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

Author:

Fournier Clémence1,Leguillette Clémence2,Leblanc Eric3ORCID,Le Deley Marie-Cécile2ORCID,Carnot Aurélien4,Pasquier David56ORCID,Escande Alexandre67,Taieb Sophie8ORCID,Ceugnart Luc7,Lebellec Loïc4

Affiliation:

1. Department of Medical Oncology, Centre Hospitalier de Roubaix, 59100 Roubaix, France

2. Clinical Research Department, Centre Oscar Lambret, 59000 Lille, France

3. Department of Surgical Oncology, Centre Oscar Lambret, 59000 Lille, France

4. Department of Medical Oncology, Centre Oscar Lambret, 59000 Lille, France

5. Academic Department of Radiation Oncology, Centre Oscar Lambret, 59000 Lille, France

6. Univ. Lille, CNRS, Centrale Lille, UMR 9189–CRIStAL, 59000 Lille, France

7. Department of Radiotherapy, Clinique Léonard de Vinci, 59187 Dechy, France

8. Department of Radiology, Centre Oscar Lambret, 59000 Lille, France

Abstract

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.

Publisher

MDPI AG

Subject

Cancer Research,Oncology

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