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Background <p>The study aimed at developing and validating a deep learning (DL) model based on the ultrasound imaging for predicting the platinum resistance of patients with epithelial ovarian cancer (EOC).</p> Methods <p>392 patients were enrolled in this retrospective study who had been diagnosed with EOC between 2014 and 2020 and underwent pelvic ultrasound before initial treatment. A DL model was developed to predict patients' platinum resistance, and the model underwent evaluation through receiver-operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curve.</p> Results <p>The ROC curves showed that the area under the curve (AUC) of the DL model for predicting patients' platinum resistance in the internal and external test sets were 0.86 (95% CI 0.83–0.90) and 0.86 (95% CI 0.84–0.89), respectively. The model demonstrated high clinical value through clinical decision curve analysis and exhibited good calibration efficiency in the training cohort. Kaplan–Meier analyses showed that the model's optimal cutoff value successfully distinguished between patients at high and low risk of recurrence, with hazard ratios of 3.1 (95% CI 2.3–4.1, <i>P</i> &lt; 0.0001) and 2.9 (95% CI 2.3–3.9; <i>P</i> &lt; 0.0001) in the high-risk group of the internal and external test sets, serving as a prognostic indicator.</p> Conclusions <p>The DL model based on ultrasound imaging can predict platinum resistance in patients with EOC and may support clinicians in making the most appropriate treatment decisions.</p>

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Deep learning based on ultrasound images to predict platinum resistance in patients with epithelial ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月13日

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  • Chang Su,
  • Kuo Miao,
  • Liwei Zhang,
  • Xiaoqiu Dong

摘要

Background

The study aimed at developing and validating a deep learning (DL) model based on the ultrasound imaging for predicting the platinum resistance of patients with epithelial ovarian cancer (EOC).

Methods

392 patients were enrolled in this retrospective study who had been diagnosed with EOC between 2014 and 2020 and underwent pelvic ultrasound before initial treatment. A DL model was developed to predict patients' platinum resistance, and the model underwent evaluation through receiver-operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curve.

Results

The ROC curves showed that the area under the curve (AUC) of the DL model for predicting patients' platinum resistance in the internal and external test sets were 0.86 (95% CI 0.83–0.90) and 0.86 (95% CI 0.84–0.89), respectively. The model demonstrated high clinical value through clinical decision curve analysis and exhibited good calibration efficiency in the training cohort. Kaplan–Meier analyses showed that the model's optimal cutoff value successfully distinguished between patients at high and low risk of recurrence, with hazard ratios of 3.1 (95% CI 2.3–4.1, P < 0.0001) and 2.9 (95% CI 2.3–3.9; P < 0.0001) in the high-risk group of the internal and external test sets, serving as a prognostic indicator.

Conclusions

The DL model based on ultrasound imaging can predict platinum resistance in patients with EOC and may support clinicians in making the most appropriate treatment decisions.