{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:09:29Z","timestamp":1790834969215,"version":"4.1.0"},"reference-count":54,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,2]]},"abstract":"Abstract<\/jats:title>Purpose<\/jats:title>To assess whether the integration between (a) functional imaging features that will be extracted from diffusion\u2010weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2\u2010weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules\u00a0classification.<\/jats:p><\/jats:sec>Patients and methods<\/jats:title>In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2\u2010weighted and diffusion\u2010weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases. To quantify the nodules' morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram\u2010based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2\u2010weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave\u2010one\u2010subject\u2010out (LOSO)<\/jats:italic> for training\/testing validation\u00a0tests.<\/jats:p><\/jats:sec>Results<\/jats:title>The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer\u2010aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a sensitivity<\/jats:italic>, specificity<\/jats:italic>, positive predictive value<\/jats:italic>, negative predictive value<\/jats:italic>, F1\u2010score<\/jats:italic>, and accuracy<\/jats:italic> of (confidence interval [CI]: ), (CI: ), (CI: ), (CI: ), (CI: ), and (CI: ), respectively, using the LOSO cross\u2010validation\u00a0approach.<\/jats:p><\/jats:sec>Conclusion<\/jats:title>The results demonstrated in this paper show the promise that integrating the functional features with morphology as well as texture features by using the current state\u2010of\u2010the\u2010art machine learning approaches will be extremely useful for identifying thyroid nodules as well as diagnosing their\u00a0malignancy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15399","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T08:30:22Z","timestamp":1639038622000},"page":"988-999","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Texture and shape analysis of diffusion\u2010weighted imaging for thyroid nodules classification using machine learning"],"prefix":"10.1002","volume":"49","author":[{"given":"Ahmed","family":"Sharafeldeen","sequence":"first","affiliation":[{"name":"BioImaging Laboratory Department of Bioengineering University of Louisville Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Elsharkawy","sequence":"additional","affiliation":[{"name":"BioImaging Laboratory Department of Bioengineering University of Louisville Louisville Kentucky 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