Yiqiu Shi, Yuwen Shen, Jie Chen, Wanying Yan, Kefu Liu
{"title":"[CT 定量参数在预测肺磨玻璃结节病理类型中的价值]。","authors":"Yiqiu Shi, Yuwen Shen, Jie Chen, Wanying Yan, Kefu Liu","doi":"10.3779/j.issn.1009-3419.2024.102.09","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The pathological types of lung ground glass nodules (GGNs) show great significance to the clinical treatment. This study was aimed to predict pathological types of GGNs based on computed tomography (CT) quantitative parameters.</p><p><strong>Methods: </strong>389 GGNs confirmed by postoperative pathology were selected, including 138 cases of precursor glandular lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], 109 cases of microinvasive adenocarcinoma (MIA) and 142 cases of invasive adenocarcinoma (IAC). The morphological characteristics of nodules were evaluated subjectively by radiologist, as well as artificial intelligence (AI).</p><p><strong>Results: </strong>In the subjective CT signs, the maximum diameter of nodule and the frequency of spiculation, lobulation and pleural traction increased from AAH+AIS, MIA to IAC. In the AI quantitative parameters, parameters related to size and CT value, proportion of solid component, energy and entropy increased from AAH+AIS, MIA to IAC. There was no significant difference between AI quantitative parameters and the subjective CT signs for distinguishing the pathological types of GGNs.</p><p><strong>Conclusions: </strong>AI quantitative parameters were valuable in distinguishing the pathological types of GGNs.</p>","PeriodicalId":39317,"journal":{"name":"中国肺癌杂志","volume":"27 2","pages":"118-125"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10918243/pdf/","citationCount":"0","resultStr":"{\"title\":\"[Value of CT Quantitative Parameters in Prediction of Pathological Types\\u2029of Lung Ground Glass Nodules].\",\"authors\":\"Yiqiu Shi, Yuwen Shen, Jie Chen, Wanying Yan, Kefu Liu\",\"doi\":\"10.3779/j.issn.1009-3419.2024.102.09\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The pathological types of lung ground glass nodules (GGNs) show great significance to the clinical treatment. This study was aimed to predict pathological types of GGNs based on computed tomography (CT) quantitative parameters.</p><p><strong>Methods: </strong>389 GGNs confirmed by postoperative pathology were selected, including 138 cases of precursor glandular lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], 109 cases of microinvasive adenocarcinoma (MIA) and 142 cases of invasive adenocarcinoma (IAC). The morphological characteristics of nodules were evaluated subjectively by radiologist, as well as artificial intelligence (AI).</p><p><strong>Results: </strong>In the subjective CT signs, the maximum diameter of nodule and the frequency of spiculation, lobulation and pleural traction increased from AAH+AIS, MIA to IAC. In the AI quantitative parameters, parameters related to size and CT value, proportion of solid component, energy and entropy increased from AAH+AIS, MIA to IAC. There was no significant difference between AI quantitative parameters and the subjective CT signs for distinguishing the pathological types of GGNs.</p><p><strong>Conclusions: </strong>AI quantitative parameters were valuable in distinguishing the pathological types of GGNs.</p>\",\"PeriodicalId\":39317,\"journal\":{\"name\":\"中国肺癌杂志\",\"volume\":\"27 2\",\"pages\":\"118-125\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10918243/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国肺癌杂志\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3779/j.issn.1009-3419.2024.102.09\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国肺癌杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3779/j.issn.1009-3419.2024.102.09","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
[Value of CT Quantitative Parameters in Prediction of Pathological Types of Lung Ground Glass Nodules].
Background: The pathological types of lung ground glass nodules (GGNs) show great significance to the clinical treatment. This study was aimed to predict pathological types of GGNs based on computed tomography (CT) quantitative parameters.
Methods: 389 GGNs confirmed by postoperative pathology were selected, including 138 cases of precursor glandular lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], 109 cases of microinvasive adenocarcinoma (MIA) and 142 cases of invasive adenocarcinoma (IAC). The morphological characteristics of nodules were evaluated subjectively by radiologist, as well as artificial intelligence (AI).
Results: In the subjective CT signs, the maximum diameter of nodule and the frequency of spiculation, lobulation and pleural traction increased from AAH+AIS, MIA to IAC. In the AI quantitative parameters, parameters related to size and CT value, proportion of solid component, energy and entropy increased from AAH+AIS, MIA to IAC. There was no significant difference between AI quantitative parameters and the subjective CT signs for distinguishing the pathological types of GGNs.
Conclusions: AI quantitative parameters were valuable in distinguishing the pathological types of GGNs.
期刊介绍:
Chinese Journal of Lung Cancer(CJLC, pISSN 1009-3419, eISSN 1999-6187), a monthly Open Access journal, is hosted by Chinese Anti-Cancer Association, Chinese Antituberculosis Association, Tianjin Medical University General Hospital. CJLC was indexed in DOAJ, EMBASE/SCOPUS, Chemical Abstract(CA), CSA-Biological Science, HINARI, EBSCO-CINAHL,CABI Abstract, Global Health, CNKI, etc. Editor-in-Chief: Professor Qinghua ZHOU.