Upgrade of high-risk breast lesions detected on mammography in the Breast Cancer Surveillance Consortium

BackgroundUpgrade rates of high-risk breast lesions after screening mammography were examined.MethodsThe Breast Cancer Surveillance Consortium registry was used to identify all Breast Imaging Reporting and Data System category 4 assessments followed by needle biopsies with high-risk lesions. Follow-up was performed for all women.ResultsHigh-risk lesions were found in 957 needle biopsies, with excision documented in 53%. Most (n = 685) were atypical ductal hyperplasia (ADH), 173 were lobular neoplasia, and 99 were papillary lesions. Upgrade to cancer varied with type of lesion (18% in ADH, 10% in lobular neoplasia, and 2% in papillary lesions). In premenopausal women with ADH, upgrade was associated with family history. Cancers associated with ADH were mostly (82%) ductal carcinoma in situ, and those associated with lobular neoplasia were mostly (56%) invasive. During a further 2 years of follow-up, cancer was documented in 1% of women with follow-up surgery and in 3% with no surgery.ConclusionsDespite low rates of surgery, low rates of cancer were documented during follow-up. Benign papillary lesions diagnosed on Breast Imaging Reporting and Data System category 4 mammograms among asymptomatic women do not justify surgical excision

Risk Modeling of Time-Varying Covariates Using an Ensemble of Survival Trees: Predicting Future Cancer Events

The challenge of survival prediction is ubiquitous in medicine, but only a handful of methods are available for survival prediction based on time-varying data. Here we propose a novel method for this problem, using a random forest of survival trees for left-truncated and right-censored data. We demonstrate the advantage of our method on prediction of breast cancer and prostate gland cancer risk among healthy individuals by analyzing routine laboratory measurements, vital signs and age. We analyze electronic medical records of 20,317 healthy individuals who underwent routine checkups and identified those who later developed cancer. In cross-validation, our method predicted future prostate and breast cancers six months before diagnosis with an area under the ROC curve of 0.62±0.05 and 0.6±0.03 respectively, outperforming standard random forest, random survival forest, cox-regression model, dynamic deep-hit and a single survival tree. Our work proposes a new framework for survival risk prediction in time-varying data and our results suggest that computational analysis of data on healthy individuals can improve the detection of those at risk of future cancer development