Shaoyu Huang , Xiuzhen Liang , Kaihua Lou , Jinlong Zhou , Jie Wang , Guodong Xu , Shibo Wu , Hongjie Hu , Haibo Dong
{"title":"Comparing Habitat, Radiomics, and Fusion Models for Predicting Micropapillary/Solid Components in Stage I Lung Adenocarcinoma","authors":"Shaoyu Huang , Xiuzhen Liang , Kaihua Lou , Jinlong Zhou , Jie Wang , Guodong Xu , Shibo Wu , Hongjie Hu , Haibo Dong","doi":"10.1016/j.acra.2025.07.035","DOIUrl":null,"url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To comprehensively compare habitat, radiomics, and fusion models for the preoperative prediction of micropapillary/solid (MP/S) status in stage I lung adenocarcinoma (LAC).</div></div><div><h3>Materials and Methods</h3><div>In this retrospective study, we enrolled 345 patients postoperatively diagnosed with stage I LAC from two medical centers, dividing them into training (<em>n<!--> </em>=<!--> <!-->207), internal validation (<em>n<!--> </em>=<!--> <!-->69), and external validation (<em>n<!--> </em>=<!--> <!-->69) cohorts. Radiomics model (RM) was developed using CT images of the primary tumor. Habitat model (HM) was built by analyzing intra-tumor subregions identified via unsupervised K-means clustering algorithm. Fusion model employed two integration strategies as follows: feature-based pre-fusion model (pre-FM) and decision-based post-fusion model (post-FM). The predictive performance of all models was comprehensively evaluated by area under the curve (AUC) and integrated discrimination improvement (IDI). Additionally, correlations between clustering and radiomics features were analyzed with Spearman’s correlation analysis.</div></div><div><h3>Results</h3><div>The HM demonstrated superior predictive performance compared to the RM in the training cohort (AUC: 0.900 vs. 0.876, <em>p<!--> </em>=<!--> <!-->0.252). The pre-FM consistently outperformed the HM and RM across all study cohorts (AUC: 0.843–0.914 vs. 0.802–0.900 and 0.841–0.876, <em>p<!--> </em>=<!--> <!-->0.041–0.484 and 0.011–0.924, respectively). The post-FM further enhanced predictive performance, as evidenced by the highest AUCs in the training and internal validation cohorts (AUC: 0.952 vs. 0.862–0.914, <em>p<!--> </em>=<!--> <!-->0.001–0.116; 0.850 [0.724–0.922] vs. 0.770–0.843, <em>p<!--> </em>=<!--> <!-->0.102–0.922). and IDI values (14.2%–36.4% increase). Additionally, clustering and radiomics features displayed a higher number of correlated feature pairs in the MP/S (+) group than MP/S (-) group.</div></div><div><h3>Conclusion</h3><div>The post-FM, integrating clustering signature, radiomics signature, and clinical characteristics, has been established as a reliable predictor for MP/S status in stage I LAC.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 10","pages":"Pages 6307-6319"},"PeriodicalIF":3.9000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Radiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1076633225007068","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Rationale and Objectives
To comprehensively compare habitat, radiomics, and fusion models for the preoperative prediction of micropapillary/solid (MP/S) status in stage I lung adenocarcinoma (LAC).
Materials and Methods
In this retrospective study, we enrolled 345 patients postoperatively diagnosed with stage I LAC from two medical centers, dividing them into training (n = 207), internal validation (n = 69), and external validation (n = 69) cohorts. Radiomics model (RM) was developed using CT images of the primary tumor. Habitat model (HM) was built by analyzing intra-tumor subregions identified via unsupervised K-means clustering algorithm. Fusion model employed two integration strategies as follows: feature-based pre-fusion model (pre-FM) and decision-based post-fusion model (post-FM). The predictive performance of all models was comprehensively evaluated by area under the curve (AUC) and integrated discrimination improvement (IDI). Additionally, correlations between clustering and radiomics features were analyzed with Spearman’s correlation analysis.
Results
The HM demonstrated superior predictive performance compared to the RM in the training cohort (AUC: 0.900 vs. 0.876, p = 0.252). The pre-FM consistently outperformed the HM and RM across all study cohorts (AUC: 0.843–0.914 vs. 0.802–0.900 and 0.841–0.876, p = 0.041–0.484 and 0.011–0.924, respectively). The post-FM further enhanced predictive performance, as evidenced by the highest AUCs in the training and internal validation cohorts (AUC: 0.952 vs. 0.862–0.914, p = 0.001–0.116; 0.850 [0.724–0.922] vs. 0.770–0.843, p = 0.102–0.922). and IDI values (14.2%–36.4% increase). Additionally, clustering and radiomics features displayed a higher number of correlated feature pairs in the MP/S (+) group than MP/S (-) group.
Conclusion
The post-FM, integrating clustering signature, radiomics signature, and clinical characteristics, has been established as a reliable predictor for MP/S status in stage I LAC.
期刊介绍:
Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.