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Pleomorphic LCIS (P-LCIS) and florid LCIS (F-LCIS) are morphologic variants distinguished from classic LCIS by marked nuclear pleomorphism and/or an expansile growth pattern with or without necrosis. Given the rarity of these LCIS variants, little data exist regarding their molecular pathogenesis, natural history, and optimal management. The purpose of this study was to genomically profile LCIS variants to gain further insight into their biology. Nineteen cases of pure LCIS variants (17 P-LCIS, 2 F-LCIS) diagnosed on core needle biopsy at our institution from 2006 to 2017 were included, five of which were upgraded to invasive cancer at excision. Macrodissected lesions were analyzed by a hybrid-capture next generation sequencing assay that surveyed exonic sequences of 447 genes for mutations and copy number variations (CNVs) and 191 regions across 60 genes for structural rearrangements. LCIS variants were all confirmed as E-cadherin negative by immunohistochemistry. Receptor profiles among the 17 P-LCIS cases included HR+/HER2− (nine cases), HR+/HER2+ (three cases), HR−/HER2+ (two cases), and HR−/HER2− (three cases). The two F-LCIS cases were HR+/HER2− and HR+/HER2+. All LCIS variants had genetic alterations consistent with a lobular phenotype including 1q gain (16 cases), 16q loss (18 cases), and CDH1mutations (18 cases). Highly recurrent ERBB2alterations were noted including mutations (13 cases) and amplifications (six cases). Other significant alterations included mutations in PIK3CA(six cases), RUNX1(four cases), ERBB3(four cases), and CBFB(three cases), as well as amplification of CCND1(five cases). A TP53mutation was identified in one case of HR−/HER2+ P-LCIS with signet ring cell features that lacked 1q gain and 16q loss. P-LCIS and F-LCIS contain genetic alterations characteristic of lobular neoplasia; however, these LCIS variants are distinguished from classical LCIS reported in the literature by their highly recurrent ERBB2alterations.

The use of artificial intelligence (AI) within pathology and health care has advanced extensively. We have accordingly witnessed an increased adoption of various AI tools that are transforming our approach to clinical decision support, personalized medicine, predictive analytics, automation, and discovery. The familiar and more reliable AI tools that have been incorporated within health care thus far fall mostly under the nongenerative AI domain, which includes supervised and unsupervised machine learning (ML) techniques. This review article explores how such nongenerative AI methods, rooted in traditional rules-based systems, enhance diagnostic accuracy, efficiency, and consistency within medicine. Key concepts and the application of supervised learning models (ie, classification and regression) such as decision trees, support vector machines, linear and logistic regression, K-nearest neighbor, and neural networks are explained along with the newer landscape of neural network-based nongenerative foundation models. Unsupervised learning techniques, including clustering, dimensionality reduction, and anomaly detection, are also discussed for their roles in uncovering novel disease subtypes or identifying outliers. Technical details related to the application of nongenerative AI algorithms for analyzing whole slide images are also highlighted. The performance, explainability, and reliability of nongenerative AI models essential for clinical decision-making is also reviewed, as well as challenges related to data quality, model interpretability, and risk of data drift. An understanding of which AI-ML models to employ and which shortcomings need to be addressed is imperative to safely and efficiently leverage, integrate, and monitor these traditional AI tools in clinical practice and research.