RESUMEN
Mass-forming ductal carcinoma in situ (DCIS) detected on core needle biopsy (CNB) is often a radiology-pathology discordance and thought to represent missed invasive carcinoma. This brief report applied supervised machine learning (ML) for image segmentation to investigate a series of 44 mass-forming DCIS cases, with the primary focus being stromal computational signatures. The area under the curve (AUC) for receiver operator curves (ROC) in relation to upgrade to invasive carcinoma from DCIS were as follows: high myxoid stromal ratio (MSR): 0.923, P = <0.001; low collagenous stromal percentage (CSP): 0.875, P = <0.001; and low proportionated stromal area (PSA): 0.682, P = 0.039. The use of ML in mass-forming DCIS could predict upgraded to invasive carcinoma with high sensitivity and specificity. The findings from this brief report are clinically useful and should be further validated by future studies.