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 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 Rep

journal_title

Scientific reports

authors

Chen JM,Qu AP,Wang LW,Yuan JP,Yang F,Xiang QM,Maskey N,Yang GF,Liu J,Li Y

doi

10.1038/srep10690

subject

Has Abstract

pub_date

2015-05-29 00:00:00

pages

10690

issn

2045-2322

pii

srep10690

journal_volume

5

pub_type

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