Gastroenterology

Gastroenterology

Volume 163, Issue 6, December 2022, Pages 1531-1546.e8
Gastroenterology

Original Research
Full Report: GI Cancer
Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival

https://doi.org/10.1053/j.gastro.2022.08.025Get rights and content

Background & Aims

To examine whether quantitative pathologic analysis of digitized hematoxylin and eosin slides of colorectal carcinoma (CRC) correlates with clinicopathologic features, molecular alterations, and prognosis.

Methods

A quantitative segmentation algorithm (QuantCRC) was applied to 6468 digitized hematoxylin and eosin slides 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 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 status, KRAS mutation, BRAF mutation, and CpG methylation. A prognostic model incorporating stage, mismatch repair, and QuantCRC resulted in a Harrell’s concordance (c)-index of 0.714 (95% confidence interval [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 = .004) for low vs high-risk stage III CRCs and 2.36 (95% CI, 1.07–5.20, P = .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 recurrence-free survival.

Section snippets

Study Populations

The study population consisted of CRCs from the Colon Cancer Family Registry (CCFR, participating sites: Seattle, Australia, Mayo, Ontario, and Hawaii) as well as 3 sites external to the CCFR: University of Pittsburgh Medical Center (UPMC), Mount Sinai Hospital Toronto, and Seattle-Puget Sound Cancer Registry. The CCFR enrolled participants after CRC diagnosis with prospective follow-up and has been described in detail.28,29 The UPMC and Mount Sinai cohorts consisted of consecutively resected

QuantCRC Is Significantly Associated With Tumor Stage, Tumor Location, and Histologic Features

QuantCRC was applied to 6468 CRCs (Figure 1). The full cohort details are shown in Supplementary Table 1. Significant differences were seen when QuantCRC results were stratified by TNM stage, tumor location, histologic category, tumor grade, venous/lymphatic/perineural invasion (VELIPI), and pathologist-derived TB grade (Table 1 and Supplementary Table 6). Increased tumor stage or presence of VELIPI were associated with decreased tumor:stroma ratio, %inflammatory stroma (both tumor bed and

Discussion

In this study, we applied our previously developed QuantCRC algorithm to 6468 CRCs from multiple centers. QuantCRC results had strong associations with pathologist-derived parameters, molecular features, and prognosis. A prognostic model was developed by combining the output from QuantCRC with MMR status and stage and shown to be prognostic in an external cohort. These results demonstrate the potential of quantitative digital pathology to provide a detailed assessment of known histologic

CRediT Authorship Contributions

Order of Authors (with Contributor Roles):

Reetesh K. Pai, MD (Conceptualization: Supporting; Formal analysis: Supporting; Writing – original draft: Supporting; Writing – review & editing: Supporting).

Imon Banerjee, PhD (Formal analysis: Equal; Methodology: Equal; Writing – original draft: Supporting; Writing – review & editing: Supporting).

Sameer Shivji, MD (Data curation: Supporting; Writing – original draft: Supporting; Writing – review & editing: Supporting).

Suchit Jain, BS (Formal analysis:

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  • Cited by (6)

    Conflicts of Interest These authors disclose the following: David F. Schaeffer reports honoraria from Alimentiv Inc., Pfizer, Merck, Diaceutics, and Astellas; and stock ownership in Satisfai Health Inc outside of the submitted work. Reetesh K. Pai, Christophe Rosty, Richard Kirsch report consulting income from Alimentiv Inc. outside of the submitted work. Rish K. Pai reports consulting income from Alimentiv Inc., Eli Lilly, AbbVie, Allergan, Genentech, and PathAI outside of the submitted work. Thomas Westerling-Bui is an employee of Aiforia Inc. The remaining authors disclose no conflicts.

    Funding The Colon Cancer Family Registry (CCFR, www.coloncfr.org) is supported in part by funding from the National Cancer Institute (NCI), National Institutes of Health (NIH) (award U01 CA167551). Support for case ascertainment was provided in part from the Surveillance, Epidemiology, and End Results (SEER) Program and the following US state cancer registries: AZ, CO, MN, NC, and NH; and by the Victoria Cancer Registry (Australia) and Ontario Cancer Registry (Canada). The content of this article does not necessarily reflect the views or policies of the NCI, NIH, or any of the collaborating centers in the CCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government, any cancer registry, or the CCFR. The CCFR data, including the images created for this study, can be requested at www.coloncfr.org. The University of Pittsburgh Medical Center and Mount Sinai data are available from the authors on reasonable request.

    Authors share co-first authorship.

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