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10.3390/diagnostics12040889
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Article

An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors

by
Chia-Ying Lin
1,
Yi-Ting Yen
2,3,
Li-Ting Huang
1,
Tsai-Yun Chen
4,
Yi-Sheng Liu
1,
Shih-Yao Tang
5,
Wei-Li Huang
2,
Ying-Yuan Chen
2,
Chao-Han Lai
6,
Yu-Hua Dean Fang
7,*,
Chao-Chun Chang
2,* and
Yau-Lin Tseng
2
1
Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
2
Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
3
Division of Trauma and Acute Care Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
4
Division of Hematology and Oncology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
5
Department of Biomedical Engineering, National Cheng Kung University, Tainan 704, Taiwan
6
Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
7
Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
*
Authors to whom correspondence should be addressed.
Diagnostics 2022, 12(4), 889; https://doi.org/10.3390/diagnostics12040889
Submission received: 7 February 2022 / Revised: 18 March 2022 / Accepted: 31 March 2022 / Published: 2 April 2022
(This article belongs to the Section Medical Imaging and Theranostics)
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Abstract

This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfusion parameters. The clinical data and preoperative DCE–MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning–based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.
Keywords: differential diagnosis; dynamic contrast-enhanced MRI; perfusion parameters; prevascular mediastinal tumor; machine learning differential diagnosis; dynamic contrast-enhanced MRI; perfusion parameters; prevascular mediastinal tumor; machine learning

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MDPI and ACS Style

Lin, C.-Y.; Yen, Y.-T.; Huang, L.-T.; Chen, T.-Y.; Liu, Y.-S.; Tang, S.-Y.; Huang, W.-L.; Chen, Y.-Y.; Lai, C.-H.; Fang, Y.-H.D.; et al. An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors. Diagnostics 2022, 12, 889. https://doi.org/10.3390/diagnostics12040889

AMA Style

Lin C-Y, Yen Y-T, Huang L-T, Chen T-Y, Liu Y-S, Tang S-Y, Huang W-L, Chen Y-Y, Lai C-H, Fang Y-HD, et al. An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors. Diagnostics. 2022; 12(4):889. https://doi.org/10.3390/diagnostics12040889

Chicago/Turabian Style

Lin, Chia-Ying, Yi-Ting Yen, Li-Ting Huang, Tsai-Yun Chen, Yi-Sheng Liu, Shih-Yao Tang, Wei-Li Huang, Ying-Yuan Chen, Chao-Han Lai, Yu-Hua Dean Fang, and et al. 2022. "An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors" Diagnostics 12, no. 4: 889. https://doi.org/10.3390/diagnostics12040889

APA Style

Lin, C.-Y., Yen, Y.-T., Huang, L.-T., Chen, T.-Y., Liu, Y.-S., Tang, S.-Y., Huang, W.-L., Chen, Y.-Y., Lai, C.-H., Fang, Y.-H. D., Chang, C.-C., & Tseng, Y.-L. (2022). An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors. Diagnostics, 12(4), 889. https://doi.org/10.3390/diagnostics12040889

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