This can be done even after an initial evaluation. If the heat persists after this additional cooling protocol, then it would be
due to malignancy, inflammation or infection. Questions could include whether the subject was exposed to some external
conditions causing heat generation.
5. Conclusions and Future Work
A subset of 108 subjects from the patient data of Central Diagnostic Research Foundation were categorized by
the radiologist using age, gender, medical history, history of cancer in the patient and her family, thermography and
sono-mammography data findings. We have demonstrated an automatic breast cancer screening tool on thermography
data from this clinic. This tool also has visualization features to assist the doctors or thermographers in their diagnosis.
Using automated thermographic breast cancer screening on 108 subjects consisting of normal subjects, benign tumor
and malignant tumor subjects, promising results of 100% sensitivity and 73% specificity was obtained. The specificity of
automated screening could be improved in future with better imaging protocols, such as cooling the subject to remove
heat generated by external causes. The specificity can also be improved with additional algorithms that determine high
risk subjects with excessive hormonal responses and that separate borderline suspicious cases into different categories.
The screening tool will also be modified in future to take into account the age and medical history along with
thermography data to improve sensitivity and specificity. Further tests on larger number of subjects needs to be done to
determine the possibility of using thermography as a first line breast cancer screening modality, followed by additional
tests to diagnose breast cancer.
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Table 1. Statistics of the subject data used in this paper
Subject category Number of subjects
Cancerous tumor 5
Benign tumor 38
Normal subjects 60
Suspicious 1
Infection 1
Lactating 2
Repeat study requested among
normal/benign tumor cases
14 out of 98
High risk due to hormonal responses among
normal/benign tumor subjects
3 out of 98
Table 2. Results of automated screening on human supervised ROI selection.
Subject category Number of subjects Classified as suspicious through
automated screening
Cancerous tumor 5 5 out of 5
Benign tumor 38 13 out of 38
Normal subjects 60 12 out of 38
Suspicious 1 1 out of 1
Infection 1 0 out of 1
Lactating 2 1 out of 2
Repeat study requested among
normal/benign tumor cases
14 out of 98 12 out of 14
High risk due to hormonal responses among
normal/benign tumor subjects
3 out of 98 3 out of 3
Fig. 1. Images of a normal subject from the Meditherm camera at a resolution of 690
478 pixels. There is high
temperature in the root of the neck and upper part of the chest due to external heat exposure.
Fig. 2. Images of a normal subject in figure 1 in the isotherm view, with each color representing a 0.5
C change.
Fig. 3: Suspected Tumor Detected for the subject with breast cancer using auto-detect algorithm
Fig. 4. No Tumor Detected for the normal subject using auto-detect algorithm
Fig. 5. Manual-select process to detect the suspected tumor
Fig. 6. Suspected tumor for the subject with cancer displayed using Manual-select process
Fig. 7. Patient data acquisition
Fig. 8. Segmented ROI outlined in purple for a subject with cancer. The automated screening located the tumor outlined
in black.
Fig. 9. Sensitivity and Specificity percentage of the automated screening results.
... Thus, breast thermography has been lately utilized for early breast cancer detection, because it is patient-friendly as it is painless, quick, non-contact, very safe, and has less price compared to other screening systems. Additionally, it is suitable for breasts of all sizes and density [3]. ...
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Breast cancer is a leading cause of death nowadays in women throughout the world. In developed countries, it is the most common type of cancer in women, and it is the second or third most common malignancy in developing countries. The cancer incidence is gradually increasing and remains a significant public health concern. The limitations of mammography as a screening and diagnostic modality, especially in young women with dense breasts, necessitated the development of novel and more effective strategies with high sensitivity and specificity. Thermal imaging (thermography) is a noninvasive imaging procedure used to record the thermal patterns using Infrared (IR) camera. The aim of this study is to evaluate the feasibility of using thermal imaging as a potential tool for detecting breast cancer. In this work, we have used 50 IR breast images (25 normal and 25 cancerous) collected from Singapore General Hospital, Singapore. Texture features were extracted from co-occurrence matrix and run length matrix. Subsequently, these features were fed to the Support Vector Machine (SVM) classifier for automatic classification of normal and malignant breast conditions. Our proposed system gave an accuracy of 88.10%, sensitivity and specificity of 85.71% and 90.48% respectively.
IntroductionRole of Statistical Dependence on Classifier Ensemble FusionAnalysis of or Rule FusionEnsemble Design for or Rule FusionEnsemble Design for and Rule FusionEnsemble Generation for Majority Rule FusionConclusions AcknowledgmentReferences
Introduction: Early detection of breast cancer is known to have a more favourable outcome. Currently clinical breast examination and imaging modalities, primarily mammography are used for screening purposes. In India, more than 85% of the population is below the age of 50 years, wherein the sensitivity of mammography is at best 64%. Additional drawbacks of the procedure are physical discomfort and ionizing radiation dose to the patient. So newer techniques have been investigated which detect cancer induced neovascularity with digital thermal imaging. The purpose of this clinical study was to determine the efficacy of a software assisted thermal image analysis tool to distinguish between benign and malignant lesions of the breast.Methods: A prospective study was conducted in women who presented to the breast clinic with clinically or mammographically suspicious breast lesions. They also underwent thermal imaging of the breast. All mammographically suspicious lumps were subjected to histopathological confirmation. The mammography and infrared (IR) reports were compared to the histopathology.Results: In 90 women, 180 breasts were independently analyzed by both digital IR software analysis and mammography. Eighty five out of these 180 had suspicious lesions on mammography or clinical examination which were subjected to pathological confirmation. Mammography being the present diagnostic gold standard, all normal mammograms in clinically normal breast were considered as non-malignant. The sensitivity and specificity of digital thermography in detecting malignant lesions was 88.24% and 70.52% respectively with NPV of 87.01% and PPV of 72.82%. While for mammography the sensitivity and specificity were 96.25% and 96.7% with NPV of 96.7% and PPV of 96.25%. In women below 50 years of age (62/90) the sensitivity and specificity of digital thermography was 89.83% and 64.61% with NPV of 87.5% and PPV of 69.74%. Further, in the same subset no statistically significant difference was detected in the sensitivity of digital thermography to that of mammography (p = 0.7263).Conclusion: Our initial experience shows that the detection rate by digital thermal imaging is comparable to mammography in clinically palpable breast tumors. We also note that there is no significant difference in sensitivity of thermal imaging in women on either side of 50 making it a potentially testable tool for screening in younger women. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 5028.
Breast cancer is the leading cause of death among women. This fact justifies researches to reach early diagnosis, improving patients’ life expectancy. Moreover, there are other pathologies, such as cysts and benign neoplasms that deserve investigation. In the last ten years, the infrared thermography has shown to be a promising technique to early diagnosis of breast pathologies. Works on this subject presented results that justify the thermography as a complementary exam to detect breast diseases. Several papers on the use of infrared imaging for breast screening can be found in the current medical literature. This survey explores and analyses these works in the light of their applications in computer vision. Consequently, the comments are organized according to the main steps of pattern recognition systems. These include: image acquisition protocols, exams storage, segmentation methods, feature extraction, classification or diagnostic and computer modelling. Main contributions of discussed papers are summarized in tables to provide a structured vision of the aspects involved in breast thermography.
To assess a schema for estimating the risk of radiation-induced breast cancer following exposure of the breast to ionizing radiation as would occur with mammography and to provide data that can be used to estimate the potential number of breast cancers, cancer deaths, and woman-years of life lost attributable to radiation exposure delivered according to a variety of screening scenarios. An excess absolute risk model was used to predict the number of radiation-induced breast cancers attributable to the radiation dose received for a single typical digital mammography examination. The algorithm was then extended to consider the consequences of various scenarios for routine screening beginning and ending at different ages, with examinations taking place at 1- or 2-year intervals. A life-table correction was applied to consider reductions of the cohort size over time owing to nonradiation-related causes of death. Finally, the numbers of breast cancer deaths and woman-years of life lost that might be attributable to the radiation exposure were calculated. Cancer incidence and cancer deaths were estimated for individual attained ages following the onset of screening, and lifetime risks were also calculated. For a cohort of 100 000 women each receiving a dose of 3.7 mGy to both breasts and who were screened annually from age 40 to 55 years and biennially thereafter to age 74 years, it is predicted that there will be 86 cancers induced and 11 deaths due to radiation-induced breast cancer. For the mammographic screening regimens considered that begin at age 40 years, this risk is small compared with the expected mortality reduction achievable through screening. The risk of radiation-induced breast cancer should not be a deterrent from mammographic screening of women over the age of 40 years.