287 Background: Traditional prognostic systems like the AJCC TNM staging for colorectal cancer (CRC) often fall short in predicting long-term patient outcomes. These systems often rely on limited pathologic features, leading to generalized approaches to treatment despite diverse tumor heterogeneity, such as the complex interaction between the tumor-host microenvironment. There is therefore a pressing need for more accurate, scalable tools to enhance decision-making and predict outcomes for patients with CRC. Methods: Tumor sections from 191 CRC patients were comprehensively stained for CD8 to examine CD8-positive tumor-infiltrating lymphocytes (TILs). To account for tumor heterogeneity, multiple areas of the tumor were evaluated to include intertumoral and peritumoral areas. Slides were digitized and quantitatively analyzed using a cloud-based platform (SpatialX Diagnostics, Inc. USA). Additionally, clinicopathologic features including patient demographics, 8th edition AJCC staging, lymphovascular invasion (LVI), perineural invasion (PNI), mismatch repair (MMR) protein status, tumor location, presence/development of distant metastases, and patient follow-up were collected. Merging CD8 AI findings with clinicopathologic features, a complete data set was available for 175 patients. Cases were randomly assigned into 70% training and 30% validation. In the training set, unsupervised artificial intelligence (AI) models integrated clinicopathologic data and CD8-positive TIL counts using vision transformer models. The final model combined spatially resolved pathologic features with clinical variables to statistically predict overall survival (OS) and distant metastasis risk (DMR). Results: In the validation cohort, the model stratified patients into two groups based on a deep learning-derived risk score, with statistically significant differences between low-risk and high-risk groups for OS (p<0.02) and DMR prediction (p<0.0001), For OS prediction, the model selected tumor site, PNI, MMR status, epithelial and stromal CD8-positive TILs, and 5 deep vision features as the top 10 clinically significant features. For DMR prediction, the age, T- and N-stage, PNI, and 6 deep vision-derived features as key predictors. Conclusions: As captured by vision transformers, the distribution and spatial arrangement of CD8-positive TILs are critical factors in patient stratification for OS and DMR. AI-driven models like the one herein offer the potential for more personalized management strategies, advancing CRC management and improving patient outcomes beyond traditional prognostic staging systems and parameters.
Read full abstract