整合放射组学和深度学习以增强IA期肺腺癌高级别模式的预测。

IF 3.5 2区 医学 Q2 ONCOLOGY
Translational lung cancer research Pub Date : 2025-04-30 Epub Date: 2025-04-15 DOI:10.21037/tlcr-24-995
Zhongxiao Chen, Hao Liu, Hua Sun, Cheng Xu, Bingyu Hu, Luyu Qu, William C Cho, Thivanka Witharana, Chengchu Zhu, Jianfei Shen
{"title":"整合放射组学和深度学习以增强IA期肺腺癌高级别模式的预测。","authors":"Zhongxiao Chen, Hao Liu, Hua Sun, Cheng Xu, Bingyu Hu, Luyu Qu, William C Cho, Thivanka Witharana, Chengchu Zhu, Jianfei Shen","doi":"10.21037/tlcr-24-995","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The presence of high-grade patterns (HGPs) often has a detrimental effect on prognosis. It is helpful to make individualized clinical treatment plans when preoperative recognition of the presence of HGPs becomes possible. So, this study aimed to develop a model based on preoperative computed tomography (CT) images to predict the presence of HPGs in invasive pulmonary non-mucinous adenocarcinoma.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 403 surgically treated patients with clinical stage IA and pathologically confirmed invasive non-mucinous adenocarcinoma. There were 256 deep learning features and 1,836 handcrafted features extracted from the regions of interest (ROIs) in preoperative CT images. The optimal subset of features was screened using <i>t</i>-test, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression to construct the fusion model. Receiver operating characteristic (ROC) curve was applied to assess the model's performance. Decision curve analysis (DCA) and calibration curve were used to assess the clinical usefulness.</p><p><strong>Results: </strong>The fusion model combining radiomics features and deep learning features using the XGBoost classifier exhibited strong predictive efficacy with the area under the curve (AUC) of 0.983, 0.862, and 0.832 in the training, validation, and test set. It means that the model can distinguish well between tumors with and without HGPs. The fusion model had better diagnostic performance when compared to the radiomics model and deep learning model. Calibration curve indicated good coherence between model prediction and the actual observation. DCA revealed the fusion model exerted the highest clinical benefit.</p><p><strong>Conclusions: </strong>The fusion model can identify the presence of HPGs in invasive lung adenocarcinoma from preoperative CT images. It assists clinicians in determining individualized treatments and monitoring strategies for patients.</p>","PeriodicalId":23271,"journal":{"name":"Translational lung cancer research","volume":"14 4","pages":"1076-1088"},"PeriodicalIF":3.5000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082195/pdf/","citationCount":"0","resultStr":"{\"title\":\"Integrating radiomics and deep learning for enhanced prediction of high-grade patterns in stage IA lung adenocarcinoma.\",\"authors\":\"Zhongxiao Chen, Hao Liu, Hua Sun, Cheng Xu, Bingyu Hu, Luyu Qu, William C Cho, Thivanka Witharana, Chengchu Zhu, Jianfei Shen\",\"doi\":\"10.21037/tlcr-24-995\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The presence of high-grade patterns (HGPs) often has a detrimental effect on prognosis. It is helpful to make individualized clinical treatment plans when preoperative recognition of the presence of HGPs becomes possible. So, this study aimed to develop a model based on preoperative computed tomography (CT) images to predict the presence of HPGs in invasive pulmonary non-mucinous adenocarcinoma.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 403 surgically treated patients with clinical stage IA and pathologically confirmed invasive non-mucinous adenocarcinoma. There were 256 deep learning features and 1,836 handcrafted features extracted from the regions of interest (ROIs) in preoperative CT images. The optimal subset of features was screened using <i>t</i>-test, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression to construct the fusion model. Receiver operating characteristic (ROC) curve was applied to assess the model's performance. Decision curve analysis (DCA) and calibration curve were used to assess the clinical usefulness.</p><p><strong>Results: </strong>The fusion model combining radiomics features and deep learning features using the XGBoost classifier exhibited strong predictive efficacy with the area under the curve (AUC) of 0.983, 0.862, and 0.832 in the training, validation, and test set. It means that the model can distinguish well between tumors with and without HGPs. The fusion model had better diagnostic performance when compared to the radiomics model and deep learning model. Calibration curve indicated good coherence between model prediction and the actual observation. DCA revealed the fusion model exerted the highest clinical benefit.</p><p><strong>Conclusions: </strong>The fusion model can identify the presence of HPGs in invasive lung adenocarcinoma from preoperative CT images. It assists clinicians in determining individualized treatments and monitoring strategies for patients.</p>\",\"PeriodicalId\":23271,\"journal\":{\"name\":\"Translational lung cancer research\",\"volume\":\"14 4\",\"pages\":\"1076-1088\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082195/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Translational lung cancer research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.21037/tlcr-24-995\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/4/15 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational lung cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tlcr-24-995","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/15 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:高级别模式(HGPs)的存在通常对预后有不利影响。术前识别hgp的存在有助于制定个性化的临床治疗方案。因此,本研究旨在建立一种基于术前计算机断层扫描(CT)图像的模型来预测侵袭性肺非粘液腺癌中HPGs的存在。方法:回顾性分析403例经手术治疗的临床分期为IA期并经病理证实的侵袭性非粘液腺癌患者。从术前CT图像的感兴趣区域(roi)中提取了256个深度学习特征和1836个手工特征。采用t检验、Pearson相关分析、最小绝对收缩和选择算子(LASSO)回归等方法筛选最优特征子集,构建融合模型。采用受试者工作特征(ROC)曲线评价模型的性能。采用决策曲线分析(DCA)和校准曲线评估临床有效性。结果:基于XGBoost分类器的放射组学特征与深度学习特征相结合的融合模型具有较强的预测效果,在训练集、验证集和测试集的曲线下面积(AUC)分别为0.983、0.862和0.832。这意味着该模型可以很好地区分有和没有hgp的肿瘤。与放射组学模型和深度学习模型相比,融合模型具有更好的诊断性能。校正曲线表明,模型预测与实际观测具有较好的一致性。DCA显示融合模型具有最高的临床疗效。结论:该融合模型可从术前CT图像识别浸润性肺腺癌中存在HPGs。它帮助临床医生确定个体化治疗和患者监测策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating radiomics and deep learning for enhanced prediction of high-grade patterns in stage IA lung adenocarcinoma.

Background: The presence of high-grade patterns (HGPs) often has a detrimental effect on prognosis. It is helpful to make individualized clinical treatment plans when preoperative recognition of the presence of HGPs becomes possible. So, this study aimed to develop a model based on preoperative computed tomography (CT) images to predict the presence of HPGs in invasive pulmonary non-mucinous adenocarcinoma.

Methods: A retrospective analysis was conducted on 403 surgically treated patients with clinical stage IA and pathologically confirmed invasive non-mucinous adenocarcinoma. There were 256 deep learning features and 1,836 handcrafted features extracted from the regions of interest (ROIs) in preoperative CT images. The optimal subset of features was screened using t-test, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression to construct the fusion model. Receiver operating characteristic (ROC) curve was applied to assess the model's performance. Decision curve analysis (DCA) and calibration curve were used to assess the clinical usefulness.

Results: The fusion model combining radiomics features and deep learning features using the XGBoost classifier exhibited strong predictive efficacy with the area under the curve (AUC) of 0.983, 0.862, and 0.832 in the training, validation, and test set. It means that the model can distinguish well between tumors with and without HGPs. The fusion model had better diagnostic performance when compared to the radiomics model and deep learning model. Calibration curve indicated good coherence between model prediction and the actual observation. DCA revealed the fusion model exerted the highest clinical benefit.

Conclusions: The fusion model can identify the presence of HPGs in invasive lung adenocarcinoma from preoperative CT images. It assists clinicians in determining individualized treatments and monitoring strategies for patients.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
7.20
自引率
2.50%
发文量
137
期刊介绍: Translational Lung Cancer Research(TLCR, Transl Lung Cancer Res, Print ISSN 2218-6751; Online ISSN 2226-4477) is an international, peer-reviewed, open-access journal, which was founded in March 2012. TLCR is indexed by PubMed/PubMed Central and the Chemical Abstracts Service (CAS) Databases. It is published quarterly the first year, and published bimonthly since February 2013. It provides practical up-to-date information on prevention, early detection, diagnosis, and treatment of lung cancer. Specific areas of its interest include, but not limited to, multimodality therapy, markers, imaging, tumor biology, pathology, chemoprevention, and technical advances related to lung cancer.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信