We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients.
- Authors
Bourbonne, Vincent; Jaouen, Vincent; Nguyen, Truong An; Tissot, Valentin; Doucet, Laurent; Hatt, Mathieu; Visvikis, Dimitris; Pradier, Olivier; Valéri, Antoine; Fournier, Georges; Schick, Ulrike
- Abstract
Simple Summary: In patients with prostate cancer, lymph node involvement is a risk factor of relapse. Current guidelines recommend extended lymph node dissection to better stage the disease. However, such a surgical procedure is associated with a higher morbidity than limited lymph node dissection. A better selection of patients is thus essential. Radiomics features are quantitative features automatically extracted from medical imaging. Combining clinical and radiomics features, a machine learning-based model seemed to provide added predictive performance compared to state of the art models regarding the risk prediction of lymph-node involvement in prostate cancer patients. Significant advances in lymph node involvement (LNI) risk modeling in prostate cancer (PCa) 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, we aimed to develop and internally validate a novel LNI risk prediction model based on radiomic features extracted from preoperative multimodal MRI. All patients who underwent a preoperative MRI and radical prostatectomy with extensive lymph node dissection were retrospectively included in a single institution. Patients were randomly divided into the training (60%) and testing (40%) sets. Radiomic features were extracted from the index tumor volumes, delineated on the apparent diffusion coefficient corrected map and the T2 sequences. A ComBat harmonization method was applied to account for inter-site heterogeneity. A prediction model was trained using a neural network approach (Multilayer Perceptron Network, SPSS v24.0©) combining clinical, radiomic and all features. It was then evaluated on the testing set and compared to the current available models using the Receiver Operative Characteristics and the C-Index. Two hundred and eighty patients were included, with a median age of 65.2 y (45.3–79.6), a mean PSA level of 9.5 ng/mL (1.04–63.0) and 79.6% of ISUP ≥ 2 tumors. LNI occurred in 51 patients (18.2%), with a median number of extracted nodes of 15 (10–19). In the testing set, with their respective cutoffs applied, the Partin, Roach, Yale, MSKCC, Briganti 2012 and 2017 models resulted in a C-Index of 0.71, 0.66, 0.55, 0.67, 0.65 and 0.73, respectively, while our proposed combined model resulted in a C-Index of 0.89 in the testing set. Radiomic features extracted from the preoperative MRI scans and combined with clinical features through a neural network seem to provide added predictive performance compared to state of the art models regarding LNI risk prediction in PCa.
- Subjects
LYMPHOMA risk factors; DIGITAL image processing; PREOPERATIVE period; RADICAL prostatectomy; MAGNETIC resonance imaging; RETROSPECTIVE studies; RISK assessment; CANCER patients; DESCRIPTIVE statistics; DATA analysis software; ARTIFICIAL neural networks; RECEIVER operating characteristic curves; PROSTATE-specific antigen; PROSTATE tumors
- Publication
Cancers, 2021, Vol 13, Issue 22, p5672
- ISSN
2072-6694
- Publication type
Article
- DOI
10.3390/cancers13225672