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 nuclei feature (HR 0.729, 95%CI [0.537-0.989], P = 0.042), TNs cell density (HR 1.625, 95%CI [1.177-2.244], P = 0.003), and stromal cell structure feature (HR 1.596, 95%CI [1.142-2.229], P = 0.006) were identified by multivariate Cox proportional hazards model to be new independent prognostic factors. The results indicated that CAI can assist the pathologist in extracting prognostic information from HE histopathology images for IDC. The TNs feature, TNs cell nuclei feature, TNs cell density, and stromal cell structure feature could be new prognostic factors.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Chen JM,Qu AP,Wang LW,Yuan JP,Yang F,Xiang QM,Maskey N,Yang GF,Liu J,Li Ydoi
10.1038/srep10690subject
Has Abstractpub_date
2015-05-29 00:00:00pages
10690issn
2045-2322pii
srep10690journal_volume
5pub_type
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