Despite lymph node involvement (LNI) being one of the main prognostic factors in patients with prostate cancer (PCa), pelvic lymph node irradiation remains debated, possibly due to an insufficient selection of patients. Significant advances in LNI risk modelling have been achieved with the addition of visual interpretation of magnetic resonance imaging (MRI) data, but it is likely that quantitative analysis could further improve prediction models. In this study, the investigators aimed to develop and internally validate a novel LNI risk prediction model based on radiomic features extracted from pre-operative multimodal MRI.
Study Type
OBSERVATIONAL
Enrollment
400
CHRU Brest
Brest, France
RECRUITINGPredicted risk of lymph-node involvement vs Briganti 2017
Comparison between the predicted risk of lymph node involvement based on the new algorithm and the predicted risk with the Briganti 2017
Time frame: immediately after the intervention/procedure/surgery
Predicted risk of lymph-node involvement vs Briganti 2012, Briganti 2018, Briganti 2019 and MSKCC
Comparison between the predicted risk of lymph node involvement based on the new algorithm and the predicted risk with the Briganti 2012, Briganti 2018, Briganti 2019 and MSKCC
Time frame: immediately after the intervention/procedure/surgery
Biochemical recurrence free survival
Prediction of Biochemical recurrence free survival
Time frame: immediately after the intervention/procedure/surgery
Extra-prostatic disease
Prediction of Extra-prostatic disease
Time frame: immediately after the intervention/procedure/surgery
Seminal vesicle invasion
Prediction of Seminal vesicle invasion
Time frame: immediately after the intervention/procedure/surgery
Automatic segmentation of the index lesion
Comparison between a manual segmentation of the index lesion and an automatic segmentation
Time frame: immediately after the intervention/procedure/surgery
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.