CT‐based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study

Abstract

Introduction Innovative biomarkers to predict treatment response in rectal cancer would be helpful in optimizing personalized treatment approaches. In this study, we aimed to develop and validate a CT‐based radiomic imaging biomarker to predict pathological response. Methods We used two independent cohorts of rectal cancer patients to develop and validate a CT‐based radiomic imaging biomarker predictive of treatment response. A total of 91 rectal cancer cases treated from 2009 to 2018 were assessed for the tumour regression grade (TRG) (0 = pathological complete response, pCR; 1 = moderate response; 2 = partial response; 3 = poor response). Exploratory analysis was performed by combining pre‐treatment non‐contrast CT images and patterns of TRG. The models built from the training cohort were further assessed using the independent validation cohort. Results The patterns of pathological response in training and validation groups were TRG 0 (n = 14, 23.3%; n = 6, 19.4%), 1 (n = 31, 51.7%; n = 15, 48.4%), 2 (n = 12, 20.0%; n = 7, 22.6%) and 3 (n = 3, 5.0%; n = 3, 9.7%), respectively. Separate predictive models were built and analysed from CT features for pathological response. For pathological response prediction, the model including 8 radiomic features by random forest method resulted in 83.9% accuracy in predicting TRG 0 vs TRG 1–3 in validation. Conclusion The pre‐treatment CT‐based radiomic signatures were developed and validated in two independent cohorts. This imaging biomarker provided a promising way to predict pCR and select patients for non‐operative management.

Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer

“…However, few of them focused on CT-based radiomics analysis, although it has been demonstrated that multiple radiomics analysis based on CT images can facilitate the prediction of lymph node metastasis [ 37 , 49 , 50 ], distant metastasis [ 51 ], therapy response [ 52 , 53 ] and prognostic outcomes [ 28 ]. Two previous studies have performed CT-based radiomics analysis for pCR prediction but came out with controversial results [ 27 , 54 ]. Both of these studies analyzed non-contrast CT images, which may not display tumor characteristics well, and were based on small sizes of cohorts with retrospective design [ 27 , 55 ].…”

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

“…Studies have confirmed that TDpositive patients have more aggressive tumors, with poorer disease-free survival (hazard ratio, HR, 1.7 to 2.0) and poorer overall survival (HR, 2.2 to 2.9) [177]. Yuan et al [178], on the other hand, used a machine learning technique to build radiomics models by extracting radiomic features on non-contrast CT images, and predict the response to treatment. Bibault et al [179] reported 80% accuracy in predicting a complete response in LARC with nCRT using radiomic features extracted from a post-contrast CT through a deep neural network algorithm.…”

Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging

“…In order to apply organ-preserving strategies, as well as to personalize treatments or to deescalate therapies [ 10 ], there is a great interest in stratifying the risk in patients with LARC, aimed at predicting pCR by exploiting various techniques which include radiomics [ 11 ]. Moreover, it would be of practical utility whether this could be accomplished by using the available CT images acquired for radiation therapy planning [ 12 – 14 ]. In this regard, we note that previous studies have assessed the potential role of CT imaging radiomics in rectal cancer both for contrast-enhanced [ 12 , 13 , 15 – 17 ] and noncontrast CT scans [ 14 , 18 ].…”

“…Moreover, it would be of practical utility whether this could be accomplished by using the available CT images acquired for radiation therapy planning [ 12 – 14 ]. In this regard, we note that previous studies have assessed the potential role of CT imaging radiomics in rectal cancer both for contrast-enhanced [ 12 , 13 , 15 – 17 ] and noncontrast CT scans [ 14 , 18 ].…”