New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images.

ABSTRACT: Computer-aided image analysis (CAI) can help objectively quantify morphologic features of hematoxylin-eosin (HE) histopathology images and provide potentially useful prognostic information on breast cancer. We performed a CAI workflow on 1,150 HE images from 230 patients with invasive ductal carcinoma (IDC) of the breast. We used a pixel-wise support vector machine classifier for tumor nests (TNs)-stroma segmentation, and a marker-controlled watershed algorithm for nuclei segmentation. 730 morphologic parameters were extracted after segmentation, and 12 parameters identified by Kaplan-Meier analysis were significantly associated with 8-year disease free survival (P < 0.05 for all). Moreover, four image features including TNs feature (HR 1.327, 95%CI [1.001-1.759], P = 0.049), TNs cell nucl

SUBMITTER: Chen JM 

PROVIDER: S-EPMC4448264 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature