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Summary <p>This study presents a preoperative prediction model for tumor-induced osteomalacia (TIO) surgery outcomes on the basis of patient characteristics. The model, which was validated in 309 patients, identifies key risk factors and aids in clinical decision-making to optimize treatment strategies, reduce the number of unnecessary surgeries, and improve patient care.</p> Purpose <p>Tumor-induced osteomalacia (TIO) should be curable by complete removal of the causative tumor. Knowledge of the prognosis of surgery is lacking. This study aimed to establish a prediction model that uses the preoperative characteristics of patients to predict the surgical treatment outcomes of patients with TIO.</p> Methods <p>This was a single-center, retrospective, case-control study. The main outcome was the surgical outcomes of patients with TIO. Patients with TIO who underwent surgical treatment were divided into a training set and a validation set. A nomogram was established in the training set, and the model was evaluated by the C-index, calibration curve, and clinical impact curve and verified in the validation set.</p> Results <p>A total of 309 patients with TIO were included, with 222 in the training set and 87 in the validation set. The C-index of the nomogram was 0.864 (<i>p</i> &lt; 0.001). The model had high goodness of fit—which is suggested by the calibration curve, and clinical benefit is indicated by the decision curve analysis and clinical impact curve. In the validation set, the area under the curve of the prediction model was 0.782 (<i>p</i> &lt; 0.001), and decision curve analysis and clinical impact curve also suggested the existence of clinical benefit.</p> Conclusion <p>This study established a prognostic model for the preoperative prediction of surgical outcomes for TIO. This model can be used as a reference in clinical practice for the development of individualized treatment strategies.</p>

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A nomogram for predicting surgery outcome of tumor-induced osteomalacia: a case-control study

  • 期刊论文

  • 发表日期：

    2025年9月18日

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  • Xiang Li,
  • Yupeng Wang,
  • Yiyi Gong,
  • Yushuo Wu,
  • Qianqian Pang,
  • Wei Yu,
  • Huanwen Wu,
  • Li Huo,
  • Yong Liu,
  • Jin Jin,
  • Xi Zhou,
  • Wei Lv,
  • Lian Zhou,
  • Yu Xia,
  • Wei Liu,
  • Yue Chi,
  • Ruizhi Jiajue,
  • Lijia Cui,
  • Ou Wang,
  • Xiaoping Xing,
  • Yan Jiang,
  • Mei Li,
  • Weibo Xia

摘要

Summary

This study presents a preoperative prediction model for tumor-induced osteomalacia (TIO) surgery outcomes on the basis of patient characteristics. The model, which was validated in 309 patients, identifies key risk factors and aids in clinical decision-making to optimize treatment strategies, reduce the number of unnecessary surgeries, and improve patient care.

Purpose

Tumor-induced osteomalacia (TIO) should be curable by complete removal of the causative tumor. Knowledge of the prognosis of surgery is lacking. This study aimed to establish a prediction model that uses the preoperative characteristics of patients to predict the surgical treatment outcomes of patients with TIO.

Methods

This was a single-center, retrospective, case-control study. The main outcome was the surgical outcomes of patients with TIO. Patients with TIO who underwent surgical treatment were divided into a training set and a validation set. A nomogram was established in the training set, and the model was evaluated by the C-index, calibration curve, and clinical impact curve and verified in the validation set.

Results

A total of 309 patients with TIO were included, with 222 in the training set and 87 in the validation set. The C-index of the nomogram was 0.864 (p < 0.001). The model had high goodness of fit—which is suggested by the calibration curve, and clinical benefit is indicated by the decision curve analysis and clinical impact curve. In the validation set, the area under the curve of the prediction model was 0.782 (p < 0.001), and decision curve analysis and clinical impact curve also suggested the existence of clinical benefit.

Conclusion

This study established a prognostic model for the preoperative prediction of surgical outcomes for TIO. This model can be used as a reference in clinical practice for the development of individualized treatment strategies.