The Breast : official journal of the European Society of Mastology
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Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. ⋯ In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.
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Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. ⋯ Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication.
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Comparative Study
Modified lymph node ratio improves the prognostic predictive ability for breast cancer patients compared with other lymph node staging systems.
Metastatic regional lymph nodes (LN) is a strong predictor of worse long-term outcome. Therefore, different LN staging systems have been proposed in recent years. In this study, we proposed a modified lymph node ratio (mLNR) as a new lymph node staging system and then compared the prognostic performance of mLNR with American Joint Committee on Cancer N stage, lymph node ratio (LNR) and log odds of metastatic lymph nodes in breast cancer patients. ⋯ The predictive ability of LNR is restricted by a limited LN harvest. However, mLNR shows superiority to LNR and other lymph node staging systems especially in a limited LN harvest cohort, making mLNR the most powerful lymph node staging systems.