MRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma

Abstract

Purpose: Chemo-radiotherapy (CRT) is the standard treatment for non-metastatic anal squamous cell carcinomas (ASCC). Despite excellent results for T1-2 stages, relapses still occur in around 35% of locally advanced tumors. Recent strategies focus on treatment intensification, but could benefit from a better patient selection. Our goal was to assess the prognostic value of pre-therapeutic MRI radiomics on 2-year disease control (DC). Methods: We retrospectively selected patients with non-metastatic ASCC treated at the CHU Bordeaux and in the French FFCD0904 multicentric trial. Radiomic features were extracted from T2-weighted pre-therapeutic MRI delineated sequences. After random division between training and testing sets on a 2:1 ratio, univariate and multivariate analysis were performed on the training cohort to select optimal features. The correlation with 2-year DC was assessed using logistic regression models, with AUC and accuracy as performance gauges, and the prediction of disease-free survival using Cox regression and Kaplan-Meier analysis. Results: A total of 82 patients were randomized in the training (n = 54) and testing sets (n = 28). At 2 years, 24 patients (29%) presented relapse. In the training set, two clinical (tumor size and CRT length) and two radiomic features (FirstOrder_Entropy and GLCM_JointEnergy) were associated with disease control in univariate analysis and included in the model. The clinical model was outperformed by the mixed (clinical and radiomic) model in both the training (AUC 0.758 versus 0.825, accuracy of 75.9% versus 87%) and testing (AUC 0.714 versus 0.898, accuracy of 78.6% versus 85.7%) sets, which led to distinctive high and low risk of disease relapse groups (HR 8.60, p = 0.005). Conclusion: A mixed model with two clinical and two radiomic features was predictive of 2-year disease control after CRT and could contribute to identify high risk patients amenable to treatment intensification with view of personalized medicine.

Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review

“…From these twelve full texts, a total of nine studies met the inclusion criteria and were included in our analysis. [16][17][18][19][20][21][22][23][24] See Figure 1.…”

“…The amalgamation of these novel parameters with pre-existing clinical factors appears to augment performance. 16,17,22,23 This is precisely where the prowess of machine learning (ML) algorithms becomes evident, having already demonstrated remarkable accomplishments across various cancer categories. [32][33][34][35] A proportion of ASCC patients undergoing curative CRT still require salvage abdominoperineal resection (APR) due to inadequate treatment response or local recurrence.…”

“…Overall, six of the included studies were retrospective in nature, [16][17][18]21,22,24 with the remaining three being prospective. 19,20,23 In total, four studies were multicentre, 17,20,21,25 with the other five being singlecenter studies. 16,18,19,22,23 The total number of participants from the nine included studies was 589.…”

“…16,18,19,[22][23][24] The remaining studies developed their models from T2-weighted MRI (2/9) or contrast-enhanced CT (1/9). 17,20,21 Acquisition parameters are illustrated in Table 2.…”

Multi-Parameter MR Radiomics Based Model to Predict 5-Year Progression-Free Survival in Endometrial Cancer

“…A study found that Kurtosis combined with several clinical and other texture parameters could predict eight-year event-free survival (EFS) in Luminal Non-Metastatic Breast Valencia ( 33 ). In addition to adenocarcinoma, another study on anal squamous cell carcinoma also found that Entropy and Joint Energy can be independent risk factors for predicting tumor recurrence rate ( 34 ). Therefore, the radiomics indicators screened in this study are not only reproducible in the evaluation of multiple biological characteristics and prognosis of endometrial cancer, but also seem to be similar in other tumors.…”