Differentiation of testicular seminomas from nonseminomas based on multiphase CT radiomics combined with machine learning : A multicenter study
Copyright © 2024 Elsevier B.V. All rights reserved..
BACKGROUND: Differentiating seminomas from nonseminomas is crucial for formulating optimal treatment strategies for testicular germ cell tumors (TGCTs). Therefore, our study aimed to develop and validate a clinical-radiomics model for this purpose.
METHODS: In this study, 221 patients with TGCTs confirmed by pathology from four hospitals were enrolled and classified into training (n = 126), internal validation (n = 55) and external test (n = 40) cohorts. Radiomics features were extracted from the CT images. After feature selection, we constructed a clinical model, radiomics models and clinical-radiomics model with different machine learning algorithms. The top-performing model was chosen utilizing receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was also conducted to assess its practical utility.
RESULTS: Compared with those of the clinical and radiomics models, the clinical-radiomics model demonstrated the highest discriminatory ability, with AUCs of 0.918 (95 % CI: 0.870 - 0.966), 0.909 (95 % CI: 0.829 - 0.988) and 0.839 (95 % CI: 0.709 - 0.968) in the training, validation and test cohorts, respectively. Moreover, DCA confirmed that the combined model had a greater net benefit in predicting seminomas and nonseminomas.
CONCLUSION: The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:175 |
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| Enthalten in: |
European journal of radiology - 175(2024) vom: 07. März, Seite 111416 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Fang, Fuxiang [VerfasserIn] |
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| Links: |
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| Themen: |
Computed tomography |
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| Anmerkungen: |
Date Revised 09.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1016/j.ejrad.2024.111416 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369501713 |
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| 100 | 1 | |a Fang, Fuxiang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Differentiation of testicular seminomas from nonseminomas based on multiphase CT radiomics combined with machine learning |b A multicenter study |
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| 500 | |a published: Print-Electronic | ||
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| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND: Differentiating seminomas from nonseminomas is crucial for formulating optimal treatment strategies for testicular germ cell tumors (TGCTs). Therefore, our study aimed to develop and validate a clinical-radiomics model for this purpose | ||
| 520 | |a METHODS: In this study, 221 patients with TGCTs confirmed by pathology from four hospitals were enrolled and classified into training (n = 126), internal validation (n = 55) and external test (n = 40) cohorts. Radiomics features were extracted from the CT images. After feature selection, we constructed a clinical model, radiomics models and clinical-radiomics model with different machine learning algorithms. The top-performing model was chosen utilizing receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was also conducted to assess its practical utility | ||
| 520 | |a RESULTS: Compared with those of the clinical and radiomics models, the clinical-radiomics model demonstrated the highest discriminatory ability, with AUCs of 0.918 (95 % CI: 0.870 - 0.966), 0.909 (95 % CI: 0.829 - 0.988) and 0.839 (95 % CI: 0.709 - 0.968) in the training, validation and test cohorts, respectively. Moreover, DCA confirmed that the combined model had a greater net benefit in predicting seminomas and nonseminomas | ||
| 520 | |a CONCLUSION: The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Testicular germ cell tumors | |
| 700 | 1 | |a Wu, Linfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Xing |e verfasserin |4 aut | |
| 700 | 1 | |a Bu, Huiping |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yueting |e verfasserin |4 aut | |
| 700 | 1 | |a Xian Wu, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Zheng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Tianyu |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Guanglin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yutong |e verfasserin |4 aut | |
| 700 | 1 | |a Weng, Hongchao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Jiawen |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Chenjun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chengyang |e verfasserin |4 aut | |
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