Ines Joye1, Annelies Debucquoy2, Christophe M Deroose3, Vincent Vandecaveye4, Eric Van Cutsem5, Albert Wolthuis6, André D'Hoore6, Xavier Sagaert7, Mu Zhou8, Olivier Gevaert8, Karin Haustermans9. 1. KU Leuven - University of Leuven, Department of Oncology, B-3000 Leuven, Belgium; University Hospitals Leuven, Radiation Oncology, B-3000 Leuven, Belgium. Electronic address: joyeines@gmail.com. 2. KU Leuven - University of Leuven, Department of Oncology, B-3000 Leuven, Belgium. 3. University Hospitals Leuven, Nuclear Medicine, B-3000 Leuven, Belgium. 4. University Hospitals Leuven, Radiology, B-3000 Leuven, Belgium. 5. University Hospitals Leuven, Digestive Oncology, B-3000 Leuven, Belgium. 6. University Hospitals Leuven, Abdominal Surgery, B-3000 Leuven, Belgium. 7. University Hospitals Leuven, Pathology, B-3000 Leuven, Belgium. 8. Stanford University, The Stanford Center for Biomedical Informatics Research, Medicine, Stanford, United States. 9. KU Leuven - University of Leuven, Department of Oncology, B-3000 Leuven, Belgium; University Hospitals Leuven, Radiation Oncology, B-3000 Leuven, Belgium.
Abstract
BACKGROUND AND PURPOSE: To explore the integration of imaging and molecular data for response prediction to chemoradiotherapy (CRT) for rectal cancer. MATERIAL AND METHODS: Eighty-five rectal cancer patients underwent preoperative CRT. 18F-FDG PET/CT and diffusion-weighted imaging (DWI) were acquired before (TP1) and during CRT (TP2) and prior to surgery (TP3). Inflammatory cytokines and gene expression were analysed. Tumour response was defined as ypT0-1N0. Multivariate models were built combining the obtained parameters. Final models were calculated on the data combination with the highest AUC. RESULTS: Twenty-two patients (26%) achieved ypT0-1N0 response. 18F-FDG PET/CT had worse predictive performance than DWI and T2-volumetry (AUC 0.61±0.04, 0.72±0.03, and 0.72±0.02, respectively). Combining all imaging parameters increased the AUC to 0.81±0.03. Adding cytokines or gene expression did not improve the AUC (AUC of 0.72±0.06 and 0.79±0.04 respectively). Final models combining 18F-FDG PET/CT, DWI, and T2-weighted volumetry at all TPs and using only TP1 and TP3, allowed ypT0-1N0 prediction with a 75% sensitivity, 94% specificity and PPV of 80%. CONCLUSIONS: Combining 18F-FDG PET/CT, DWI, and T2-weighted MRI volumetry obtained before CRT and prior to surgery may help physicians in selecting rectal cancer patients for organ-preservation.
BACKGROUND AND PURPOSE: To explore the integration of imaging and molecular data for response prediction to chemoradiotherapy (CRT) for rectal cancer. MATERIAL AND METHODS: Eighty-five rectal cancerpatients underwent preoperative CRT. 18F-FDG PET/CT and diffusion-weighted imaging (DWI) were acquired before (TP1) and during CRT (TP2) and prior to surgery (TP3). Inflammatory cytokines and gene expression were analysed. Tumour response was defined as ypT0-1N0. Multivariate models were built combining the obtained parameters. Final models were calculated on the data combination with the highest AUC. RESULTS: Twenty-two patients (26%) achieved ypT0-1N0 response. 18F-FDG PET/CT had worse predictive performance than DWI and T2-volumetry (AUC 0.61±0.04, 0.72±0.03, and 0.72±0.02, respectively). Combining all imaging parameters increased the AUC to 0.81±0.03. Adding cytokines or gene expression did not improve the AUC (AUC of 0.72±0.06 and 0.79±0.04 respectively). Final models combining 18F-FDG PET/CT, DWI, and T2-weighted volumetry at all TPs and using only TP1 and TP3, allowed ypT0-1N0 prediction with a 75% sensitivity, 94% specificity and PPV of 80%. CONCLUSIONS: Combining 18F-FDG PET/CT, DWI, and T2-weighted MRI volumetry obtained before CRT and prior to surgery may help physicians in selecting rectal cancerpatients for organ-preservation.
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