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.

Classification of breast cancer stroma as a tool for prognosis

“…Three stromal morphological features were significantly associated with survival -all related with the spatial relationships between different populations of stromal cell types -and were more closely related to cancer progression than the morphological features obtained from epithelial compartments. Chen et al 10 extracted 730 morphologic parameters after tumour-stroma segmentation. Twelve parameters were significantly associated with 8-year disease free survival; one of these was related to the stromal component (stromal cell structure).…”

Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

“…Third, informatics and big data analytic methods (8) are providing unprecedented detail about data from the subcellular to the tissue level [in, e.g., nuclei (9–13), mitoses (14, 15), and lymphocytes (12, 16)]. Moreover, recently proposed machine learning–based approaches, in conjunction with “subvisual” image biomarkers of disease architecture, could provide information about the state of aggressiveness of the disease and enable prognostic prediction of therapeutic outcome (17). …”

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

“…Stromal cell structure feature has also been proved to be an independent prognostic factor for invasive ductal carcinoma of the breast, which is the major type of BC. 18 Other stromal features like the spatial distribution of stromal cells quantified by Ripley's K function have significant prognostic value for ER-negative BC. 75 …”

“…11 Histological features in H&E images are measured to evaluate tumor grade and prognosis for BC. Recently, various image analysis approaches have been developed to help pathologists quantify morphological features, 12 detect malignant lesions, 13,14 and predict prognosis [15][16][17][18] for BC. In this article, we summarized recent works in image analysis of H&E histopathology images for BC prognosis.…”