{ "abstract": "High-risk subtypes of invasive lung adenocarcinoma (IAC), particularly micropapillary- or solid-predominant patterns, are closely associated with poor prognosis. This multicenter retrospective study developed and validated a predictive model for the preoperative identification of these high-risk subtypes using topologically distinct intratumoral heterogeneity (ITH) scores derived from CT images. The study included 1,051 patients with IAC. Two complementary ITH scores were developed: a two-dimensional ITH score, which integrated local radiomics features with global pixel distribution patterns on the largest cross-sectional CT slice, and a three-dimensional ITH score, which extended this quantification across the entire tumor volume. Clinicoradiological features and ITH scores were incorporated as model inputs to construct six base machine learning classifiers and a final stacking ensemble classifier. Model interpretability and robustness were evaluated using SHapley Additive exPlanations (SHAP)-based ablation analyses. An independent dataset from The Cancer Imaging Archive (TCIA) was used for external validation to investigate associations between ITH scores and pathological characteristics, genomic features, recurrence-free survival, and overall survival. The stacking ensemble classifier achieved the best predictive performance, with an area under the receiver operating characteristic curve of 0.875, outperforming models based solely on radiomics features (0.834) or clinicoradiological features (0.792). SHAP analysis identified the 3D ITH score as the most influential contributor to model output, and TCIA validation showed that higher 3D ITH scores were associated with more aggressive tumor biology and poorer survival outcomes. The topologically distinct 3D ITH score may provide a clinically meaningful imaging biomarker for preoperative risk stratification in IAC. Author summary: We studied whether patterns hidden inside lung tumors on routine CT scans can help identify patients with more aggressive disease before surgery. Pathologists can determine tumor grade after resection, but treatment decisions often need to be made earlier. We developed a way to measure how fragmented and spatially complex the internal structure of a tumor is, using both a single representative image slice and the full tumor volume. We then combined these measurements with standard clinical and imaging features in a machine learning model. In a multicenter patient cohort, the model using the three-dimensional heterogeneity score performed better than models based only on conventional radiomics or routine clinical imaging features. This score also showed meaningful associations with adverse pathological findings and survival outcomes in an external public dataset. Our findings suggest that spatial tumor heterogeneity on CT images may provide useful preoperative information for risk stratification and individualized surgical planning.", "abstract_shown": true, "affiliations": [ "Department of Radiology, Xiangtan Central Hospital(The Affiliated Hospital of Hunan University), Xiangtan, Hunan, China", "Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China", "Department of Radiology, Liuyang Hospital of Traditional Chinese Medicine, Changsha, Hunan, China", "Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China" ], "article_type": "Research Article", "arxiv": null, "author_total": 7, "authors": [ { "affs": [ 0 ], "name": "Shanyue Lin" }, { "affs": [ 1 ], "name": "Yudian Mao" }, { "affs": [ 2 ], "name": "Qian He" }, { "affs": [ 3 ], "name": "Wanyin Qi" }, { "name": "Sanhong Zhang" }, { "name": "Wei Li" }, { "name": "Zhichao Zuo" } ], "bibtex": "https://research.draconitic.com/paper/5e0095bmqyy81.bib", "cited_by": 0, "doi": "10.1371/journal.pdig.0001646", "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001646", "id": "5e0095bmqyy81", "issns": [ "2767-3170" ], "landing_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001646", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2741|Radiomics and Machine Learning in Medical Imaging" ], [ "plos", "/Biology and life sciences/Anatomy/Histology" ], [ "plos", "/Biology and life sciences/Neuroscience/Neuroimaging/Computed axial tomography" ], [ "plos", "/Computer and information sciences/Artificial intelligence/Machine learning" ], [ "plos", "/Medicine and health sciences/Anatomy/Histology" ], [ "plos", "/Medicine and health sciences/Clinical medicine/Clinical oncology/Surgical oncology" ], [ "plos", "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Tomography/Computed axial tomography" ], [ "plos", "/Medicine and health sciences/Epidemiology/Medical risk factors/Cancer risk factors" ], [ "plos", "/Medicine and health sciences/Oncology/Cancer risk factors" ], [ "plos", "/Medicine and health sciences/Oncology/Cancer treatment/Surgical oncology" ], [ "plos", "/Medicine and health sciences/Oncology/Cancers and neoplasms" ], [ "plos", "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors" ], [ "plos", "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors/Non-small cell lung cancer" ], [ "plos", "/Medicine and health sciences/Oncology/Clinical oncology/Surgical oncology" ], [ "plos", "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Tomography/Computed axial tomography" ], [ "plos", "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Tomography/Computed axial tomography" ], [ "plos", "/Research and analysis methods/Imaging techniques/Neuroimaging/Computed axial tomography" ] ], "openalex": "W7211933387", "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001646&type=printable", "pmcid": null, "pmid": null, "published": "2026-09-08", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/5e0095bmqyy81.ris", "sources": [ { "first_seen": "2026-10-30T12:17:56Z", "native_id": "10.1371/journal.pdig.0001646", "source": "plos", "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001646" } ], "subjects": [ { "field": "medicine", "origin": "plos:diagnostic radiology", "subfield": "imaging" }, { "field": "medicine", "origin": "plos:oncology", "subfield": "oncology" } ], "title": "Topologically distinct intratumoral heterogeneity scores for predicting high-risk pathological grades in invasive lung adenocarcinoma: A multicenter study across four institutions", "title_text": "Topologically distinct intratumoral heterogeneity scores for predicting high-risk pathological grades in invasive lung adenocarcinoma: A multicenter study across four institutions", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:56Z", "url": "https://research.draconitic.com/paper/5e0095bmqyy81", "venue": "PLOS Digital Health", "versions": [] }