Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning

IF 1.9 4区 医学 Q3 RESPIRATORY SYSTEM Clinical Respiratory Journal Pub Date : 2024-05-12 DOI:10.1111/crj.13769
Qiao Liu, Xue Lv, Daiquan Zhou, Na Yu, Yuqin Hong, Yan Zeng
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Abstract

Background

Lung cancer is the leading cause of cancer-related death worldwide. This study aimed to establish novel multiclassification prediction models based on machine learning (ML) to predict the probability of malignancy in pulmonary nodules (PNs) and to compare with three published models.

Methods

Nine hundred fourteen patients with PNs were collected from four medical institutions (A, B, C and D), which were organized into tables containing clinical features, radiologic features and laboratory test features. Patients were divided into benign lesion (BL), precursor lesion (PL) and malignant lesion (ML) groups according to pathological diagnosis. Approximately 80% of patients in A (total/male: 632/269, age: 57.73 ± 11.06) were randomly selected as a training set; the remaining 20% were used as an internal test set; and the patients in B (total/male: 94/53, age: 60.04 ± 11.22), C (total/male: 94/47, age: 59.30 ± 9.86) and D (total/male: 94/61, age: 62.0 ± 11.09) were used as an external validation set. Logical regression (LR), decision tree (DT), random forest (RF) and support vector machine (SVM) were used to establish prediction models. Finally, the Mayo model, Peking University People's Hospital (PKUPH) model and Brock model were externally validated in our patients.

Results

The AUC values of RF model for MLs, PLs and BLs were 0.80 (95% CI: 0.73–0.88), 0.90 (95% CI: 0.82–0.99) and 0.75 (95% CI: 0.67–0.88), respectively. The weighted average AUC value of the RF model for the external validation set was 0.71 (95% CI: 0.67–0.73), and its AUC values for MLs, PLs and BLs were 0.71 (95% CI: 0.68–0.79), 0.98 (95% CI: 0.88–1.07) and 0.68 (95% CI: 0.61–0.74), respectively. The AUC values of the Mayo model, PKUPH model and Brock model were 0.68 (95% CI: 0.62–0.74), 0.64 (95% CI: 0.58–0.70) and 0.57 (95% CI: 0.49–0.65), respectively.

Conclusions

The RF model performed best, and its predictive performance was better than that of the three published models, which may provide a new noninvasive method for the risk assessment of PNs.

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基于机器学习的肺结节多分类预测模型的建立和验证。
背景:肺癌是全球癌症相关死亡的主要原因。本研究旨在建立基于机器学习(ML)的新型多分类预测模型,以预测肺结节(PNs)的恶性概率,并与已发表的三种模型进行比较:从四家医疗机构(A、B、C 和 D)收集了 914 名肺结节患者,并将其整理成包含临床特征、放射学特征和实验室检查特征的表格。根据病理诊断将患者分为良性病变组(BL)、前驱病变组(PL)和恶性病变组(ML)。随机抽取 A 组(总人数/男性:632/269,年龄:57.73±11.06)约 80% 的患者作为训练集,其余 20% 的患者作为内部测试集,B 组(总人数/男性:94/53,年龄:60.04±11.22)、C 组(总人数/男性:94/47,年龄:59.30±9.86)和 D 组(总人数/男性:94/61,年龄:62.0±11.09)的患者作为外部验证集。使用逻辑回归(LR)、决策树(DT)、随机森林(RF)和支持向量机(SVM)建立预测模型。最后,梅奥模型、北京大学人民医院(PKUPH)模型和布洛克模型在本院患者中进行了外部验证:RF模型对MLs、PLs和BLs的AUC值分别为0.80(95% CI:0.73-0.88)、0.90(95% CI:0.82-0.99)和0.75(95% CI:0.67-0.88)。外部验证集的 RF 模型加权平均 AUC 值为 0.71(95% CI:0.67-0.73),ML、PL 和 BL 的 AUC 值分别为 0.71(95% CI:0.68-0.79)、0.98(95% CI:0.88-1.07)和 0.68(95% CI:0.61-0.74)。梅奥模型、PKUPH 模型和布洛克模型的 AUC 值分别为 0.68(95% CI:0.62-0.74)、0.64(95% CI:0.58-0.70)和 0.57(95% CI:0.49-0.65):射频模型表现最佳,其预测性能优于已发表的三种模型,可为 PN 风险评估提供一种新的无创方法。
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来源期刊
Clinical Respiratory Journal
Clinical Respiratory Journal 医学-呼吸系统
CiteScore
3.70
自引率
0.00%
发文量
104
审稿时长
>12 weeks
期刊介绍: Overview Effective with the 2016 volume, this journal will be published in an online-only format. Aims and Scope The Clinical Respiratory Journal (CRJ) provides a forum for clinical research in all areas of respiratory medicine from clinical lung disease to basic research relevant to the clinic. We publish original research, review articles, case studies, editorials and book reviews in all areas of clinical lung disease including: Asthma Allergy COPD Non-invasive ventilation Sleep related breathing disorders Interstitial lung diseases Lung cancer Clinical genetics Rhinitis Airway and lung infection Epidemiology Pediatrics CRJ provides a fast-track service for selected Phase II and Phase III trial studies. Keywords Clinical Respiratory Journal, respiratory, pulmonary, medicine, clinical, lung disease, Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Embase (Elsevier) Health & Medical Collection (ProQuest) Health Research Premium Collection (ProQuest) HEED: Health Economic Evaluations Database (Wiley-Blackwell) Hospital Premium Collection (ProQuest) Journal Citation Reports/Science Edition (Clarivate Analytics) MEDLINE/PubMed (NLM) ProQuest Central (ProQuest) Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier)
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