{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T13:17:19Z","timestamp":1792675039264,"version":"4.5.0"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["PREDICT - ITN - No. 766276"],"award-info":[{"award-number":["PREDICT - ITN - No. 766276"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["ImmunoSABR no. 733008, FETOPEN- SCANnTREAT no. 899549, CHAIMELEON no. 952172, EuCanImage no. 952103"],"award-info":[{"award-number":["ImmunoSABR no. 733008, FETOPEN- SCANnTREAT no. 899549, CHAIMELEON no. 952172, EuCanImage no. 952103"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013297","name":"Eurostars","doi-asserted-by":"publisher","award":["DART, DECIDE, COMPACT-12053"],"award-info":[{"award-number":["DART, DECIDE, COMPACT-12053"]}],"id":[{"id":"10.13039\/100013297","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["ERC-ADG-2015 no. 694812, ERC-2018-PoC: 813200-CL-IO, ERC-2020-PoC: 957565-AUTO.DISTINCT"],"award-info":[{"award-number":["ERC-ADG-2015 no. 694812, ERC-2018-PoC: 813200-CL-IO, ERC-2020-PoC: 957565-AUTO.DISTINCT"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011202","name":"FP7 Research for the Benefit of SMEs","doi-asserted-by":"publisher","award":["RAIL no.673780"],"award-info":[{"award-number":["RAIL no.673780"]}],"id":[{"id":"10.13039\/100011202","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013276","name":"Interreg","doi-asserted-by":"publisher","award":["EURADIOMICS no. EMR4"],"award-info":[{"award-number":["EURADIOMICS no. EMR4"]}],"id":[{"id":"10.13039\/100013276","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004622","name":"KWF Kankerbestrijding","doi-asserted-by":"publisher","award":["12085\/2018\u20132"],"award-info":[{"award-number":["12085\/2018\u20132"]}],"id":[{"id":"10.13039\/501100004622","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>There is a cumulative risk of 20\u201340% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (\n                      <jats:italic>N<\/jats:italic>\n                      = 1,404) and a validation cohort (\n                      <jats:italic>N<\/jats:italic>\n                      = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2022.920393","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T02:12:05Z","timestamp":1657678325000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics"],"prefix":"10.3389","volume":"12","author":[{"given":"Simon A.","family":"Keek","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manon","family":"Beuque","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergey","family":"Primakov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry C.","family":"Woodruff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Avishek","family":"Chatterjee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janita E.","family":"van Timmeren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Valli\u00e8res","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizza E. L.","family":"Hendriks","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Kraft","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolaus","family":"Andratschke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve E.","family":"Braunstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Morin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Lambin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1212\/wnl.35.2.219","article-title":"Epidemiology of Brain Tumors: The National Survey of Intracranial Neoplasms","volume":"35","author":"Walker","year":"1985","journal-title":"Neurology"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/s1042-3680(18)30365-6","article-title":"Demographics of Brain Metastasis","volume":"7","author":"Johnson","year":"1996","journal-title":"Neurosurg Clinics North America"},{"key":"B3","first-page":"941","article-title":"Management of Brain Metastases","volume":"13","author":"Wen","year":"1999","journal-title":"Oncology"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.10541","article-title":"Incidence of Brain Metastases in a Cohort of Patients With Carcinoma of the Breast, Colon, Kidney, and Lung and Melanoma","volume":"94","author":"Schouten","year":"2002","journal-title":"Cancer"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2004.12.149","article-title":"Incidence Proportions of Brain Metastases in Patients Diagnosed, (1993 to 2001) in the Metropolitan Detroit Cancer Surveillance System","volume":"22","author":"Barnholtz-Sloan","year":"2004","journal-title":"J Clin Oncol: Off J Am Soc Clin Oncol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.lungcan.2015.01.020","article-title":"Brain Metastases in Patients With EGFR -Mutated or ALK -Rearranged non-Small-Cell Lung Cancers","volume":"88","author":"Rangachari","year":"2015","journal-title":"Lung Cancer"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtho.2019.11.004","article-title":"Brigatinib in Crizotinib-Refractory ALK+ NSCLC: 2-Year Follow-Up on Systemic and Intracranial Outcomes in the Phase 2 ALTA Trial","volume":"15","author":"Huber","year":"2020","journal-title":"J Thorac Oncol: Off Publ Int Assoc Study Lung Cancer"},{"key":"B8","article-title":"Systemic Therapy for Brain Metastases","volume-title":"Handbook of Clinical Neurology","author":"Venur","year":""},{"key":"B9","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1200\/JCO.21.02314","article-title":"Treatment for Brain Metastases: ASCO-SNO-ASTRO Guideline","volume":"40","author":"Vogelbaum","year":"2022","journal-title":"J Clin Oncol: Off J Am Soc Clin Oncol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.4103\/2152-7806.111301","article-title":"Whole Brain Radiotherapy for Brain Metastasis","volume":"4","author":"McTyre","year":"2013","journal-title":"Surg Neurol Int"},{"key":"B11","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/s11940-019-0548-3","article-title":"Stereotactic Radiosurgery for Multiple Brain Metastases","volume":"21","author":"Kraft","year":"2019","journal-title":"Curr Treat Options Neurol"},{"key":"B12","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11060-021-03714-w","article-title":"Management of Multiple Brain Metastases: A Patterns of Care Survey Within the German Society for Radiation Oncology","volume":"152","author":"Kraft","year":"2021","journal-title":"J Neuro Oncol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1200\/JOP.2016.012922","article-title":"Stereotactic Radiosurgery for Treatment of Brain Metastases","volume":"12","author":"Badiyan","year":"2016","journal-title":"J Oncol Pract \/ Am Soc Clin Oncol"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.2217\/fon.13.271","article-title":"Postradiation Imaging Changes in the CNS: How can We Differentiate Between Treatment Effect and Disease Progression","volume":"10","author":"Walker","year":"2014","journal-title":"Future Oncol"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.3171\/2014.10.JNS141610","article-title":"Adverse Radiation Effect After Stereotactic Radiosurgery for Brain Metastases: Incidence, Time Course, and Risk Factors","volume":"123","author":"Sneed","year":"2015","journal-title":"J Neurosurg"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3171\/jns.2002.97.supplement_5.0515","article-title":"Gamma Knife Radiosurgery for Brain Metastases: A Primary Therapeutic Option","volume":"97","author":"Gerosa","year":"2002","journal-title":"J Neurosurg"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.02.091","article-title":"Radiation Dose\u2013Volume Effects in the Brain","volume":"76","author":"Lawrence","year":"2010","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B18","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s11060-014-1388-3","article-title":"Fractionated Stereotactic Radiosurgery for Patients With Brain Metastases","volume":"117","author":"Minniti","year":"2014","journal-title":"J Neuro Oncol"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2018.00395","article-title":"Diagnosis and Management of Radiation Necrosis in Patients With Brain Metastases","volume":"8","author":"Vellayappan","year":"2018","journal-title":"Front Oncol"},{"key":"B20","doi-asserted-by":"publisher","first-page":"499","DOI":"10.3171\/jns.2002.97.supplement_5.0499","article-title":"Survival and Pattern of Failure in Brain Metastasis Treated With Stereotactic Gamma Knife Radiosurgery","volume":"97","author":"Petrovich","year":"2002","journal-title":"J Neurosurg"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting More Information From Medical Images Using Advanced Feature Analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer"},{"key":"B22","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding Tumour Phenotype by Noninvasive Imaging Using a Quantitative Radiomics Approach","author":"Aerts","year":"2014","journal-title":"Nat Commun 5"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.a5391","article-title":"Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches","volume":"39","author":"Zhou","year":"2018","journal-title":"Am J Neuroradiol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1093\/noajnl\/vdz011","article-title":"Integrated Models Incorporating Radiologic and Radiomic Features Predict Meningioma Grade, Local Failure, and Overall Survival","volume":"1","author":"Morin","year":"2019","journal-title":"Neuro Oncol Adv"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13678","article-title":"Machine and Deep Learning Methods for Radiomics","volume":"47","author":"Avanzo","year":"2020","journal-title":"Med Physics"},{"key":"B26","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":"B27","first-page":"36","article-title":"Investigating a Quantitative Radiomics Approach for Brain Tumor Classification. In","volume-title":"Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging. Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, SPIE","author":"Abidin","year":"2019"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06460-w","article-title":"Differentiation of Supratentorial Single Brain Metastasis and Glioblastoma by Using Peri-Enhancing Oedema Region-Derived Radiomic Features and Multiple Classifiers","volume":"30","author":"Dong","year":"2020","journal-title":"Eur Radiol"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1007\/s11060-019-03343-4","article-title":"Radiomics as Prognostic Factor in Brain Metastases Treated With Gamma Knife Radiosurgery","volume":"146","author":"Huang","year":"2020","journal-title":"J Neuro Oncol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1093\/neuonc\/noaa007","article-title":"Use of Radiomics for the Prediction of Local Control of Brain Metastases After Stereotactic Radiosurgery","volume":"22","author":"Mouraviev","year":"2020","journal-title":"Neuro-oncology"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5463-6","article-title":"Classifying Brain Metastases by Their Primary Site of Origin Using a Radiomics Approach Based on Texture Analysis: A Feasibility Study","volume":"28","author":"Ortiz-Ram\u00f3n","year":"2018","journal-title":"Eur Radiol"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180946","article-title":"Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type","volume":"28","author":"Kniep","year":"2019","journal-title":"Radiology"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noz141","article-title":"MRI Radiomic Features are Associated With Survival in Melanoma Brain Metastases Treated With Immune Checkpoint Inhibitors","volume":"21","author":"Bhatia","year":"2019","journal-title":"Neuro-oncology"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1177\/0284185119831692","article-title":"A 3D Quantitative Imaging Biomarker in Pre-Treatment MRI Predicts Overall Survival After Stereotactic Radiation Therapy of Patients With a Singular Brain Metastasis","volume":"60","author":"Della Seta","year":"2019","journal-title":"Acta Radiol"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.739639","article-title":"Deep Learning-Based Computer-Aided Detection System for Automated Treatment Response Assessment of Brain Metastases on 3D MRI","volume":"11","author":"Cho","year":"2021","journal-title":"Front Oncol"},{"key":"B36","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1080\/23808993.2019.1585805","article-title":"Deep Learning and Radiomics in Precision Medicine","volume":"4","author":"Parekh","year":"2019","journal-title":"Expert Rev Precis Med Drug Dev"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2046908","article-title":"N4ITK: Improved N3 Bias Correction","volume":"29","author":"Tustison","year":"2010","journal-title":"IEEE Trans Med Imaging"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-32390-2_64","article-title":"Bias Field Correction for MRI Images","author":"Juntu","year":"2005","journal-title":"Adv Soft Computing"},{"key":"B39","doi-asserted-by":"publisher","first-page":"165011","DOI":"10.1088\/1361-6560\/ab2f44","article-title":"Impact of Image Preprocessing on the Scanner Dependence of Multi-Parametric MRI Radiomic Features and Covariate Shift in Multi-Institutional Glioblastoma Datasets","volume":"64","author":"Um","year":"2019","journal-title":"Phys Med Biol"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12795","article-title":"Impact of Image Preprocessing Methods on Reproducibility of Radiomic Features in Multimodal Magnetic Resonance Imaging in Glioblastoma","volume":"21","author":"Moradmand","year":"2020","journal-title":"J Appl Clin Med Phys \/ Am Coll Med Phys"},{"key":"B41","doi-asserted-by":"publisher","first-page":"010901","DOI":"10.1117\/1.JMI.8.1.010901","article-title":"Quick Guide on Radiology Image Pre-Processing for Deep Learning Applications in Prostate Cancer Research","volume":"8","author":"Masoudi","year":"2021","journal-title":"J Med Imaging (Bellingham Wash)"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0213459","article-title":"Gray-Level Discretization Impacts Reproducible MRI Radiomics Texture Features","volume":"14","author":"Duron","year":"2019","journal-title":"PLoS One"},{"key":"B43","doi-asserted-by":"publisher","first-page":"12340","DOI":"10.1038\/s41598-020-69298-z","article-title":"Standardization of Brain MR Images Across Machines and Protocols: Bridging the Gap for MRI-Based Radiomics","volume":"10","author":"Carr\u00e9","year":"2020","journal-title":"Sci Rep"},{"key":"B44","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":"B45","unstructured":"Radiomic Features \u2014 Pyradiomics V3.0.1.Post9+Gdfe2c14 Documentation2019"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1109\/trpms.2019.2893860","article-title":") Creating Robust Predictive Radiomic Models for Data From Independent Institutions Using Normalization","volume":"3","author":"Chatterjee","year":"2019","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195","article-title":"Xception: Deep Learning With Depthwise Separable Convolutions","author":"Chollet","year":"2017","journal-title":"Proc IEEE Conf Comput Vision Pattern Recognition"},{"key":"B48","volume-title":"Adam: A Method for Stochastic Optimization","author":"Kingma","year":"2014"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for Rating Diagnostic Tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"key":"B50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1200\/EDBK_352320","article-title":"Emerging Systemic Treatment Perspectives on Brain Metastases: Moving Toward a Better Outlook for Patients","volume":"42","author":"Alvarez-Breckenridge","year":"2022","journal-title":"Am Soc Clin Oncol Educ Book Am Soc Clin Oncol Annu Meeting"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-017-5154-8","article-title":"A Predictive Model for Distinguishing Radiation Necrosis From Tumour Progression After Gamma Knife Radiosurgery Based on Radiomic Features From MR Images","volume":"28","author":"Zhang","year":"2018","journal-title":"Eur Radiol"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.05.041","article-title":"Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics","volume":"102","author":"Peng","year":"2018","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B53","doi-asserted-by":"publisher","first-page":"2913","DOI":"10.1038\/s41598-021-82467-y","article-title":"Differentiation of Recurrent Glioblastoma From Radiation Necrosis Using Diffusion Radiomics With Machine Learning Model Development and External Validation","volume":"11","author":"Park","year":"2021","journal-title":"Sci Rep"},{"key":"B54","doi-asserted-by":"publisher","first-page":"101275","DOI":"10.1016\/j.tranon.2021.101275","article-title":"The Role of Feature-Based Radiomics for Predicting Response and Radiation Injury After Stereotactic Radiation Therapy for Brain Metastases: A Critical Review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)","volume":"15","author":"Salvestrini","year":"2022","journal-title":"Trans Oncol"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2015.60.9503","article-title":"Treatment of Brain Metastases","volume":"33","author":"Lin","year":"2015","journal-title":"J Clin Oncol: Off J Am Soc Clin Oncol"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2019.00269","article-title":"Dosiomics: Extracting 3d Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis","volume":"9","author":"Liang","year":"2019","journal-title":"Front Oncol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2022.920393\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T02:12:20Z","timestamp":1657678340000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2022.920393\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,13]]},"references-count":56,"alternative-id":["10.3389\/fonc.2022.920393"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2022.920393","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,13]]},"article-number":"920393"}}