A morphometric signature to identify ductal carcinoma in situ with a low risk of progression.

IF 6.8 1区 医学 Q1 ONCOLOGY
Marcelo Sobral-Leite, Simon P Castillo, Shiva Vonk, Hendrik A Messal, Xenia Melillo, Noomie Lam, Brandi de Bruijn, Yeman B Hagos, Myrna van den Bos, Joyce Sanders, Mathilde Almekinders, Lindy L Visser, Emma J Groen, Petra Kristel, Caner Ercan, Leyla Azarang, Jacco van Rheenen, E Shelley Hwang, Yinyin Yuan, Renee Menezes, Esther H Lips, Jelle Wesseling
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引用次数: 0

Abstract

Ductal carcinoma in situ (DCIS) may progress to ipsilateral invasive breast cancer (iIBC), but often never will. Because DCIS is treated as early breast cancer, many women with harmless DCIS face overtreatment. To identify features associated with progression, we developed an artificial intelligence-based DCIS morphometric analysis pipeline (AIDmap) on hematoxylin-eosin-stained (H&E) tissue sections. We analyzed 689 digitized H&Es of pure primary DCIS of which 226 were diagnosed with subsequent iIBC and 463 were not. The distribution of 15 duct morphological measurements was summarized in 55 morphometric variables. A ridge regression classifier with cross validation predicted 5-years-free of iIBC with an area-under the curve of 0.67 (95% CI 0.57-0.77). A combined clinical-morphometric signature, characterized by small-sized ducts, a low number of cells and a low DCIS/stroma ratio, was associated with outcome (HR = 0.56; 95% CI 0.28-0.78). AIDmap has potential to identify harmless DCIS that may not need treatment.

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来源期刊
CiteScore
9.90
自引率
1.30%
发文量
87
审稿时长
18 weeks
期刊介绍: Online-only and open access, npj Precision Oncology is an international, peer-reviewed journal dedicated to showcasing cutting-edge scientific research in all facets of precision oncology, spanning from fundamental science to translational applications and clinical medicine.
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