{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T07:15:26Z","timestamp":1787901326090,"version":"build-2784847793"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Gliomas are still considered as challenging in oncologic management despite the developments in treatment approaches. The complete elimination of a glioma might not be possible even after a treatment and assessment of therapeutic response is important to determine the future course of actions for patients with such cancers. In the recent years radiomics has emerged as a promising solution with potential applications including prediction of therapeutic response. Hence, this study was focused on investigating whether morphometry-based radiomics signature could be used to predict therapeutic response in patients with gliomas following radiotherapy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>105 magnetic resonance (MR) images including segmented and non-segmented images were used to extract morphometric features and develop a morphometry-based radiomics signature. After determining the appropriate machine learning algorithm, a prediction model was developed to predict the therapeutic response eliminating the highly correlated features as well as without eliminating the highly correlated features. Then the model performance was evaluated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Tumor grade had the highest contribution to develop the morphometry-based signature. Random forest provided the highest accuracy to train the prediction model derived from the morphometry-based radiomics signature. An accuracy of 86% and area under the curve (AUC) value of 0.91 were achieved for the prediction model evaluated without eliminating the highly correlated features whereas accuracy and AUC value were 84% and 0.92 respectively for the prediction model evaluated after eliminating the highly correlated features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Nonetheless, the developed morphometry-based radiomics signature could be utilized as a noninvasive biomarker for therapeutic response in patients with gliomas following radiotherapy.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2023.1139902","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:04:09Z","timestamp":1692342249000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy"],"prefix":"10.3389","volume":"13","author":[{"given":"Lahanda Purage G.","family":"Sherminie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohan L.","family":"Jayatilake","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Badra","family":"Hewavithana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bimali S.","family":"Weerakoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahan M.","family":"Vijithananda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noaa200","article-title":"CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2013-2017","volume":"22","author":"Ostrom","year":"2020","journal-title":"Neuro Oncol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noab106","article-title":"The 2021 WHO classification of tumors of the central nervous system: A summary","volume":"23","author":"Louis","year":"2021","journal-title":"Neuro Oncol"},{"key":"B3","unstructured":"Glioma2022"},{"key":"B4","unstructured":"Gliomas2022"},{"key":"B5","unstructured":"Classification of brain tumors2022"},{"key":"B6","doi-asserted-by":"publisher","first-page":"10373","DOI":"10.3390\/ijms221910373","article-title":"Prognostic and predictive biomarkers in gliomas","volume":"22","author":"\u015aledzi\u0144ska","year":"2021","journal-title":"Int J Mol Sci MDPI"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2014.00047","article-title":"Prognostic and predictive biomarkers in adult and pediatric gliomas: Toward personalized treatment","volume":"4","author":"Haynes","year":"2014","journal-title":"Front Oncol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1136\/bmjno-2020-000069","article-title":"Potential biomarkers and challenges in glioma diagnosis, therapy and prognosis","volume":"2","author":"Kan","year":"2020","journal-title":"BMJ Neurology Open"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.19080\/CTOIJ.2017.05.555653","article-title":"Radiomics in radiation oncology precision medicine","volume":"5","author":"Abuhijla","year":"2017","journal-title":"Cancer Ther Oncol Int J"},{"key":"B10","unstructured":"A web accessible platform for imaging analytics2022"},{"key":"B11","doi-asserted-by":"publisher","first-page":"628.e17","DOI":"10.1016\/j.crad.2021.03.019","article-title":"Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma","volume":"76","author":"Patel","year":"2021","journal-title":"Clin Radiol"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1712604","DOI":"10.1155\/2020\/1712604","article-title":"Machine learning based on a multiparametric and multiregional radiomics signature predicts radiotherapeutic response in patients with glioblastoma","volume":"2020","author":"Pan","year":"2020","journal-title":"Behav Neurology."},{"key":"B13","first-page":"109500B","article-title":"Radiomics of the lesion habitat on pre-treatment MRI predicts response to chemo-radiation therapy in Glioblastoma","volume-title":"Medical Imaging 2019: Computer-Aided Diagnosis (Prog. Biomed. Opt. Imaging - Proc. SPIE)","author":"Correa","year":"2019"},{"key":"B14","doi-asserted-by":"publisher","first-page":"7830909","DOI":"10.1155\/2021\/7830909","article-title":"Predicting the grade of prostate cancer based on a biparametric MRI radiomics signature","volume":"2021","author":"Zhang","year":"2021","journal-title":"Contrast Media Mol Imaging"},{"key":"B15","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput Sci"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-021-00239-1","article-title":"Deep limitations? Examining expert disagreement over deep learning","volume":"10","author":"Cremer","year":"2021","journal-title":"Prog Artif Intelligence."},{"key":"B17","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1186\/s12916-023-02779-w","article-title":"There is no such thing as a validated prediction model","volume":"21","author":"Van Calster","year":"2023","journal-title":"BMC Med"},{"key":"B18","doi-asserted-by":"publisher","first-page":"20190046","DOI":"10.1259\/bjro.20190046","article-title":"Radiomics for radiation oncologists: are we ready to go","volume":"2","author":"Ferrer","year":"2020","journal-title":"BJR Open"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cancers13020240","article-title":"Image contrast, image pre-processing, and t1 mapping affect MRI radiomic feature repeatability in patients with colorectal cancer liver metastases","volume":"13","author":"McHugh","year":"2021","journal-title":"Cancers (Basel)."},{"key":"B20","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s40644-021-00388-5","article-title":"Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer","volume":"21","author":"Park","year":"2021","journal-title":"Cancer Imaging"},{"key":"B21","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1177\/1533034616661852","article-title":"Sensitivity of image features to noise in conventional and respiratory-gated PET\/CT images of lung cancer: uncorrelated noise effects","volume":"16","author":"Oliver","year":"2017","journal-title":"Technol Cancer Res Treat"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28058","article-title":"Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer","volume":"83","author":"Merisaari","year":"2020","journal-title":"Magn Reson Med"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.116.180919","article-title":"Reliability of PET\/CT shape and heterogeneity features in functional and morphologic components of non-small cell lung cancer tumors: A repeatability analysis in a prospective multicenter cohort","volume":"58","author":"Desseroit","year":"2017","journal-title":"J Nucl Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"14163","DOI":"10.1038\/s41598-020-70940-z","article-title":"MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability","volume":"10","author":"Granzier","year":"2020","journal-title":"Sci Rep"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1097\/rli.0000000000000530","article-title":"Robustness and reproducibility of radiomics in magnetic resonance imaging: A phantom study","volume":"54","author":"Bae\u00dfler","year":"2019","journal-title":"Invest Radiol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0169172","article-title":"Associations of radiomic data extracted from static and respiratory-gated CT scans with disease recurrence in lung cancer patients treated with SBRT","volume":"12","author":"Huynh","year":"2017","journal-title":"PloS One"},{"key":"B27","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s12880-020-0416-3","article-title":"Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging","volume":"20","author":"Sha","year":"2020","journal-title":"BMC Med Imaging"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0166550","article-title":"Assessing agreement between radiomic features computed for multiple CT imaging settings","volume":"11","author":"Lu","year":"2016","journal-title":"PloS One"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-06957-9","article-title":"Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform","volume":"30","author":"Fornacon-Wood","year":"2020","journal-title":"Eur Radiol"},{"key":"B30","article-title":"Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method","volume":"45","author":"Hatt","year":"2017","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"B31","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ejmp.2019.04.009","article-title":"Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing","volume":"61","author":"Traverso","year":"2019","journal-title":"Physica Medica."},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.18383\/j.tom.2016.00235","article-title":"Radiomics of lung nodules: A multi-institutional study of robustness and agreement of quantitative imaging features","volume":"2","author":"Kalpathy-Cramer","year":"2016","journal-title":"Tomography."},{"key":"B33","doi-asserted-by":"publisher","first-page":"23428","DOI":"10.1038\/srep23428","article-title":"Reproducibility of radiomics for deciphering tumor phenotype with imaging","volume":"6","author":"Zhao","year":"2016","journal-title":"Sci Rep"},{"key":"B34","doi-asserted-by":"publisher","first-page":"5985","DOI":"10.3390\/cancers13235985","article-title":"Impact of interobserver variability in manual segmentation of non-small cell lung cancer (NSCLC) applying low-rank radiomic representation on computed tomography","volume":"13","author":"Hershman","year":"2021","journal-title":"Cancers (Basel)."},{"key":"B35","unstructured":"HopperK0\n            KasalesCJ\n            vanSMA\n            SchwartzTA\n            TenhaveTR\n            JozefiakJA\n          Analysis of Interobserver and lntraobserver Variability in CT Tumor Measurements1996"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1080\/0284186X.2018.1445283","article-title":"Influence of inter-observer delineation variability on radiomics stability in different tumor sites","volume":"57","author":"Pavic","year":"2018","journal-title":"Acta Oncol (Madr)."},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.21037\/qims-21-86","article-title":"Radiomics feature reliability assessed by intraclass correlation coefficient: A systematic review","volume":"11","author":"Xue","year":"2021","journal-title":"Quant Imaging Med Surg"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2021.03.009","article-title":"Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization","volume":"83","author":"Papadimitroulas","year":"2021","journal-title":"Physica Medica."},{"key":"B39","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1038\/sdata.2017.117","article-title":"Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features","volume":"5","author":"Bakas","year":"2017","journal-title":"Sci Data."},{"key":"B40","unstructured":"Harmonized cancer datasets2022"},{"key":"B41","article-title":"The cancer imaging archive","volume-title":"Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection [Data set]","author":"Bakas","year":"2017"},{"key":"B42","article-title":"The cancer imaging archive","volume-title":"Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection [Data set]","author":"Bakas","year":"2017"},{"key":"B43","unstructured":"BakasS\n            ReyesM\n            JakabA\n            BauerS\n            RempflerM\n            CrimiA\n          Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge2018"},{"key":"B44","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2015","journal-title":"IEEE Trans Med Imaging."},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7","article-title":"The cancer imaging archive (TCIA): maintaining and operating a public information repository","volume":"26","author":"Clark","year":"2013","journal-title":"J Digit Imaging."},{"key":"B46","doi-asserted-by":"publisher","first-page":"10186","DOI":"10.1038\/s41598-022-13937-0","article-title":"Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy","volume":"12","author":"Marfisi","year":"2022","journal-title":"Sci Rep"},{"key":"B47","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":"B48","unstructured":"TCGA forms and documents"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J Artif Intell Res"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2023.1139902\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:04:15Z","timestamp":1692342255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2023.1139902\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,17]]},"references-count":49,"alternative-id":["10.3389\/fonc.2023.1139902"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2023.1139902","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,17]]},"article-number":"1139902"}}