Artificial intelligence-based morphometric signature to identify ductal carcinoma in situ with low risk of progression to invasive breast cancer

Author:

Sobral-Leite Marcelo1ORCID,Castillo Simon2ORCID,Vonk Shiva1,Melillo Xenia1,Lam Noomie1,de Bruijn Brandi1,Hagos Yeman3,Sanders Joyce1,Almekinders Mathilde1,Visser Lindy1ORCID,Groen Emma1,Kristel Petra1,Ercan Caner2ORCID,Azarang Leyla1,Yuan Yinyin4,Consortium Grand Challenge PRECISION1,Menezes Renee1,Lips Esther1,Wesseling Jelle1

Affiliation:

1. Netherlands Cancer Institute

2. The University of Texas MD Anderson Cancer Center

3. The Institute of Cancer Research

4. The Institute of Cancer Research, London

Abstract

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 these women that may forego treatment, we hypothesized that DCIS morphometric features relate to the risk of subsequent iIBC. We developed an artificial intelligence-based DCIS morphometric analysis pipeline (AIDmap) to detect DCIS as a pathologist and measure morphological structures in hematoxylin-eosin-stained (H&E) tissue sections. These were from a case-control study of patients diagnosed with primary DCIS, treated by breast-conserving surgery without radiotherapy. We analyzed 689 WSIs of DCIS of which 226 were diagnosed with subsequent iIBC (cases) and 463 were not (controls). The distribution of 15 duct morphological measurements in each H&E 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 0.65 (95% CI 0.55–0.76). A morphometric signature based on the 30 variables most associated with outcome, identified lesions containing small-sized ducts, low number of cells and low DCIS/stroma area ratio. This signature was associated with lower iIBC risk in a multivariate regression model including grade, ER, HER2 and COX-2 expression (HR = 0.56; 95% CI 0.28–0.78). AIDmap has potential to identify harmless DCIS that may not need treatment.

Publisher

Research Square Platform LLC

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