The average lifetime risk of breast cancer for a woman in the United States has been estimated at 12.3% (ie, 1 in 8 women). 1 For 2018, the American Cancer Society (ACS) estimates that 63,960 cases of female carcinoma in situ of the breast and 268,670 cases of invasive breast cancer (266,120 women and 2,550 men) will be diagnosed in the United States. 2 About 41,400 deaths are estimated for 2018. 3 The good news is that death rates have been falling on average NCCN

There is substantial overlap in the imaging characteristics of benign and malignant phyllodes tumors. A tumor diameter of 3 cm or greater appears to be associated with a higher likelihood of malignancy.

Women in the United States have a 12.3% estimated lifetime risk for developing breast cancer (i.e., 1 in 8 women). 1 In 2009, an estimated 194,290 cases of invasive breast cancer (192,370 women and 1919 men) and 62,280 cases of female carcinoma in situ of the breast will be diagnosed in the United States, with 40,610 deaths from invasive breast cancer predicted. 2 However, mortality from breast cancer has decreased slightly, attributed partly to mammographic screening. 3 The NCCN Breast Cancer Screening and Diagnosis Panel designed these practice guidelines to fa-The NCCN

Stereotaxic core biopsy obviated surgical biopsy for most nonpalpable lesions sampled, resulting in a greater than 50% reduction in biopsy costs. If these results were generalizable to the national level, annual savings would approach $200 million.

In this retrospective review of this small subset of cancers, it appears that CAD has the potential to decrease the FN rate at double reading by more than one-third (from 31% to 19%). The CAD system correctly marked 37 (71%) of 52 actionable findings read as negative in previous screening years.

Background To demonstrate that computer-extracted image phenotypes (CEIPs) of biopsy-proven breast cancer on MRI can accurately predict pathologic stage. Methods We used a dataset of de-identified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. We analyzed 91 biopsy-proven breast cancer cases with pathologic stage (stage I = 22; stage II = 58; stage III = 11) and surgically proven nodal status (negative nodes = 46, ≥ 1 positive node = 44, no nodes examined = 1). We characterized tumors by (a) radiologist measured size, and (b) CEIP. We built models combining two CEIPs to predict tumor pathologic stage and lymph node involvement, evaluated them in leave-one-out cross-validation with area under the ROC curve (AUC) as figure of merit. Results Tumor size was the most powerful predictor of pathologic stage but CEIPs capturing biologic behavior also emerged as predictive (e.g. stage I+II vs. III demonstrated AUC = 0.83). No size measure was successful in the prediction of positive lymph nodes but adding a CEIP describing tumor “homogeneity,” significantly improved this discrimination (AUC = 0.62, p=.003) over chance. Conclusions Our results indicate that MRI phenotypes show promise for predicting breast cancer pathologic stage and lymph node status.

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BackgroundIn this study, we sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute.MethodsOur retrospective interpretation study involved analysis of Health Insurance Portability and Accountability Act-compliant breast MRI data from The Cancer Imaging Archive, an open-source database from the TCGA project. This study was exempt from institutional review board approval at Memorial Sloan Kettering Cancer Center and the need for informed consent was waived. Ninety-one pre-operative breast MRIs with verified invasive breast cancers were analysed. Three fellowship-trained breast radiologists evaluated the index cancer in each case according to size and the BI-RADS lexicon for shape, margin, and enhancement (human-extracted image phenotypes [HEIP]). Human inter-observer agreement was analysed by the intra-class correlation coefficient (ICC) for size and Krippendorff’s α for other measurements. Quantitative MRI radiomics of computerised three-dimensional segmentations of each cancer generated computer-extracted image phenotypes (CEIP). Spearman’s rank correlation coefficients were used to compare HEIP and CEIP.ResultsInter-observer agreement for HEIP varied, with the highest agreement seen for size (ICC 0.679) and shape (ICC 0.527). The computer-extracted maximum linear size replicated the human measurement with p < 10−12. CEIP of shape, specifically sphericity and irregularity, replicated HEIP with both p values < 0.001. CEIP did not demonstrate agreement with HEIP of tumour margin or internal enhancement.ConclusionsQuantitative radiomics of breast cancer may replicate human-extracted tumour size and BI-RADS imaging phenotypes, thus enabling precision medicine.