[A prediction model of pathological complete response in patients with locally advanced rectal cancer after PD-1 antibody combined with total neoadjuvant chemoradiotherapy based on MRI radiomics].

Objective: To construct a prediction model of pathologic complete response (pCR) in locally advanced rectal cancer patients who received programmed cell death protein-1 (PD-1) antibody and total neoadjuvant chemoradiotherapy by using radiomics based on MR imaging data and to investigate its predictive value. Methods: A clinical diagnostic test study was carried out. Clinicopathalogical and radiological data of 38 patients with middle-low rectal cancer who received PD-1 antibody combined with total neoadjuvant chemoradiotherapy and underwent TME surgery from January 2019 to September 2021 in our hospital were retrospectively collected. Among 38 patients, 23 were males and 15 were females with a median age of 68 (47-79) years and 13 (34.2%) a chieved pCR. These 38 patients were stratified and randomly divided into the training group (n=26) and test group (n=12) for modeling. All the patients underwent rectal MRI before treatment. The clinical, imaging and radiomics features of all the patients were collected, and the clinical feature model and radiomics model were constructed. The receiver operating characteristic (ROC) curves of each model were drawn, and the constructed model was evaluated through the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Results: There were no significant differences in age, gender, primary location of tumor and postoperative pathology between the two groups (all P>0.05). Forty-one features were extracted from region of interest in each modality, including 9 first-order features, 24 gray level co-occurrence matrix features and 8 shape features. From 38 patients, 41 features were extracted from each imaging modality of baseline and preoperative DWI and T2WI images, totally 164 features. Only 4 features were preserved after correlation analysis between each pair of features and t-test between pCR and non-pCR subjects. After LASSO cross validation, only the first-order skewness of the baseline DWI image before treatment and the volume in the baseline T2WI image before treatment were retained. The area under the curve, sensitivity, specificity, positive and negative predictive values of the prediction model established by applying these two features in the training group and the test group were 0.856 and 0.844, 77.8% and 100.0%, 88.2% and 75.0%, 77.8% and 66.7%, 88.2% and 100.0%, respectively. The decision curve analysis of the radiomics model showed that the strategy of this model in predicting pCR was better than that in treating all the patients as pCR and that in treating all the patients as non-pCR. Conclusion: The pCR prediction model for rectal cancer patients receiving PD-1 antibody combined with total neoadjuvant radiochemotherapy based on MRI radiomics has the potential to be used in clinical screening or rectal cancer patients who can be spared from radical surgery.

A prediction model of pathological complete response in patients with locally advanced rectal cancer after PD-1 antibody combined with total neoadjuvant chemoradiotherapy based on MRI radiomics / 中华胃肠外科杂志

Objective: To construct a prediction model of pathologic complete response (pCR) in locally advanced rectal cancer patients who received programmed cell death protein-1 (PD-1) antibody and total neoadjuvant chemoradiotherapy by using radiomics based on MR imaging data and to investigate its predictive value. Methods: A clinical diagnostic test study was carried out. Clinicopathalogical and radiological data of 38 patients with middle-low rectal cancer who received PD-1 antibody combined with total neoadjuvant chemoradiotherapy and underwent TME surgery from January 2019 to September 2021 in our hospital were retrospectively collected. Among 38 patients, 23 were males and 15 were females with a median age of 68 (47-79) years and 13 (34.2%) a chieved pCR. These 38 patients were stratified and randomly divided into the training group (n=26) and test group (n=12) for modeling. All the patients underwent rectal MRI before treatment. The clinical, imaging and radiomics features of all the patients were collected, and the clinical feature model and radiomics model were constructed. The receiver operating characteristic (ROC) curves of each model were drawn, and the constructed model was evaluated through the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Results: There were no significant differences in age, gender, primary location of tumor and postoperative pathology between the two groups (all P>0.05). Forty-one features were extracted from region of interest in each modality, including 9 first-order features, 24 gray level co-occurrence matrix features and 8 shape features. From 38 patients, 41 features were extracted from each imaging modality of baseline and preoperative DWI and T2WI images, totally 164 features. Only 4 features were preserved after correlation analysis between each pair of features and t-test between pCR and non-pCR subjects. After LASSO cross validation, only the first-order skewness of the baseline DWI image before treatment and the volume in the baseline T2WI image before treatment were retained. The area under the curve, sensitivity, specificity, positive and negative predictive values of the prediction model established by applying these two features in the training group and the test group were 0.856 and 0.844, 77.8% and 100.0%, 88.2% and 75.0%, 77.8% and 66.7%, 88.2% and 100.0%, respectively. The decision curve analysis of the radiomics model showed that the strategy of this model in predicting pCR was better than that in treating all the patients as pCR and that in treating all the patients as non-pCR. Conclusion: The pCR prediction model for rectal cancer patients receiving PD-1 antibody combined with total neoadjuvant radiochemotherapy based on MRI radiomics has the potential to be used in clinical screening or rectal cancer patients who can be spared from radical surgery.

Adjuvant treatment in older patients with rectal cancer: a population-based review.

Background: Little is known about the benefits of adjuvant chemotherapy (adj) in the older population with locally advanced rectal cancer (larc). We evaluated use of adj, survival outcomes, and adj-related toxicity in older patients with larc. Methods: Our retrospective review included 286 patients with larc (stages ii and iii) diagnosed between January 2010 and December 2013 in Nova Scotia who underwent curative-intent surgery. Baseline patient, tumour, and treatment characteristics were collected. The survival analysis used the Kaplan-Meier method and Cox regression statistics. Results: Of 286 identified patients, 152 were 65 years of age or older, and 92 were 70 years of age or older. Median follow-up was 46 months, and 163 patients (57%) received neoadjuvant chemoradiation. Although adj was given to 81% of patients (n = 109) less than 65 years of age, only 29% patients (n = 27) 70 years of age and older received adj. Kaplan-Meier analysis suggested a potential survival advantage for adj regardless of age. In multivariate Cox regression analysis, Eastern Cooperative Oncology Group performance status, T stage, and adj were significant predictors of overall survival (p < 0.04); age was not. Similarly, N stage, neoadjuvant chemoradiation, and adj were significant predictors of disease-free survival (p < 0.01). Poor Eastern Cooperative Oncology Group performance status was the most common cause of adj omission. In patients 70 years of age and older, grade 1 or greater chemotherapy-related toxicities were experienced significantly more often by those treated with adj (85% vs. 68% for those not treated with adj, p < 0.05). Conclusions: Regardless of age, patients with larc seem to experience a survival benefit with adj. However, older patients are less likely to receive adj, and when they do, they experience more chemotherapy-related toxicities.