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Hammer S, Nunes DW, Hammer M, Zeman F, Akers M, Götz A, Balla A, Doppler MC, Fellner C, Platz Batista da Silva N, Thurn S, Verloh N, Stroszczynski C, Wohlgemuth WA, Palm C, Uller W. Deep learning-based differentiation of peripheral high-flow and low-flow vascular malformations in T2-weighted short tau inversion recovery MRI. Clin Hemorheol Microcirc 2024; 87:221-235. [PMID: 38306026 DOI: 10.3233/ch-232071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
BACKGROUND Differentiation of high-flow from low-flow vascular malformations (VMs) is crucial for therapeutic management of this orphan disease. OBJECTIVE A convolutional neural network (CNN) was evaluated for differentiation of peripheral vascular malformations (VMs) on T2-weighted short tau inversion recovery (STIR) MRI. METHODS 527 MRIs (386 low-flow and 141 high-flow VMs) were randomly divided into training, validation and test set for this single-center study. 1) Results of the CNN's diagnostic performance were compared with that of two expert and four junior radiologists. 2) The influence of CNN's prediction on the radiologists' performance and diagnostic certainty was evaluated. 3) Junior radiologists' performance after self-training was compared with that of the CNN. RESULTS Compared with the expert radiologists the CNN achieved similar accuracy (92% vs. 97%, p = 0.11), sensitivity (80% vs. 93%, p = 0.16) and specificity (97% vs. 100%, p = 0.50). In comparison to the junior radiologists, the CNN had a higher specificity and accuracy (97% vs. 80%, p < 0.001; 92% vs. 77%, p < 0.001). CNN assistance had no significant influence on their diagnostic performance and certainty. After self-training, the junior radiologists' specificity and accuracy improved and were comparable to that of the CNN. CONCLUSIONS Diagnostic performance of the CNN for differentiating high-flow from low-flow VM was comparable to that of expert radiologists. CNN did not significantly improve the simulated daily practice of junior radiologists, self-training was more effective.
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Affiliation(s)
- Simone Hammer
- Department of Radiology, Faculty of Medicine, Medical Center University of Regensburg, University of Regensburg, Regensburg, Germany
| | - Danilo Weber Nunes
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany
| | - Michael Hammer
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany
| | - Florian Zeman
- Faculty of Medicine, Center for Clinical Trials, Medical Center University of Regensburg, University of Regensburg, Regensburg, Germany
| | - Michael Akers
- Department of Radiology, Faculty of Medicine, Medical Center University of Regensburg, University of Regensburg, Regensburg, Germany
| | - Andrea Götz
- Department of Radiology, Faculty of Medicine, Medical Center University of Regensburg, University of Regensburg, Regensburg, Germany
| | - Annika Balla
- Department of Radiology, Faculty of Medicine, Medical Center University of Regensburg, University of Regensburg, Regensburg, Germany
| | - Michael Christian Doppler
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Claudia Fellner
- Department of Radiology, Faculty of Medicine, Medical Center University of Regensburg, University of Regensburg, Regensburg, Germany
| | - Natascha Platz Batista da Silva
- Department of Radiology, Faculty of Medicine, Medical Center University of Regensburg, University of Regensburg, Regensburg, Germany
| | - Sylvia Thurn
- Department of Radiology, Faculty of Medicine, Medical Center University of Regensburg, University of Regensburg, Regensburg, Germany
| | - Niklas Verloh
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Christian Stroszczynski
- Department of Radiology, Faculty of Medicine, Medical Center University of Regensburg, University of Regensburg, Regensburg, Germany
| | - Walter Alexander Wohlgemuth
- Department of Radiology, Faculty of Medicine, Medical Center University of Halle (Saale), University of Halle (Saale), Halle, Germany
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany
- Regensburg Center of Biomedical Engineering (RCBE), OTH Regensburg and University of Regensburg, Regensburg, Germany
| | - Wibke Uller
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany
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Liu H, Hou CJ, Tang JL, Sun LT, Lu KF, Liu Y, Du P. Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images. Sci Rep 2023; 13:10500. [PMID: 37380667 DOI: 10.1038/s41598-023-37319-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 06/20/2023] [Indexed: 06/30/2023] Open
Abstract
This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultrasound features and basic clinical information was established. A deep learning prediction model was used to train the training group images and derive the deep learning prediction model. The two models were validated, and their accuracy rates were compared using the data and images of the test group, respectively. A logistic regression method was used to combine the two models to derive a combination diagnostic model and validate it in the test group. The diagnostic performance of each model was represented by the receiver operating characteristic curve and the area under the curve. In the test cohort, the diagnostic efficacy of the deep learning model was better than traditional statistical model, and the combined diagnostic model was better and outperformed the other two models (combination model vs traditional statistical model: AUC: 0.95 > 0.70, P = 0.001; combination model vs deep learning model: AUC: 0.95 > 0.87, P = 0.04). A combination model based on deep learning and ultrasound features has good diagnostic value.
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Affiliation(s)
- Han Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Chun-Jie Hou
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Jing-Lan Tang
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China.
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China.
| | - Li-Tao Sun
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Ke-Feng Lu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Ying Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Pei Du
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
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