This study aims at evaluating the value of various artificial intelligence based techniques to improve the characterization and image post-processing for patients with musculoskeletal tumors.
Comparison of values relating to the texture parameters of tumors evaluated by MRI and ultra-high resolution CT between benign and malignant lesions using histological analysis as the standard of reference. Comparison of the diagnostic performance of texture parameters derived from different MRI sequences and ultra-high resolution CT for musculoskeletal tumor characterization. Evaluate the impact of ultra-high resolution with respect to standard resolution on CT images Comparison of the diagnostic performance of the texture parameters for the tumor on the diagnostic performance of texture analysis derived parameters for the characterization of musculoskeletal tumors. Evaluate the effectiveness and accuracy of automatic artificial intelligence (AI) based tumor segmentation tools. Evaluate the use of trabecular analysis on ultra-high resolution CT images for the evaluation of tumor-bone interfaces.
Study Type
OBSERVATIONAL
Enrollment
740
Medical imaging
CHU-Nancy
Nancy, Lorraine, France
RECRUITINGLesion benignancy or malignancy
Histologic determination of lesion aggressiveness (benign versus malignant) on core biopsy material
Time frame: Performed up to 6 months after CT and Magnetic Resonance (MR) imaging
Sarcoma FNCLCC (fédération Nationale des Centres de Lutte Contre le Cancer) grade
Histologic grade of the sarcomas included in the study population with surgical resection material
Time frame: Performed up to 1 year after CT and MR imaging
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