Diagnostic performance of commercially available vs. in-house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
| Autor: | Satomi Kawamoto, Berkan Solmaz, Alan L. Yuille, Ralph H. Hruban, Linda C. Chu, Seyoun Park, Elliot K. Fishman |
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| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty Pancreatic ductal adenocarcinoma Urology Computed tomography Sensitivity and Specificity 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Software Radiomics medicine Humans Radiology Nuclear Medicine and imaging Retrospective Studies Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Gastroenterology Random forest Pancreatic Neoplasms Binary classification Case-Control Studies 030220 oncology & carcinogenesis Normal pancreas Female Radiology Tomography X-Ray Computed business Carcinoma Pancreatic Ductal |
| Zdroj: | Abdominal Radiology. 45:2469-2475 |
| ISSN: | 2366-0058 2366-004X |
| Popis: | The purpose of this study is to evaluate diagnostic performance of a commercially available radiomics research prototype vs. an in-house radiomics software in the binary classification of CT images from patients with pancreatic ductal adenocarcinoma (PDAC) vs. healthy controls. In this retrospective case–control study, 190 patients with PDAC (97 men, 93 women; 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. 3D volume of the pancreas was manually segmented from preoperative CT scans. Four hundred and seventy-eight radiomics features were extracted using in-house radiomics software. Eight hundred and fifty-four radiomics features were extracted using a commercially available research prototype. Random forest classifier was used for binary classification of PDAC vs. normal pancreas. Accuracy, sensitivity, and specificity of commercially available radiomics software were compared to in-house software. When 40 radiomics features were used in the random forest classification, in-house software achieved superior sensitivity (1.00) and accuracy (0.992) compared to the commercially available research prototype (sensitivity = 0.950, accuracy = 0.968). When the number of features was reduced to five features, diagnostic performance of the in-house software decreased to sensitivity (0.950), specificity (0.923), and accuracy (0.936). Diagnostic performance of the commercially available research prototype was unchanged. Commercially available and in-house radiomics software achieve similar diagnostic performance, which may lower the barrier of entry for radiomics research and allow more clinician-scientists to perform radiomics research. |
| Databáze: | OpenAIRE |
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