Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation. (April 2021)

• Record Type: Journal Article
• Authors: Cusumano, Davide; Boldrini, Luca; Yadav, Poonam; Yu, Gao; Musurunu, Bindu; Chiloiro, Giuditta; Piras, Antonio; Lenkowicz, Jacopo; Placidi, Lorenzo; Romano, Angela; De Luca, Viola; Votta, Claudio; Barbaro, Brunella; Gambacorta, Maria Antonietta; Bassetti, Michael F.; Yang, Yingli; Indovina, Luca; Valentini, Vincenzo
• Is Part Of: Physica medica. Volume 84(2021)
• Page Start: 186
• Page End: 191
• Publication Date: 2021-04
• Subjects: Delta radiomics -- Predictive modelling -- Rectal CANCER -- Radiomics -- Response prediction; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.03.038
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Two radiomic features were identified as response predictors in rectal cancer. This study aims to validate such features on an external larger dataset. The analysis was performed considering clinical and pathological complete response. The variation of length least showed good performance in predicting both outcomes. Grey Level non uniformity reported limited performance as predictor. Abstract: Introduction: A recent study performed on 16 locally advanced rectal cancer (LARC) patients treated using magnetic resonance guided radiotherapy (MRgRT) has identified two delta radiomics features as predictors of clinical complete response (cCR) after neoadjuvant radio-chemotherapy (nCRT). This study aims to validate these features (ΔLleast and Δglnu) on an external larger dataset, expanding the analysis also for pathological complete response (pCR) prediction. Methods: A total of 43 LARC patients were enrolled: Gross Tumour Volume (GTV) was delineated on T2/T1* MR images acquired during MRgRT and the two delta features were calculated. Receiver Operating Characteristic (ROC) curve analysis was performed on the 16 cases of the original study and the best cut-off value was identified. The performance of ΔLleast and Δglnu was evaluated at the best cut-off value. Results: On the original dataset of 16 patients, ΔLleast reported an AUC of 0.81 for cCR and 0.93 for pCR, while Δglnu 0.72 and 0.54 respectively. The best cut-off values of ΔLleast was 0.73 for both outcomes, while Δglnu reported 0.54 for cCR and 0.93 for pCR. At the external validation, ΔLleast showed an accuracy of 81% for cCR and 79% for pCR, while Δglnu reported 63% for cCR and 40% for pCR. Conclusion: The accuracy of ΔLleast in predicting cCR and pCR is significantly higher than those obtained considering Δglnu, but inferior if compared with other image-based biomarker, such as the early-regression index. Studies with larger cohorts of patients are recommended to further investigate the role of delta radiomic features in MRgRT.