Digitally assessed lymphocyte infiltration in rectal cancer biopsies is associated with pathological response to neoadjuvant therapy.

A frequently used treatment strategy in locally advanced rectal cancer (RC) is neoadjuvant therapy followed by surgery. Patients treated with neoadjuvant therapy achieve varying pathological response, and currently, predicting the degree of response is challenging. This study examined the association between digitally assessed histopathological features in the diagnostic biopsies and pathological response to neoadjuvant therapy, aiming to find potential predictive biomarkers. 50 patients with RC treated with neoadjuvant chemotherapy and/or radiotherapy followed by surgery were included. Deep learning-based digital algorithms were used to assess the epithelium tumor area percentage (ETP) based on H&E-stained slides, and to quantify the density of CD3+ and CD8+ lymphocytes, as well as the CD8+/CD3+ lymphocyte percentage, based on immunohistochemically stained slides, from the diagnostic tumor biopsies. Pathological response was assessed according to the Mandard method. A good pathological response was defined as tumor regression grade (TRG) 1-2, and a complete pathological response was defined as Mandard TRG 1. Associations between the ETP and lymphocyte densities in the diagnostic biopsies and the pathological response were examined. The density of CD8+ lymphocytes, and the CD8+/CD3+ lymphocyte percentage, were associated with both good and complete response to neoadjuvant therapy, while the density of CD3+ lymphocytes was associated with complete response. The ETP did not correlate with response to neoadjuvant therapy. It is well-known that infiltration of lymphocytes in colorectal cancer is a prognostic biomarker. However, assessment of CD8+ and CD3+ lymphocytes in the diagnostic tumor biopsies of patients with RC may also be useful in predicting response to neoadjuvant therapy.

Development of a method for digital assessment of tumor regression grade in patients with rectal cancer following neoadjuvant therapy.

Neoadjuvant chemo-radiotherapy (nCRT) followed by surgical resection is the standard treatment strategy in patients with locally advanced rectal cancer (RC). The pathological effect of nCRT is assessed by determining the tumor regression grade (TRG) of the resected tumor. Various methods exist for assessing TRG and all are performed manually by the pathologist with an accompanying risk of interobserver variation. Automated digital image analysis could be a more objective and reproducible approach to evaluate TRG. This study aimed at developing a digital method to assess TRG in RC following nCRT, and correlate the results to the currently used Mandard method. A deep learning-based semi-automatic Epithelium-Tumor area Percentage (ETP) algorithm enabling quantification of tumor regression by determining the percentage of residual tumor epithelium out of the total tumor area was developed. The ETP was quantified in 50 cases treated with nCRT and 25 cases with no prior nCRT served as controls. Median ETP was 39.25% in untreated compared with 6.64% in patients who received nCRT (P < .001). The ETP of the resected tumors treated with nCRT increased along with increasing Mandard grade (P < .001). As new treatment strategies in RC are emerging, performing an accurate and reproducible evaluation of TRG is important in the assessment of treatment response and prognosis. TRG is often used as an outcome point in clinical trials. The ETP algorithm is capable of performing a precise and objective value of tumor regression.