Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets
Abstract Background To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) from renal cell carcinoma (RCC) prior to surgical intervention. Methods Preoperative computed tomography venous-phase datasets from patients t...
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2025-05-01
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| Online Access: | https://doi.org/10.1186/s12880-025-01727-9 |
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| author | Julian Marcon Philipp Weinhold Mona Rzany Matthias P. Fabritius Michael Winkelmann Alexander Buchner Lennert Eismann Jan-Friedrich Jokisch Jozefina Casuscelli Gerald B. Schulz Thomas Knösel Michael Ingrisch Jens Ricke Christian G. Stief Severin Rodler Philipp M. Kazmierczak |
| author_facet | Julian Marcon Philipp Weinhold Mona Rzany Matthias P. Fabritius Michael Winkelmann Alexander Buchner Lennert Eismann Jan-Friedrich Jokisch Jozefina Casuscelli Gerald B. Schulz Thomas Knösel Michael Ingrisch Jens Ricke Christian G. Stief Severin Rodler Philipp M. Kazmierczak |
| author_sort | Julian Marcon |
| collection | DOAJ |
| description | Abstract Background To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) from renal cell carcinoma (RCC) prior to surgical intervention. Methods Preoperative computed tomography venous-phase datasets from patients that underwent procedures for histopathologically confirmed UTUC or RCC were retrospectively analyzed. Tumor segmentation was performed manually, and radiomic features were extracted according to the International Image Biomarker Standardization Initiative. Features were normalized using z-scores, and a predictive model was developed using the least absolute shrinkage and selection operator (LASSO). The dataset was split into a training cohort (70%) and a test cohort (30%). Results A total of 236 patients [30.5% female, median age 70.5 years (IQR: 59.5–77), median tumor size 5.8 cm (range: 4.1–8.2 cm)] were included. For differentiating UTUC from RCC, the model achieved a sensitivity of 88.4% and specificity of 81% (AUC: 0.93, radiomics score cutoff: 0.467) in the training cohort. In the validation cohort, the sensitivity was 80.6% and specificity 80% (AUC: 0.87, radiomics score cutoff: 0.601). Subgroup analysis of the validation cohort demonstrated robust performance, particularly in distinguishing clear cell RCC from high-grade UTUC (sensitivity: 84%, specificity: 73.1%, AUC: 0.84) and high-grade from low-grade UTUC (sensitivity: 57.7%, specificity: 88.9%, AUC: 0.68). Limitations include the need for independent validation in future randomized controlled trials (RCTs). Conclusions Machine learning-based radiomics models can reliably differentiate between RCC and UTUC in preoperative CT imaging. With a suggested performance benefit compared to conventional imaging, this technology might be added to the current preoperative diagnostic workflow. Clinical trial number Local ethics committee no. 20–179 |
| format | Article |
| id | doaj-art-648febe9faab46fe8d13d18481b0e792 |
| institution | DOAJ |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-05-01 |
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| series | BMC Medical Imaging |
| spelling | doaj-art-648febe9faab46fe8d13d18481b0e7922025-08-20T03:16:39ZengBMCBMC Medical Imaging1471-23422025-05-0125111010.1186/s12880-025-01727-9Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasetsJulian Marcon0Philipp Weinhold1Mona Rzany2Matthias P. Fabritius3Michael Winkelmann4Alexander Buchner5Lennert Eismann6Jan-Friedrich Jokisch7Jozefina Casuscelli8Gerald B. Schulz9Thomas Knösel10Michael Ingrisch11Jens Ricke12Christian G. Stief13Severin Rodler14Philipp M. Kazmierczak15Department of Urology, University Hospital, LMU MunichDepartment of Urology, University Hospital, LMU MunichDepartment of Urology, University Hospital, LMU MunichDepartment of Radiology, University Hospital, LMU MunichDepartment of Radiology, University Hospital, LMU MunichDepartment of Urology, University Hospital, LMU MunichDepartment of Urology, University Hospital, LMU MunichDepartment of Urology, University Hospital, LMU MunichDepartment of Urology, University Hospital, LMU MunichDepartment of Urology, University Hospital, LMU MunichDepartment of Pathology, University Hospital, LMU MunichDepartment of Radiology, University Hospital, LMU MunichDepartment of Radiology, University Hospital, LMU MunichDepartment of Urology, University Hospital, LMU MunichDepartment of Urology, University Hospital, LMU MunichDepartment of Radiology, University Hospital, LMU MunichAbstract Background To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) from renal cell carcinoma (RCC) prior to surgical intervention. Methods Preoperative computed tomography venous-phase datasets from patients that underwent procedures for histopathologically confirmed UTUC or RCC were retrospectively analyzed. Tumor segmentation was performed manually, and radiomic features were extracted according to the International Image Biomarker Standardization Initiative. Features were normalized using z-scores, and a predictive model was developed using the least absolute shrinkage and selection operator (LASSO). The dataset was split into a training cohort (70%) and a test cohort (30%). Results A total of 236 patients [30.5% female, median age 70.5 years (IQR: 59.5–77), median tumor size 5.8 cm (range: 4.1–8.2 cm)] were included. For differentiating UTUC from RCC, the model achieved a sensitivity of 88.4% and specificity of 81% (AUC: 0.93, radiomics score cutoff: 0.467) in the training cohort. In the validation cohort, the sensitivity was 80.6% and specificity 80% (AUC: 0.87, radiomics score cutoff: 0.601). Subgroup analysis of the validation cohort demonstrated robust performance, particularly in distinguishing clear cell RCC from high-grade UTUC (sensitivity: 84%, specificity: 73.1%, AUC: 0.84) and high-grade from low-grade UTUC (sensitivity: 57.7%, specificity: 88.9%, AUC: 0.68). Limitations include the need for independent validation in future randomized controlled trials (RCTs). Conclusions Machine learning-based radiomics models can reliably differentiate between RCC and UTUC in preoperative CT imaging. With a suggested performance benefit compared to conventional imaging, this technology might be added to the current preoperative diagnostic workflow. Clinical trial number Local ethics committee no. 20–179https://doi.org/10.1186/s12880-025-01727-9Machine learningRadiomicsRenal cell carcinomaUpper tract urothelial carcinoma |
| spellingShingle | Julian Marcon Philipp Weinhold Mona Rzany Matthias P. Fabritius Michael Winkelmann Alexander Buchner Lennert Eismann Jan-Friedrich Jokisch Jozefina Casuscelli Gerald B. Schulz Thomas Knösel Michael Ingrisch Jens Ricke Christian G. Stief Severin Rodler Philipp M. Kazmierczak Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets BMC Medical Imaging Machine learning Radiomics Renal cell carcinoma Upper tract urothelial carcinoma |
| title | Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets |
| title_full | Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets |
| title_fullStr | Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets |
| title_full_unstemmed | Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets |
| title_short | Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets |
| title_sort | radiomics based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets |
| topic | Machine learning Radiomics Renal cell carcinoma Upper tract urothelial carcinoma |
| url | https://doi.org/10.1186/s12880-025-01727-9 |
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