MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Abstract

ObjectivesTo develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT).MethodsBetween October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC).ResultsThere was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model.ConclusionMRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study

“…may have the potential to further enhance the expression of this heterogeneity, contributing to the diagnostic performance ( Jiang et al, 2022a ; Jiang et al, 2022b ). The mRMR, ADASYN, and Bayesian optimization algorithms also showed the excellent selection performance in radiomics analysis, which is consistent with the findings of Xie et al (2021) , Hou et al (2022) , Wang et al (2023) , and Wu et al (2023) .…”

The effect of data resampling methods in radiomics

“…Another bleak explanation could be that some studies did not apply the resampling correctly. If only cross-validation is used without an independent test set, it is of utmost importance that resampling is applied only to the training set and does not utilize the validation set in any way 20 , 21 . If this is not followed, a large bias can be expected 22 , 23 ; yet, this kind of error is common 24 and often cannot be detected without access to the code, which is most often not provided in radiomic studies.…”

Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics

“…In addition, SMOTE, bSMOTE, and KMeans SMOTE also demonstrated good performance, which can provide a reference for other imbalanced biomedical data processing. A recent study ( 38 ) reported that machine learning mining results using the SMOTE method were largely consistent with the baseline patterns or trends, and synthetic data generated using machine learning have shown advantages in clinical modeling ( 39 ). XGBoost is a novel tree-based algorithm for sparse data processing that provides the gradient-boosted decision tree, which is widely recognized in data mining challenges and tasks ( 40 , 41 ).…”