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Peer-reviewedCC BY 4.0Frontiers in Oncology

Lymphocyte to C-reactive protein ratio and improved prognostication of renal cell carcinoma: machine learning-based analysis

  • Melis Guer1,2
  • Cesare Saitta2
  • Viraj Master3
  • Hajime Tanaka4
  • Dattatraya Patil3
  • Margaret F. Meagher2
  • Dhruv Puri2
  • Kit Yuen2
  • Natalie Birouty2
  • Giacomo Musso5
  • Mai Dabbas2
  • Aaron Ahdoot2
10 more authors
  • Masaki Kobayashi4
  • Mimi Nguyen2
  • Alexander Hill5
  • Omer Baker2
  • Shohei Fukuda4
  • Mohamed Elshaer5
  • Michael A. Liss2
  • Edward Cherullo5
  • Yasuhisa Fujii4
  • Ithaar H. Derweesh5
5 affiliations
  1. 1Department of Urology, University Hospital Tübingen
  2. 2Department of Urology, UC San Diego School of Medicine
  3. 3Department of Urology, Emory University School of Medicine
  4. 4Department of Urology, Institute of Science Tokyo
  5. 5Department of Urology, RUSH-MD Anderson Cancer Center, Rush University Medical Center
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Abstract

Purpose: To investigate utility of a novel ratio, lymphocyte to C-reactive protein ratio (LCR), for improved prognostic stratification in RCC.

Methods: We conducted a retrospective machine learning–based analysis in a multicenter RCC cohort who underwent surgery (Radical or Partial Nephrectomy). Cut-off for “low” or “high” LCR was delineated by receiver operating characteristics/area under curve (ROC/AUC). Multivariable Analysis was conducted for predictors of all-cause mortality (ACM) and cancer-specific mortality (CSM). LCR, along with UCLA Integrated Staging System (UISS) factors, were included into MVA model to evaluate CSM. Employing Classification and Regression Tree (CART), we developed a decision tree based on hazard ratios (HR). The predictive performance was calculated using ROC/AUC.

Results: We analyzed 2071 patients [low LCR n=598/high LCR n=1473; cut-off=237.87 (AUC = 0.80)], median follow-up 60 months). Low LCR is an independent risk factor for ACM (HR = 2.75, p<0.001) and CSM (HR = 2.70, p<0.001). Including LCR (HR = 2.60, p<0.001), tumor stage (Stage II: HR = 6.7, Stage III: HR = 6.5, Stage IV: HR = 26.8, p<0.001) nuclear grade (HR = 3.0, p<0.001) and ECOG (HR = 1.6, p=0.002) in the CART, patients were re-stratified [(HR<1.0 (low-risk), HR = 1.0-2.0 (intermediate-risk), HR>2.0 (high-risk)]. AUC of 0.91 was revealed for the modified UISS model (AUC = 0.85 in the classic UISS model). Our findings are limited by retrospective design.

Conclusion: This study provides evidence supporting the prognostic significance of LCR in RCC. While low LCR was associated with poorer survival outcomes, incorporating LCR into the UISS model enhanced prognostic stratification in RCC patients. Further investigation is requisite to validate our findings.

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Cite

BibTeXRIS
BibTeX
@article{guer2026lymphocyte,
  title = {{Lymphocyte to C-reactive protein ratio and improved prognostication of renal cell carcinoma: machine learning-based analysis}},
  author = {Melis Guer and Cesare Saitta and Viraj Master and Hajime Tanaka and Dattatraya Patil and Margaret F. Meagher and Dhruv Puri and Kit Yuen and Natalie Birouty and Giacomo Musso and Mai Dabbas and Aaron Ahdoot and Masaki Kobayashi and Mimi Nguyen and Alexander Hill and Omer Baker and Shohei Fukuda and Mohamed Elshaer and Michael A. Liss and Edward Cherullo and Yasuhisa Fujii and Ithaar H. Derweesh},
  journal = {Frontiers in Oncology},
  year = {2026},
  month = oct,
  doi = {10.3389/fonc.2026.1907942},
  url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1907942/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Lymphocyte to C-reactive protein ratio and improved prognostication of renal cell carcinoma: machine learning-based analysis
AU  - Melis Guer
AU  - Cesare Saitta
AU  - Viraj Master
AU  - Hajime Tanaka
AU  - Dattatraya Patil
AU  - Margaret F. Meagher
AU  - Dhruv Puri
AU  - Kit Yuen
AU  - Natalie Birouty
AU  - Giacomo Musso
AU  - Mai Dabbas
AU  - Aaron Ahdoot
AU  - Masaki Kobayashi
AU  - Mimi Nguyen
AU  - Alexander Hill
AU  - Omer Baker
AU  - Shohei Fukuda
AU  - Mohamed Elshaer
AU  - Michael A. Liss
AU  - Edward Cherullo
AU  - Yasuhisa Fujii
AU  - Ithaar H. Derweesh
PY  - 2026
DA  - 2026/10/13
JO  - Frontiers in Oncology
DO  - 10.3389/fonc.2026.1907942
UR  - https://www.frontiersin.org/articles/10.3389/fonc.2026.1907942/full
AB  - Purpose: To investigate utility of a novel ratio, lymphocyte to C-reactive protein ratio (LCR), for improved prognostic stratification in RCC.  Methods: We conducted a retrospective machine learning–based analysis in a multicenter RCC cohort who underwent surgery (Radical or Partial Nephrectomy). Cut-off for “low” or “high” LCR was delineated by receiver operating characteristics/area under curve (ROC/AUC). Multivariable Analysis was conducted for predictors of all-cause mortality (ACM) and cancer-specific mortality (CSM). LCR, along with UCLA Integrated Staging System (UISS) factors, were included into MVA model to evaluate CSM. Employing Classification and Regression Tree (CART), we developed a decision tree based on hazard ratios (HR). The predictive performance was calculated using ROC/AUC.  Results: We analyzed 2071 patients [low LCR n=598/high LCR n=1473; cut-off=237.87 (AUC = 0.80)], median follow-up 60 months). Low LCR is an independent risk factor for ACM (HR = 2.75, p<0.001) and CSM (HR = 2.70, p<0.001). Including LCR (HR = 2.60, p<0.001), tumor stage (Stage II: HR = 6.7, Stage III: HR = 6.5, Stage IV: HR = 26.8, p<0.001) nuclear grade (HR = 3.0, p<0.001) and ECOG (HR = 1.6, p=0.002) in the CART, patients were re-stratified [(HR<1.0 (low-risk), HR = 1.0-2.0 (intermediate-risk), HR>2.0 (high-risk)]. AUC of 0.91 was revealed for the modified UISS model (AUC = 0.85 in the classic UISS model). Our findings are limited by retrospective design.  Conclusion: This study provides evidence supporting the prognostic significance of LCR in RCC. While low LCR was associated with poorer survival outcomes, incorporating LCR into the UISS model enhanced prognostic stratification in RCC patients. Further investigation is requisite to validate our findings.
SN  - 2234-943X
ER  -