4.6 Article

Diagnostic Value of the Texture Analysis Parameters of Retroperitoneal Residual Masses on Computed Tomographic Scan after Chemotherapy in Non-Seminomatous Germ Cell Tumors

Journal

CANCERS
Volume 15, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/cancers15112997

Keywords

non-seminomatous germ cell tumor; surgery; residual lesions; radiomics; score; prediction

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This study aimed to develop a radiomics score to predict the malignant nature of residual masses in patients with NSGCTs after chemotherapy, in order to avoid surgical overtreatment. The researchers used post-chemotherapy contrast-enhanced CT scans to delineate the residual masses and obtained tumor textures using LifeX software. They constructed a radiomics score based on eight texture features and evaluated its performance in predicting malignancy. The results suggest that the radiomics score can help predict the malignant character of residual post-chemotherapy masses in NSGCTs before surgery, thus limiting overtreatment.
After chemotherapy, patients with non-seminomatous germ cell tumors (NSGCTs) with residual masses >1 cm on computed tomography (CT) undergo surgery. However, in approximately 50% of cases, these masses only consist of necrosis/fibrosis. We aimed to develop a radiomics score to predict the malignant character of residual masses to avoid surgical overtreatment. Patients with NSGCTs who underwent surgery for residual masses between September 2007 and July 2020 were retrospectively identified from a unicenter database. Residual masses were delineated on post-chemotherapy contrast-enhanced CT scans. Tumor textures were obtained using the free software LifeX. We constructed a radiomics score using a penalized logistic regression model in a training dataset, and evaluated its performance on a test dataset. We included 76 patients, with 149 residual masses; 97 masses were malignant (65%). In the training dataset (n = 99 residual masses), the best model (ELASTIC-NET) led to a radiomics score based on eight texture features. In the test dataset, the area under the curve (AUC), sensibility, and specificity of this model were respectively estimated at 0.82 (95%CI, 0.69-0.95), 90.6% (75.0-98.0), and 61.1% (35.7-82.7). Our radiomics score may help in the prediction of the malignant nature of residual post-chemotherapy masses in NSGCTs before surgery, and thus limit overtreatment. However, these results are insufficient to simply select patients for surgery.

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