Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning
Background Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to inv...
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| Published in: | BMC cancer Vol. 25; no. 1; pp. 563 - 11 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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London
BioMed Central
28.03.2025
BioMed Central Ltd Springer Nature B.V BMC |
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| ISSN: | 1471-2407, 1471-2407 |
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| Abstract | Background
Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization.
Methods
Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results.
Results
Utilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985–0.993) and 0.909 (95% CI: 0.893–0.923) in the training and testing cohorts, respectively.
Conclusions
AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions. |
|---|---|
| AbstractList | BackgroundDistinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization.MethodsRetrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results.ResultsUtilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985–0.993) and 0.909 (95% CI: 0.893–0.923) in the training and testing cohorts, respectively.ConclusionsAutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions. Background Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization. Methods Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results. Results Utilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985-0.993) and 0.909 (95% CI: 0.893-0.923) in the training and testing cohorts, respectively. Conclusions AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions. Keywords: Automatic machine learning, Magnetic resonance imaging, Radiomics, Testicular cancer Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization. Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results. Utilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985-0.993) and 0.909 (95% CI: 0.893-0.923) in the training and testing cohorts, respectively. AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions. Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization.BACKGROUNDDistinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization.Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results.METHODSRetrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results.Utilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985-0.993) and 0.909 (95% CI: 0.893-0.923) in the training and testing cohorts, respectively.RESULTSUtilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985-0.993) and 0.909 (95% CI: 0.893-0.923) in the training and testing cohorts, respectively.AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions.CONCLUSIONSAutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions. Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization. Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results. Utilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985-0.993) and 0.909 (95% CI: 0.893-0.923) in the training and testing cohorts, respectively. AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions. Background Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization. Methods Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results. Results Utilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985–0.993) and 0.909 (95% CI: 0.893–0.923) in the training and testing cohorts, respectively. Conclusions AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions. Abstract Background Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization. Methods Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results. Results Utilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985–0.993) and 0.909 (95% CI: 0.893–0.923) in the training and testing cohorts, respectively. Conclusions AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions. |
| ArticleNumber | 563 |
| Audience | Academic |
| Author | Liu, Zhiyong Lv, Wenzhi Feng, Zhaoyan Zhang, PeiPei Feng, Yanhui Min, Xiangde Wang, Liang Li, Jin |
| Author_xml | – sequence: 1 givenname: Liang surname: Wang fullname: Wang, Liang organization: Computer Center, Tongji Hospital, Tongji Medical College, Uazhong University of Science and Technology – sequence: 2 givenname: PeiPei surname: Zhang fullname: Zhang, PeiPei organization: Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology – sequence: 3 givenname: Yanhui surname: Feng fullname: Feng, Yanhui organization: Computer Center, Tongji Hospital, Tongji Medical College, Uazhong University of Science and Technology, School of Medicine and Health Management, Huazhong University of Science and Technology – sequence: 4 givenname: Wenzhi surname: Lv fullname: Lv, Wenzhi organization: Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Britton Chance Center, MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology – sequence: 5 givenname: Xiangde surname: Min fullname: Min, Xiangde organization: Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology – sequence: 6 givenname: Zhiyong surname: Liu fullname: Liu, Zhiyong organization: School of Medicine and Health Management, Huazhong University of Science and Technology – sequence: 7 givenname: Jin surname: Li fullname: Li, Jin email: lijin@tjh.tjmu.edu.cn organization: Computer Center, Tongji Hospital, Tongji Medical College, Uazhong University of Science and Technology – sequence: 8 givenname: Zhaoyan surname: Feng fullname: Feng, Zhaoyan email: fzm198822@126.com organization: Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology |
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| Cites_doi | 10.3390/cancers14163882 10.1038/s41598-021-83023-4 10.1002/jmri.27910 10.1158/0008-5472.CAN-17-0339 10.1145/2908812.2908918 10.1038/nrurol.2016.164 10.1016/j.ejrad.2019.02.032 10.3322/caac.21660 10.1007/s00330-017-4944-3 10.1148/radiol.14132718 10.1007/s00330-019-06495-z 10.1007/s00261-018-1560-x 10.1148/ryai.2020200029 10.4103/aja202158 10.1016/j.crad.2017.10.007 10.1148/rg.2018170150 10.1002/hbm.25028 10.1016/j.ejrad.2022.110158 10.4103/aja.aja_119_18 10.3390/jpm11100978 10.1111/andr.13032 10.3390/diagnostics12112733 10.1186/s13058-022-01516-0 10.1038/ncomms5006 10.1007/s00261-020-02621-4 10.3322/caac.21763 10.1117/1.JBO.28.4.045001 10.1002/jso.25203 10.1093/brain/awab340 10.1038/s42256-023-00717-2 10.3389/fonc.2019.01330 10.3389/fonc.2022.963612 10.1007/s00330-017-5013-7 10.3390/curroncol29100542 10.1007/s13244-017-0592-z 10.1002/jmri.27690 10.3174/ajnr.A6621 10.1093/bioinformatics/btz470 |
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| Keywords | Automatic machine learning Magnetic resonance imaging Testicular cancer Radiomics |
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| References | Y Li (13844_CR20) 2022; 29 H Zhou (13844_CR25) 2020; 41 C Fan (13844_CR21) 2022; 148 J Meng (13844_CR27) 2023; 28 L Manganaro (13844_CR35) 2018; 28 J Yu (13844_CR11) 2022; 24 A Luzurier (13844_CR12) 2018; 73 J Gu (13844_CR19) 2022; 12 GC Parenti (13844_CR31) 2018; 9 KP Dieckmann (13844_CR4) 2011; 34 13844_CR16 M Khanna (13844_CR34) 2021; 46 J Dafflon (13844_CR23) 2020; 41 G Song (13844_CR6) 2019; 21 13844_CR39 T Bian (13844_CR32) 2022; 55 AC Tsili (13844_CR7) 2018; 28 13844_CR13 G Li (13844_CR18) 2022; 145 TT Le (13844_CR24) 2020; 36 J Pan (13844_CR33) 2021; 54 G Feliciani (13844_CR37) 2021; 11 M Chovanec (13844_CR5) 2016; 13 AC Tsili (13844_CR9) 2021; 9 AS Tejani (13844_CR30) 2023; 5 J Bergstra (13844_CR40) 2012; 13 RL Siegel (13844_CR2) 2023; 73 S Ramanathan (13844_CR3) 2018; 43 AM Isidori (13844_CR10) 2014; 273 M Xu (13844_CR36) 2019; 114 H Sung (13844_CR1) 2021; 71 13844_CR26 13844_CR29 B Baessler (13844_CR22) 2020; 30 JJM van Griethuysen (13844_CR28) 2017; 77 HJ Aerts (13844_CR15) 2014; 5 PK Mittal (13844_CR14) 2018; 38 S Lagabrielle (13844_CR8) 2018; 118 P Zhang (13844_CR38) 2019; 9 X Wang (13844_CR17) 2022; 24 |
| References_xml | – ident: 13844_CR13 doi: 10.3390/cancers14163882 – volume: 11 start-page: 3456 issue: 1 year: 2021 ident: 13844_CR37 publication-title: Sci Rep doi: 10.1038/s41598-021-83023-4 – volume: 13 start-page: 281 issue: 1 year: 2012 ident: 13844_CR40 publication-title: J Mach Learn Res – volume: 55 start-page: 772 issue: 3 year: 2022 ident: 13844_CR32 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.27910 – volume: 77 start-page: e104 issue: 21 year: 2017 ident: 13844_CR28 publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-17-0339 – ident: 13844_CR39 doi: 10.1145/2908812.2908918 – volume: 13 start-page: 663 issue: 11 year: 2016 ident: 13844_CR5 publication-title: Nat Rev Urol doi: 10.1038/nrurol.2016.164 – volume: 114 start-page: 38 year: 2019 ident: 13844_CR36 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2019.02.032 – volume: 71 start-page: 209 issue: 3 year: 2021 ident: 13844_CR1 publication-title: CA Cancer J Clin doi: 10.3322/caac.21660 – volume: 28 start-page: 31 issue: 1 year: 2018 ident: 13844_CR7 publication-title: Eur Radiol doi: 10.1007/s00330-017-4944-3 – volume: 273 start-page: 606 issue: 2 year: 2014 ident: 13844_CR10 publication-title: Radiology doi: 10.1148/radiol.14132718 – volume: 30 start-page: 2334 issue: 4 year: 2020 ident: 13844_CR22 publication-title: Eur Radiol doi: 10.1007/s00330-019-06495-z – volume: 43 start-page: 3125 issue: 11 year: 2018 ident: 13844_CR3 publication-title: Abdom Radiol (NY) doi: 10.1007/s00261-018-1560-x – ident: 13844_CR29 doi: 10.1148/ryai.2020200029 – volume: 24 start-page: 201 issue: 2 year: 2022 ident: 13844_CR11 publication-title: Asian J Androl doi: 10.4103/aja202158 – volume: 73 start-page: 322 issue: 3 year: 2018 ident: 13844_CR12 publication-title: Clin Radiol doi: 10.1016/j.crad.2017.10.007 – volume: 38 start-page: 806 issue: 3 year: 2018 ident: 13844_CR14 publication-title: Radiographics doi: 10.1148/rg.2018170150 – volume: 41 start-page: 3555 issue: 13 year: 2020 ident: 13844_CR23 publication-title: Hum Brain Mapp doi: 10.1002/hbm.25028 – volume: 148 start-page: 110158 year: 2022 ident: 13844_CR21 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2022.110158 – volume: 21 start-page: 196 issue: 2 year: 2019 ident: 13844_CR6 publication-title: Asian J Androl doi: 10.4103/aja.aja_119_18 – ident: 13844_CR26 doi: 10.3390/jpm11100978 – volume: 9 start-page: 1395 issue: 5 year: 2021 ident: 13844_CR9 publication-title: Andrology doi: 10.1111/andr.13032 – ident: 13844_CR16 doi: 10.3390/diagnostics12112733 – volume: 24 start-page: 20 issue: 1 year: 2022 ident: 13844_CR17 publication-title: Breast Cancer Res doi: 10.1186/s13058-022-01516-0 – volume: 5 start-page: 4006 year: 2014 ident: 13844_CR15 publication-title: Nat Commun doi: 10.1038/ncomms5006 – volume: 46 start-page: 319 issue: 1 year: 2021 ident: 13844_CR34 publication-title: Abdom Radiol (NY) doi: 10.1007/s00261-020-02621-4 – volume: 73 start-page: 17 issue: 1 year: 2023 ident: 13844_CR2 publication-title: CA Cancer J Clin doi: 10.3322/caac.21763 – volume: 28 start-page: 045001 issue: 4 year: 2023 ident: 13844_CR27 publication-title: J Biomed Opt doi: 10.1117/1.JBO.28.4.045001 – volume: 118 start-page: 630 issue: 4 year: 2018 ident: 13844_CR8 publication-title: J Surg Oncol doi: 10.1002/jso.25203 – volume: 145 start-page: 1151 issue: 3 year: 2022 ident: 13844_CR18 publication-title: Brain doi: 10.1093/brain/awab340 – volume: 5 start-page: 950 issue: 9 year: 2023 ident: 13844_CR30 publication-title: Nat Mach Intell doi: 10.1038/s42256-023-00717-2 – volume: 9 start-page: 1330 year: 2019 ident: 13844_CR38 publication-title: Front Oncol doi: 10.3389/fonc.2019.01330 – volume: 12 start-page: 963612 year: 2022 ident: 13844_CR19 publication-title: Front Oncol doi: 10.3389/fonc.2022.963612 – volume: 34 start-page: e7 issue: 4 Pt 2 year: 2011 ident: 13844_CR4 publication-title: Int J Androl – volume: 28 start-page: 554 issue: 2 year: 2018 ident: 13844_CR35 publication-title: Eur Radiol doi: 10.1007/s00330-017-5013-7 – volume: 29 start-page: 6893 year: 2022 ident: 13844_CR20 publication-title: Curr Oncol doi: 10.3390/curroncol29100542 – volume: 9 start-page: 137 issue: 2 year: 2018 ident: 13844_CR31 publication-title: Insights Imaging doi: 10.1007/s13244-017-0592-z – volume: 54 start-page: 1314 issue: 4 year: 2021 ident: 13844_CR33 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.27690 – volume: 41 start-page: 1279 issue: 7 year: 2020 ident: 13844_CR25 publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A6621 – volume: 36 start-page: 250 issue: 1 year: 2020 ident: 13844_CR24 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz470 |
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Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment... Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However,... Background Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment... BackgroundDistinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment... Abstract Background Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding... |
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| Title | Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning |
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