{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T06:38:36Z","timestamp":1777012716804,"version":"3.51.4"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"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. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Differentiating sellar region germ cell tumors (GCTs) from Langerhans cell histiocytosis (LCH) is challenging due to highly similar MRI features, especially in tumor marker-negative patients. In this study, we aimed to develop and validate a radiomics model to distinguish tumor marker-negative sellar GCTs from LCH.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective study enrolled a total of 93 patients diagnosed pathologically or by therapeutic diagnosis at our single institution between April 2012 and April 2024, including 40 cases of LCH and 53 cases of GCTs. Radiomics features extracted from multiparametric MRI, including T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). We manually segmented the regions of interests (ROIs) of tumors. Feature selection was subsequently performed using LASSO regression with five-fold cross-validation. We have chosen three machine learning classifiers-Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) to construct models based on the 7 features which were retained. Additionally, by integrating clinically significant features and imaging semantic features, classification models based on radiomics features, imaging semantic features, and clinical features were developed separately. There are 7 models in total. Furthermore, combined prediction models were constructed based on different fusion feature sets, respectively. The performance of the diagnostic model was evaluated using the receiver operating characteristic (ROC) curve. The mean area under the curve (AUC), sensitivity, specificity, accuracy, and F1-score were calculated for both the development set and the test set. Differences in AUC between models were assessed using DeLong's test, and the resulting\n                      <jats:italic>P<\/jats:italic>\n                      -values were adjusted using the Bonferroni false discovery rate (FDR) correction method. Code available upon request.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The best diagnostic performance was achieved by the combined model of radiomics with clinical features and imaging semantic features using the RF classifier, with an AUC value of 0.81. A statistically significant difference (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.05) was confirmed by the DeLong test, indicating robust diagnostic capability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Radiomics-based machine learning is a promising, non-invasive approach to distinguish tumor marker-negative sellar GCTs from LCH, which has good predictive performance and may help with treatment decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2026.1775150","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T05:43:35Z","timestamp":1777009415000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning based on radiomics for discriminating sellar region langerhans cell histiocytosis from germ cell tumors"],"prefix":"10.3389","volume":"14","author":[{"given":"Hongting","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Radiology, Children\u2019s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents\u2019 Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders","place":["Chongqing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zanyong","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Radiology, Children\u2019s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents\u2019 Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders","place":["Chongqing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Children\u2019s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents\u2019 Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders","place":["Chongqing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenxian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Children\u2019s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents\u2019 Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders","place":["Chongqing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanxue","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Children\u2019s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents\u2019 Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders","place":["Chongqing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lusheng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Children\u2019s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents\u2019 Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders","place":["Chongqing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Children\u2019s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents\u2019 Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders","place":["Chongqing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"946","DOI":"10.3171\/jns.1996.84.6.0946","article-title":"Epidemiological study of primary intracranial tumors: a regional survey in kumamoto prefecture in the southern part of Japan","volume":"84","author":"Kuratsu","year":"1996","journal-title":"J Neurosurg"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e470","DOI":"10.1016\/S1470-2045(15)00244-2","article-title":"Consensus on the management of intracranial germ-cell tumours","volume":"16","author":"Murray","year":"2015","journal-title":"Lancet Oncol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s00401-007-0243-4","article-title":"The 2007 WHO classification of tumours of the central nervous system","volume":"114","author":"Louis","year":"2007","journal-title":"Acta Neuropathol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1016\/j.jaad.2017.05.059","article-title":"Langerhans cell histiocytosis in children: history, classification, pathobiology, clinical manifestations, and prognosis","volume":"78","author":"Krooks","year":"2018","journal-title":"J Am Acad Dermatol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1002\/pbc.24367","article-title":"Langerhans cell histiocytosis (LCH): guidelines for diagnosis, clinical work-up, and treatment for patients till the age of 18 years","volume":"60","author":"Haupt","year":"2013","journal-title":"Pediatr Blood Cancer"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1093\/neuonc\/noab107","article-title":"Histiocytosis and the nervous system: from diagnosis to targeted therapies","volume":"23","author":"Cohen Aubart","year":"2021","journal-title":"Neuro Oncol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s11060-017-2513-x","article-title":"MRI Features of pediatric intracranial germ cell tumor subtypes","volume":"134","author":"Wu","year":"2017","journal-title":"J Neurooncol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3109\/0284186X.2011.586000","article-title":"Intracranial germ cell tumours. A review with special reference to endocrine manifestations","volume":"51","author":"Jorsal","year":"2012","journal-title":"Acta Oncol"},{"key":"B9","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3389\/fendo.2020.00076","article-title":"Clinical characteristics of pediatric patients with sellar and suprasellar lesions who initially present with central diabetes insipidus: a retrospective study of 55 cases from a large pituitary center in China","volume":"11","author":"Ji","year":"2020","journal-title":"Front Endocrinol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s40001-016-0204-2","article-title":"An update on the clinical diagnostic value of \u03b2-hCG and \u03b1FP for intracranial germ cell tumors","volume":"21","author":"Hu","year":"2016","journal-title":"Eur J Med Res"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1007\/s00381-012-1762-4","article-title":"Diagnostic utility and correlation of tumor markers in the serum and cerebrospinal fluid of children with intracranial germ cell tumors","volume":"28","author":"Qaddoumi","year":"2012","journal-title":"Childs Nerv Syst"},{"key":"B12","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1093\/neuonc\/noab252","article-title":"EANO, SNO and euracan consensus review on the current management and future development of intracranial germ cell tumors in adolescents and young adults","volume":"24","author":"Frappaz","year":"2022","journal-title":"Neuro Oncol"},{"key":"B13","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.jcms.2013.03.005","article-title":"Langerhans cell histiocytosis in childhood - review, symptoms in the oral cavity, differential diagnosis and report of two cases","volume":"42","author":"Merglov\u00e1","year":"2014","journal-title":"J Craniomaxillofac Surg"},{"key":"B14","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s00234-017-1928-6","article-title":"Loco-regional extensions of central nervous system germ cell tumors: a retrospective radiological analysis of 100 patients","volume":"60","author":"Duron","year":"2018","journal-title":"Neuroradiology"},{"key":"B15","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.lpm.2016.09.014","article-title":"Neuro-Langerhans cell histiocytosis","volume":"46","author":"Le Guennec","year":"2017","journal-title":"Presse Med"},{"key":"B16","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1093\/neuonc\/not019","article-title":"SIOP CNS GCT 96: final report of outcome of a prospective, multinational nonrandomized trial for children and adults with intracranial germinoma, comparing craniospinal irradiation alone with chemotherapy followed by focal primary site irradiation for patients with localized disease","volume":"15","author":"Calaminus","year":"2013","journal-title":"Neuro Oncol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"2908","DOI":"10.1200\/JCO.1996.14.11.2908","article-title":"Chemotherapy without irradiation\u2013a novel approach for newly diagnosed CNS germ cell tumors: results of an international cooperative trial. The first international central nervous system germ cell tumor study","volume":"14","author":"Balmaceda","year":"1996","journal-title":"J Clin Oncol"},{"key":"B18","doi-asserted-by":"publisher","first-page":"441","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":"B19","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"B20","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.crad.2022.08.149","article-title":"Introduction to radiomics for a clinical audience","volume":"78","author":"McCague","year":"2023","journal-title":"Clin Radiol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"801","DOI":"10.3760\/cma.j.issn.0253-3766.2018.11.001","article-title":"Radiomics: the process and applications in tumor research","volume":"40","author":"Li","year":"2018","journal-title":"Zhonghua Zhong Liu Za Zhi"},{"key":"B22","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: the bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat Rev Clin Oncol"},{"key":"B23","doi-asserted-by":"publisher","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J Nucl Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"90352B","DOI":"10.1117\/12.2043969","article-title":"Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI","volume":"9035","author":"Pallavi","year":"2014","journal-title":"Proc SPIE Int Soc Opt Eng"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.21037\/tp-2025-210","article-title":"Application of machine learning based on radiomics in the discrimination of intracranial germ cell tumours","volume":"14","author":"Tong","year":"2025","journal-title":"Transl Pediatr"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e0255500","DOI":"10.1371\/journal.pone.0255500","article-title":"Magnetic resonance radiomics features and prognosticators in different molecular subtypes of pediatric medulloblastoma","volume":"16","author":"Chang","year":"2021","journal-title":"PLoS One"},{"key":"B27","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s13244-023-01415-8","article-title":"Checklist for EvaluAtion of radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII","volume":"14","author":"Kocak","year":"2023","journal-title":"Insights Imaging"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1153784","DOI":"10.3389\/fradi.2023.1153784","article-title":"uRP: an integrated research platform for one-stop analysis of medical images","volume":"3","author":"Wu","year":"2023","journal-title":"Front Radiol"},{"key":"B29","volume-title":"_R: A language and environment for statistical computing_. [Internet]","year":"2024"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12010045","article-title":"Differentiation between germinoma and craniopharyngioma using radiomics-based machine learning","volume":"12","author":"Chen","year":"2022","journal-title":"J Pers Med"},{"key":"B31","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.radonc.2022.01.005","article-title":"Non-invasive preoperative imaging differential diagnosis of pineal region tumor: a novel developed and validated multiparametric MRI-based clinicoradiomic model","volume":"167","author":"Fan","year":"2022","journal-title":"Radiother Oncol"},{"key":"B32","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s13244-012-0196-6","article-title":"Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?","volume":"3","author":"Davnall","year":"2012","journal-title":"Insights Imaging"},{"key":"B33","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/s11060-018-2984-4","article-title":"Diagnostic accuracy of MRI texture analysis for grading gliomas","volume":"140","author":"Ditmer","year":"2018","journal-title":"J Neurooncol"},{"key":"B34","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1016\/j.ejrad.2016.01.013","article-title":"Diagnostic performance of texture analysis on MRI in grading cerebral gliomas","volume":"85","author":"Skogen","year":"2016","journal-title":"Eur J Radiol"},{"key":"B35","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1016\/j.jtho.2016.11.2230","article-title":"Imaging phenotyping using radiomics to predict micropapillary pattern within lung adenocarcinoma","volume":"12","author":"Song","year":"2017","journal-title":"J Thorac Oncol"},{"key":"B36","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1002\/jmri.26243","article-title":"Prostate cancer differentiation and aggressiveness: assessment with a radiomic-based model vs. PI-RADS v2","volume":"49","author":"Chen","year":"2019","journal-title":"J Magn Reson Imaging"},{"key":"B37","doi-asserted-by":"publisher","first-page":"3922","DOI":"10.1158\/0008-5472.CAN-17-0122","article-title":"Somatic mutations drive distinct imaging phenotypes in lung cancer","volume":"77","author":"Rios Velazquez","year":"2017","journal-title":"Cancer Res"}],"container-title":["Frontiers in Pediatrics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fped.2026.1775150\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T05:43:36Z","timestamp":1777009416000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fped.2026.1775150\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":37,"alternative-id":["10.3389\/fped.2026.1775150"],"URL":"https:\/\/doi.org\/10.3389\/fped.2026.1775150","relation":{},"ISSN":["2296-2360"],"issn-type":[{"value":"2296-2360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"article-number":"1775150"}}