Scholarlink

Purpose <p>Identifying patients who may benefit from multiple drilling are crucial. Hence, the purpose of the study is to utilize radiomics and deep learning for predicting no-collapse survival in patients with femoral head osteonecrosis.</p> Methods <p>Patients who underwent multiple drilling were enrolled. Radiomics and deep learning features were extracted from pelvic radiographs and selected by LASSO-COX regression, radiomics and DL signature were then built. The clinical variables were selected through univariate and multivariate Cox regression analysis, and the clinical, radiomics, DL and DLRC model were constructed. Model performance was evaluated using the concordance index (C-index), area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curves, and Decision Curve Analysis (DCA).</p> Results <p>A total of 144 patients (212 hips) were included in the study. ARCO classification, bone marrow edema, and combined necrotic angle were identified as independent risk factors for collapse. The DLRC model exhibited superior discrimination ability with higher C-index of 0.78 (95%CI: 0.73–0.84) and AUC values (0.83 and 0.87) than other models. The DLRC model demonstrated superior predictive performance with a higher C-index of 0.78 (95% CI: 0.73–0.84) and area under the curve (AUC) values of 0.83 for 3-year survival and 0.87 for 5-year survival, outperforming other models. The DLRC model also exhibited favorable calibration and clinical utility, with Kaplan–Meier survival curves revealing significant differences in survival rates between high-risk and low-risk cohorts.</p> Conclusion <p>This study introduces a novel approach that integrates radiomics and deep learning techniques and demonstrates superior predictive performance for no-collapse survival after multiple drilling. It offers enhanced discrimination ability, favorable calibration, and strong clinical utility, making it a valuable tool for stratifying patients into high-risk and low-risk groups. The model has the potential to provide personalized risk assessment, guiding treatment decisions and improving outcomes in patients with osteonecrosis of the femoral head.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A radiomics and deep learning nomogram developed and validated for predicting no-collapse survival in patients with osteonecrosis after multiple drilling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月15日

查看全文
  • Fan Liu,
  • De-bao Zhang,
  • Shi-huan Cheng,
  • Gui-shan Gu

摘要

Purpose

Identifying patients who may benefit from multiple drilling are crucial. Hence, the purpose of the study is to utilize radiomics and deep learning for predicting no-collapse survival in patients with femoral head osteonecrosis.

Methods

Patients who underwent multiple drilling were enrolled. Radiomics and deep learning features were extracted from pelvic radiographs and selected by LASSO-COX regression, radiomics and DL signature were then built. The clinical variables were selected through univariate and multivariate Cox regression analysis, and the clinical, radiomics, DL and DLRC model were constructed. Model performance was evaluated using the concordance index (C-index), area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curves, and Decision Curve Analysis (DCA).

Results

A total of 144 patients (212 hips) were included in the study. ARCO classification, bone marrow edema, and combined necrotic angle were identified as independent risk factors for collapse. The DLRC model exhibited superior discrimination ability with higher C-index of 0.78 (95%CI: 0.73–0.84) and AUC values (0.83 and 0.87) than other models. The DLRC model demonstrated superior predictive performance with a higher C-index of 0.78 (95% CI: 0.73–0.84) and area under the curve (AUC) values of 0.83 for 3-year survival and 0.87 for 5-year survival, outperforming other models. The DLRC model also exhibited favorable calibration and clinical utility, with Kaplan–Meier survival curves revealing significant differences in survival rates between high-risk and low-risk cohorts.

Conclusion

This study introduces a novel approach that integrates radiomics and deep learning techniques and demonstrates superior predictive performance for no-collapse survival after multiple drilling. It offers enhanced discrimination ability, favorable calibration, and strong clinical utility, making it a valuable tool for stratifying patients into high-risk and low-risk groups. The model has the potential to provide personalized risk assessment, guiding treatment decisions and improving outcomes in patients with osteonecrosis of the femoral head.