{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T23:58:08Z","timestamp":1793404688602,"version":"4.5.0"},"reference-count":27,"publisher":"Georg Thieme Verlag KG","issue":"04","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["World J Nucl Med"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Introduction\u2003Texture and radiomic analysis characterize the lesion's phenotype and evaluate its microenvironment in quantitative terms. The aim of this study was to investigate the role of textural features of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography\u2013computed tomography (PET\/CT) images in differentiating patients with cardiac sarcoidosis (CS) from patients with physiologic myocardial uptake.<\/jats:p><jats:p>\n          Methods\u2003This is a retrospective, single-center study of 67 patients, 17 diagnosed CS patients, and 50 non-CS patients. These patients underwent FDG PET\/CT for the diagnosis of CS. The non-CS group underwent 18F-FDG PET\/CT for other oncological indications. The PET\/CT images were then processed in a commercially available textural analysis software. Region of interest was drawn over primary tumor with a 40% threshold and was processed further to derive 92 textural and radiomic parameters. These parameters were then compared between the CS group and the non-CS group. Receiver operating characteristics (ROC) curves were used to identify cutoff values for textural features with a p-value\u2009&lt;\u20090.05 for statistical significance. These parameters were then passed through a principle component analysis algorithm. Five different machine learning classifiers were then tested on the derived parameters.<\/jats:p><jats:p>\n          Results\u2003A retrospective study of 67 patients, 17 diagnosed CS patients, and 50 non-CS patients, was done. Twelve textural analysis parameters were significant in differentiating between the CS group and the non-CS group. Cutoff values were calculated for these parameters according to the ROC curves. The parameters were Discretized_HISTO_Entropy, GLCM_Homogeneity, GLCM_Energy, GLRLM_LRE, GLRLM_LGRE, GLRLM_SRLGE, GLRLM_LRLGE, NGLDM_Coarseness, GLZLM_LZE, GLZLM_LGZE, GLZLM_SZLGE, and GLZLM_LZLGE. The gradient boosting classifier gave best results on these parameters with 85.71% accuracy and an F1 score of 0.86 (max 1.0) on both classes, indicating the classifier is performing well on both classes.<\/jats:p><jats:p>\n          Conclusion\u2003Textural analysis parameters could successfully differentiate between the CS and non-CS groups noninvasively. Larger multicenter studies are needed for better clinical prognostication of these parameters.<\/jats:p>","DOI":"10.1055\/s-0044-1788336","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T23:09:01Z","timestamp":1720825741000},"page":"256-263","source":"Crossref","is-referenced-by-count":3,"title":["Role of Textural Analysis Parameters Derived from FDG PET\/CT in Diagnosing Cardiac Sarcoidosis"],"prefix":"10.1055","volume":"23","author":[{"given":"Rutuja","family":"Kote","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, All India Institute of Medical Sciences Raipur, Raipur, Chhattisgarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mudalsha","family":"Ravina","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, All India Institute of Medical Sciences Raipur, Raipur, Chhattisgarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rangnath","family":"Thippanahalli Ganga","sequence":"additional","affiliation":[{"name":"Department of Pulmonary Medicine, All India Institute of Medical Sciences Raipur, Raipur, Chhattisgarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2634-7876","authenticated-orcid":false,"given":"Satyajt","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Cardiology, All India Institute of Medical Sciences Raipur, Raipur, Chhattisgarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moulish","family":"Reddy","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, All India Institute of Medical Sciences Raipur, Raipur, Chhattisgarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pratheek","family":"Prasanth","sequence":"additional","affiliation":[{"name":"Department of 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