Sajad Shayesteh1, Mostafa Nazari2, Ali Salahshour3, Saleh Sandoughdaran4, Ghasem Hajianfar5, Maziar Khateri6, Ali Yaghobi Joybari4, Fariba Jozian4, Seyed Hasan Fatehi Feyzabad5, Hossein Arabi7, Isaac Shiri7, Habib Zaidi7,8,9,10. 1. Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran. 2. Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 3. Department of Radiology, Alborz University of Medical Sciences, Karaj, Iran. 4. Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 5. Rajaie Cardiovascular, Medical & Research Centre, Iran University of Medical Science, Tehran, Iran. 6. Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. 7. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland. 8. Geneva University Neurocenter, Geneva University, Geneva, Switzerland. 9. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. 10. Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
Abstract
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.
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.
Authors: José Marcio Luna; Andrew R Barsky; Russell T Shinohara; Leonid Roshkovan; Michelle Hershman; Alexandra D Dreyfuss; Hannah Horng; Carolyn Lou; Peter B Noël; Keith A Cengel; Sharyn Katz; Eric S Diffenderfer; Despina Kontos Journal: Cancers (Basel) Date: 2022-01-29 Impact factor: 6.639