Automated clear cell renal carcinoma grade classification with prognostic significance


Autoři: Katherine Tian aff001;  Christopher A. Rubadue aff001;  Douglas I. Lin aff001;  Mitko Veta aff003;  Michael E. Pyle aff001;  Humayun Irshad aff001;  Yujing J. Heng aff001
Působiště autorů: Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, United States of America aff001;  The Harker School, San Jose, CA, United States of America aff002;  Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, The Netherlands aff003;  Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, United States of America aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222641

Souhrn

We developed an automated 2-tiered Fuhrman’s grading system for clear cell renal cell carcinoma (ccRCC). Whole slide images (WSI) and clinical data were retrieved for 395 The Cancer Genome Atlas (TCGA) ccRCC cases. Pathologist 1 reviewed and selected regions of interests (ROIs). Nuclear segmentation was performed. Quantitative morphological, intensity, and texture features (n = 72) were extracted. Features associated with grade were identified by constructing a Lasso model using data from cases with concordant 2-tiered Fuhrman’s grades between TCGA and Pathologist 1 (training set n = 235; held-out test set n = 42). Discordant cases (n = 118) were additionally reviewed by Pathologist 2. Cox proportional hazard model evaluated the prognostic efficacy of the predicted grades in an extended test set which was created by combining the test set and discordant cases (n = 160). The Lasso model consisted of 26 features and predicted grade with 84.6% sensitivity and 81.3% specificity in the test set. In the extended test set, predicted grade was significantly associated with overall survival after adjusting for age and gender (Hazard Ratio 2.05; 95% CI 1.21–3.47); manual grades were not prognostic. Future work can adapt our computational system to predict WHO/ISUP grades, and validating this system on other ccRCC cohorts.

Klíčová slova:

Cancer detection and diagnosis – Diagnostic medicine – Ellipses – Chromatin – Image processing – Machine learning – Renal cell carcinoma – Computational systems


Zdroje

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Článek vyšel v časopise

PLOS One


2019 Číslo 10

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