Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma.
This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospi...
| Published in: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2135 - 2149 |
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| Main Authors: | , , , , , , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515429&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515429 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515429 181515429 181515429 10.1007/s10278-024-00967-5 181515429 ppf: 2135 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma. aug: au: Ren, He Wang, Qiubo Xiao, Zhengguang Mo, Runwei Guo, Jiachen Hide, Gareth Richard Tu, Mengting Zeng, Yanan Ling, Chen Li, Ping affil: https://ror.org/03ns6aq57 Respiratory Department, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China sug: subj: Adenocarcinoma of Lung Diagnosis Radiomics Adenocarcinoma of Lung Pathology Imaging, Three-Dimensional Methods Human Male Female Adult Middle Age Minimally Invasive Procedures Adenocarcinoma in Situ Learning Methods Random Forest Boosting Machine Learning Algorithms Comparative Studies Prospective Studies ROC Curve Prediction Models Support, Psychosocial Health Care Delivery Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model's performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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