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<p>Although neoadjuvant chemotherapy (NACT) is commonly used for advanced ovarian cancer, patient outcomes vary substantially. We developed a graph convolutional network (GCN) that integrates patient-specific baseline clinical variables and computed tomography–derived radiomic features while modeling inter-patient relationships to improve outcome prediction beyond standard models. The GCN operates without reliance on high-performance computing resources and predicts long-term overall survival (OS) while stratifying short-term surgical outcomes (R0 resection). The GCN was compared with the CA-125 ELIMination rate constant K (KELIM) score and three Cox-based comparator models. Model performance was evaluated using the concordance index (C-index) for OS, area under the receiver operating characteristic curve for 3-year OS, Kaplan–Meier survival analysis, and R0 resection stratification. The GCN demonstrated strong OS prognosis performance (C-index = 0.73, 0.72, and 0.70 across the training and two external test datasets), stratified surgical outcomes, and identified 16.30% of patients with low KELIM scores but favorable survival.</p>

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Multimodal data-based graph convolutional networks for predicting outcomes in ovarian cancer receiving neoadjuvant chemotherapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Shimin Zhang,
  • Yinlong Liu,
  • Zhuonan Liu,
  • Xinyue Li,
  • Guan Wang,
  • Zhuo Yang,
  • Yutong Liu,
  • Meiyao Li,
  • Jiarui Wang,
  • Jiage Zhang,
  • Bosinan Chen,
  • Jingyi Liu,
  • Yi Zhang,
  • Jiangdian Song,
  • Xin Zhou

摘要

Although neoadjuvant chemotherapy (NACT) is commonly used for advanced ovarian cancer, patient outcomes vary substantially. We developed a graph convolutional network (GCN) that integrates patient-specific baseline clinical variables and computed tomography–derived radiomic features while modeling inter-patient relationships to improve outcome prediction beyond standard models. The GCN operates without reliance on high-performance computing resources and predicts long-term overall survival (OS) while stratifying short-term surgical outcomes (R0 resection). The GCN was compared with the CA-125 ELIMination rate constant K (KELIM) score and three Cox-based comparator models. Model performance was evaluated using the concordance index (C-index) for OS, area under the receiver operating characteristic curve for 3-year OS, Kaplan–Meier survival analysis, and R0 resection stratification. The GCN demonstrated strong OS prognosis performance (C-index = 0.73, 0.72, and 0.70 across the training and two external test datasets), stratified surgical outcomes, and identified 16.30% of patients with low KELIM scores but favorable survival.