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Article: A Classifier for Improving Early Lung Cancer Diagnosis Incorporating Artificial Intelligence and Liquid Biopsy

TitleA Classifier for Improving Early Lung Cancer Diagnosis Incorporating Artificial Intelligence and Liquid Biopsy
Authors
Keywordsartificial intelligence
early diagnosis
liquid biopsy
lung cancer
prediction model
Issue Date2-Mar-2022
PublisherFrontiers Media
Citation
Frontiers in Oncology, 2022, v. 12 How to Cite?
Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide and in China. Screening for lung cancer by low dose computed tomography (LDCT) can reduce mortality but has resulted in a dramatic rise in the incidence of indeterminate pulmonary nodules, which presents a major diagnostic challenge for clinicians regarding their underlying pathology and can lead to overdiagnosis. To address the significant gap in evaluating pulmonary nodules, we conducted a prospective study to develop a prediction model for individuals at intermediate to high risk of developing lung cancer. Univariate and multivariate logistic analyses were applied to the training cohort (n = 560) to develop an early lung cancer prediction model. The results indicated that a model integrating clinical characteristics (age and smoking history), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the training cohort (sensitivity 89.53%, specificity 81.31%, area under the curve [AUC] = 0.880). In the independent validation cohort (n = 168), this model had an AUC of 0.895, which was greater than that of the Mayo Clinic Model (AUC = 0.772) and Veterans’ Affairs Model (AUC = 0.740). These results were significantly better for predicting the presence of cancer than radiological features and artificial intelligence risk scores alone. Applying this classifier prospectively may lead to improved early lung cancer diagnosis and early treatment for patients with malignant nodules while sparing patients with benign entities from unnecessary and potentially harmful surgery.


Persistent Identifierhttp://hdl.handle.net/10722/343929
ISSN
2023 Impact Factor: 3.5
2023 SCImago Journal Rankings: 1.066

 

DC FieldValueLanguage
dc.contributor.authorYe, Maosong-
dc.contributor.authorTong, Lin-
dc.contributor.authorZheng, Xiaoxuan-
dc.contributor.authorWang, Hui-
dc.contributor.authorZhou, Haining-
dc.contributor.authorZhu, Xiaoli-
dc.contributor.authorZhou, Chengzhi-
dc.contributor.authorZhao, Peige-
dc.contributor.authorWang, Yan-
dc.contributor.authorWang, Qi-
dc.contributor.authorBai, Li-
dc.contributor.authorCai, Zhigang-
dc.contributor.authorKong, FS-
dc.contributor.authorWang, Yuehong-
dc.contributor.authorLi, Yafei-
dc.contributor.authorFeng, Mingxiang-
dc.contributor.authorYe, Xin-
dc.contributor.authorYang, Dawei-
dc.contributor.authorLiu, Zilong-
dc.contributor.authorZhang, Quncheng-
dc.contributor.authorWang, Ziqi-
dc.contributor.authorHan, Shuhua-
dc.contributor.authorSun, Lihong-
dc.contributor.authorZhao, Ningning-
dc.contributor.authorYu, Zubin-
dc.contributor.authorZhang, Juncheng-
dc.contributor.authorZhang, Xiaoju-
dc.contributor.authorKatz, Ruth L-
dc.contributor.authorSun, Jiayuan-
dc.contributor.authorBai, Chunxue-
dc.date.accessioned2024-06-18T03:42:54Z-
dc.date.available2024-06-18T03:42:54Z-
dc.date.issued2022-03-02-
dc.identifier.citationFrontiers in Oncology, 2022, v. 12-
dc.identifier.issn2234-943X-
dc.identifier.urihttp://hdl.handle.net/10722/343929-
dc.description.abstract<p>Lung cancer is the leading cause of cancer-related deaths worldwide and in China. Screening for lung cancer by low dose computed tomography (LDCT) can reduce mortality but has resulted in a dramatic rise in the incidence of indeterminate pulmonary nodules, which presents a major diagnostic challenge for clinicians regarding their underlying pathology and can lead to overdiagnosis. To address the significant gap in evaluating pulmonary nodules, we conducted a prospective study to develop a prediction model for individuals at intermediate to high risk of developing lung cancer. Univariate and multivariate logistic analyses were applied to the training cohort (n = 560) to develop an early lung cancer prediction model. The results indicated that a model integrating clinical characteristics (age and smoking history), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the training cohort (sensitivity 89.53%, specificity 81.31%, area under the curve [AUC] = 0.880). In the independent validation cohort (n = 168), this model had an AUC of 0.895, which was greater than that of the Mayo Clinic Model (AUC = 0.772) and Veterans’ Affairs Model (AUC = 0.740). These results were significantly better for predicting the presence of cancer than radiological features and artificial intelligence risk scores alone. Applying this classifier prospectively may lead to improved early lung cancer diagnosis and early treatment for patients with malignant nodules while sparing patients with benign entities from unnecessary and potentially harmful surgery.<br></p>-
dc.languageeng-
dc.publisherFrontiers Media-
dc.relation.ispartofFrontiers in Oncology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectartificial intelligence-
dc.subjectearly diagnosis-
dc.subjectliquid biopsy-
dc.subjectlung cancer-
dc.subjectprediction model-
dc.titleA Classifier for Improving Early Lung Cancer Diagnosis Incorporating Artificial Intelligence and Liquid Biopsy-
dc.typeArticle-
dc.identifier.doi10.3389/fonc.2022.853801-
dc.identifier.scopuseid_2-s2.0-85126823472-
dc.identifier.volume12-
dc.identifier.eissn2234-943X-
dc.identifier.issnl2234-943X-

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