A retrospective study utilizing archived CT scans of patients diagnosed with retroperitoneal fibrosis, sarcoma or other malignancies (i.e. lymphoma, germ cell tumors, metastasis, infections, ganglioneuromas) in order to implement a radiomics algorithm which is able to differentiate between these malignancies.
The aim of this project is to develop a radiomics algorithm that can reliably identify retroperitoneal fibrosis (Ormond's disease) and retroperitoneal sarcomas, automatically segment them and differentiate them from other retroperitoneal diseases. Radiomics is a technique that uses artificial intelligence to extract characteristics from radiological image data that are not visible to humans and to identify image morphological patterns of diseases. As it is difficult to differentiate between diseases using image data alone, clinical data such as symptoms and laboratory values are to be correlated with the image data and utilized by the algorithm. Among other things, this should increase the sensitivity, accuracy and specificity of image-based diagnostics in order to enable faster, non-invasive diagnosis.
Study Type
OBSERVATIONAL
Enrollment
600
A radiomics algorithm designed to distinguish retroperitoneal fibrosis from other retroperitoneal tumors and provide recommendations for clinical treatment decisions.
Peking University International Hospital
Beijing, China
Universitätsklinikum Mannheim
Mannheim, Baden-Wurttemberg, Germany
Radiomic accuracy for retroperitoneal fibrosis
Accuracy of the algorithm in differentiating between retroperitoneal fibrosis and other retroperitoneal diseases
Time frame: 6 months
Radiomic accuracy for retroperitoneal sarcomas
Accuracy of the algorithm in differentiating between retroperitoneal sarcoma and other retroperitoneal diseases using CT images
Time frame: 10 Months
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