Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer
<b>Background/Objectives</b>: Delta-radiomics involves analyzing feature variations at different acquisition time-points. This study aimed to assess the utility of delta-radiomics feature analysis applied to contrast-enhanced (CE) and non-contrast-enhanced (NE) T1-weighted images (WI) in...
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2025-03-01
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| author | Kohei Isemoto Yuma Waseda Motohiro Fujiwara Koichiro Kimura Daisuke Hirahara Tatsunori Saho Eichi Takaya Yuki Arita Thomas C. Kwee Shohei Fukuda Hajime Tanaka Soichiro Yoshida Yasuhisa Fujii |
| author_facet | Kohei Isemoto Yuma Waseda Motohiro Fujiwara Koichiro Kimura Daisuke Hirahara Tatsunori Saho Eichi Takaya Yuki Arita Thomas C. Kwee Shohei Fukuda Hajime Tanaka Soichiro Yoshida Yasuhisa Fujii |
| author_sort | Kohei Isemoto |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Delta-radiomics involves analyzing feature variations at different acquisition time-points. This study aimed to assess the utility of delta-radiomics feature analysis applied to contrast-enhanced (CE) and non-contrast-enhanced (NE) T1-weighted images (WI) in predicting the therapeutic response to chemoradiotherapy (CRT) in patients diagnosed with muscle-invasive bladder cancer (MIBC). <b>Methods</b>: Forty-three patients with non-metastatic MIBC (cT2–4N0M0) who underwent partial or radical cystectomy after induction CRT were, retrospectively, reviewed. Pathological complete response (pCR) to CRT was defined as the absence of residual viable tumor cells in the cystectomy specimen. Identical volumes of interest corresponding to the index bladder cancer lesions on CE- and NE-T1WI on pre-therapeutic 1.5-T MRI were collaboratively delineated by one radiologist and one urologist. Texture analysis was performed using “LIFEx” software. The subtraction of radiological features between CE- and NE-T1WI yielded 112 delta-radiomics features, which were utilized in multiple machine-learning algorithms to construct optimal predictive models for CRT responsiveness. Additionally, the predictive performance of the radiomics model constructed using CE-T1WI alone was assessed. <b>Results</b>: Twenty-one patients (49%) achieved pCR. The best-performing delta-radiomics model, employing the “Extreme Gradient Boosting” algorithm, yielded an area under the receiver operating characteristic curve (AUC) of 0.85 (95% confidence interval [CI]: 0.75–0.95), utilizing four signal intensity-based delta-radiomics features. This outperformed the best model derived from CE-T1WI alone (AUC: 0.63, 95% CI: 0.50–0.75), which incorporated two morphological features and one signal intensity-based radiomics feature. <b>Conclusions</b>: Delta-radiomics analysis applied to pre-therapeutic CE- and NE-MRI demonstrated promising predictive ability for CRT responsiveness prior to treatment initiation. |
| format | Article |
| id | doaj-art-1bdfa746253540919e65fe04dc3d77ee |
| institution | OA Journals |
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| language | English |
| publishDate | 2025-03-01 |
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| spelling | doaj-art-1bdfa746253540919e65fe04dc3d77ee2025-08-20T02:09:18ZengMDPI AGDiagnostics2075-44182025-03-0115780110.3390/diagnostics15070801Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder CancerKohei Isemoto0Yuma Waseda1Motohiro Fujiwara2Koichiro Kimura3Daisuke Hirahara4Tatsunori Saho5Eichi Takaya6Yuki Arita7Thomas C. Kwee8Shohei Fukuda9Hajime Tanaka10Soichiro Yoshida11Yasuhisa Fujii12Department of Urology, Institute of Science Tokyo, Tokyo 113-8519, JapanDepartment of Urology, Insured Medical Care Management, Tokyo Medical and Dental University, Tokyo 113-8519, JapanDepartment of Urology, Institute of Science Tokyo, Tokyo 113-8519, JapanDepartment of Radiology, Institute of Science Tokyo, Tokyo 113-8519, JapanDepartment of Management Planning Division, Harada Academy, Kagoshima 891-0113, JapanDepartment of Radiological Technology, Kokura Memorial Hospital, Kitakyushu 802-8555, JapanAI Lab, Tohoku University Hospital, Sendai 980-8574, JapanDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USADepartment of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Boston, MA 02114, USADepartment of Urology, Institute of Science Tokyo, Tokyo 113-8519, JapanDepartment of Urology, Institute of Science Tokyo, Tokyo 113-8519, JapanDepartment of Urology, Institute of Science Tokyo, Tokyo 113-8519, JapanDepartment of Urology, Institute of Science Tokyo, Tokyo 113-8519, Japan<b>Background/Objectives</b>: Delta-radiomics involves analyzing feature variations at different acquisition time-points. This study aimed to assess the utility of delta-radiomics feature analysis applied to contrast-enhanced (CE) and non-contrast-enhanced (NE) T1-weighted images (WI) in predicting the therapeutic response to chemoradiotherapy (CRT) in patients diagnosed with muscle-invasive bladder cancer (MIBC). <b>Methods</b>: Forty-three patients with non-metastatic MIBC (cT2–4N0M0) who underwent partial or radical cystectomy after induction CRT were, retrospectively, reviewed. Pathological complete response (pCR) to CRT was defined as the absence of residual viable tumor cells in the cystectomy specimen. Identical volumes of interest corresponding to the index bladder cancer lesions on CE- and NE-T1WI on pre-therapeutic 1.5-T MRI were collaboratively delineated by one radiologist and one urologist. Texture analysis was performed using “LIFEx” software. The subtraction of radiological features between CE- and NE-T1WI yielded 112 delta-radiomics features, which were utilized in multiple machine-learning algorithms to construct optimal predictive models for CRT responsiveness. Additionally, the predictive performance of the radiomics model constructed using CE-T1WI alone was assessed. <b>Results</b>: Twenty-one patients (49%) achieved pCR. The best-performing delta-radiomics model, employing the “Extreme Gradient Boosting” algorithm, yielded an area under the receiver operating characteristic curve (AUC) of 0.85 (95% confidence interval [CI]: 0.75–0.95), utilizing four signal intensity-based delta-radiomics features. This outperformed the best model derived from CE-T1WI alone (AUC: 0.63, 95% CI: 0.50–0.75), which incorporated two morphological features and one signal intensity-based radiomics feature. <b>Conclusions</b>: Delta-radiomics analysis applied to pre-therapeutic CE- and NE-MRI demonstrated promising predictive ability for CRT responsiveness prior to treatment initiation.https://www.mdpi.com/2075-4418/15/7/801chemoradiotherapycontrast mediamachine learningmagnetic resonance imagingurinary bladder neoplasms |
| spellingShingle | Kohei Isemoto Yuma Waseda Motohiro Fujiwara Koichiro Kimura Daisuke Hirahara Tatsunori Saho Eichi Takaya Yuki Arita Thomas C. Kwee Shohei Fukuda Hajime Tanaka Soichiro Yoshida Yasuhisa Fujii Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer Diagnostics chemoradiotherapy contrast media machine learning magnetic resonance imaging urinary bladder neoplasms |
| title | Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer |
| title_full | Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer |
| title_fullStr | Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer |
| title_full_unstemmed | Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer |
| title_short | Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer |
| title_sort | predictive potential of contrast enhanced mri based delta radiomics for chemoradiation responsiveness in muscle invasive bladder cancer |
| topic | chemoradiotherapy contrast media machine learning magnetic resonance imaging urinary bladder neoplasms |
| url | https://www.mdpi.com/2075-4418/15/7/801 |
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