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Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Metastatic neuroendocrine neoplasms to the breast may show considerable morphologic overlap with primary mammary carcinomas, particularly those showing evidence of neuroendocrine differentiation, and may be misdiagnosed as such. Accurate distinction between these two entities is crucial for determination of appropriate clinical management. The histologic and immunohistochemical features of metastatic neuroendocrine neoplasms to the breast were studied and compared with the features of primary invasive mammary carcinomas with neuroendocrine differentiation, which served as controls. Of the metastatic neuroendocrine neoplasms, 15 were well-differentiated neuroendocrine tumors with carcinoid tumor-type morphology and 7 were poorly differentiated/high-grade neuroendocrine carcinomas with small-cell or large-cell neuroendocrine carcinoma morphology. The majority of the metastatic neoplasms originated in the lung and gastrointestinal tract. There were histologic similarities between metastatic neuroendocrine neoplasms and invasive mammary carcinomas with neuroendocrine differentiation, both of which exhibited neuroendocrine histologic features (nested and trabecular architecture, minimal tubular differentiation, and characteristic nuclear features). Only one case of the invasive mammary carcinomas with neuroendocrine differentiation was modified Bloom-Richardson grade 1 (largely due to minimal tubular differentiation on most such tumors), and the invasive mammary carcinomas with neuroendocrine differentiation were often associated with in situ carcinoma. Immunohistochemistry was helpful in distinguishing metastatic neuroendocrine neoplasms from invasive mammary carcinomas with neuroendocrine differentiation. Whereas the majority of invasive mammary carcinomas with neuroendocrine differentiation were positive for estrogen receptor and GATA3, metastatic neuroendocrine neoplasms were typically negative for estrogen receptor and GATA3, and metastatic well-differentiated neuroendocrine tumors often showed immunoreactivity for site-specific markers. Although the histologic and immunohistochemical features of a breast tumor may raise the suspicion of a metastatic neuroendocrine neoplasm, the pathologic findings should be interpreted in the context of the clinical history and imaging findings in order to establish an accurate diagnosis.

A high frequency of precursor lesions is a risk factor for cancer in many organ systems but must be precisely quantified. Pelvic serous neoplasia is associated with an estimated increase in frequency of secretory cell outgrowths (SCOUTs) with loss of PAX2 protein (PAX2p) expression (PAX2p-null SCOUTs) in the fallopian tube. However, to confirm this, PAX2p-null SCOUTs must be precisely quantified relative to the epithelial surface. We developed a method by which fallopian tube sections were digitized using an iScan brightfield scanner (BioImagene) and uploaded in Adobe Photoshop CS3 Extended. Pixel length was translated into microns and epithelial length measured with the Magic Wand tool. SCOUTs were expressed as a function of total epithelial perimeter. Frequency, required perimeter length, topographic clustering tendency and effects of age were ascertained. SCOUT frequency per 10 cm was 0-4.60 for cases and 0-1.66 for controls, averaging 0.84 and 0.27, respectively, (P=0.007). Required perimeter length for SCOUT detection was less in serous cancer cases and topographic distribution followed a random pattern without aberrant clustering. Age was also associated with SCOUT frequency (P=0.025) and differences between cancers and controls were still significant after adjusting for age (P=0.001). We describe an efficient method for quantifying epithelial perimeter in the fallopian tube and verify its relevance to precursor frequency. This has important implications for assessing precursor frequency both in the fallopian tube and in other organs-such as prostate, pancreas and colon-where epithelial precursors are integral to carcinogenesis.