Differentiation of follicular carcinomas from adenomas using histogram obtained from diffusion-weighted MRI

Chung, Sae Rom; Lee, J.H.; Yoon, Richard K; Sung, Tae‐Yon; Song, Dong Eun; Pfeuffer, Josef; Kim, I.S.

doi:10.1016/j.crad.2020.07.018

Cited by 3 publications

(4 citation statements)

References 39 publications

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“…Moreover, the proposed system, whose AUC is 99.4% (CI: 97.1%-100%), is better than the study done by Brown et al 29 that revealed an AUC of 97%, in spite of using higher magnetic field strength (3T MRI) and multiinstitutional design of their study. Also, our results are higher than the study done by Chung et al 41 as their best AUC was 86%. The lower value may be due to very small sample size used in their study (17 patients only).…”

Section: Discussioncontrasting

confidence: 91%

“…that revealed an AUC of

97\%

, in spite of using higher magnetic field strength (3T MRI) and multiinstitutional design of their study. Also, our results are higher than the study done by Chung et al 41 . as their best AUC was

86\%

.…”

Section: Discussioncontrasting

confidence: 87%

“…Finally, a linear discriminant analysis (LDA) model was built to stratify the malignancy of the nodules. Another study by Chung et al 41 evaluated the role of histogram analysis of ADC maps from DWI in differentiating follicular thyroid carcinoma (FTC) from follicular adenoma (FA) on 17 patients. Their study showed that 25th percentiles of ADC were the best parameter in discriminating FTC from FA with an AUC 0.86.…”

Section: Introductionmentioning

confidence: 99%

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Texture and shape analysis of diffusion‐weighted imaging for thyroid nodules classification using machine learning

et al. 2021

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To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification. Patients and methods: In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted 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-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted 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-one-subject-out (LOSO) for training/testing validation tests. Results: The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a

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Section: Discussioncontrasting

confidence: 91%

“…that revealed an AUC of

97\%

, in spite of using higher magnetic field strength (3T MRI) and multiinstitutional design of their study. Also, our results are higher than the study done by Chung et al 41 . as their best AUC was

86\%

.…”

Section: Discussioncontrasting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Texture and shape analysis of diffusion‐weighted imaging for thyroid nodules classification using machine learning

et al. 2021

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“…the value of histogram analysis of ADC maps in the differentiation of follicular thyroid carcinoma from follicular adenoma in 17 Bethesda III and IV indeterminate nodules on US-guided core needle biopsy[254]. Histogram parameters were derived from ADC values (acquired with b-values of 0 and 800 s/mm²) obtained from the entire tumour volume and compared with the histopathological diagnosis.…”

mentioning

confidence: 99%

Radiomics in [18F]FDG PET/CT: A leap in the dark?

Noortman¹

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be reproduced, stored in a retrieval system or transmitted in any form or by any means without permission of the author. Alle rechten voorbehouden. Niets uit deze uitgave mag worden vermenigvuldigd, in enige vorm of op enige wijze, zonder voorafgaande schriftelijke toestemming van de auteur. RADIOMICS IN [18 F]FDG PET/CT: A LEAP IN THE DARK? PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit Twente, op gezag van de rector magnificus, prof. dr. ir. A. Veldkamp, volgens besluit van het College voor Promoties in het openbaar te verdedigen op woensdag 10 mei 2023 om 12.45 uur door

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Non-invasive Imaging Biomarkers of Thyroid Nodules with Indeterminate Cytology

Noortman

Koster

Velden

et al. 2023

Integrated Diagnostics and Theranostics of Thyroid Diseases

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Stratified by ultrasonography and fine-needle aspiration cytology, indeterminate nodules are lesions with an intermediate risk of being malignant (approximately 25%). Diagnostic resection of half the thyroid gland provides the true nature of these lesions but at the cost of 75% of the patients being futilely operated. In case a malignancy is found, a second surgery to remove the whole thyroid gland is often necessary. Different approaches either on cytological material or using imaging have been investigated to further stratify these lesions. This chapter reviewed biomarkers obtained using conventional as well as artificial intelligence-based non-invasive imaging strategies for the differentiation of thyroid nodules with indeterminate cytology. An overview of the abilities of different tests to differentiate between benign and malignant nodules was provided, taking into account the clinical readiness and cost-effectiveness.

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Differentiation of follicular carcinomas from adenomas using histogram obtained from diffusion-weighted MRI

Cited by 3 publications

References 39 publications

Texture and shape analysis of diffusion‐weighted imaging for thyroid nodules classification using machine learning

Texture and shape analysis of diffusion‐weighted imaging for thyroid nodules classification using machine learning

Radiomics in [18F]FDG PET/CT: A leap in the dark?

Non-invasive Imaging Biomarkers of Thyroid Nodules with Indeterminate Cytology

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