LAUSR

BACKGROUND AND AIMS To examine whether quantitative pathologic analysis of digitized hematoxylin and eosin (H&E) slides of colorectal carcinoma (CRC) correlates with clinicopathologic features, molecular alterations, and prognosis. METHODS A quantitative segmentation algorithm (QuantCRC) was applied to 6,468 digitized H&Es of CRCs. Fifteen parameters were recorded from each image and tested for associations with clinicopathologic features and molecular alterations. A prognostic model was developed to predict recurrence-free survival (RFS) using data from the internal cohort (N=1928) and validated on an internal test (N=483) and external cohort (N=938). RESULTS There were significant differences in QuantCRC according to stage, histologic subtype, grade, venous/lymphatic/perineural invasion, tumor budding, CD8 immunohistochemistry, mismatch repair (MMR) status, KRAS mutation, BRAF mutation, and CpG methylation. A prognostic model incorporating stage, MMR, and QuantCRC resulted in a Harrell's concordance (c)-index of 0.714 (95%CI 0.702-0.724) in the internal test and 0.744 (95%CI 0.741-0.754) in the external cohort. Removing QuantCRC from the model reduced the c-index to 0.679 (95%CI 0.673-0.694) in the external cohort. Prognostic risk groups were identified, which provided a hazard ratio of 2.24 (95%CI 1.33-3.87, P=0.004) for low vs. high-risk stage III CRCs and 2.36 (95%CI 1.07-5.20, P=0.03) for low vs. high-risk stage II CRCs, in the external cohort after adjusting for established risk factors. The predicted median 36-month recurrence rate for high-risk stage III CRCs was 32.7% vs. 13.4% for low-risk stage III and 15.8% for high-risk stage II vs. 5.4% for low-risk stage II CRCs. CONCLUSIONS QuantCRC provides a powerful adjunct to routine pathologic reporting of CRC. A prognostic model using QuantCRC improves prediction of RFS.