{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T11:27:30Z","timestamp":1776338850339,"version":"3.51.2"},"reference-count":14,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Health with Ricerca Corrente and 5x1000 funds","award":["952159 (ProCAncer-I)"],"award-info":[{"award-number":["952159 (ProCAncer-I)"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation program","award":["952159 (ProCAncer-I)"],"award-info":[{"award-number":["952159 (ProCAncer-I)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Radiomics is emerging as a promising tool to extract quantitative biomarkers\u2014called radiomic features\u2014from medical images, potentially contributing to the improvement in diagnosis and treatment of oncological patients. However, technical limitations might impair the reliability of radiomic features and their ability to quantify clinically relevant tissue properties. Among these, sampling the image signal in a too-small region can reduce the ability to discriminate tissues with different properties. However, a volume threshold guaranteeing a reliable analysis, which might vary according to the imaging modality and clinical scenario, has not been assessed yet. In this study, an MRI phantom specifically developed for radiomic investigation of gynecological malignancies was used to explore how the ability of radiomic features to discriminate different image textures varies with the volume of the analyzed region. The phantom, embedding inserts with different textures, was scanned on two 1.5T and one 3T scanners, each using the T2-weighted sequence of the clinical protocol implemented for gynecological studies. Within each of the three inserts, six cylindrical regions were drawn with volumes ranging from 0.8 cm3 to 29.8 cm3, and 944 radiomic features were extracted from both original images and from images processed with different filters. For each scanner, the ability of each feature to discriminate the different textures was quantified. Despite differences observed among the scanner models, the overall percentage of discriminative features across scanners was &gt;70%, with the smallest volume having the lowest percentage of discriminative features for all scanners. Stratification by feature class, still aggregating data for original and filtered images, showed statistical significance for the association between the percentage of discriminative features with VOI sizes for features classes GLCM, GLDM, and GLSZM on the first 1.5T scanner and for first-order and GLSZM classes on the second 1.5T scanner. Poorer results in terms of features\u2019 discriminative ability were found for the 3T scanner. Focusing on original images only, the analysis of discriminative features stratified by feature class showed that the first-order and GLCM were robust to VOI size variations (&gt;85% discriminative features for all sizes), while for the 1.5T scanners, the GLSZM and NGTDM feature classes showed a percentage of discriminative features &gt;80% only for volumes no smaller than 3.3 cm3, and equal or larger than 7.4 cm3 for the GLRLM. As for the 3T scanner, only the GLSZM showed a percentage of discriminative features &gt;80% for all volume sizes above 3.3 cm3. Analogous considerations were obtained for each filter, providing useful indications for feature selection in this clinical case. Similar studies should be replicated with suitably adapted phantoms to derive useful data for other clinical scenarios and imaging modalities.<\/jats:p>","DOI":"10.3390\/app12115465","type":"journal-article","created":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T01:40:45Z","timestamp":1653702045000},"page":"5465","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Discrimination of Tumor Texture Based on MRI Radiomic Features: Is There a Volume Threshold? A Phantom Study"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8174-2943","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Santinha","sequence":"first","affiliation":[{"name":"Champalimaud Experimental Clinical Research Programme, Champalimaud Foundation, 1400-038 Lisboa, Portugal"},{"name":"Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal"}]},{"given":"Linda","family":"Bianchini","sequence":"additional","affiliation":[{"name":"Department of Physics, Universit\u00e0 degli Studi di Pavia, 27100 Pavia, Italy"}]},{"given":"M\u00e1rio","family":"Figueiredo","sequence":"additional","affiliation":[{"name":"Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, 1049-001 Lisboa, Portugal"}]},{"given":"Celso","family":"Matos","sequence":"additional","affiliation":[{"name":"Champalimaud Experimental Clinical Research Programme, Champalimaud Foundation, 1400-038 Lisboa, Portugal"},{"name":"Champalimaud Clinical Centre, Champalimaud Foundation, 1400-038 Lisboa, Portugal"}]},{"given":"Alessandro","family":"Lascialfari","sequence":"additional","affiliation":[{"name":"Department of Physics, Universit\u00e0 degli Studi di Pavia, 27100 Pavia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3298-2072","authenticated-orcid":false,"given":"Nikolaos","family":"Papanikolaou","sequence":"additional","affiliation":[{"name":"Champalimaud Experimental Clinical Research Programme, Champalimaud Foundation, 1400-038 Lisboa, Portugal"},{"name":"Department of Radiology, Royal Marsden Hospital, Downs Road, Sutton SM2 5PT, UK"}]},{"given":"Marta","family":"Cremonesi","sequence":"additional","affiliation":[{"name":"Radiation Research Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy"}]},{"given":"Barbara A.","family":"Jereczek-Fossa","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy"},{"name":"Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3693-8899","authenticated-orcid":false,"given":"Francesca","family":"Botta","sequence":"additional","affiliation":[{"name":"Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy"}]},{"given":"Daniela","family":"Origgi","sequence":"additional","affiliation":[{"name":"Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","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. 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Cancer Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1002\/mp.12123","article-title":"Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels","volume":"44","author":"Zhang","year":"2017","journal-title":"Med. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/1748-717X-6-69","article-title":"Current measures of metabolic heterogeneity within cervical cancer do not predict disease outcome","volume":"6","author":"Brooks","year":"2011","journal-title":"Radiat. Oncol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.2967\/jnumed.112.116715","article-title":"The Effect of Small Tumor Volumes on Studies of Intratumoral Heterogeneity of Tracer Uptake","volume":"55","author":"Brooks","year":"2014","journal-title":"J. Nucl. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.ejmp.2020.02.003","article-title":"PETER PHAN: An MRI phantom for the optimisation of radiomic studies of the female pelvis","volume":"71","author":"Bianchini","year":"2020","journal-title":"Phys. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1002\/mrm.28521","article-title":"A multicenter study on radiomic features from T2-weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics","volume":"85","author":"Bianchini","year":"2021","journal-title":"Magn. Reson. 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Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5058","DOI":"10.1118\/1.3622605","article-title":"The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms","volume":"38","author":"Waugh","year":"2011","journal-title":"Med. Phys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"238","DOI":"10.3390\/tomography7020022","article-title":"Stability of Radiomic Features across Different Region of Interest Sizes\u2014A CT and MR Phantom Study","volume":"7","author":"Jensen","year":"2021","journal-title":"Tomography"}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/11\/5465\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:20:13Z","timestamp":1760138413000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/11\/5465"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,27]]},"references-count":14,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["app12115465"],"URL":"https:\/\/doi.org\/10.3390\/app12115465","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,27]]}}}