Artificial Intelligence in Head and Neck Imaging

Werth, Kyle; Ledbetter, Luke

doi:10.1016/j.nic.2020.04.004

Cited by 12 publications

(10 citation statements)

References 38 publications

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“…Research is needed on integrating the use of novel MRI sequences into existing MRgRT platforms, including the development of optimal imaging techniques for identifying predictive imaging biomarkers that may allow for correlation with toxicity and outcomes. Finally, the frequent imaging obtained during MRgRT provides a rich data source from which machine learning and artificial intelligence approaches [ 42 , 43 ] may allow for further refinement of treatment, prognostication, or personalization of treatment for patients with HNSCC. Further study of the imaging information obtained during MRgRT may point toward improvements in delivery of RT for HNSCC.…”

Section: Developing Areasmentioning

confidence: 99%

Magnetic Resonance Guided Radiotherapy for Head and Neck Cancers

Gharzai

Rosen

Mittal

et al. 2022

JCM

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Radiotherapy is an integral component of head/neck squamous cell carcinomas (HNSCCs) treatment, and technological developments including advances in image-guided radiotherapy over the past decades have offered improvements in the technical treatment of these cancers. Integration of magnetic resonance imaging (MRI) into image guidance through the development of MR-guided radiotherapy (MRgRT) offers further potential for refinement of the techniques by which HNSCCs are treated. This article provides an overview of the literature supporting the current use of MRgRT for HNSCC, challenges with its use, and developing research areas.

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Section: Developing Areasmentioning

confidence: 99%

Magnetic Resonance Guided Radiotherapy for Head and Neck Cancers

Gharzai

Rosen

Mittal

et al. 2022

JCM

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“…Finally, the vast amount of rich data obtained from numerous high-quality anatomical and functional imaging during and after treatments may provide a unique opportunity to exploit machine learning and artificial intelligence, possibly uncovering new predictive and prognostic biomarkers and improving treatment individualization of head and neck cancer patients [ 60 , 61 ].…”

Section: Assessment Of Responsementioning

confidence: 99%

Magnetic Resonance-Guided Radiation Therapy for Head and Neck Cancers

Lavigne

O’Sullivan

et al. 2022

Current Oncology

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Despite the significant evolution of radiation therapy (RT) techniques in recent years, many patients with head and neck cancer still experience significant toxicities during and after treatments. The increased soft tissue contrast and functional sequences of magnetic resonance imaging (MRI) are particularly attractive in head and neck cancer and have led to the increasing development of magnetic resonance-guided RT (MRgRT). This approach refers to the inclusion of the additional information acquired from a diagnostic or planning MRI in radiation treatment planning, and now extends to online high-quality daily imaging generated by the recently developed MR-Linac. MRgRT holds numerous potentials, including enhanced baseline and planning evaluations, anatomical and functional treatment adaptation, potential for hypofractionation, and multiparametric assessment of response. This article offers a structured review of the current literature on these established and upcoming roles of MRI for patients with head and neck cancer undergoing RT.

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“…The majority of artificial intelligence applications in head and neck imaging are in their infancy; however, widespread use and integration into healthcare are not far off. A working knowledge of fundamental words and ideas enables improved interpretation of the medical literature, cooperation with data scientists, and involvement in the decision-making processes that is necessary prior to workflow integration [2]. Artificial intelligence is a broad concept that covers several techniques to make machines think like humans [26] and encompasses two major fields: machine learning and deep learning.…”

Section: Radiomics and Deep Learning In Medical Imagingmentioning

confidence: 99%

“…There are several applications of machine learning and deep learning that are of clinical interest for head and neck imaging, including delineation of organs/tissues at risk or primary tumor for radiation therapy, tumor segmentation, detection, and phenotyping, and precision oncology applications, such as prediction of histopathology or molecular phenotype, response to treatment, and survival [2,[9][10][11][12]. In addition, machine learning and deep learningbased studies on specific organs of the head and neck, such as cervical lymph nodes, parotid, thyroid, and oral cavity, in which neoplasms can raise issues in the differentiation of benign vs. malignant lesions or in determining histotypes, are increasing.…”

Section: Introductionmentioning

confidence: 99%

“…The idea led to the coining of the term ‘artificial intelligence’, defined differently depending on the source but based on the overall notion that a computer can replicate human cognitive capabilities. Advances in computer technology and growing expectations from computer-aided systems have led artificial intelligence to evolve into subsets, such as machine learning and deep learning [2]. The use of these systems in radiological diagnosis is revolutionizing the artificial intelligence field [3 ▪ ,4], with machine learning and deep learning in medical imaging ushering in an exciting era with re-engineered and re-imagined clinical and research capabilities [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors

Gündüz

Alçın

Kızılay

et al. 2021

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of reviewAdvances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolutionizing modern radiological diagnosis. In this review, artificial intelligence applications developed with radiomics and deep learning methods in the differential diagnosis of parotid gland tumors (PGTs) will be overviewed. Recent findingsThe development of artificial intelligence models has opened new scenarios owing to the possibility of assessing features of medical images that usually are not evaluated by physicians. Radiomics and deep learning models come to the forefront in computer-aided diagnosis of medical images, even though their applications in the differential diagnosis of PGTs have been limited because of the scarcity of data sets related to these rare neoplasms. Nevertheless, recent studies have shown that artificial intelligence tools can classify common PGTs with reasonable accuracy.

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Artificial Intelligence in Head and Neck Imaging

Cited by 12 publications

References 38 publications

Magnetic Resonance Guided Radiotherapy for Head and Neck Cancers

Magnetic Resonance Guided Radiotherapy for Head and Neck Cancers

Magnetic Resonance-Guided Radiation Therapy for Head and Neck Cancers

Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors

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