Current Use of Molecular Profiling for Indeterminate Thyroid Nodules

Rojo, Irene López; Valdazo, A.; Ramírez, Joaquín Gómez

doi:10.1016/j.cireng.2018.07.013

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…1 The main challenge is the detection of malignancy of thyroid nodules as it affects the method and planning of patients as well as their prognosis. [1][2][3][4] Biopsy, either fineneedle aspiration or surgical excision of the nodule, is the definitive means of evaluating nodules. This invasive procedure is not always accurate, with a false negative rate depending on the biopsy technique and size of the nodule being aspirated.…”

Section: Introductionmentioning

confidence: 99%

“…This invasive procedure is not always accurate, with a false negative rate depending on the biopsy technique and size of the nodule being aspirated. [3][4][5][6] However, it is still being the gold standard to identify tumor malignancy. On the other hand, there are imaging approaches that may be used to noninvasively classify thyroid nodules as having a potential of being malignant.…”

Section: Introductionmentioning

confidence: 99%

“…A thyroid nodule is a common clinical finding, reported in about

5\%

of the population, that nonetheless calls for additional diagnostic procedures to rule out cancer, as

7\%-15\%

of these nodules end up being malignant 1 . The main challenge is the detection of malignancy of thyroid nodules as it affects the method and planning of patients as well as their prognosis 1–4 . Biopsy, either fine‐needle aspiration or surgical excision of the nodule, is the definitive means of evaluating nodules.…”

Section: Introductionmentioning

confidence: 99%

“…Biopsy, either fine‐needle aspiration or surgical excision of the nodule, is the definitive means of evaluating nodules. This invasive procedure is not always accurate, with a false negative rate depending on the biopsy technique and size of the nodule being aspirated 3–6 . However, it is still being the gold standard to identify tumor malignancy.…”

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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