Predicting Survival in Metastatic Castration-Resistant Prostate Cancer Patients: Development of a Prognostic Nomogram

Patients with metastatic castration-resistant prostate cancer (mCRPC) have a 5-year survival rate of approximately 30%. Accurate prediction of survival in these patients is critical for optimal choice of patient treatment. This study aimed to develop a nomogram to accurately predict overall survival in mCRPC patients in routine clinical practice. We developed a nomogram based on a Cox proportional hazards model with predictors including treatment groups, ALP, LDH, albumin, hemoglobin, and PSA. Model performance was evaluated by AUC, calibration curves, and C-index with internal validation via bootstrapping. High ALP, high LDH, high PSA, low Albumin, and low hemoglobin were significantly associated with an increased risk of death. The nomogram showed good predictive accuracy, with a C-index of 0.637 and AUC of 0.736, 0.686, and 0.712 for 1-, 2-, and 3-year survival predictions. Calibration plots showed strong alignment between predicted and observed survival. The nomogram can be successfully used in clinical practice.

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Publication Details

Journal
Studies in health technology and informatics
Published
2025-04-08
DOI
https://doi.org/10.3233/shti250070
Citations
1
Primary Topic
Prostate Cancer Treatment and Research
Type
article
Field-Weighted Citation Impact
23.18
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article

Predicting Survival in Metastatic Castration-Resistant Prostate Cancer Patients: Development of a Prognostic Nomogram

Manish Kohli, Xingyue Huo, Joseph Finkelstein
1 citations
Studies in health technology and informatics
Prostate Cancer Treatment and Research
23.18
article

Predicting Survival in Metastatic Castration-Resistant Prostate Cancer Patients: Development of a Prognostic Nomogram

Manish Kohli, Xingyue Huo, Joseph Finkelstein
article en
1 citations

Abstract

Patients with metastatic castration-resistant prostate cancer (mCRPC) have a 5-year survival rate of approximately 30%. Accurate prediction of survival in these patients is critical for optimal choice of patient treatment. This study aimed to develop a nomogram to accurately predict overall survival in mCRPC patients in routine clinical practice. We developed a nomogram based on a Cox proportional hazards model with predictors including treatment groups, ALP, LDH, albumin, hemoglobin, and PSA. Model performance was evaluated by AUC, calibration curves, and C-index with internal validation via bootstrapping. High ALP, high LDH, high PSA, low Albumin, and low hemoglobin were significantly associated with an increased risk of death. The nomogram showed good predictive accuracy, with a C-index of 0.637 and AUC of 0.736, 0.686, and 0.712 for 1-, 2-, and 3-year survival predictions. Calibration plots showed strong alignment between predicted and observed survival. The nomogram can be successfully used in clinical practice.

Studies in health technology and informaticsVol. 323
University of Utah (US)
Good health and well-being
Openalex Percentile: Top 2%
Prostate Cancer Treatment and Research
23.18
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