ESTRO 2022 - Abstract Book

S1590

Abstract book

ESTRO 2022

Figure 1. Reported AUC/C-index of the included studies with number of good items were classified by Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). Type 1a: Development only; Type 1b: Development and validation using resampling; Type 2a: Random split-sample development and validation; Type 2b: Nonrandom split-sample development and validation; Type 3: Development and validation using separate data; Type 4: Validation only. Conclusion A substantial number of studies lacked prospective registration, external validation, model calibration, and support for clinical use. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration and multi-institution validation are recommended.

PO-1783 Leverage radiomic and clinical data in predicting SRS treatment outcomes in patients with brain mets

G. Carloni 1,2 , G. Marvaso 2,3 , C. Garibaldi 4 , M. Zaffaroni 2 , S. Volpe 2,3 , M. Pepa 2 , S. Raimondi 5 , G. Lo Presti 5 , V. Positano 1,6 , R. Orecchia 7 , B.A. Jereczek-Fossa 2,3 1 University of Pisa, Department of Information Engineering, Pisa, Italy; 2 IEO European Institute of Oncology IRCCS, Division of Radiation Oncology, Milan, Italy; 3 University of Milan, Department of Oncology and Hemato-Oncology, Milan, Italy; 4 IEO European Institute of Oncology IRCCS, Unit of Radiation Research, Milan, Italy; 5 IEO European Institute of Oncology IRCCS, Molecular and Pharmaco-Epidemiology unit, Department of Experimental Oncology, Milan, Italy; 6 National Research