Myron Moskowitz

Feasibility of a randomized trial of a low-fat diet for the prevention of breast cancer: dietary compliance in the Women's Health Trial Vanguard Study.

The Womens Health Trial Vanguard Study was conducted to examine the feasibility of a nationwide, randomized multicenter intervention trial to test the hypothesis that a low-fat diet followed for a period of 10 years will reduce breast cancer risk. Women ages 45-69 years at increased risk of breast cancer were randomized into intervention (low-fat diet, n = 184) and control (usual diet, n = 119) groups. On the basis of 4-day food records, baseline fat intakes were comparable in the two groups, averaging 1,718 kcal with 39% of energy as fat. Intervention women reported substantially lower fat intake at 6 (20.9% kcal), 12 (21.6%), and 24 months (22.6% kcal). In contrast, control women reported only slight reductions in fat intake (37.3% kcal at 12 months and 36.8% kcal at 24 months). Evidence that these women were indeed complying with the low-fat dietary intervention comes from (a) the reasonable nature of reported nutrient changes within food groups in the intervention women and (b) agreement between observed and expected differences in plasma total cholesterol between the control and the intervention groups. At 12 months, the observed control - intervention plasma cholesterol difference was 13.1 +/- 4.6 mg/dl while the expected difference based on the Keys equation was 15.1 +/- 1.1 mg/dl; at 24 months, the observed difference was 15.5 +/- 4.3 mg/dl and the expected difference was 12.0 +/- 1.2 mg/dl. These analyses indicate that the intervention women made substantial dietary changes and have successfully maintained these changes over a 2-year period. This study thus demonstrates the feasibility of a randomized trial with an intensive low-fat dietary intervention.

Lack of efficacy of thermography as a screening tool for minimal and stage I breast cancer.

In a study of thermograms of 42 patients with Stage 1 or smaller carcinomas of the breast, 44 confounding cases and 64 randomly selected subjects being screened, we found that the ability of expert thermographers to identify the patients with carcinoma correctly (true positive = 0.238) varied little from the ability of untrained readers (true positive = 0.301). Furthermore, in the expert group, the indexes of suspicion were so high (0.436) and the true-positive levels were so relatively low (0.238, P = 0.0005) that thermography may well have a very limited role as a screening or pre-screening modality for the detection of minimal or Stage 1 breast cancers.

Artificial neural network based classification of mammographic microcalcifications using image structure and cluster features

Breast cancer is the leading cause of death among women. Mammography is the only effective and viable technique to detect breast cancer, sometimes before the cancer becomes invasive. About 30% to 50% of breast cancers demonstrate clustered microcalcifications. We investigate the potential of using second-order histogram textural features for their correlation with malignancy. A combination of image structure features extracted from the second histogram was used with binary cluster features extracted from segmented calcifications. Several architectures of neural networks were used for analyzing the features. The neural network yielded good results for the classification of hard-to-diagnose cases of mammographic microcalcification into benign malignant categories using the selected set of features.<<ETX>>

Screening is not diagnosis.

Mammography and physical examination, usually employed as diagnostic tools, may be used to screen for early detection of breast cancer. A study of these modalities used to aggressively screen patients in Cincinnati and Milwaukee is presented and compared to more traditional methods of breast cancer detection and diagnosis as done in Louisville. For a similar-sized group over the same period, the rate of cancers detected in Louisville did not exceed that in Cincinnati-Milwaukee. Aggressive screening will not increase the overall number of detected cancers but will decrease the number of advanced cancers. Mammography as a screening device is not in itself a diagnostic tool, since its potential benefit is maximized only through the use of nondiagnostic, indirect radiographic criteria.

On the diagnosis of minimal breast cancer in a screenee population.

As a result of screening of 8100 consecutive volunteers, 67 breast cancers have been detected. Fourteen percent of these cancers were found in women aged 35–44 years. Eighty percent of the cancers found in this age group were minimal breast cancer. This percentage decreased significantly in each subsequent decade until age 65 years, or older, when it began to rise once more. While mammography was responsible for detecting the majority of in situ or minimal cancers, 13 were found on physical examination, six of which were found only by physical examination.

Observer variation in the classification of mammographic parenchymal patterns

Wolfe has described different cancer risks associated with a classification of four patterns of the breast parenchyma on mammography, but there is however little information available on the ability of radiologists to agree on the classification of the different patterns. We have assessed inter-rater agreement on the assignment of films to one of the four mammographic patterns described by Wolfe. One hundred xeromammograms were selected, copied and distributed to 10 radiologists who were experts in mammography. Films were classified according to the presence or absence of several radiological signs, according to diagnosis and recommendation, and according to mammographic pattern. Agreement was assessed after correction for agreement expected by chance, using the Kappa statistic. In general, high levels of agreement were found for the classification of mammographic pattern. Agreement on the classification of mammographic pattern was substantially greater than agreement for any other feature of mammographic interpretation, including diagnosis and recommendation.

Effects of lead time, length bias, and false-negative assurance on screening for breast cancer

This article presents a model of breast cancer screening programs. The model shows the effects of the screening threshold, screen sensitivity, and false‐negative assurance on the cumulative mortality rate in the screened population. It shows that factors of screen design and a penalty associated with false‐negative assurance can result in excess mortality rates in screened populations‐especially in those age classes in which the incidence of rapidly growing tumors is high. Factors related to the magnitude of this effect are described.

ARTIFICIAL NEURAL NETWORK BASED CLASSIFICATION OF MAMMOGRAPHIC MICROCALCIFICATIONS USING IMAGE STRUCTURE FEATURES

Mammography associated with clinical breast examination and breast self-examination is the only effective and viable method for mass breast screening. Most of the minimal breast cancers are detected by the presence of microcalcifications. It is however difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized grey-level image into regions representing microcalcifications. Since mammographic images usually suffer from poorly defined microcalcification features, the extraction of microcalcification features based on segmentation process is not reliable and accurate. We present a second-order grey-level histogram based feature extraction approach which does not require the segmentation of microcalcifications into binary regions to extract features to be used in classification. The image structure features, computed from the second-order grey-level histogram statistics, are used for classification of microcalcifications. Several image structure features were computed for 100 cases of “difficult to diagnose” microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. Four networks were trained for different combinations of training and test cases, and number of nodes in hidden layers. False Positive (FP) and True Positive (TP) rates for microcalcification classification were computed to compare the performance of the trained networks. The results of the neural network based classification were compared with those obtained using multivariate Baye’s classifiers, and the k-nearest neighbor classifier. The neural network yielded good results for classification of “difficult-to-diagnose” micro-calcifications into benign and malignant categories using the selected image structure features.

Cost-benefit determinations in screening mammography.

A cost-benefit analysis clearly shows that the costs for screening a large population of asymptomatic women are well within the cost-benefit range accepted for other areas within the medical care system. Reduction in cancer deaths is not easy to come by. When a method is available which can achieve this result, every effort should be made to make it available until it can be replaced satisfactorily with a less expensive, equally effective method.

Breast cancer screening: Preliminary report of 207 biopsies performed in 4,128 volunteer screenees

Thirty‐six carcinomas of the breast were pathologically verified in the first 207 biopsies performed to date on women attending the Breast Cancer Detection Center at The University of Cincinnati. Fifty‐three percent of these have been minimal breast cancer, 14 being totally in situ. This is compared with a similar number of breast biopsies performed prior to the opening of the Breast Cancer Project. In the latter group of cases there were 45 carcinomas proven, only 1 of which was in situ, 24 of which were invasive Stage I and 20 Stage II or higher. In addition, 65 biopsies demonstrated proliferative disease of varying degrees of severity as compared to a total of 13 in the non screened population.