Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation
Abstract Preoperative detection of pT3a invasion in non-metastatic renal cell carcinoma (RCC) remains challenging with CT. This study developed and validated radiomic models using preoperative CT to identify pT3a invasions. Six models were trained and internally validated via nested cross-validation...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01723-x |
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| author | Enyu Yuan Yuntian Chen Lei Ye Ben He ChunLei He Junchao Ma Ting Yang Hao Zeng Ling Yang Jin Yao Bin Song |
| author_facet | Enyu Yuan Yuntian Chen Lei Ye Ben He ChunLei He Junchao Ma Ting Yang Hao Zeng Ling Yang Jin Yao Bin Song |
| author_sort | Enyu Yuan |
| collection | DOAJ |
| description | Abstract Preoperative detection of pT3a invasion in non-metastatic renal cell carcinoma (RCC) remains challenging with CT. This study developed and validated radiomic models using preoperative CT to identify pT3a invasions. Six models were trained and internally validated via nested cross-validation on 999 patients from one hospital. External validation included 313 patients from two hospitals and 204 patients from four TCIA datasets. A multi-reader multi-case study with seven radiologists evaluated the model’s incremental value. The morphology model achieved the highest internal AUC (0.867, 95% CI: 0.866–0.869) and maintained performance in external validations (AUC = 0.895 and 0.842). When used as a second reader, it significantly improved junior radiologists’ sensitivity and discrimination (AUC: 0.790 vs. 0.831, p < 0.001) without compromising specificity. This study demonstrates that CT-based radiomic models, particularly the morphology model, can reliably detect pT3a invasion and enhance diagnostic accuracy for junior radiologists, offering potential clinical utility in preoperative staging. |
| format | Article |
| id | doaj-art-b1e9fb161c8d412ca189a0a9e36fd80d |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-b1e9fb161c8d412ca189a0a9e36fd80d2025-08-20T01:53:19ZengNature Portfolionpj Digital Medicine2398-63522025-05-018111010.1038/s41746-025-01723-xEnhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validationEnyu Yuan0Yuntian Chen1Lei Ye2Ben He3ChunLei He4Junchao Ma5Ting Yang6Hao Zeng7Ling Yang8Jin Yao9Bin Song10Department of Radiology, West China Hospital, Sichuan UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityDepartment of Urology, The Third People’s Hospital of Chengdu/The Affiliated Hospital of Southwest Jiaotong UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityDepartment of Urology, West China Hospital, Sichuan UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityAbstract Preoperative detection of pT3a invasion in non-metastatic renal cell carcinoma (RCC) remains challenging with CT. This study developed and validated radiomic models using preoperative CT to identify pT3a invasions. Six models were trained and internally validated via nested cross-validation on 999 patients from one hospital. External validation included 313 patients from two hospitals and 204 patients from four TCIA datasets. A multi-reader multi-case study with seven radiologists evaluated the model’s incremental value. The morphology model achieved the highest internal AUC (0.867, 95% CI: 0.866–0.869) and maintained performance in external validations (AUC = 0.895 and 0.842). When used as a second reader, it significantly improved junior radiologists’ sensitivity and discrimination (AUC: 0.790 vs. 0.831, p < 0.001) without compromising specificity. This study demonstrates that CT-based radiomic models, particularly the morphology model, can reliably detect pT3a invasion and enhance diagnostic accuracy for junior radiologists, offering potential clinical utility in preoperative staging.https://doi.org/10.1038/s41746-025-01723-x |
| spellingShingle | Enyu Yuan Yuntian Chen Lei Ye Ben He ChunLei He Junchao Ma Ting Yang Hao Zeng Ling Yang Jin Yao Bin Song Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation npj Digital Medicine |
| title | Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation |
| title_full | Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation |
| title_fullStr | Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation |
| title_full_unstemmed | Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation |
| title_short | Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation |
| title_sort | enhanced staging of renal cell carcinoma using tumor morphology features model development and multi source validation |
| url | https://doi.org/10.1038/s41746-025-01723-x |
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