Assessment of 4DCT imaging findings of parathyroid adenomas in correlation with biochemical and histopathological findings

Gümeler, Ekim; Kurtulan, Olcay; Arslan, Sevtap; Karakaya, Jale; Sökmensüer, Cenk; Ünlütürk, Uğur; Oğuz, Kader K.; Karaosmanoglu, Ayca Akgoz

doi:10.1007/s11604-021-01220-7

Cited by 1 publication

(1 citation statement)

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“…Numerous radiological investigations have described lesion enhancement patterns, lesion morphological shapes, and morphological changes in lesions over a given follow-up period, and several additional parameters obtained from functional imaging techniques are valuable for the diagnostic prediction of the histological type or differentiation of benign and malignant lesions in various head and neck diseases. [81][82][83] However, there are still many nonspecific imaging findings in head and neck MRI, and standard imaging sequences such as T1WI and T2WI can be limited in their ability to reliably differentiate, for example, benign from malignant lesions, resulting in difficulty reaching a diagnosis even among experienced radiologists. Advanced analytical methods such as a texture analysis or radiomics approach have been reported to provide mostly equivalent diagnostic accuracy (or sometimes better accuracy) in classifying head and neck diseases compared to classifications by expert radiologists who specialize in head and neck imaging.…”

Section: Disease Classification and Diagnosismentioning

confidence: 99%

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Fujima,

Kamagata,

Ueda

et al. 2023

MRMS

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Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

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Section: Disease Classification and Diagnosismentioning

confidence: 99%