AIMS: After local excision of early rectal cancer, definitive lymph node status is not available. An alternative means for accurate assessment of recurrence risk is required to determine the most appropriate subsequent management. Currently used measures are suboptimal. We assess three measures of tumour stromal content to determine their predictive value after local excision in a well-characterized cohort of rectal cancer patients without prior radiotherapy. METHODS AND RESULTS: 143 patients were included. H&E sections were scanned for 1) deep neural network (DNN, a machine learning algorithm) tumour segmentation into compartments including desmoplastic stroma and inflamed stroma; and 2) digital assessment of tumour stromal fraction (TSR) and optical DNA ploidy analysis. 3' mRNA sequencing was performed to obtain gene expression data, from which stromal and immune scores were calculated using the ESTIMATE method. Full results were available for 139 samples and compared with disease-free survival. All three methods were prognostic. Most strongly predictive was a DNN-determined ratio of desmoplastic to inflamed stroma >5.41 (p<0.0001). A ratio of ESTIMATE stromal to immune score <1.19 was also predictive of disease-free survival (p=0.00051), as was stromal fraction >36.5% (p=0.037). CONCLUSIONS: The DNN-determined ratio of desmoplastic to inflamed ratio is a novel and powerful predictor of disease recurrence in locally excised early rectal cancer. It can be assessed on a single H&E section so could be applied in routine clinical practice to improve the prognostic information available to patients and clinicians to inform the decision about further management.
desmoplastic stroma, early rectal cancer, inflamed stroma, local excision, prognostic factor, stroma