用于预测实性 I 期非小细胞肺癌术后进展的多模态深度学习放射组学模型。

IF 3.5 2区 医学 Q2 ONCOLOGY
Qionglian Kuang, Bao Feng, Kuncai Xu, Yehang Chen, Xiaojuan Chen, Xiaobei Duan, Xiaoyan Lei, Xiangmeng Chen, Kunwei Li, Wansheng Long
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引用次数: 0

摘要

目的:探讨多模态深度学习放射组学(MDLR)模型在预测实性I期非小细胞肺癌(NSCLC)术后进展风险状态中的应用价值:回顾性研究了2014年1月至2019年9月在我院接受手术切除的459例组织学确诊的实性I期NSCLC患者。在另一家医疗中心,根据相同的标准对 104 例患者进行了回顾性研究,作为外部验证队列。对进展组和非进展组的临床病理特征和主观CT结果进行了单变量分析。表现出显著差异的临床病理特征和主观CT结果被用作极端学习机(ELM)分类器的输入变量,以构建临床模型。我们使用迁移学习策略训练 ResNet18 模型,利用该模型从所有 CT 图像中提取深度学习特征,然后使用 ELM 分类器对深度学习特征进行分类,从而获得深度学习特征(DLS)。结合临床病理特征、主观CT结果和DLS构建了MDLR模型。临床模型、DLS模型和MDLR模型的诊断效率通过曲线下面积(AUC)进行评估:单变量分析表明,肿瘤大小(p = 0.004)、神经元特异性烯醇化酶(NSE)(p = 0.03)、碳水化合物抗原 19 - 9(CA199)(p = 0.003)和病理分期(p = 0.027)与术后实性 I 期 NSCLC 的进展显著相关。因此,这些临床特征被纳入临床模型,以预测术后实体期NSCLC患者的进展风险。共选取了294个系数不为零的深度学习特征。进展组的 DLS 为(0.721 ± 0.371),高于非进展组的 DLS(0.113 ± 0.350)(P 结论:MDLR 模型能有效预测术后实体期 NSCLC 患者的进展风险:MDLR模型能有效预测实性I期NSCLC术后进展的风险,有助于实性I期NSCLC患者的治疗和随访。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer.

Purpose: To explore the application value of a multimodal deep learning radiomics (MDLR) model in predicting the risk status of postoperative progression in solid stage I non-small cell lung cancer (NSCLC).

Materials and methods: A total of 459 patients with histologically confirmed solid stage I NSCLC who underwent surgical resection in our institution from January 2014 to September 2019 were reviewed retrospectively. At another medical center, 104 patients were reviewed as an external validation cohort according to the same criteria. A univariate analysis was conducted on the clinicopathological characteristics and subjective CT findings of the progression and non-progression groups. The clinicopathological characteristics and subjective CT findings that exhibited significant differences were used as input variables for the extreme learning machine (ELM) classifier to construct the clinical model. We used the transfer learning strategy to train the ResNet18 model, used the model to extract deep learning features from all CT images, and then used the ELM classifier to classify the deep learning features to obtain the deep learning signature (DLS). A MDLR model incorporating clinicopathological characteristics, subjective CT findings and DLS was constructed. The diagnostic efficiencies of the clinical model, DLS model and MDLR model were evaluated by the area under the curve (AUC).

Results: Univariate analysis indicated that size (p = 0.004), neuron-specific enolase (NSE) (p = 0.03), carbohydrate antigen 19 - 9 (CA199) (p = 0.003), and pathological stage (p = 0.027) were significantly associated with the progression of solid stage I NSCLC after surgery. Therefore, these clinical characteristics were incorporated into the clinical model to predict the risk of progression in postoperative solid-stage NSCLC patients. A total of 294 deep learning features with nonzero coefficients were selected. The DLS in the progressive group was (0.721 ± 0.371), which was higher than that in the nonprogressive group (0.113 ± 0.350) (p < 0.001). The combination of size、NSE、CA199、pathological stage and DLS demonstrated the superior performance in differentiating postoperative progression status. The AUC of the MDLR model was 0.885 (95% confidence interval [CI]: 0.842-0.927), higher than that of the clinical model (0.675 (95% CI: 0.599-0.752)) and DLS model (0.882 (95% CI: 0.835-0.929)). The DeLong test and decision in curve analysis revealed that the MDLR model was the most predictive and clinically useful model.

Conclusion: MDLR model is effective in predicting the risk of postoperative progression of solid stage I NSCLC, and it is helpful for the treatment and follow-up of solid stage I NSCLC patients.

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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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