An Integrated Nomogram Combining Deep Learning and Radiomics for Predicting Malignancy of Pulmonary Nodules Using CT-Derived Nodules and Adipose Tissue: A Multicenter Study

IF 2.9 2区医学 Q2 ONCOLOGY Cancer Medicine Pub Date : 2024-11-04 DOI:10.1002/cam4.70372

Shidi Miao, Qifan Xuan, Hanbing Xie, Yuyang Jiang, Mengzhuo Sun, Wenjuan Huang, Jing Li, Hongzhuo Qi, Ao Li, Qiujun Wang, Zengyao Liu, Ruitao Wang

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Abstract

Background

Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary nodules.

Methods

One thousand and ninety-eight patients with 6–30 mm pulmonary nodules who received histopathologic diagnosis at 3 centers were included and divided into a primary cohort (PC), an internal test cohort (I-T), and two external test cohorts (E-T1, E-T2). The DLRCN was built by integrating adipose tissue radiomics features, intranodular and perinodular deep learning features, and clinical characteristics for diagnosing malignancy of pulmonary nodules. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. The performance of DLRCN was assessed with respect to its calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Furthermore, we compared it with three radiologists. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and subgroup analysis were also taken into account.

Results

The incorporation of adipose tissue radiomics features led to significant NRI and IDI (NRI = 1.028, p < 0.05, IDI = 0.137, p < 0.05). In the I-T, E-T1, and E-T2, the AUCs of DLRCN were 0.946 (95% CI: 0.936, 0.955), 0.948 (95% CI: 0.933, 0.963) and 0.962 (95% CI: 0.945, 0.979), The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (p > 0.05). DCA showed that the DLRCN was clinically useful. Under equal specificity, the sensitivity of DLRCN increased by 8.6% compared to radiologist assessments. The subgroup analysis conducted on adipose tissue radiomics features further demonstrated their supplementary value in determining the malignancy of pulmonary nodules.

Conclusion

The DLRCN demonstrated good performance in predicting the malignancy of pulmonary nodules, which was comparable to radiologist assessments. The adipose tissue radiomics features have notably enhanced the performance of DLRCN.

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利用 CT 导出的结节和脂肪组织预测肺结节恶性程度的深度学习与放射组学相结合的综合提名图：一项多中心研究。

背景：正确区分肺结节的良性和恶性可避免不必要的侵入性手术。本研究旨在构建一种深度学习放射组学临床提名图（DLRCN），用于预测肺结节的恶性程度：方法：纳入在3个中心接受组织病理学诊断的198例6-30毫米肺结节患者，并将其分为一个原发队列（PC）、一个内部测试队列（I-T）和两个外部测试队列（E-T1、E-T2）。DLRCN 是通过整合脂肪组织放射组学特征、结节内和结节周围深度学习特征以及临床特征而建立的，用于诊断肺结节的恶性程度。特征选择采用最小绝对收缩和选择算子（LASSO）。通过校准曲线、曲线下面积（AUC）和决策曲线分析（DCA）评估了 DLRCN 的性能。此外，我们还将其与三位放射科医生进行了比较。我们还考虑了净再分类改进（NRI）、综合辨别改进（IDI）和亚组分析：结果：纳入脂肪组织放射组学特征后，NRI 和 IDI 显著提高（NRI = 1.028，P 0.05）。DCA显示，DLRCN对临床有用。在特异性相同的情况下，DLRCN 的灵敏度比放射科医生的评估提高了 8.6%。对脂肪组织放射组学特征进行的亚组分析进一步证明了其在判断肺结节恶性方面的辅助价值：DLRCN 在预测肺结节恶性程度方面表现良好，与放射科医生的评估结果相当。脂肪组织放射组学特征显著提高了 DLRCN 的性能。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.