Automated assessment of necrosis tumor ratio in colorectal cancer using an artificial intelligence‐based digital pathology analysis

Abstract

Abstract Background With the advance in digital pathology and artificial intelligence (AI)‐powered approaches, necrosis is proposed as a marker of poor prognosis in colorectal cancer (CRC). However, most previous studies quantified necrosis merely as a tissue type and patch‐level segmentation. Thus, it was worth exploring and validating the prognostic and predictive value of necrosis proportion with a pixel‐level segmentation in large multicenter cohorts. Methods A semantic segmentation model was trained with 12 tissue types labeled by pathologists. Segmentation was performed using the U‐net model with a subsequently derived necrosis tumor ratio (NTR). We proposed the NTR score (NTR‐low or NTR‐high) to evaluate the prognostic and predictive value of necrosis for disease‐free survival (DFS) and overall survival (OS) in the development (N = 443) and validation cohorts (N = 333) using 75% as a threshold. Results The 2‐category NTR was an independent prognostic factor and NTR‐low was associated with significant prolonged DFS (unadjusted HR for high vs. low 1.72 [95% CI 1.19–2.49] and 1.98 [1.22–3.23] in the development and validation cohorts). Similar trends were observed for OS. The prognostic value of NTR was maintained in the multivariate analysis for both cohorts. Furthermore, a stratified analysis showed that NTR‐high was a high risk with adjuvant chemotherapy for OS in stage II CRC (p = 0.047). Conclusion AI‐based pixel‐level quantified NTR has a stable prognostic value in CRC associated with unfavorable survival. Additionally, adjuvant chemotherapy provided survival benefits for patients with a high NTR score in stage II CRC.

Keywords

• artificial intelligence
• colorectal cancer
• digital pathology
• necrosis
• whole‐slide images