Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer.

OBJECTIVES: We evaluate the feasibility of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT). MATERIALS AND METHODS: This retrospective study included 53 LARC patients divided into a training set (Center#1, n = 36) and external validation set (Center#2, n = 17). T2-weighted (T2W) MRI was acquired for all patients, 2 weeks before and 4 weeks after nCRT. Ninety-six radiomic features, including intensity, morphological and second- and high-order texture features were extracted from segmented 3D volumes from T2W MRI. All features were harmonized using ComBat algorithm. Max-Relevance-Min-Redundancy (MRMR) algorithm was used as feature selector and k-nearest neighbors (KNN), Naïve Bayes (NB), Random forests (RF), and eXtreme Gradient Boosting (XGB) algorithms were used as classifiers. The evaluation was performed using the area under the receiver operator characteristic (ROC) curve (AUC), sensitivity, specificity and accuracy. RESULTS: In univariate analysis, the highest AUC in pre-, post-, and delta-radiomic features were 0.78, 0.70, and 0.71, for GLCM_IMC1, shape (surface area and volume) and GLSZM_GLNU features, respectively. In multivariate analysis, RF and KNN achieved the highest AUC (0.85 ± 0.04 and 0.81 ± 0.14, respectively) among pre- and post-treatment features. The highest AUC was achieved for the delta-radiomic-based RF model (0.96 ± 0.01) followed by NB (0.96 ± 0.04). Overall. Delta-radiomics model, outperformed both pre- and post-treatment features (P-value <0.05). CONCLUSION: Multivariate analysis of delta-radiomic T2W MRI features using machine learning algorithms could potentially be used for response prediction in LARC patients undergoing nCRT. We also observed that multivariate analysis of delta-radiomic features using RF classifiers can be used as powerful biomarkers for response prediction in LARC.

ORGAN EQUIVALENT DOSE AND LIFETIME ATTRIBUTABLE RISK OF CANCER INCIDENCE AND MORTALITY ASSOCIATED WITH CARDIAC CT ANGIOGRAPHY.

The aim of this study is the calculation of equivalent organ dose and estimation of lifetime attributable risk (LAR) of cancer incidence and mortality related to cardiac computed tomography angiography (CCTA) because the use of CT angiography as a noninvasive diagnostic method has increased. The organ dose has been calculated by ImPACT software based on the volumetric CT dose index (CTDIvol), and LAR of cancer risk incidence and mortality from CCTA has estimated according to the BEIR VII report. The median value of the effective dose was 13.78 ± 6.88 mSv for both genders. In all scanners, the highest median value for LAR of cancer incidence in males and females for lung cancer was 44.20 and 109.17 per 100 000, respectively. And in infants was 5.89 and 12 for lung cancer in males and breast cancer in females, respectively. Also, the median value of LAR of all cancer incidence from single CCTA in adult patients for males and females was 122 and 238 cases, respectively. Maximum LAR of cancer mortality in adults for lung cancer was 40.28 and 91.84 and in pediatrics was 5.69 and 8.50 in males and females, respectively. Despite many benefits of CTA in the heart disease evaluation, according to a high radiation dose in CCTA, to reduce the cancer risk: CCTA should be used cautiously, especially for pediatric and females.