Predicting rectal tumor response to neoadjuvant chemoradiotherapy using plasma levels of carcinoembryonic antigen (CEA): Results from a tertiary center in Iran

Predictive value of flexible proctosigmoidoscopy and laboratory findings for complete clinical responses after neoadjuvant chemoradiotherapy in patients with locally advanced primary rectal cancer: a retrospective cohort study

PURPOSE

Colorectal cancer is the second leading cause of cancer death worldwide. Standard treatments for locally advanced rectal cancer include neoadjuvant chemoradiotherapy and total mesorectal excision (TME), which are associated with significant morbidity. After neoadjuvant therapy, one-third of patients achieve a pathological complete response (pCR) and are eligible for a watch-and-wait approach without TME. The purpose of this study was to determine the potential predictors of pCR before surgery.

METHODS

The demographic, clinical, and endoscopic data of 119 patients with primary locally advanced rectal cancer without distant metastasis who underwent restaging endoscopy and TME 6-8 weeks after the end of neoadjuvant therapy were collected. The absence of tumor cells in the histological examination of the TME specimen after neoadjuvant therapy was considered pCR. Binary logistic regression and receiver operating characteristic curves were utilized for analysis.

RESULTS

According to the multivariate logistic regression analysis, flattening of marginal tumor swelling (p value < 0.001, odds ratio = 100.605) emerged as an independent predictor of pCR in rectal cancer patients. Additionally, receiver operating characteristic curve analysis revealed that lower preoperative carcinoembryonic antigen and erythrocyte sedimentation rate levels predict pCR, with cutoffs of 2.15 ng/ml and 19.0 mm/h, respectively.

CONCLUSION

Carcinoembryonic antigen and erythrocyte sedimentation rate, along with the presence of flattening of marginal tumor swelling, can predict pCR after neoadjuvant chemoradiotherapy in patients with primary rectal cancer. These factors offer a potential method for selecting candidates for conservative treatment based on endoscopic and laboratory findings.

Machine Learning-Based Prediction of Responsiveness to Neoadjuvant Chemoradiotheapy in Locally Advanced Rectal Cancer Patients from Endomicroscopy

The protocol for treating locally advanced rectal cancer consists of the application of chemoradiotherapy (neoCRT) followed by surgical intervention. One issue for clinical oncologists is predicting the efficacy of neoCRT in order to adjust the dosage and avoid treatment toxicity in cases when surgery should be conducted promptly. Biomarkers may be used for this purpose along with in vivo cell-level images of the colorectal mucosa obtained by probe-based confocal laser endomicroscopy (pCLE) during colonoscopy. The aim of this article is to report our experience with Motiro, a computational framework that we developed for machine learning (ML) based analysis of pCLE videos for predicting neoCRT response in locally advanced rectal cancer patients. pCLE videos were collected from 47 patients who were diagnosed with locally advanced rectal cancer (T3/T4, or N+). The patients received neoCRT. Response to treatment by all patients was assessed by endoscopy along with biopsy and magnetic resonance imaging (MRI). Thirty-seven patients were classified as non-responsive to neoCRT because they presented a visible macroscopic neoplastic lesion, as confirmed by pCLE examination. Ten remaining patients were considered responsive to neoCRT because they presented lesions as a scar or small ulcer with negative biopsy, at post-treatment follow-up. Motiro was used for batch mode analysis of pCLE videos. It automatically characterized the tumoral region and its surroundings. That enabled classifying a patient as responsive or non-responsive to neoCRT based on pre-neoCRT pCLE videos. Motiro classified patients as responsive or non-responsive to neoCRT with an accuracy of ~ 0.62 when using images of the tumor. When using images of regions surrounding the tumor, it reached an accuracy of ~ 0.70. Feature analysis showed that spatial heterogeneity in fluorescence distribution within regions surrounding the tumor was the main contributor to predicting response to neoCRT. We developed a computational framework to predict response to neoCRT by locally advanced rectal cancer patients based on pCLE images acquired pre-neoCRT. We demonstrate that the analysis of the mucosa of the region surrounding the tumor provides stronger predictive power.