基于CT的深度学习模型:腹膜转移患者术前分期的一种新方法。

IF 4.2 3区 医学 Q2 ONCOLOGY
Clinical & Experimental Metastasis Pub Date : 2023-12-01 Epub Date: 2023-10-05 DOI:10.1007/s10585-023-10235-5
Jipeng Wang, Yuannan Hu, Hao Xiong, Tiantian Song, Shuyi Wang, Haibo Xu, Bin Xiong
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引用次数: 0

摘要

腹膜转移(PM)是晚期腹部恶性肿瘤的常见表现。术前准确评估PM的程度对于患者接受最佳治疗至关重要。因此,我们建议构建一个基于增强型计算机断层扫描(CT)图像的深度学习(DL)模型,以在患者术前对PM进行分期。所有168例PM患者在开放手术或腹腔镜探查前均进行了腹部CT增强扫描,并在手术过程中使用腹膜癌症指数(PCI)对患者进行评估。DL特征从门静脉期腹部CT扫描中提取,并使用Spearman相关系数和LASSO进行特征选择。在验证队列中评估了术前分期模型的性能,并与基于临床和放射组学(Rad)特征的模型进行了比较。DenseNet121 SVM模型在训练和验证队列中都表现出较强的患者辨别力,在训练和确认队列中实现的AUC分别为0.996和0.951,均高于Clinic模型和Rad模型。决策曲线分析(DCA)表明,使用DL-SVM模型,患者可能会从治疗中受益更多,校准曲线与实际结果吻合良好。基于门静脉期腹部CT的DL模型准确预测了患者术前PM的程度,有助于最大限度地提高治疗效果,优化患者的治疗计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

CT-based deep learning model: a novel approach to the preoperative staging in patients with peritoneal metastasis.

CT-based deep learning model: a novel approach to the preoperative staging in patients with peritoneal metastasis.

Peritoneal metastasis (PM) is a frequent manifestation of advanced abdominal malignancies. Accurately assessing the extent of PM before surgery is essential for patients to receive optimal treatment. Therefore, we propose to construct a deep learning (DL) model based on enhanced computed tomography (CT) images to stage PM preoperatively in patients. All 168 patients with PM underwent contrast-enhanced abdominal CT before either open surgery or laparoscopic exploration, and peritoneal cancer index (PCI) was used to evaluate patients during the surgical procedure. DL features were extracted from portal venous-phase abdominal CT scans and subjected to feature selection using the Spearman correlation coefficient and LASSO. The performance of models for preoperative staging was assessed in the validation cohort and compared against models based on clinical and radiomics (Rad) signature. The DenseNet121-SVM model demonstrated strong patient discrimination in both the training and validation cohorts, achieving AUC was 0.996 in training and 0.951 validation cohort, which were both higher than those of the Clinic model and Rad model. Decision curve analysis (DCA) showed that patients could potentially benefit more from treatment using the DL-SVM model, and calibration curves demonstrated good agreement with actual outcomes. The DL model based on portal venous-phase abdominal CT accurately predicts the extent of PM in patients before surgery, which can help maximize the benefits of treatment and optimize the patient's treatment plan.

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来源期刊
CiteScore
7.80
自引率
5.00%
发文量
55
审稿时长
12 months
期刊介绍: The Journal''s scope encompasses all aspects of metastasis research, whether laboratory-based, experimental or clinical and therapeutic. It covers such areas as molecular biology, pharmacology, tumor biology, and clinical cancer treatment (with all its subdivisions of surgery, chemotherapy and radio-therapy as well as pathology and epidemiology) insofar as these disciplines are concerned with the Journal''s core subject of metastasis formation, prevention and treatment.
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