{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T04:56:07Z","timestamp":1791608167351,"version":"4.3.4"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"1175","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and validate a CT-based radiomic-clinical-dosimetric model to assess the treatment response of lung metastasis following stereotactic body radiation therapy (SBRT).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Eighty lung metastases treated with SBRT curative intent in a single institution were analysed. The treatment responses of lung lesions were categorized as a complete responding (CR) group vs a non-complete responding (NCR) group according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria. For each lesion, 107 features were extracted from the CT planning images. The least absolute shrinkage and selection operator (LASSO) was used for features selection. An eXtreme Gradient Boosting (XGBoost) model was trained and validated. Shapley additive explanations (SHAP) analysis was used to provide insights into the impact of each variable on the model\u2019s predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Eight radiomic features, 1 dosimetric variable, and no clinical variables were identified by LASSO and used to build the XGBoost model. The model yielded areas under the curve (AUCs) of 0.897 (95% CI 0.860-0.935) and 0.864 (95% CI 0.803-0.924) in the training cohort and validation cohort, respectively. Skewness, surface-to-volume ratio, sphericity, and biological equivalent dose (BED10) were the most significant variables in predicting CR. The SHAP plots illustrated the feature\u2019s global and local impact to the model, explaining the model output in a clinician-friendly way.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The integration of the XGBoost model with the SHAP strategy was able to assess lung lesions CR following SBRT, with the potential to assist clinicians in directing personalized SBRT strategies in an understandable manner.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>The explainable radiomics model we propose can better predict the treatment response of lung metastasis after SBRT and provide further guidance for clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqaf043","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:36:46Z","timestamp":1760103406000},"page":"1988-1996","source":"Crossref","is-referenced-by-count":3,"title":["Radiomics-based explainable artificial intelligence to predict treatment response following lung stereotactic body radiation therapy"],"prefix":"10.1093","volume":"98","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6711-350X","authenticated-orcid":false,"given":"Savino","family":"Cilla","sequence":"first","affiliation":[{"name":"Medical Physics Unit, Responsible Research Hospital , Campobasso 86100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmela","family":"Romano","sequence":"additional","affiliation":[{"name":"Medical Physics Unit, Responsible Research Hospital , Campobasso 86100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriella","family":"Macchia","sequence":"additional","affiliation":[{"name":"Radiation Oncology Unit, Responsible Research Hospital , Campobasso 86100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6676-1169","authenticated-orcid":false,"given":"Donato","family":"Pezzulla","sequence":"additional","affiliation":[{"name":"Radiation Oncology Unit, Responsible Research Hospital , Campobasso 86100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabetta","family":"Lepre","sequence":"additional","affiliation":[{"name":"Istituto di Radiologia, Universit\u00e0 Cattolica del Sacro Cuore , Roma 00100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milly","family":"Buwenge","sequence":"additional","affiliation":[{"name":"Radiation Oncology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna , Bologna 40100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Costanza Maria","family":"Donati","sequence":"additional","affiliation":[{"name":"Radiation Oncology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna , Bologna 40100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erika","family":"Galietta","sequence":"additional","affiliation":[{"name":"Radiation Oncology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna , Bologna 40100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio Giuseppe","family":"Morganti","sequence":"additional","affiliation":[{"name":"Radiation Oncology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna , Bologna 40100,","place":["Italy"]},{"name":"Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum\u2014Bologna University , Bologna 40100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Deodato","sequence":"additional","affiliation":[{"name":"Radiation Oncology Unit, Responsible Research Hospital , Campobasso 86100,","place":["Italy"]},{"name":"Istituto di Radiologia, Universit\u00e0 Cattolica del Sacro Cuore , Roma 00100,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"2025112712224117900_tqaf043-B1","doi-asserted-by":"publisher","first-page":"570","DOI":"10.18632\/oncotarget.2677","article-title":"The landscape of metastatic progression patterns across major human cancers","volume":"6","author":"Budczies","year":"2015","journal-title":"Oncotarget"},{"key":"2025112712224117900_tqaf043-B2","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.radonc.2020.04.003","article-title":"Defining oligometastatic disease from a radiation oncology perspective: an ESTRO-ASTRO consensus document","volume":"148","author":"Lievens","year":"2020","journal-title":"Radiother Oncol"},{"key":"2025112712224117900_tqaf043-B3","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.lungcan.2016.04.012","article-title":"Stereotactic body radiotherapy (SBRT) for medically inoperable lung metastases-a pooled analysis of the German working group \u201cstereotactic radiotherapy\u201d","volume":"97","author":"Rieber","year":"2016","journal-title":"Lung Cancer"},{"key":"2025112712224117900_tqaf043-B4","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.lungcan.2011.04.021","article-title":"Stereotactic body radiation therapy for lung metastases","volume":"75","author":"Ricardi","year":"2012","journal-title":"Lung Cancer"},{"key":"2025112712224117900_tqaf043-B5","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1200\/JCO.2008.19.6386","article-title":"Multi-institutional phase I\/II trial of stereotactic body radiation therapy for lung metastases","volume":"27","author":"Rusthoven","year":"2009","journal-title":"J Clin Oncol"},{"key":"2025112712224117900_tqaf043-B6","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.ijrobp.2014.10.021","article-title":"Stereotactic body radiation therapy for oligometastases to the lung: a phase 2 study","volume":"91","author":"Nuyttens","year":"2015","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"2025112712224117900_tqaf043-B7","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1038\/nrclinonc.2017.166","article-title":"Tumour heterogeneity and resistance to cancer therapies","volume":"15","author":"Dagogo-Jack","year":"2018","journal-title":"Nat Rev Clin Oncol"},{"key":"2025112712224117900_tqaf043-B8","doi-asserted-by":"publisher","first-page":"20190948","DOI":"10.1259\/bjr.20190948","article-title":"Radiomics: from qualitative to quantitative imaging","volume":"93","author":"Rogers","year":"2020","journal-title":"Br J Radiol"},{"key":"2025112712224117900_tqaf043-B9","doi-asserted-by":"publisher","first-page":"20160665","DOI":"10.1259\/bjr.20160665","article-title":"Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures","volume":"90","author":"Larue","year":"2017","journal-title":"Br J Radiol"},{"key":"2025112712224117900_tqaf043-B10","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.acra.2023.03.040","article-title":"Predicting EGFR mutation status in non-small cell lung cancer using artificial intelligence: a systematic review and meta-analysis","volume":"31","author":"Nguyen","year":"2024","journal-title":"Acad Radiol"},{"key":"2025112712224117900_tqaf043-B11","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ejmp.2020.03.018","article-title":"Bridging the gap between micro- and macro-scales in medical imaging with textural analysis\u2014a biological basis for CT radiomics classifiers?","volume":"72","author":"Geady","year":"2020","journal-title":"Phys Med"},{"key":"2025112712224117900_tqaf043-B12","doi-asserted-by":"publisher","first-page":"102546","DOI":"10.1016\/j.ejmp.2023.102546","article-title":"CT-derived radiomic analysis for predicting the survival rate of patients with non-small cell lung cancer receiving radiotherapy","volume":"107","author":"Zhang","year":"2023","journal-title":"Phys Med"},{"key":"2025112712224117900_tqaf043-B13","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.radonc.2018.06.025","article-title":"Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy","volume":"129","author":"Li","year":"2018","journal-title":"Radiother Oncol"},{"key":"2025112712224117900_tqaf043-B14","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1007\/s10278-023-00778-0","article-title":"Development and validation of CT-based radiomics signature for overall survival prediction in multi-organ cancer","volume":"36","author":"Le","year":"2023","journal-title":"J Digit Imaging"},{"key":"2025112712224117900_tqaf043-B15","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1007\/s00066-019-01542-6","article-title":"A radiomic approach to predicting nodal relapse and disease-specific survival in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer","volume":"196","author":"Franceschini","year":"2020","journal-title":"Strahlenther Onkol"},{"key":"2025112712224117900_tqaf043-B16","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.radonc.2016.05.024","article-title":"CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer","volume":"120","author":"Huynh","year":"2016","journal-title":"Radiother Oncol"},{"key":"2025112712224117900_tqaf043-B17","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1007\/s00066-023-02086-6","article-title":"CT-based radiomics prediction of complete response after stereotactic body radiation therapy for patients with lung metastases","volume":"199","author":"Cilla","year":"2023","journal-title":"Strahlenther Onkol"},{"key":"2025112712224117900_tqaf043-B18","doi-asserted-by":"publisher","first-page":"254","DOI":"10.3857\/roj.2021.00311","article-title":"Computed tomography-based radiomic model predicts radiological response following stereotactic body radiation therapy in early-stage non-small-cell lung cancer and pulmonary oligo-metastases","volume":"39","author":"Cheung","year":"2021","journal-title":"Radiat Oncol J"},{"key":"2025112712224117900_tqaf043-B19","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1002\/mp.13570","article-title":"Machine learning for radiation outcome modeling and prediction","volume":"47","author":"Luo","year":"2020","journal-title":"Med Phys"},{"key":"2025112712224117900_tqaf043-B20","first-page":"4768","author":"Lundberg","year":"2017"},{"key":"2025112712224117900_tqaf043-B21","first-page":"785","author":"Chen","year":"2016"},{"key":"2025112712224117900_tqaf043-B22","doi-asserted-by":"publisher","first-page":"320","DOI":"10.25007\/ajnu.v12n2a1612","article-title":"Exploring the power of eXtreme gradient boosting algorithm in machine learning: a review","volume":"12","author":"Ali","year":"2023","journal-title":"Acad J Nawroz University"},{"key":"2025112712224117900_tqaf043-B23","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20180422","volume-title":"Br J Radiol","author":"Deodato","year":"2019"},{"key":"2025112712224117900_tqaf043-B24","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1093\/jnci\/92.3.205","article-title":"New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada","volume":"92","author":"Therasse","year":"2000","journal-title":"J Natl Cancer Inst"},{"key":"2025112712224117900_tqaf043-B25","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"van Griethuysen","year":"2017","journal-title":"Cancer Res"},{"key":"2025112712224117900_tqaf043-B26","doi-asserted-by":"publisher","first-page":"328","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":"2025112712224117900_tqaf043-B27","volume-title":"WHO Guidance on Ethics and Governance of Artificial Intelligence for Health","author":"World Health Organization","year":"2021"},{"key":"2025112712224117900_tqaf043-B28","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ygyno.2024.01.023","article-title":"Efficacy of stereotactic body radiotherapy and response prediction using artificial intelligence in oligometastatic gynaecologic cancer","volume":"184","author":"Macchia","year":"2024","journal-title":"Gynecol Oncol"},{"key":"2025112712224117900_tqaf043-B29","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.clinimag.2020.12.01731.1371\/journal.pone.0206108","article-title":"Quantifying intratumor heterogeneity of lung neoplasms with radiomics","volume":"74","author":"Ferreira Junior","year":"2021","journal-title":"Clin Imaging"},{"key":"2025112712224117900_tqaf043-B30","doi-asserted-by":"publisher","first-page":"2023042","DOI":"10.1259\/bjr.2023042","article-title":"Interpretable artificial intelligence in radiology and radiation oncology","volume":"96","author":"Cui","year":"2023","journal-title":"Br J Radiol"},{"key":"2025112712224117900_tqaf043-B31","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1007\/s00066-019-01452-7","article-title":"Radiomic analysis of planning computed tomograms for predicting radiation induced lung injury and outcome in lung cancer patients treated with robotic stereotactic body radiation therapy","volume":"195","author":"Bousabarah","year":"2019","journal-title":"Strahlenther Onkol"},{"key":"2025112712224117900_tqaf043-B32","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.radonc.2016.04.004","article-title":"Radiomic phenotype features predict pathological response in non-small cell lung cancer","volume":"119","author":"Coroller","year":"2016","journal-title":"Radiother Oncol"},{"key":"2025112712224117900_tqaf043-B33","doi-asserted-by":"publisher","first-page":"e88598","DOI":"10.1371\/journal.pone.0088598","article-title":"Quantitative CT variables enabling response prediction in neoadjuvant therapy with EGFR TKIs: are they different from those in neoadjuvant concurrent chemoradiotherapy?","volume":"9","author":"Chong","year":"2014","journal-title":"PLoS One"},{"key":"2025112712224117900_tqaf043-B34","doi-asserted-by":"publisher","first-page":"109874","DOI":"10.1016\/j.ejrad.2021.109874","article-title":"Radiomics and functional imaging in lung cancer: the importance of radiological heterogeneity beyond FDG PET\/CT and lung biopsy","volume":"142","author":"Caruso","year":"2021","journal-title":"Eur J Radiol"},{"key":"2025112712224117900_tqaf043-B35","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1245\/s10434-012-2754-z","article-title":"KRAS mutation in patients with lung cancer: a predictor for poor prognosis but not for EGFR KIs or chemotherapy","volume":"20","author":"Guan","year":"2013","journal-title":"Ann Surg Oncol"},{"key":"2025112712224117900_tqaf043-B36","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.radonc.2019.08.003","article-title":"Is tumour sphericity an important prognostic factor in patients with lung cancer?","volume":"143","author":"Davey","year":"2020","journal-title":"Radiother Oncol"},{"key":"2025112712224117900_tqaf043-B37","doi-asserted-by":"publisher","first-page":"100626","DOI":"10.1016\/j.phro.2024.100626","article-title":"Delta radiomics to track radiation response in lung tumors receiving stereotactic magnetic resonance-guided radiotherapy","volume":"31","author":"Zha","year":"2024","journal-title":"Phys Imaging Radiat Oncol"},{"key":"2025112712224117900_tqaf043-B38","doi-asserted-by":"publisher","first-page":"955","DOI":"10.21037\/tlcr-21-44","article-title":"A machine learning-based prediction of the micropapillary\/solid growth pattern in invasive lung adenocarcinoma with radiomics","volume":"10","author":"He","year":"2021","journal-title":"Transl Lung Cancer Res"},{"key":"2025112712224117900_tqaf043-B39","doi-asserted-by":"publisher","first-page":"3650","DOI":"10.1007\/s00330-020-06776-y","article-title":"Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction","volume":"30","author":"Sun","year":"2020","journal-title":"Eur Radiol"},{"key":"2025112712224117900_tqaf043-B40","doi-asserted-by":"publisher","first-page":"19062","DOI":"10.1038\/s41598-023-46391-7","article-title":"Radiomics as a non-invasive adjunct to Chest CT in distinguishing benign and malignant lung nodules","volume":"13","author":"Selvam","year":"2023","journal-title":"Sci Rep"},{"key":"2025112712224117900_tqaf043-B41","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1093\/annonc\/mdz108","article-title":"Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers","volume":"30","author":"Trebeschi","year":"2019","journal-title":"Ann Oncol"},{"key":"2025112712224117900_tqaf043-B42","doi-asserted-by":"publisher","first-page":"LMT38","DOI":"10.2217\/lmt-2020-0002","article-title":"Computed tomography texture analysis of response to second-line nivolumab in metastatic non-small cell lung cancer","volume":"9","author":"Ladwa","year":"2020","journal-title":"Lung Cancer Manag"},{"key":"2025112712224117900_tqaf043-B43","doi-asserted-by":"publisher","first-page":"e100244","DOI":"10.1371\/journal.pone.0100244","article-title":"Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic","volume":"9","author":"Weiss","year":"2014","journal-title":"PLoS One"},{"key":"2025112712224117900_tqaf043-B44","doi-asserted-by":"crossref","first-page":"S94","DOI":"10.1097\/JTO.0b013e318074de34","article-title":"Hypofractionated stereotactic radiotherapy (HypoFXSRT) for stage I non-small cell lung cancer: updated results of 257 patients in a Japanese multi-institutional study","volume":"2","author":"Onishi","year":"2007","journal-title":"J Thorac Oncol"},{"key":"2025112712224117900_tqaf043-B45","doi-asserted-by":"publisher","first-page":"2242","DOI":"10.1002\/cncr.30058","article-title":"Clinical and molecular markers of long-term survival after oligometastasis-directed stereotactic body radiotherapy (SBRT)","volume":"122","author":"Wong","year":"2016","journal-title":"Cancer"}],"container-title":["British Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bjr\/advance-article-pdf\/doi\/10.1093\/bjr\/tqaf043\/64612819\/tqaf043.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/98\/1175\/1988\/64612819\/tqaf043.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/98\/1175\/1988\/64612819\/tqaf043.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T17:22:49Z","timestamp":1764264169000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bjr\/article\/98\/1175\/1988\/8280405"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,10]]},"references-count":45,"journal-issue":{"issue":"1175","published-online":{"date-parts":[[2025,10,10]]},"published-print":{"date-parts":[[2025,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bjr\/tqaf043","relation":{},"ISSN":["0007-1285","1748-880X"],"issn-type":[{"value":"0007-1285","type":"print"},{"value":"1748-880X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,11]]},"published":{"date-parts":[[2025,10,10]]}}}