Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. (November 2018)

• Record Type: Journal Article
• Authors: Lu, Cheng; Romo-Bucheli, David; Wang, Xiangxue; Janowczyk, Andrew; Ganesan, Shridar; Gilmore, Hannah; Rimm, David; Madabhushi, Anant
• Is Part Of: Laboratory investigation. Volume 98:Number 11(2018)
• Page Start: 1438
• Page End: 1448
• Publication Date: 2018-11
• Subjects: Medicine, Experimental -- Periodicals; 616.0756
• DOI: 10.1038/s41374-018-0095-7
• Languages: English
• ISSNs: 0023-6837

Abstract

Abstract Early-stage estrogen receptor-positive (ER+) breast cancer (BCa) is the most common type of BCa in the United States. One critical question with these tumors is identifying which patients will receive added benefit from adjuvant chemotherapy. Nuclear pleomorphism (variance in nuclear shape and morphology) is an important constituent of breast grading schemes, and in ER+ cases, the grade is highly correlated with disease outcome. This study aimed to investigate whether quantitative computer-extracted image features of nuclear shape and orientation on digitized images of hematoxylin-stained and eosin-stained tissue of lymph node-negative (LN−), ER+ BCa could help stratify patients into discrete (<10 years short-term vs. >10 years long-term survival) outcome groups independent of standard clinical and pathological parameters. We considered a tissue microarray (TMA) cohort of 276 ER+, LN− patients comprising 150 patients with long-term and 126 patients with short-term overall survival, wherein 177 randomly chosen cases formed the modeling set, and 99 remaining cases the test set. Segmentation of individual nuclei was performed using multiresolution watershed; subsequently, 615 features relating to nuclear shape/texture and orientation disorder were extracted from each TMA spot. The Wilcoxon's rank-sum test identified the 15 most prognostic quantitative histomorphometric features within the modeling set. These features were then subsequently combined via a linear discriminant analysis classifier and evaluated on the test set to assign a probability of long-term vs. short-term disease-specific survival. In univariate survival analysis, patients identified by the image classifier as high risk had significantly poorer survival outcome: hazard ratio (95% confident interval) = 2.91(1.23–6.92), p = 0.02786. Multivariate analysis controlling for T-stage, histology grade, and nuclear grade showed the classifier to be independently predictive of poorer survival: hazard ratio (95% confident interval) = 3.17(0.33–30.46), p = 0.01039. Our results suggest that quantitative histomorphometric features of nuclear shape and orientation are strongly and independently predictive of patient survival in ER+, LN− BCa. This study investigated whether quantitative computer-extracted images of tissue of lymph node (LN)-, estrogen receptor (ER)+ breast cancer could help stratify patients into discrete outcome groups. The results suggest that quantitative histomorphometric features of nuclear shape and orientation are strongly and independently predictive of patient survival in ER+, LN- breast cancer.