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Peer-reviewedCC BY-NC-NDBiomedical reports

Machine learning-based classification of renal cell carcinoma and upper tract urothelial carcinoma using preoperative hematological markers.

  • Kanrapee Nuwatkrisin1
  • Chanita Panwoon2
  • Kiartisak Senchan3
  • Sakkarn Sangkhamanon3
  • Wunchana Seubwai4,5
5 affiliations
  1. 1Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand.
  2. 2Faculty of Medicine, Vongchavalitkul University, Nakhon Ratchasima 30000, Thailand.
  3. 3Department of Pathology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand.
  4. 4Department of Forensic Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand.
  5. 5Center for Translational Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand.
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Abstract

Renal tumors are the second most common type of urological cancer. These tumors consist of renal cell carcinoma (RCC; 80-85%) and upper tract urothelial carcinoma (UTUC; 5-10%). Accurate preoperative differentiation between these two entities remains challenging despite advances in imaging and endoscopic techniques, often leading to potential misdiagnoses and inappropriate treatment strategies. Therefore, the development of novel diagnostic methods to improve preoperative diagnosis remains an area of notable clinical interest. At present, the potential for using machine learning (ML) based on preoperative hematological markers as non-invasive tools for cancer diagnosis and prognosis is being investigated. However, the application of ML based on preoperative hematological markers to differentiate RCC from UTUC remains unclear. The present study aimed to develop a classification system to differentiate between RCC and UTUC using ML based on preoperative hematological markers and clinical data from 172 patients with renal cancer, including 115 cases (66.9%) of RCC and 57 cases (33.1%) of UTUC. To address class imbalance, the Synthetic Minority Oversampling Technique was applied within a leakage-free pipeline restricted to the training data. Feature selection was performed using recursive feature elimination with cross-validation. A panel of ML algorithms was screened. The six best-performing models were tuned and evaluated, with performance assessed by accuracy, balanced accuracy, precision, recall, F1 score and the area under the receiver operating characteristic curve (AUC). The Extra Trees classifier showed the most favorable overall performance, with an accuracy of 75.0%, a balanced accuracy of 0.71 and an AUC value of 0.755 (bootstrap AUC, 0.71; 95% CI, 0.59-0.83). These preliminary findings suggest that ML models based on routinely available preoperative hematological markers may serve as a useful, low-cost adjunct to support the differentiation of RCC and UTUC.

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Cite

BibTeXRIS
BibTeX
@article{nuwatkrisin2026machine,
  title = {{Machine learning-based classification of renal cell carcinoma and upper tract urothelial carcinoma using preoperative hematological markers.}},
  author = {Kanrapee Nuwatkrisin and Chanita Panwoon and Kiartisak Senchan and Sakkarn Sangkhamanon and Wunchana Seubwai},
  journal = {Biomedical reports},
  year = {2026},
  month = oct,
  doi = {10.3892/br.2026.2212},
  url = {https://europepmc.org/article/MED/42948584},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Machine learning-based classification of renal cell carcinoma and upper tract urothelial carcinoma using preoperative hematological markers.
AU  - Kanrapee Nuwatkrisin
AU  - Chanita Panwoon
AU  - Kiartisak Senchan
AU  - Sakkarn Sangkhamanon
AU  - Wunchana Seubwai
PY  - 2026
DA  - 2026/10/09
JO  - Biomedical reports
DO  - 10.3892/br.2026.2212
UR  - https://europepmc.org/article/MED/42948584
AB  - Renal tumors are the second most common type of urological cancer. These tumors consist of renal cell carcinoma (RCC; 80-85%) and upper tract urothelial carcinoma (UTUC; 5-10%). Accurate preoperative differentiation between these two entities remains challenging despite advances in imaging and endoscopic techniques, often leading to potential misdiagnoses and inappropriate treatment strategies. Therefore, the development of novel diagnostic methods to improve preoperative diagnosis remains an area of notable clinical interest. At present, the potential for using machine learning (ML) based on preoperative hematological markers as non-invasive tools for cancer diagnosis and prognosis is being investigated. However, the application of ML based on preoperative hematological markers to differentiate RCC from UTUC remains unclear. The present study aimed to develop a classification system to differentiate between RCC and UTUC using ML based on preoperative hematological markers and clinical data from 172 patients with renal cancer, including 115 cases (66.9%) of RCC and 57 cases (33.1%) of UTUC. To address class imbalance, the Synthetic Minority Oversampling Technique was applied within a leakage-free pipeline restricted to the training data. Feature selection was performed using recursive feature elimination with cross-validation. A panel of ML algorithms was screened. The six best-performing models were tuned and evaluated, with performance assessed by accuracy, balanced accuracy, precision, recall, F1 score and the area under the receiver operating characteristic curve (AUC). The Extra Trees classifier showed the most favorable overall performance, with an accuracy of 75.0%, a balanced accuracy of 0.71 and an AUC value of 0.755 (bootstrap AUC, 0.71; 95% CI, 0.59-0.83). These preliminary findings suggest that ML models based on routinely available preoperative hematological markers may serve as a useful, low-cost adjunct to support the differentiation of RCC and UTUC.
SN  - 2049-9434
SN  - 2049-9442
ER  -