{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T20:14:46Z","timestamp":1789071286529,"version":"build-2803163510"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T00:00:00Z","timestamp":1710547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T00:00:00Z","timestamp":1710547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cancer Res Clin Oncol"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>To develop a radiomics model based on diffusion-weighted imaging (DWI) utilizing automated machine learning method to differentiate cerebral cystic metastases from brain abscesses.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>A total of 186 patients with cerebral cystic metastases (<jats:italic>n<\/jats:italic>\u2009=\u200998) and brain abscesses (<jats:italic>n<\/jats:italic>\u2009=\u200988) from two clinical institutions were retrospectively included. The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were extracted from DWI images using two subregions of the lesion (cystic core and solid wall). A thorough image preprocessing method was applied to DWI images to ensure the robustness of radiomics features before feature extraction. Then the Tree-based Pipeline Optimization Tool (TPOT) was utilized to search for the best optimized machine learning pipeline, using a fivefold cross-validation in the training set. The external test set (57 from institution B) was used to evaluate the model\u2019s performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Seven distinct TPOT models were optimized to distinguish between cerebral cystic metastases and abscesses either based on different features combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in the internal test set, based on the combination of cystic core and solid wall radiomics signature using wavelet transform. In the external test set, this model reached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The DWI-based radiomics model established by TPOT\u00a0exhibits a promising predictive capacity in distinguishing cerebral cystic metastases from abscesses.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00432-024-05642-4","type":"journal-article","created":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T09:02:03Z","timestamp":1710579723000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses"],"prefix":"10.1007","volume":"150","author":[{"given":"Linyang","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyuan","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dexin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,16]]},"reference":[{"issue":"1","key":"5642_CR1","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1186\/s13244-021-01102-6","volume":"12","author":"AAK Abdel Razek","year":"2021","unstructured":"Abdel Razek AAK, Alksas A, Shehata M et al (2021) Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 12(1):152. https:\/\/doi.org\/10.1186\/s13244-021-01102-6","journal-title":"Insights Imaging"},{"issue":"10","key":"5642_CR2","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1093\/neuonc\/noac118","volume":"24","author":"AA Aizer","year":"2022","unstructured":"Aizer AA, Lamba N, Ahluwalia MS et al (2022) Brain metastases: a Society for Neuro-Oncology (SNO) consensus review on current management and future directions. Neuro Oncol 24(10):1613\u20131646. https:\/\/doi.org\/10.1093\/neuonc\/noac118","journal-title":"Neuro Oncol"},{"issue":"7","key":"5642_CR3","first-page":"428","volume":"22","author":"MS Alam","year":"2012","unstructured":"Alam MS, Sajjad Z, Azeemuddin M, Khan ZA, Mubarak F, Akhtar W (2012) Diffusion weighted MR imaging of ring enhancing brain lesions. J Coll Physicians Surg Pak 22(7):428\u2013431","journal-title":"J Coll Physicians Surg Pak"},{"issue":"4","key":"5642_CR4","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1093\/brain\/awac312","volume":"146","author":"J Bodilsen","year":"2023","unstructured":"Bodilsen J, Duerlund LS, Mariager T et al (2023) Clinical features and prognostic factors in adults with brain abscess. Brain 146(4):1637\u20131647. https:\/\/doi.org\/10.1093\/brain\/awac312","journal-title":"Brain"},{"key":"5642_CR5","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac7fd7","author":"Y Cui","year":"2022","unstructured":"Cui Y, Yin FF (2022) Impact of image quality on radiomics applications. Phys Med Biol. https:\/\/doi.org\/10.1088\/1361-6560\/ac7fd7","journal-title":"Phys Med Biol"},{"issue":"1","key":"5642_CR6","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.ejrad.2009.03.004","volume":"74","author":"G Duygulu","year":"2010","unstructured":"Duygulu G, Ovali GY, Calli C et al (2010) Intracerebral metastasis showing restricted diffusion: correlation with histopathologic findings. Eur J Radiol 74(1):117\u2013120. https:\/\/doi.org\/10.1016\/j.ejrad.2009.03.004","journal-title":"Eur J Radiol"},{"issue":"1","key":"5642_CR7","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s13244-019-0771-1","volume":"10","author":"A Falk Delgado","year":"2019","unstructured":"Falk Delgado A, Van Westen D, Nilsson M et al (2019) Diagnostic value of alternative techniques to gadolinium-based contrast agents in MR neuroimaging-a comprehensive overview. Insights Imaging 10(1):84. https:\/\/doi.org\/10.1186\/s13244-019-0771-1","journal-title":"Insights Imaging"},{"issue":"4","key":"5642_CR8","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.2020190047","volume":"2","author":"R Forghani","year":"2020","unstructured":"Forghani R (2020) Precision digital oncology: emerging role of radiomics-based biomarkers and artificial intelligence for advanced imaging and characterization of brain tumors. Radiol Imaging Cancer 2(4):e190047. https:\/\/doi.org\/10.1148\/rycan.2020190047","journal-title":"Radiol Imaging Cancer"},{"issue":"9","key":"5642_CR9","first-page":"1738","volume":"22","author":"M Hartmann","year":"2001","unstructured":"Hartmann M, Jansen O, Heiland S, Sommer C, M\u00fcnkel K, Sartor K (2001) Restricted diffusion within ring enhancement is not pathognomonic for brain abscess. AJNR Am J Neuroradiol 22(9):1738\u20131742","journal-title":"AJNR Am J Neuroradiol"},{"issue":"13","key":"5642_CR10","doi-asserted-by":"publisher","first-page":"3201","DOI":"10.3390\/cancers14133201","volume":"14","author":"Q Hu","year":"2022","unstructured":"Hu Q, Wang G, Song X et al (2022) Machine learning based on MRI DWI radiomics features for prognostic prediction in nasopharyngeal carcinoma. Cancers (basel) 14(13):3201. https:\/\/doi.org\/10.3390\/cancers14133201","journal-title":"Cancers (basel)"},{"issue":"17","key":"5642_CR11","doi-asserted-by":"publisher","first-page":"4952","DOI":"10.1002\/hbm.24750","volume":"40","author":"F Isensee","year":"2019","unstructured":"Isensee F, Schell M, Pflueger I et al (2019) Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp 40(17):4952\u20134964. https:\/\/doi.org\/10.1002\/hbm.24750","journal-title":"Hum Brain Mapp"},{"issue":"3","key":"5642_CR43","doi-asserted-by":"publisher","first-page":"836","DOI":"10.3390\/cancers14030836","volume":"14","author":"D Kalasauskas","year":"2022","unstructured":"Kalasauskas D, Kosterhon M, Keric N et al (2022) Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 14(3):836. https:\/\/doi.org\/10.3390\/cancers14030836","journal-title":"Cancers (Basel)"},{"issue":"11","key":"5642_CR44","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.3348\/kjr.2022.0299","volume":"23","author":"M Kim","year":"2022","unstructured":"Kim M, Lee JH, Joo L et al (2022) Development and validation of a model using radiomics features from an apparent diffusion coefficient map to diagnose local tumor recurrence in patients treated for head and neck squamous cell carcinoma. Korean J Radiol 23(11):1078\u20131088. https:\/\/doi.org\/10.3348\/kjr.2022.0299","journal-title":"Korean J Radiol"},{"key":"5642_CR12","doi-asserted-by":"publisher","first-page":"e226","DOI":"10.1016\/j.wneu.2019.07.123","volume":"131","author":"M Kolakshyapati","year":"2019","unstructured":"Kolakshyapati M, Hashizume A, Ochi K et al (2019) Usefulness of histogram-profile analysis in ring-enhancing intracranial lesions. World Neurosurg 131:e226\u2013e236. https:\/\/doi.org\/10.1016\/j.wneu.2019.07.123","journal-title":"World Neurosurg"},{"key":"5642_CR13","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.ejrad.2019.05.021","volume":"117","author":"PH Lai","year":"2019","unstructured":"Lai PH, Chung HW, Chang HC et al (2019) Susceptibility-weighted imaging provides complementary value to diffusion-weighted imaging in the differentiation between pyogenic brain abscesses, necrotic glioblastomas, and necrotic metastatic brain tumors. Eur J Radiol 117:56\u201361. https:\/\/doi.org\/10.1016\/j.ejrad.2019.05.021","journal-title":"Eur J Radiol"},{"issue":"1","key":"5642_CR14","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1093\/bioinformatics\/btz470","volume":"36","author":"TT Le","year":"2020","unstructured":"Le TT, Fu W, Moore JH (2020) Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinformatics 36(1):250\u2013256. https:\/\/doi.org\/10.1093\/bioinformatics\/btz470","journal-title":"Bioinformatics"},{"issue":"9","key":"5642_CR15","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1016\/j.acra.2022.12.030","volume":"30","author":"Y Li","year":"2022","unstructured":"Li Y, Lv X, Wang B et al (2022a) Predicting EGFR T790M mutation in brain metastases using multisequence MRI-based radiomics signature. Acad Radiol 30(9):1887\u20131895. https:\/\/doi.org\/10.1016\/j.acra.2022.12.030","journal-title":"Acad Radiol"},{"issue":"11","key":"5642_CR16","doi-asserted-by":"publisher","first-page":"8039","DOI":"10.1007\/s00330-022-08828-x","volume":"32","author":"Y Li","year":"2022","unstructured":"Li Y, Liu Y, Liang Y et al (2022b) Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol 32(11):8039\u20138051. https:\/\/doi.org\/10.1007\/s00330-022-08828-x","journal-title":"Eur Radiol"},{"key":"5642_CR17","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.855990","volume":"16","author":"Y Liu","year":"2022","unstructured":"Liu Y, Li T, Fan Z et al (2022) Image-based differentiation of intracranial metastasis from glioblastoma using automated machine learning. Front Neurosci 16:855990. https:\/\/doi.org\/10.3389\/fnins.2022.855990","journal-title":"Front Neurosci"},{"issue":"11","key":"5642_CR18","doi-asserted-by":"publisher","first-page":"e841","DOI":"10.1016\/S2589-7500(22)00144-3","volume":"4","author":"P Lohmann","year":"2022","unstructured":"Lohmann P, Franceschi E, Vollmuth P et al (2022) Radiomics in neuro-oncological clinical trials. Lancet Digit Health 4(11):e841\u2013e849. https:\/\/doi.org\/10.1016\/S2589-7500(22)00144-3","journal-title":"Lancet Digit Health"},{"key":"5642_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2021.109900","volume":"143","author":"T Mart\u00edn-Noguerol","year":"2021","unstructured":"Mart\u00edn-Noguerol T, Mohan S, Santos-Armentia E, Cabrera-Zubizarreta A, Luna A (2021) Advanced MRI assessment of non-enhancing peritumoral signal abnormality in brain lesions. Eur J Radiol 143:109900. https:\/\/doi.org\/10.1016\/j.ejrad.2021.109900","journal-title":"Eur J Radiol"},{"issue":"1","key":"5642_CR20","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1002\/acm2.12795","volume":"21","author":"H Moradmand","year":"2020","unstructured":"Moradmand H, Aghamiri SMR, Ghaderi R (2020) Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma. J Appl Clin Med Phys 21(1):179\u2013190. https:\/\/doi.org\/10.1002\/acm2.12795","journal-title":"J Appl Clin Med Phys"},{"issue":"3","key":"5642_CR21","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neurad.2014.05.004","volume":"41","author":"CF Muccio","year":"2014","unstructured":"Muccio CF, Caranci F, D\u2019Arco F et al (2014) Magnetic resonance features of pyogenic brain abscesses and differential diagnosis using morphological and functional imaging studies: a pictorial essay. J Neuroradiol 41(3):153\u2013167. https:\/\/doi.org\/10.1016\/j.neurad.2014.05.004","journal-title":"J Neuroradiol"},{"issue":"2","key":"5642_CR22","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1148\/radiol.2019190913","volume":"294","author":"JE Park","year":"2020","unstructured":"Park JE, Kim HS, Park SY et al (2020) Prediction of core signaling pathway by using diffusion- and perfusion-based MRI radiomics and next-generation sequencing in isocitrate dehydrogenase wild-type glioblastoma. Radiology 294(2):388\u2013397. https:\/\/doi.org\/10.1148\/radiol.2019190913","journal-title":"Radiology"},{"issue":"2","key":"5642_CR23","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1007\/s00330-021-08228-7","volume":"32","author":"WL Peng","year":"2022","unstructured":"Peng WL, Zhang TJ, Shi K et al (2022) Automatic machine learning based on native T1 mapping can identify myocardial fibrosis in patients with hypertrophic cardiomyopathy. Eur Radiol 32(2):1044\u20131053. https:\/\/doi.org\/10.1007\/s00330-021-08228-7","journal-title":"Eur Radiol"},{"issue":"3","key":"5642_CR24","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.230004","volume":"5","author":"V P\u00e9rez-Riverola","year":"2023","unstructured":"P\u00e9rez-Riverola V, V\u00e1zquez Mui\u00f1os O, Prenafeta Moreno M et al (2023) Single cerebral metastasis mimicking pyogenic abscess in a patient with lung adenocarcinoma. Radiol Imaging Cancer 5(3):e230004. https:\/\/doi.org\/10.1148\/rycan.230004","journal-title":"Radiol Imaging Cancer"},{"issue":"11","key":"5642_CR25","doi-asserted-by":"publisher","first-page":"2568","DOI":"10.3390\/cancers13112568","volume":"13","author":"S Priya","year":"2021","unstructured":"Priya S, Liu Y, Ward C et al (2021) Radiomic based machine learning performance for a three class problem in neuro-oncology: time to test the waters? Cancers (basel) 13(11):2568. https:\/\/doi.org\/10.3390\/cancers13112568","journal-title":"Cancers (basel)"},{"issue":"10","key":"5642_CR26","doi-asserted-by":"publisher","first-page":"978","DOI":"10.3390\/jpm11100978","volume":"11","author":"SFM Radzi","year":"2021","unstructured":"Radzi SFM, Karim MKA, Saripan MI et al (2021) Hyperparameter tuning and pipeline optimization via grid search method and tree-based AutoML in breast cancer prediction. J Pers Med 11(10):978. https:\/\/doi.org\/10.3390\/jpm11100978","journal-title":"J Pers Med"},{"issue":"3","key":"5642_CR27","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.surneu.2006.03.032","volume":"66","author":"JS Reddy","year":"2006","unstructured":"Reddy JS, Mishra AM, Behari S et al (2006) The role of diffusion-weighted imaging in the differential diagnosis of intracranial cystic mass lesions: a report of 147 lesions. Surg Neurol 66(3):246\u2013250. https:\/\/doi.org\/10.1016\/j.surneu.2006.03.032","journal-title":"Surg Neurol"},{"issue":"3","key":"5642_CR28","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1148\/radiol.2018181928","volume":"290","author":"JD Rudie","year":"2019","unstructured":"Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S (2019) Emerging applications of artificial intelligence in neuro-oncology. Radiology 290(3):607\u2013618. https:\/\/doi.org\/10.1148\/radiol.2018181928","journal-title":"Radiology"},{"key":"5642_CR29","doi-asserted-by":"publisher","first-page":"9141870","DOI":"10.1155\/2019\/9141870","volume":"2019","author":"T Sakatani","year":"2019","unstructured":"Sakatani T, Kage H, Takayanagi S et al (2019) Brain metastasis mimicking brain abscess in ALK-positive non-small-cell lung cancer. Case Rep Oncol Med 2019:9141870. https:\/\/doi.org\/10.1155\/2019\/9141870","journal-title":"Case Rep Oncol Med"},{"issue":"7","key":"5642_CR30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0159047","volume":"11","author":"S Salice","year":"2016","unstructured":"Salice S, Esposito R, Ciavardelli D et al (2016) Combined 3 tesla MRI biomarkers improve the differentiation between benign vs malignant single ring enhancing brain masses. PLoS ONE 11(7):e0159047. https:\/\/doi.org\/10.1371\/journal.pone.0159047","journal-title":"PLoS ONE"},{"issue":"7","key":"5642_CR31","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1111\/j.1468-1331.2009.02613.x","volume":"16","author":"K Schoenegger","year":"2009","unstructured":"Schoenegger K, Oberndorfer S, Wuschitz B et al (2009) Peritumoral edema on MRI at initial diagnosis: an independent prognostic factor for glioblastoma? Eur J Neurol 16(7):874\u2013878. https:\/\/doi.org\/10.1111\/j.1468-1331.2009.02613.x","journal-title":"Eur J Neurol"},{"issue":"3","key":"5642_CR32","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s00234-005-0024-5","volume":"48","author":"KM Schwartz","year":"2006","unstructured":"Schwartz KM, Erickson BJ, Lucchinetti C (2006) Pattern of T2 hypointensity associated with ring-enhancing brain lesions can help to differentiate pathology. Neuroradiology 48(3):143\u2013149. https:\/\/doi.org\/10.1007\/s00234-005-0024-5","journal-title":"Neuroradiology"},{"issue":"3","key":"5642_CR33","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1093\/neuonc\/noz184","volume":"22","author":"X Su","year":"2020","unstructured":"Su X, Chen N, Sun H et al (2020) Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain. Neuro Oncol 22(3):393\u2013401. https:\/\/doi.org\/10.1093\/neuonc\/noz184","journal-title":"Neuro Oncol"},{"issue":"8","key":"5642_CR34","doi-asserted-by":"publisher","first-page":"e11","DOI":"10.1016\/j.crad.2021.04.012","volume":"76","author":"CQ Su","year":"2021","unstructured":"Su CQ, Chen XT, Duan SF et al (2021) A radiomics-based model to differentiate glioblastoma from solitary brain metastases. Clin Radiol 76(8):e11\u2013e18. https:\/\/doi.org\/10.1016\/j.crad.2021.04.012","journal-title":"Clin Radiol"},{"issue":"1","key":"5642_CR35","doi-asserted-by":"publisher","first-page":"11","DOI":"10.2176\/nmccrj.cr.2018-0126","volume":"6","author":"T Takayasu","year":"2018","unstructured":"Takayasu T, Yamasaki F, Shishido T et al (2018) Abscess formation in metastatic brain tumor with history of immune checkpoint inhibitor: a case report. NMC Case Rep J 6(1):11\u201315. https:\/\/doi.org\/10.2176\/nmccrj.cr.2018-0126","journal-title":"NMC Case Rep J"},{"issue":"9","key":"5642_CR36","doi-asserted-by":"publisher","first-page":"2196","DOI":"10.3390\/diagnostics12092196","volume":"12","author":"G Veres","year":"2022","unstructured":"Veres G, Kiss J, Vas NF et al (2022) Phantom study on the robustness of MR radiomics features: comparing the applicability of 3D printed and biological phantoms. Diagnostics (basel) 12(9):2196. https:\/\/doi.org\/10.3390\/diagnostics12092196","journal-title":"Diagnostics (basel)"},{"key":"5642_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2020.11.014","author":"G Wang","year":"2021","unstructured":"Wang G, Jian W, Cen X et al (2021) Prediction of microvascular invasion of hepatocellular carcinoma based on preoperative diffusion-weighted MR using deep learning. Acad Radiol. https:\/\/doi.org\/10.1016\/j.acra.2020.11.014","journal-title":"Acad Radiol"},{"issue":"3","key":"5642_CR38","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1002\/cam4.5097","volume":"12","author":"H Wang","year":"2023","unstructured":"Wang H, Zhang S, Xing X et al (2023) Radiomic study on preoperative multi-modal magnetic resonance images identifies IDH-mutant TERT promoter-mutant gliomas. Cancer Med 12(3):2524\u20132537. https:\/\/doi.org\/10.1002\/cam4.5097","journal-title":"Cancer Med"},{"issue":"9","key":"5642_CR39","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1016\/j.crad.2014.04.012","volume":"69","author":"XX Xu","year":"2014","unstructured":"Xu XX, Li B, Yang HF et al (2014) Can diffusion-weighted imaging be used to differentiate brain abscess from other ring-enhancing brain lesions? A meta-analysis. Clin Radiol 69(9):909\u2013915. https:\/\/doi.org\/10.1016\/j.crad.2014.04.012","journal-title":"Clin Radiol"},{"issue":"3","key":"5642_CR40","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1007\/s00330-019-06484-2","volume":"30","author":"S Xu","year":"2020","unstructured":"Xu S, Yao Q, Liu G et al (2020) Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer. Eur Radiol 30(3):1804\u20131812. https:\/\/doi.org\/10.1007\/s00330-019-06484-2","journal-title":"Eur Radiol"},{"issue":"6","key":"5642_CR41","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1177\/197140090802100605","volume":"21","author":"A Yikilmaz","year":"2009","unstructured":"Yikilmaz A, Durak AC, Mavili E, Donmez H, Kurtsoy A, Kontas O (2009) The role of diffusion-weighted magnetic resonance imaging in intracranial cystic lesions. Neuroradiol J 21(6):781\u2013790. https:\/\/doi.org\/10.1177\/197140090802100605","journal-title":"Neuroradiol J"},{"issue":"1","key":"5642_CR42","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1002\/jmri.27498","volume":"54","author":"S Zhang","year":"2021","unstructured":"Zhang S, Sun H, Su X et al (2021) Automated machine learning to predict the co-occurrence of isocitrate dehydrogenase mutations and O6 -methylguanine-DNA methyltransferase promoter methylation in patients with gliomas. J Magn Reson Imaging 54(1):197\u2013205. https:\/\/doi.org\/10.1002\/jmri.27498","journal-title":"J Magn Reson Imaging"}],"container-title":["Journal of Cancer Research and Clinical Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00432-024-05642-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00432-024-05642-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00432-024-05642-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T11:06:53Z","timestamp":1713006413000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00432-024-05642-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,16]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["5642"],"URL":"https:\/\/doi.org\/10.1007\/s00432-024-05642-4","relation":{},"ISSN":["1432-1335"],"issn-type":[{"value":"1432-1335","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,16]]},"assertion":[{"value":"14 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was approved by the ethics committee of Qilu Hospital of Shandong University, and informed content was waived. The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Since this was a retrospective study, the ethics committee of Qilu Hospital of Shandong University waived the need to obtain informed consent from the human participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}}],"article-number":"132"}}