Role of MR Imaging in Head and Neck Squamous Cell Carcinoma

Razek, Ahmed Abdel Khalek Abdel; Elsebaie, Nermeen; Gamaleldin, Omneya; AbdelKhalek, Amro; Mukherji, Suresh K.

doi:10.1016/j.mric.2021.08.001

Cited by 5 publications

(2 citation statements)

References 68 publications

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“…Besides clinical examination [ 9 , 10 ], the detection of pathologic cervical LNs is currently based on contrast-enhanced computed tomography scans (CTs), magnetic resonance imaging (MRIs) or fluorine-18-fluorodeoxyglucose positron emission tomography scans ([ 18 F]FDG-PET) [ 11 , 12 , 13 ]. Once potentially pathologic cervical LNs are detected [ 11 , 12 , 13 ], current criteria to classify LNs as pathologic or pathologic with ECS are mainly shape-based [ 14 , 15 , 16 , 17 , 18 , 19 ]. Mainly, and without further quantification, head and neck radiologists consider LN maximum diameter and margins.…”

Section: Introductionmentioning

confidence: 99%

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

Santer

Kloppenburg

Gottfried

et al. 2022

Cancers

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Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10–258) and of LNs was 340 (SD ± 268; range 21–791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43–99%) and for testing sets 86% (SD ± 5%; range 76–92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI’s role in LN-classification in locally-advanced HNSCC.

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Section: Introductionmentioning

confidence: 99%

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

Santer

Kloppenburg

Gottfried

et al. 2022

Cancers

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“…In conventional imaging, MRI is known to be a superior imaging method compared to CT in the head and neck region due to excellent soft-tissue contrast, diffusion-weighted imaging (DWI), and DWI-derived apparent diffusion coefficient (ADC) values, which aid distinguishing likely malignant tissues from benign post-treatment changes or localizing anatomically normal sized lymph node metastases [ 20 ]. Moreover, MRI has the ability to detect many anatomically challenging findings like laryngeal cartilage and skull base invasion or perineural disease spread [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Accuracy of ¹⁸F‐FDG‐PET/CT and ¹⁸F‐FDG‐PET/MRI in Detecting Locoregional Recurrence of HNSCC 12 Weeks after the End of Chemoradiotherapy: Single‐Center Experience with PET/MRI

Murtojärvi

Malaspina

Kinnunen

et al. 2022

Contrast Media & Molecular Imaging

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Purpose. In head and neck squamous cell carcinoma (HNSCC), the early diagnosis and efficient detection of recurrences and/or residual tumor after treatment play a very important role in patient’s prognosis. Positron emission tomography (PET) using 2-deoxy-2-18F-fluoro-D-glucose (18F-FDG) has become an established method for the diagnosis of suspected recurrence in head and neck carcinomas. In particular, integrated PET/MRI imaging that provides optimal soft tissue contrast and less dental implant artifacts compared to PET/CT is an intriguing technique for the follow-up imaging of HNSCC patients. The aim of this study was to evaluate the benefit of PET/MRI compared to PET/CT in post-treatment follow-up imaging of HNSCC patients. Methods. This retrospective observational cohort study consists of 104 patients from our center with histologically confirmed HNSCC. All patients received chemoradiotherapy (CRT) and underwent 18F-FDG-PET/CT (n = 52) or 18F-FDG-PET/MRI (n = 52) scan 12 weeks after the end of treatment. Image analysis was performed by two independent readers according to a five-point Likert scale analysis. Results. PET/MRI was more sensitive (1.00 vs. 0.77) than PET/CT in the detection of locoregional recurrence. PET/MRI also had better negative (1.00 vs. 0.87) predictive values. AUCs for PET/MRI and PET/CT on patient-based analysis were 0.997 (95% CI 0.989–1.000) and 0.890 (95% CI 0.806–0.974), respectively. The comparison of sensitivity, AUCs, and negative predictive values revealed a statistically significant difference, p < 0.05 . In PET/CT, false-negative and positive findings were observed in the more advanced disease stages, where PET/MRI performed better. Also, false-negative findings were located in the oropharyngeal, laryngeal, and nasopharyngeal regions, where PET/MRI made no false-negative interpretations. Conclusion. Based on these results, PET/MRI might be considered the modality of choice in detecting locoregional recurrence in HNSCC patients, especially in the more advanced stages in the oral cavity, larynx, or nasopharynx.

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CT and MR anatomy of the larynx and hypopharynx

Ravanelli,

Rondi,

Ferrari

et al. 2024

Neuroradiology

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Role of MR Imaging in Head and Neck Squamous Cell Carcinoma

Cited by 5 publications

References 68 publications

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

Diagnostic Accuracy of ¹⁸F‐FDG‐PET/CT and ¹⁸F‐FDG‐PET/MRI in Detecting Locoregional Recurrence of HNSCC 12 Weeks after the End of Chemoradiotherapy: Single‐Center Experience with PET/MRI

CT and MR anatomy of the larynx and hypopharynx

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Role of MR Imaging in Head and Neck Squamous Cell Carcinoma

Cited by 5 publications

References 68 publications

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

Diagnostic Accuracy of 18F‐FDG‐PET/CT and 18F‐FDG‐PET/MRI in Detecting Locoregional Recurrence of HNSCC 12 Weeks after the End of Chemoradiotherapy: Single‐Center Experience with PET/MRI

CT and MR anatomy of the larynx and hypopharynx

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Diagnostic Accuracy of ¹⁸F‐FDG‐PET/CT and ¹⁸F‐FDG‐PET/MRI in Detecting Locoregional Recurrence of HNSCC 12 Weeks after the End of Chemoradiotherapy: Single‐Center Experience with PET/MRI