Applying Radiomics to Predict Pathology of Postchemotherapy Retroperitoneal Nodal Masses in Germ Cell Tumors

Abstract

PurposeAfter chemotherapy, approximately 50% of patients with metastatic testicular germ cell tumors (GCTs) who undergo retroperitoneal lymph node dissections (RPNLDs) for residual masses have fibrosis. Radiomics uses image processing techniques to extract quantitative textures/features from regions of interest (ROIs) to train a classifier that predicts outcomes. We hypothesized that radiomics would identify patients with a high likelihood of fibrosis who may avoid RPLND.Patients and MethodsPatients with GCT who had an RPLND for nodal masses > 1 cm after first-line platinum chemotherapy were included. Preoperative contrast-enhanced axial computed tomography images of retroperitoneal ROIs were manually contoured. Radiomics features (n = 153) were used to train a radial basis function support vector machine classifier to discriminate between viable GCT/mature teratoma versus fibrosis. A nested 10-fold cross-validation protocol was used to determine classifier accuracy. Clinical variables/restricted size criteria were used to optimize the classifier.ResultsSeventy-seven patients with 102 ROIs were analyzed (GCT, 21; teratoma, 41; fibrosis, 40). The discriminative accuracy of radiomics to identify GCT/teratoma versus fibrosis was 72 ± 2.2% (area under the curve [AUC], 0.74 ± 0.028); sensitivity was 56.2 ± 15.0%, and specificity was 81.9 ± 9.0% (P = .001). No major predictive differences were identified when data were restricted by varying maximal axial diameters (AUC range, 0.58 ± 0.05 to 0.74 ± 0.03). The prediction algorithm using clinical variables alone identified an AUC of 0.76. When these variables were added to the radiomics signature, the best performing classifier was identified when axial masses were limited to diameter < 2 cm (accuracy, 88.2 ± 4.4; AUC, 0.80 ± 0.05; P = .02).ConclusionA predictive radiomics algorithm had a discriminative accuracy of 72% that improved to 88% when combined with clinical predictors. Additional independent validation is required to assess whether radiomics allows patients with a high predicted likelihood of fibrosis to avoid RPLND.

Development of a radiomic signature for predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer

“…34,35 In addition, there is data supporting the use of this technology as a predictive biomarker in breast cancer, esophageal carcinoma, germ-cell tumors, nasopharyngeal carcinoma and rectal cancer, with discriminative accuracies greater than 70%. 9,10,14,[36][37][38] To date, computer-based image analysis has not been shown to out-perform validated clinical prediction tools, in settings whereby such tools exist; however, their combination with clinical predictors have been shown to improve discriminative accuracy. 36 The utility of this technology in these early applications underscore the need for further study, particularly in settings where clinical predictors alone are ineffective.…”

“…9,10,14,[36][37][38] To date, computer-based image analysis has not been shown to out-perform validated clinical prediction tools, in settings whereby such tools exist; however, their combination with clinical predictors have been shown to improve discriminative accuracy. 36 The utility of this technology in these early applications underscore the need for further study, particularly in settings where clinical predictors alone are ineffective.…”

T2-Weighted Image-Based Radiomics Signature for Discriminating Between Seminomas and Nonseminoma

“…Radiomics may help find potentially valuable information through the high-throughput extraction of quantitative features (14, 15). The newly proposed radiomics method has been successfully applied to various diseases (17, 18, 27–30). In a recent study, Lewin et al applied radiomics to predict the pathology of postchemotherapy retroperitoneal nodal masses in germ cell tumors (27).…”

“…The newly proposed radiomics method has been successfully applied to various diseases (17, 18, 27–30). In a recent study, Lewin et al applied radiomics to predict the pathology of postchemotherapy retroperitoneal nodal masses in germ cell tumors (27). Their results showed that the discriminative accuracy, sensitivity, and specificity of radiomics to identify GCT/teratoma vs. fibrosis was 72, 56.2, and 81.9%, respectively.…”

Narrative review of developing new biomarkers for decision making in advanced testis cancer

“…Moreover, there is a lot of enthusiasm in the GCT community for joint efforts to build up integrated clinical and biomarkers models for the identification of teratoma and benign residual disease in patients with non-seminoma presenting with postchemotherapy residual disease. In this setting, radiomics has showed promising results and need to be further explored (74,75).…”