Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Oncology, Radiology, Nuclear Medicine & Medical Imaging, Response prediction, PET imaging, Machine learning, Rectal cancer, External validation, POSITRON-EMISSION-TOMOGRAPHY, PREOPERATIVE CHEMORADIATION, NEOADJUVANT CHEMORADIATION, TUMOR RESPONSE, PROGNOSTIC VALUE, F-18-FDG PET, FDG-PET, THERAPY, RADIOCHEMOTHERAPY, REGRESSION, Adult, Aged, Area Under Curve, Female, Humans, Male, Middle Aged, Positron-Emission Tomography, Rectal Neoplasms, Tomography, X-Ray Computed, PSI_MIC, 0299 Other Physical Sciences, 1112 Oncology and Carcinogenesis, Oncology & Carcinogenesis, 3202 Clinical sciences, 3211 Oncology and carcinogenesis, 5105 Medical and biological physics

Abstract:

PURPOSE: To develop and validate an accurate predictive model and a nomogram for pathologic complete response (pCR) after chemoradiotherapy (CRT) for rectal cancer based on clinical and sequential PET-CT data. Accurate prediction could enable more individualised surgical approaches, including less extensive resection or even a wait-and-see policy. METHODS AND MATERIALS: Population based databases from 953 patients were collected from four different institutes and divided into three groups: clinical factors (training: 677 patients, validation: 85 patients), pre-CRT PET-CT (training: 114 patients, validation: 37 patients) and post-CRT PET-CT (training: 107 patients, validation: 55 patients). A pCR was defined as ypT0N0 reported by pathology after surgery. The data were analysed using a linear multivariate classification model (support vector machine), and the model's performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. RESULTS: The occurrence rate of pCR in the datasets was between 15% and 31%. The model based on clinical variables (AUC(train)=0.61±0.03, AUC(validation)=0.69±0.08) resulted in the following predictors: cT- and cN-stage and tumour length. Addition of pre-CRT PET data did not result in a significantly higher performance (AUC(train)=0.68±0.08, AUC(validation)=0.68±0.10) and revealed maximal radioactive isotope uptake (SUV(max)) and tumour location as extra predictors. The best model achieved was based on the addition of post-CRT PET-data (AUC(train)=0.83±0.05, AUC(validation)=0.86±0.05) and included the following predictors: tumour length, post-CRT SUV(max) and relative change of SUV(max). This model performed significantly better than the clinical model (p(train)<0.001, p(validation)=0.056). CONCLUSIONS: The model and the nomogram developed based on clinical and sequential PET-CT data can accurately predict pCR, and can be used as a decision support tool for surgery after prospective validation.