SummaryBackgroundMenarche and menopause mark the onset and cessation, respectively, of ovarian activity associated with reproduction, and affect breast cancer risk. Our aim was to assess the strengths of their effects and determine whether they depend on characteristics of the tumours or the affected women.MethodsIndividual data from 117 epidemiological studies, including 118 964 women with invasive breast cancer and 306 091 without the disease, none of whom had used menopausal hormone therapy, were included in the analyses. We calculated adjusted relative risks (RRs) associated with menarche and menopause for breast cancer overall, and by tumour histology and by oestrogen receptor expression.FindingsBreast cancer risk increased by a factor of 1·050 (95% CI 1·044–1·057; p<0·0001) for every year younger at menarche, and independently by a smaller amount (1·029, 1·025–1·032; p<0·0001), for every year older at menopause. Premenopausal women had a greater risk of breast cancer than postmenopausal women of an identical age (RR at age 45–54 years 1·43, 1·33–1·52, p<0·001). All three of these associations were attenuated by increasing adiposity among postmenopausal women, but did not vary materially by women's year of birth, ethnic origin, childbearing history, smoking, alcohol consumption, or hormonal contraceptive use. All three associations were stronger for lobular than for ductal tumours (p<0·006 for each comparison). The effect of menopause in women of an identical age and trends by age at menopause were stronger for oestrogen receptor-positive disease than for oestrogen receptor-negative disease (p<0·01 for both comparisons).InterpretationThe effects of menarche and menopause on breast cancer risk might not be acting merely by lengthening women's total number of reproductive years. Endogenous ovarian hormones are more relevant for oestrogen receptor-positive disease than for oestrogen receptor-negative disease and for lobular than for ductal tumours.FundingCancer Research UK.

These data indicate that computerized nuclear texture analysis as well as up regulation of sialyl Lewis(x) molecules may be new important prognostic factors in metastatic prostate cancer. Furthermore the prognostic importance of sedimentation rate, alkaline phosphatase and hemoglobin was confirmed.

A new texture operator, gray‐level entropy matrix (GLEM), was developed, and nine new textural features were extracted from this matrix. These textural features were applied to light microscopy images of nuclei taken from monolayers of advanced prostate cancer cells representing two different prognostic groups: hormone‐sensitive (good prognosis) and hormone‐resistant (poor prognosis) tumors. A comparison between the classification results obtained from GLEM features and those obtained from standard textural estimators is also discussed. Single features that gave correct classification rates better than 65% were included in a discriminant analysis in order to find the optimal set of features to discriminate between the two prognostic groups in the training data set. The best combination of features includes three GLEM features together with ENTROPY extracted from gray‐level cooccurrence matrix, and this combination gave a correct classification rate of 95% using the leaving‐one‐out technique. The influences of image sharpness and number of cells were also investigated. The features based on entropy or degree of scatter of minute structures can be used to discriminate between hormone‐sensitive and hormone‐resistant prostate carcinomas. © 1996 Wiley‐Liss, Inc.

This report describes the prognostic value of computerized nuclear texture analysis in metastatic prostate cancer. Seventy‐seven patients with histologically verified prostate carcinomas and skeletal metastases were selected from a Scandinavian multicenter study (SPCG‐2). Thirty‐six therapy‐resistant patients experienced objective progression and cancer‐related death within 2 years after orchiectomy. Thirty patients responded well to orchiectomy, i.e., showed objective disease remission and no signs of progression during 3 years of follow‐up. From this data set, 10 randomly chosen therapy‐resistant and 10 randomly chosen therapy‐sensitive carcinomas were used in our previous study to find the optimal combination of features that can discriminate between the two groups (Yogesan et al.: Cytometry 24:268–276, 1996). In addition to these two groups, 11 patients experienced stable disease or disease remission during the first year and a secondary progression during the second or third year of follow‐up, with subsequent cancer‐related death. Traditional clinical prognostic factors such as histopathological grading and serum markers could not discriminate between these groups of patients. Therefore, image analysis techniques based on texture analysis have been utilized in this study of prognosis of prostate cancer. Feulgen‐stained monolayers of nuclei were prepared from paraffin‐embedded material taken from the primary tumor before endocrine ablation. Four different textural features were selected from the training data set to calculate the discriminating function. This function separated the therapy‐sensitive and the therapy‐resistant patients with 87% accuracy in the independent data set. This study demonstrates that it is possible to predict tumor progression and survival for endocrine‐ablated metastatic prostate carcinomas using computerized nuclear texture analysis on light microscopy images from prostate biopsies taken at the time of diagnosis. © 1996 Wiley‐Liss, Inc.