Utilizing quantitative pathologic analysis of digitized images of rectal cancer (RC) to predict response to neoadjuvant therapy (NAT).

Abstract

200 Background: We developed and validated a quantitative segmentation algorithm (QuantCRC) to predict disease recurrence in stage I-III colorectal cancer patients (pts) based on the analysis of digitized hematoxylin and eosin (H+E) stained pathology slides. In this study, we aim to determine if QuantCRC analysis on pre-treated RC biopsies can predict pathological complete response (pCR) after NAT which include total neoadjuvant therapy (TNT), chemoradiation (CRT) and others. Methods: Stage II-IV RC pts treated with curative intent modalities between 2005-2023 at Mayo Clinic and University of Pittsburgh were evaluated. Eligible pts had pre and post-treatment tissue available for analysis. Clinical characteristics, treatment, and outcome data were extracted from the medical record. QuantCRC extracted 15 features: %tumor, %stroma, tumor:stroma ratio, %TB/PDC, %mucin, %necrosis, %high-grade, %signet ring cells, tumor-infiltrating lymphocytes (TILs) per cm2 of a tumor, %immature stroma (tumor bed), %inflammatory stroma (tumor bed), %mature stroma (tumor bed), %immature (stromal region), %inflammatory (stromal region), and %mature (stromal region). The associations between the QuantCRC features and pCR were evaluated using Wilcoxon rank-sum tests and logistic regression models while adjusting for primary tumor location and NAT. Results: The identified 288 pts had the following demographics: median age 60, 59% male, 52% distal tumor, 42% TNT. Pts who achieved pCR had smaller pre-treatment tumors (p=0.03), higher TILS, and lower %immature (stroma) (Table 1). After multivariable adjustment, higher TILS and lower %immature (stroma) remain to be significantly associated with a higher likelihood of pCR. A higher %mature (stroma) and %mature stroma (tumor bed) were also identified, in multivariable models, to be associated with a higher rate of pCR. NAT protocol and tumor location (proximal vs. distal) were not associated with pCR. Conclusions: The ability to integrate pathological features with AI may allow for personalized treatment. Using this technology, we recognize that patients who harbor a more mature stromal archetype and inflammatory tumor predisposition, are more likely to achieve pCR to conventional NAT. These pathological features are not currently incorporated into risk stratification in RC. Additional validation and integration into current risk stratification methods is warranted. [Table: see text]