{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T08:28:44Z","timestamp":1771921724128,"version":"3.50.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>CT-guided percutaneous transthoracic needle biopsy (PTNB) is a cornerstone diagnostic procedure for lung cancer. However, its diagnostic accuracy is frequently compromised by sampling errors arising from tumor heterogeneity and operator-dependent target selection, leading to false-negative outcomes. This study aimed to develop and validate a clinical\u2013radiomics model based on biopsy-slot regions of interest (ROIs) to preoperatively predict tumor-rich targets and improve the diagnostic yield of CT-guided PTNB.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective study, a cohort of 350 patients with surgically confirmed lung cancer who underwent CT-guided PTNB was analyzed. Patients were classified into true-positive group (TPG) and false-negative group (FNG) based on pathological results and randomly allocated into training and validation sets. Radiomic features were extracted from standardized biopsy-slot ROIs, and feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression. Independent clinical predictors were identified from a comprehensive set of candidate variables, including patient demographics, lesion characteristics, procedural factors, and classical lung cancer risk factors, using multivariate logistic regression and integrated with radiomic features to develop a combined prediction model. Model performance and clinical utility were assessed using receiver operating characteristic (ROC) and decision curve analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multivariate analysis identified age and vascular proximity (&amp;lt;0.5 cm) as the only independent clinical predictors of diagnostic success from among the candidate factors evaluated. The radiomics signature comprised 10 robust features derived from first-order statistics, neighboring gray tone difference matrix (NGTDM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), and wavelet transforms. The combined clinical\u2013radiomics model demonstrated superior discriminative performance, achieving AUCs of 0.942 and 0.926 in the training and validation cohorts, respectively, significantly outperforming both the clinical model (AUCs: 0.703 and 0.696) and the radiomics model alone (AUCs: 0.883 and 0.867). ROC analysis established an optimal radiomics score (Rad score) cutoff of 0.42 (corresponding to a nomogram score of \u2248165), yielding sensitivities of 89.6%\u201388.9% and specificities of 86.3%\u201384.7%, providing a clinically applicable threshold for biopsy target prioritization. The ROC curves visually confirm the performance of all three models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed biopsy-slot ROI-based clinical\u2013radiomics model accurately predicts tumor-rich targets in CT-guided PTNB for lung cancer. By synergistically integrating quantitative imaging biomarkers with key clinical variables, this model facilitates personalized biopsy planning and promotes precision-guided sampling strategies, potentially reducing nondiagnostic procedures. However, because this retrospective single-center study only included patients who subsequently underwent surgical resection, the findings may not be directly generalizable to inoperable patients or the broader population undergoing CT-guided PTNB.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2025.1701146","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T05:13:49Z","timestamp":1768454029000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiomics-based optimization of target selection in CT-guided percutaneous lung cancer biopsy: a retrospective study"],"prefix":"10.3389","volume":"15","author":[{"given":"JiWu","family":"Wang","sequence":"first","affiliation":[{"name":"Radiological Imaging Department, Panzhihua Central Hospital","place":["Panzhihua, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZeMing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Radiological Imaging Department, Panzhihua Central Hospital","place":["Panzhihua, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"XiaoDong","family":"Liu","sequence":"additional","affiliation":[{"name":"Radiological Imaging Department, Panzhihua Central Hospital","place":["Panzhihua, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"XinYu","family":"Wang","sequence":"additional","affiliation":[{"name":"Radiological Imaging Department, Panzhihua Central Hospital","place":["Panzhihua, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YaoKang","family":"Chen","sequence":"additional","affiliation":[{"name":"Radiological Imaging Department, Panzhihua Central Hospital","place":["Panzhihua, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Liu","sequence":"additional","affiliation":[{"name":"Radiological Imaging Department, Jianyang Chinese Medicine Hospital","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-023-00798-3","article-title":"The global burden of lung cancer: current status and future trends","volume":"20","author":"Leiter","year":"2023","journal-title":"Nat Rev Clin Oncol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1916623","article-title":"The effect of advances in lung-cancer treatment on population mortality","volume":"383","author":"Howlader","year":"2020","journal-title":"New Engl J Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.annonc.2021.09.008","article-title":"Hesitancy around low-dose CT screening for lung cancer","volume":"33","author":"Dickson","year":"2022","journal-title":"Ann Oncol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.14333","article-title":"Lung cancer risk prediction models based on pulmonary nodules: A systematic review","volume":"13","author":"Wu","year":"2022","journal-title":"Thorac Cancer"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2021.0377","article-title":"Screening for lung cancer with low-dose computed tomography updated evidence report and systematic review for the US preventive services task force","volume":"325","author":"Jonas","year":"2021","journal-title":"Jama-Journal Am Med Assoc"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1136\/thorax.58.11.920","article-title":"Guidelines for radiologically guided lung biopsy","volume":"58","author":"Manhire","year":"2003","journal-title":"Thorax"},{"key":"B7","doi-asserted-by":"publisher","first-page":"94","DOI":"10.5152\/dir.2020.20051","article-title":"Safety and diagnostic accuracy of percutaneous CT-guided transthoracic biopsy of small lung nodules (\u2264 20 mm) adjacent to the pericardium or great vessels","volume":"27","author":"Chen","year":"2021","journal-title":"Diagn Interventional Radiol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.167.1.8659351","article-title":"Diagnostic accuracy and safety of CT-guided percutaneous needle aspiration biopsy of the lung: comparison of small and large pulmonary nodules","volume":"167","author":"Li","year":"1996","journal-title":"AJR. Am J roentgenology"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.169.3.9275873","article-title":"Routine addition of an automated biopsy device to fine-needle aspiration of the lung: a prospective assessment","volume":"169","author":"Boiselle","year":"1997","journal-title":"AJR. Am J Roentgenol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1002\/dc.25012","article-title":"Core needle biopsy for the diagnosis of primary soft tissue lesions: Accuracy and diagnostic challenges","volume":"50","author":"Layfield","year":"2022","journal-title":"Diagn Cytopathology"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1097\/JTO.0b013e318290868f","article-title":"Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors guideline from the college of american pathologists, international association for the study of lung cancer, and association for molecular pathology","volume":"8","author":"Lindeman","year":"2013","journal-title":"J Thorac Oncol"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018181482","article-title":"Nondiagnostic percutaneous transthoracic needle biopsy of lung lesions: A multicenter study of Malignancy risk","volume":"290","author":"Lee","year":"2019","journal-title":"Radiology"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-023-00792-2","article-title":"Classification of histological types and stages in non-small cell lung cancer using radiomic features based on CT images","volume":"36","author":"Lin","year":"2023","journal-title":"J Digital Imaging"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-05505-z","article-title":"Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis","volume":"15","author":"Wang","year":"2025","journal-title":"Sci Rep"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.15410","article-title":"Computed tomography-based radiomics and clinical-genetic features for brain metastasis prediction in patients with stage III\/IV epidermal growth factor receptor-mutant non-small-cell lung cancer","volume":"15","author":"Zheng","year":"2024","journal-title":"Thorac Cancer"},{"key":"B16","doi-asserted-by":"publisher","first-page":"39","DOI":"10.5334\/jbsr.2429","article-title":"CT-guided transthoracic biopsy of lung lesions using a non-coaxial biopsy needle technique: CT characteristics predictive for diagnostic accuracy and pneumothorax","volume":"105","author":"Geeroms","year":"2021","journal-title":"J Belgian Soc Radiol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1007\/s12022-024-09838-9","article-title":"Effective Preparation of FFPE Tissue Samples for Preserving Appropriate Nucleic Acid Quality for Genomic Analysis in Thyroid Carcinoma","volume":"35","author":"Okubo","year":"2024","journal-title":"Endocr Pathol"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2014.08.040","article-title":"Macroscopic on-site quality evaluation of biopsy specimens to improve the diagnostic accuracy during EUS-guided FNA using a 19-gauge needle for solid lesions: a single-center prospective pilot study (MOSE study)","volume":"81","author":"Iwashita","year":"2015","journal-title":"Gastrointestinal Endoscopy"},{"key":"B19","doi-asserted-by":"publisher","first-page":"R1","DOI":"10.1088\/0031-9155\/46\/3\/201","article-title":"Medical image registration","volume":"46","author":"Hill","year":"2001","journal-title":"Phys Med Biol"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/4909846","article-title":"Lung cancer detection using image segmentation by means of various evolutionary algorithms","volume":"2019","author":"Senthil Kumar","year":"2019","journal-title":"Comput Math Methods Med"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"B22","doi-asserted-by":"publisher","first-page":"11075","DOI":"10.1038\/srep11075","article-title":"The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis","volume":"5","author":"Leijenaar","year":"2015","journal-title":"Sci Rep"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.13104\/imri.2020.24.4.179","article-title":"A Comparative Study of Unsupervised Deep Learning Methods for MRI Reconstruction","volume":"24","author":"He","year":"2020","journal-title":"Investig Magn Reson Imaging"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20210434","article-title":"Diagnostic yield of CT-guided lung biopsies: how can we limit negative sampling","volume":"95","author":"Tipaldi","year":"2022","journal-title":"Br J Radiol"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-017-9941-1","article-title":"Clinical applications of a CT window blending algorithm: RADIO (Relative attenuation-dependent image overlay)","volume":"30","author":"Mandell","year":"2017","journal-title":"J Digital Imaging"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000001896","article-title":"False-positive uptake on positron emission tomography\/computed tomography immediately after lung biopsy: A case report","volume":"94","author":"Bae","year":"2015","journal-title":"Medicine"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.1101448","article-title":"Diagnosing lung involvement in inflammatory rheumatic diseases-Where do we currently stand","volume":"9","author":"Hoffmann","year":"2023","journal-title":"Front Med"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J Nucl Med"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2021.103407","article-title":"CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas","volume":"68","author":"Gitto","year":"2021","journal-title":"Ebiomedicine"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.1020349","article-title":"Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer","volume":"12","author":"Ma","year":"2022","journal-title":"Front Oncol"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21147\/j.issn.1000-9604.2019.01.01","article-title":"Chinese guidelines for diagnosis and treatment of primary lung cancer 2018","volume":"31","year":"2019","journal-title":"Chin J Cancer Res"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.3390\/jcm8060821","article-title":"Initial experience in CT-guided percutaneous transthoracic needle biopsy of lung lesions performed by a pulmonologist","volume":"8","author":"Ahn","year":"2019","journal-title":"J Clin Med"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.4103\/jcrt.jcrt_1655_21","article-title":"Magnetic resonance-guided repeat biopsy of suspicious Malignant lung lesions after an initial negative computed tomography-guided Biopsy","volume":"17","author":"Yan","year":"2021","journal-title":"J Cancer Res Ther"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-017-5267-0","article-title":"Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature","volume":"28","author":"Li","year":"2018","journal-title":"Eur Radiol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1701146\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T06:54:26Z","timestamp":1771916066000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1701146\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,15]]},"references-count":36,"alternative-id":["10.3389\/fonc.2025.1701146"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2025.1701146","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,15]]},"article-number":"1701146"}}