Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma
Background and objective: Clear cell renal cell carcinoma (ccRCC) with tumor thrombus (TT) presents a significant prognostic challenge due to its high recurrence risk. Radiomics, an imaging-based biomarker approach, has primarily focused on the primary tumor, while the prognostic potential of TT rad...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | European Urology Open Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266616832500237X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850068409266470912 |
|---|---|
| author | Zine-Eddine Khene Isamu Tachibana Raj Bhanvadia Ivan Trevino Prajwal Sharma William Graber Nicholas Bingham Theophile Bertail Raphael Fleury Kris Gaston Solomon L. Woldu Karim Bensalah Yair Lotan Vitaly Margulis |
| author_facet | Zine-Eddine Khene Isamu Tachibana Raj Bhanvadia Ivan Trevino Prajwal Sharma William Graber Nicholas Bingham Theophile Bertail Raphael Fleury Kris Gaston Solomon L. Woldu Karim Bensalah Yair Lotan Vitaly Margulis |
| author_sort | Zine-Eddine Khene |
| collection | DOAJ |
| description | Background and objective: Clear cell renal cell carcinoma (ccRCC) with tumor thrombus (TT) presents a significant prognostic challenge due to its high recurrence risk. Radiomics, an imaging-based biomarker approach, has primarily focused on the primary tumor, while the prognostic potential of TT radiomics remains largely unexplored. This study aimed to assess the added value of tumor thrombus radiomic signatures (RSs) in predicting recurrence in ccRCC patients with TT. Methods: We conducted a retrospective analysis of patients undergoing surgical resection for nonmetastatic ccRCC with TT. Preoperative contrast-enhanced computed tomography images were used to extract radiomic features from the primary tumor and TT. Features were selected using least absolute shrinkage and selection operator (LASSO) Cox regression and incorporated into predictive models. Performance was assessed using the integrated area under the curve (iAUC), calibration, decision curve analysis (DCA), and incremental value over clinical models (pTNM, UISS, and Leibovich) for the prediction of disease-free survival (DFS). Key findings and limitations: A total of 166 patients (training set: n = 117; test set: n = 49) were included. The primary tumor RS achieved an iAUC of 0.69, the TT RS achieved 0.78, and the primary tumor + TT RS achieved 0.82. Incorporation of TT radiomics enhanced the predictive accuracy of clinical models significantly, with iAUC increases from 0.58 to 0.83 for pTNM, 0.64 to 0.83 for UISS, and 0.66 to 0.83 for Leibovich scores (all p < 0.001). The DCA confirmed the clinical utility of integrating radiomic features, particularly TT radiomics, into recurrence risk assessment. The retrospective design and absence of external validation in independent, multicenter cohorts limit the generalizability of these findings. Conclusions and clinical implications: Tumor thrombus radiomic profiling improves DFS prediction significantly and adds complementary prognostic value to established models in patients with ccRCC. Incorporation of these features into clinical workflows may enhance risk stratification and guide personalized treatment planning. Prospective validation in large, multicenter cohorts is warranted to support clinical adoption. Patient summary: This study focused on kidney cancer with tumor thrombus and demonstrated that an analysis of the imaging features from the thrombus improved the prediction of cancer recurrence significantly. This approach could enhance the understanding of individual patient risks and support more personalized treatment strategies. |
| format | Article |
| id | doaj-art-d9d18351b6da42379d1d3be88afb739e |
| institution | DOAJ |
| issn | 2666-1683 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Urology Open Science |
| spelling | doaj-art-d9d18351b6da42379d1d3be88afb739e2025-08-20T02:48:05ZengElsevierEuropean Urology Open Science2666-16832025-09-01791810.1016/j.euros.2025.06.005Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell CarcinomaZine-Eddine Khene0Isamu Tachibana1Raj Bhanvadia2Ivan Trevino3Prajwal Sharma4William Graber5Nicholas Bingham6Theophile Bertail7Raphael Fleury8Kris Gaston9Solomon L. Woldu10Karim Bensalah11Yair Lotan12Vitaly Margulis13Department of Urology, UT Southwestern Medical Center, Dallas, TX, USA; Department of Urology, University of Rennes, Rennes, France; Image and Signal Processing Laboratory, Inserm U1099, University of Rennes, Rennes, FranceDepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USADepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USADepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USADepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USADepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USADepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USADepartment of Urology, University of Rennes, Rennes, FranceDepartment of Urology, University of Rennes, Rennes, FranceDepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USADepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USADepartment of Urology, University of Rennes, Rennes, FranceDepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USADepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USA; Corresponding author. Department of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX 75390, USA.Background and objective: Clear cell renal cell carcinoma (ccRCC) with tumor thrombus (TT) presents a significant prognostic challenge due to its high recurrence risk. Radiomics, an imaging-based biomarker approach, has primarily focused on the primary tumor, while the prognostic potential of TT radiomics remains largely unexplored. This study aimed to assess the added value of tumor thrombus radiomic signatures (RSs) in predicting recurrence in ccRCC patients with TT. Methods: We conducted a retrospective analysis of patients undergoing surgical resection for nonmetastatic ccRCC with TT. Preoperative contrast-enhanced computed tomography images were used to extract radiomic features from the primary tumor and TT. Features were selected using least absolute shrinkage and selection operator (LASSO) Cox regression and incorporated into predictive models. Performance was assessed using the integrated area under the curve (iAUC), calibration, decision curve analysis (DCA), and incremental value over clinical models (pTNM, UISS, and Leibovich) for the prediction of disease-free survival (DFS). Key findings and limitations: A total of 166 patients (training set: n = 117; test set: n = 49) were included. The primary tumor RS achieved an iAUC of 0.69, the TT RS achieved 0.78, and the primary tumor + TT RS achieved 0.82. Incorporation of TT radiomics enhanced the predictive accuracy of clinical models significantly, with iAUC increases from 0.58 to 0.83 for pTNM, 0.64 to 0.83 for UISS, and 0.66 to 0.83 for Leibovich scores (all p < 0.001). The DCA confirmed the clinical utility of integrating radiomic features, particularly TT radiomics, into recurrence risk assessment. The retrospective design and absence of external validation in independent, multicenter cohorts limit the generalizability of these findings. Conclusions and clinical implications: Tumor thrombus radiomic profiling improves DFS prediction significantly and adds complementary prognostic value to established models in patients with ccRCC. Incorporation of these features into clinical workflows may enhance risk stratification and guide personalized treatment planning. Prospective validation in large, multicenter cohorts is warranted to support clinical adoption. Patient summary: This study focused on kidney cancer with tumor thrombus and demonstrated that an analysis of the imaging features from the thrombus improved the prediction of cancer recurrence significantly. This approach could enhance the understanding of individual patient risks and support more personalized treatment strategies.http://www.sciencedirect.com/science/article/pii/S266616832500237XKidney cancerRenal cell carcinomaRadiomicsTumor thrombusMachine learningSurvival |
| spellingShingle | Zine-Eddine Khene Isamu Tachibana Raj Bhanvadia Ivan Trevino Prajwal Sharma William Graber Nicholas Bingham Theophile Bertail Raphael Fleury Kris Gaston Solomon L. Woldu Karim Bensalah Yair Lotan Vitaly Margulis Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma European Urology Open Science Kidney cancer Renal cell carcinoma Radiomics Tumor thrombus Machine learning Survival |
| title | Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma |
| title_full | Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma |
| title_fullStr | Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma |
| title_full_unstemmed | Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma |
| title_short | Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma |
| title_sort | radiomic profiling of tumor thrombus for predicting recurrence in renal cell carcinoma |
| topic | Kidney cancer Renal cell carcinoma Radiomics Tumor thrombus Machine learning Survival |
| url | http://www.sciencedirect.com/science/article/pii/S266616832500237X |
| work_keys_str_mv | AT zineeddinekhene radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT isamutachibana radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT rajbhanvadia radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT ivantrevino radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT prajwalsharma radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT williamgraber radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT nicholasbingham radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT theophilebertail radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT raphaelfleury radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT krisgaston radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT solomonlwoldu radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT karimbensalah radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT yairlotan radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma AT vitalymargulis radiomicprofilingoftumorthrombusforpredictingrecurrenceinrenalcellcarcinoma |