Lisson, C.S.; Manoj, S.; Wolf, D.; Lisson, C.G.; Schmidt, S.A.; Beer, M.; Thaiss, W.; Bolenz, C.; Zengerling, F.; Goetz, M. Radiomics and Clinicopathological Characteristics for Predicting Lymph Node Metastasis in Testicular Cancer. Cancers2023, 15, 5630.
Lisson, C.S.; Manoj, S.; Wolf, D.; Lisson, C.G.; Schmidt, S.A.; Beer, M.; Thaiss, W.; Bolenz, C.; Zengerling, F.; Goetz, M. Radiomics and Clinicopathological Characteristics for Predicting Lymph Node Metastasis in Testicular Cancer. Cancers 2023, 15, 5630.
Lisson, C.S.; Manoj, S.; Wolf, D.; Lisson, C.G.; Schmidt, S.A.; Beer, M.; Thaiss, W.; Bolenz, C.; Zengerling, F.; Goetz, M. Radiomics and Clinicopathological Characteristics for Predicting Lymph Node Metastasis in Testicular Cancer. Cancers2023, 15, 5630.
Lisson, C.S.; Manoj, S.; Wolf, D.; Lisson, C.G.; Schmidt, S.A.; Beer, M.; Thaiss, W.; Bolenz, C.; Zengerling, F.; Goetz, M. Radiomics and Clinicopathological Characteristics for Predicting Lymph Node Metastasis in Testicular Cancer. Cancers 2023, 15, 5630.
Abstract
Radiomics refers to the extraction and analysis of a wide range of medical imaging features in a non-invasive and cost-effective manner to comprehensively characterise tumours. In this study, machine learning models combining radiomics and clinical factors were developed to predict retroperitoneal lymph node metastasis in testicular germ cell tumours (TGCTs), with the aim of reducing unnecessary treatment in this group of young patients.
Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. After segmenting 273 retroperitoneal lymph nodes using dedicated radiomics software, we developed machine-learning prediction models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). For each classifier, we developed a radiomics-only, clinical-only, and combined radiomics-clinical prediction model.
The models’ performances were evaluated using the area under the receiver operating characteristic curves (AUCs).
The RF-based combined clinical and radiomic model showed the most robust performance in predicting LNM with an area under the curve (AUC) of 0.95 (±0.03; 95% CI), accuracy 87%, precision 89%, recall 86% and F1 score 87%, followed by the LGBM model with an area under the curve (AUC) of 0.93 (±0.05; 95% CI), accuracy 83%, precision 87%, recall 80% and F1 score 82%. Decision curve analysis demonstrated the clinical utility of our proposed RF-based combined clinical–radiomics model.
Our study has identified reliable and predictive machine-learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine-learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment. Multi-centre validation is required to provide high-quality evidence for clinical application.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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