Su Chang, Miao Kuo, Zhang Liwei, Dong Xiaoqiu
Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, 37 Yi Yuan Street, Harbin, 150086, China.
Biomed Eng Online. 2025 May 13;24(1):58. doi: 10.1186/s12938-025-01391-8.
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).
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.
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.
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.
本研究旨在开发并验证一种基于超声成像的深度学习（DL）模型，用于预测上皮性卵巢癌（EOC）患者的铂耐药性。
本回顾性研究纳入了392例在2014年至2020年间被诊断为EOC且在初始治疗前接受盆腔超声检查的患者。开发了一种DL模型来预测患者的铂耐药性，并通过受试者操作特征（ROC）曲线、决策曲线分析（DCA）和校准曲线对该模型进行评估。
ROC曲线显示，DL模型在内部和外部测试集中预测患者铂耐药性的曲线下面积（AUC）分别为0.86（95%CI 0.83-0.90）和0.86（95%CI 0.84-0.89）。通过临床决策曲线分析，该模型显示出较高的临床价值，并且在训练队列中表现出良好的校准效率。Kaplan-Meier分析表明，该模型的最佳截断值成功区分了高复发风险和低复发风险的患者，在内部和外部测试集的高风险组中，风险比分别为3.1（95%CI 2.3-4.1，P<0.0001）和2.9（95%CI 2.3-3.9；P<0.0001），可作为预后指标。
基于超声成像的DL模型可以预测EOC患者的铂耐药性，并可能有助于临床医生做出最合适的治疗决策。
