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Fazekas S, Budai BK, Stollmayer R, Kaposi PN, Bérczi V. Artificial intelligence and neural networks in radiology – Basics that all radiology residents should know. IMAGING 2022. [DOI: 10.1556/1647.2022.00104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
AbstractThe area of Artificial Intelligence is developing at a high rate. In the medical field, an extreme amount of data is created every day. As the images and the reports are quantifiable, the field of radiology aspires to deliver better, more efficient clinical care. Artificial intelligence (AI) means the simulation of human intelligence by a system or machine. It has been developed to enable machines to “think”, which means to be able to learn, reason, predict, categorize, and solve problems concerning high amounts of data and make decisions in a more effective manner than before. Different AI methods can help radiologists with pre-screening images and identifying features. In this review, we summarize the basic concepts which are needed to understand AI. As the AI methods are expected to exceed the threshold for clinical usefulness soon, in the near future it will be inevitable to use AI in medicine.
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Affiliation(s)
- Szuzina Fazekas
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Bettina Katalin Budai
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Róbert Stollmayer
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Pál Novák Kaposi
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Viktor Bérczi
- Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
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Budai BK, Stollmayer R, Rónaszéki AD, Körmendy B, Zsombor Z, Palotás L, Fejér B, Szendrõi A, Székely E, Maurovich-Horvat P, Kaposi PN. Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols. Front Med (Lausanne) 2022; 9:974485. [PMID: 36314024 PMCID: PMC9606401 DOI: 10.3389/fmed.2022.974485] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction This study aimed to construct a radiomics-based machine learning (ML) model for differentiation between non-clear cell and clear cell renal cell carcinomas (ccRCC) that is robust against institutional imaging protocols and scanners. Materials and methods Preoperative unenhanced (UN), corticomedullary (CM), and excretory (EX) phase CT scans from 209 patients diagnosed with RCCs were retrospectively collected. After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. For the ML analysis, the cases were randomly split into training and test sets with a 3:1 ratio. Highly correlated RFs were filtered out based on Pearson’s correlation coefficient (r > 0.95). Intraclass correlation coefficient analysis was used to select RFs with excellent reproducibility (ICC ≥ 0.90). The most predictive RFs were selected by the least absolute shrinkage and selection operator (LASSO). A support vector machine algorithm-based binary classifier (SVC) was constructed to predict tumor types and its performance was evaluated based-on receiver operating characteristic curve (ROC) analysis. The “Kidney Tumor Segmentation 2019” (KiTS19) publicly available dataset was used during external validation of the model. The performance of the SVC was also compared with an expert radiologist’s. Results The training set consisted of 121 ccRCCs and 38 non-ccRCCs, while the independent internal test set contained 40 ccRCCs and 13 non-ccRCCs. For external validation, 50 ccRCCs and 23 non-ccRCCs were identified from the KiTS19 dataset with the available UN, CM, and EX phase CTs. After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. During external validation, the SVC achieved an area under the ROC curve (AUC) value, accuracy, sensitivity, and specificity of 0.83, 0.78, 0.80, and 0.74, respectively. UN and/or EX phase RFs did not further increase the model’s performance. Meanwhile, in the same comparison, the expert radiologist achieved similar performance with an AUC of 0.77, an accuracy of 0.79, a sensitivity of 0.84, and a specificity of 0.69. Conclusion Radiomics analysis of CM phase CT scans combined with ML can achieve comparable performance with an expert radiologist in differentiating ccRCCs from non-ccRCCs.
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Affiliation(s)
- Bettina Katalin Budai
- Department of Radiology, Faculty of Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary,*Correspondence: Bettina Katalin Budai,
| | - Róbert Stollmayer
- Department of Radiology, Faculty of Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Aladár Dávid Rónaszéki
- Department of Radiology, Faculty of Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Borbála Körmendy
- Department of Radiology, Faculty of Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Zita Zsombor
- Department of Radiology, Faculty of Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Lõrinc Palotás
- Department of Radiology, Faculty of Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Bence Fejér
- Department of Radiology, Faculty of Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Attila Szendrõi
- Department of Urology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Eszter Székely
- Department of Pathology, Forensic and Insurance Medicine, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Pál Maurovich-Horvat
- Department of Radiology, Faculty of Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Pál Novák Kaposi
- Department of Radiology, Faculty of Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
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