Clinical Study of Artificial Intelligence-assisted Diagnosis System in Predicting the Invasive Subtypes of Early-stage Lung Adenocarcinoma Appearing as Pulmonary Nodules.
Background and objective Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule...
| Publicado en: | Chinese Journal of Lung Cancer Vol. 25; no. 4; pp. 245 - 253 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Chinese Journal of Lung Cancer
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156537688&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156537688 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10093419 9014 jtl: Chinese Journal of Lung Cancer issn: 10093419 maglogo: N pubinfo: dt: Apr2022 vid: 25 iid: 4 pid: 54191 pub: Chinese Journal of Lung Cancer artinfo: ui: 156537688 156537688 156537688 10.3779/j.issn.1009-3419.2022.102.12 156537688 ppf: 245 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Clinical Study of Artificial Intelligence-assisted Diagnosis System in Predicting the Invasive Subtypes of Early-stage Lung Adenocarcinoma Appearing as Pulmonary Nodules. aug: au: Zhipeng SU Wenjie MAO Bin LI Zhizhong ZHENG Bo YANG Meiyu REN Tieniu SONG Haiming FENG Yuqi MENG affil: Department of Thoracic Surgery, Lanzhou University Second Hospital, Lanzhou University Second Clinical Medical College, Lanzhou 730030, China sug: subj: Artificial Intelligence Adenocarcinoma of Lung Diagnosis Lung Neoplasms Human Cancer Patients Inpatients China Academic Medical Centers Retrospective Design Tomography, X-Ray Computed Diagnosis, Computer Assisted Sensitivity and Specificity ROC Curve Neoplasm Staging Algorithms Male Female Adult Middle Age Descriptive Statistics Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background and objective Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule detection, differentiation between benign and malignant and diagnosis of invasive subtypes, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the effectiveness of artificial intelligence-assisted diagnosis system in predicting the invasive subtypes of early-stage lung adenocarcinoma appearing as pulmonary nodules. Methods Clinical data of 223 patients with early-stage lung adenocarcinoma appearing as pulmonary nodules admitted to the Lanzhou University Second Hospital from January 1st, 2016 to December 31th, 2021 were retrospectively analyzed, which were divided into invasive adenocarcinoma group (n=170) and non-invasive adenocarcinoma group (n=53), and the non-invasive adenocarcinoma group was subdivided into minimally invasive adenocarcinoma group (n=31) and preinvasive lesions group (n=22). The malignant probability and imaging characteristics of each group were compared to analyze their predictive ability for the invasive subtypes of early-stage lung adenocarcinoma. The concordance between qualitative diagnostic results of artificial intelligence-assisted diagnosis of the invasive subtypes of early-stage lung adenocarcinoma and postoperative pathology was then analyzed. Results In different invasive subtypes of early-stage lung adenocarcinoma, the mean CT value of pulmonary nodules .001), malignant probability (P<0.001), pleural retraction sign (P<0.001), lobulation (P<0.001), spiculation (P<0.001) were significantly different. At the same time, it was also found that with the increased invasiveness of different invasive subtypes of early-stage lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. On the issue of binary classification, the sensitivity, specificity, and area under the curve (AUC) values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 81.76%, 92.45% and 0.871 respectively. On the issue of three classification, the accuracy, recall rate, F1 score, and AUC values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 83.86%, 85.03%, 76.46% and 0.879 respectively. Conclusion Artificial intelligence-assisted diagnosis system could predict the invasive subtypes of early-stage lung adenocarcinoma appearing as pulmonary nodules, and has a certain predictive value. With the optimization of algorithms and the improvement of data, it may provide guidance for individualized treatment of patients. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: Chinese refInfo: holdings: @attributes: islocal: N |
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