Radiomic Features of Primary Rectal Cancers on Baseline T₂‐Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study

Abstract

Background Twenty‐five percent of rectal adenocarcinoma patients achieve pathologic complete response (pCR) to neoadjuvant chemoradiation and could avoid proctectomy. However, pretreatment clinical or imaging markers are lacking in predicting response to chemoradiation. Radiomic texture features from MRI have recently been associated with therapeutic response in other cancers. Purpose To construct a radiomics texture model based on pretreatment MRI for identifying patients who will achieve pCR to neoadjuvant chemoradiation in rectal cancer, including validation across multiple scanners and sites. Study Type Retrospective. Subjects In all, 104 rectal cancer patients staged with MRI prior to long‐course chemoradiation followed by proctectomy; curated from three institutions. Field Strength/Sequence 1.5T–3.0T, axial higher resolution T2‐weighted turbo spin echo sequence. Assessment Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single‐slice sections of rectal tumors on processed pretreatment T2‐weighted MRI. Statistical Tests Three feature selection schemes were compared for identifying radiomic texture descriptors associated with pCR via a discovery cohort (one site, N = 60, cross‐validation). The top‐selected radiomic texture features were used to train and validate a random forest classifier model for pretreatment identification of pCR (two external sites, N = 44). Model performance was evaluated via area under the curve (AUC), accuracy, sensitivity, and specificity. Results Laws kernel responses and gradient organization features were most associated with pCR (P ≤ 0.01); as well as being commonly identified across all feature selection schemes. The radiomics model yielded a discovery AUC of 0.699 ± 0.076 and a hold‐out validation AUC of 0.712 with 70.5% accuracy (70.0% sensitivity, 70.6% specificity) in identifying pCR. Radiomic texture features were resilient to variations in magnetic field strength as well as being consistent between two different expert annotations. Univariate analysis revealed no significant associations of baseline clinicopathologic or MRI findings with pCR (P = 0.07–0.96). Data Conclusion Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites. Level of Evidence 3 Technical Efficacy Stage 2

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

“…The contrast-enhanced T1-WI sequence is not routinely included in the MRI protocol for rectal cancer staging. The potential of radiomics features extracted from MRI T2-weighted images for predicting a pathological complete response of rectal cancer was demonstrated in several recent studies, which reported promising results of their radiomics models with AUCs ranging from 0.69 to 0.93 [ 51 , 52 , 57 , 69 , 70 , 71 ]. In contrast to MRI, a recent study had demonstrated that radiomics features extracted from CT images showed no predictive power for complete pathological response in LARC [ 72 ], while another research showed that MRI T2-WI radiomics model performed better than CT radiomics model for predicting the LARC response to nCRT [ 73 ].…”

Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study

“…This set of radiomic texture and shape descriptors appear to be driven by intuitive histopathological and physiological differences between pathologic stage groupings of rectal tumors after nCRT. Future work will include integrating our analysis with pre-treatment imaging prediction models [62] for a more comprehensive assessment of tumor evolution after chemoradiation in rectal cancers. We also plan to evaluate the performance of our predictor in a more prospective setting, as well as across different platforms and implementations to confirm generalizability of identified radiomic descriptors.…”

Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer

“…Unlike traditional image evaluation methods, radiomics is an emerging and effective method for quantitatively analyzing the classification and prognosis of diseases using medical imaging ( 10 ). From standard-of-care medical images, data can be extracted via high-throughput mining of quantitative image features, which are undetectable by the naked eye, and applied within clinical-decision support systems ( 9 – 13 ); radiomics plays an important role in early diagnosis, treatment evaluation, and tumor prognosis prediction, ultimately aiding in the achievement of precision medicine ( 11 , 14 , 15 ).…”