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

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.