Imaging of Patients with Head and Neck Cancer

Seeburg, Daniel P.; Baer, Aaron H.; Aygün, Nafi

doi:10.1016/j.coms.2018.06.004

Cited by 36 publications

(26 citation statements)

References 59 publications

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“…Despite these regimes and recent advancements in immune therapies the long-term prognosis is still poor, due to a high rate of locoregional recurrence and new malignant conversions [8][9][10]. Imaging enhances information beyond medical history and physical examination by assessing the tumor extent, possible bone infiltration and the presence of cervical nodal metastases in OSCC [11]. Regarding to this, computed tomography (CT) and magnetic resonance imaging (MRI) are the primary techniques for evaluation.…”

Section: Introductionmentioning

confidence: 99%

Total lesion glycolysis in oral squamous cell carcinoma as a biomarker derived from pre-operative FDG PET/CT outperforms established prognostic factors in a newly developed multivariate prediction model

et al. 2021

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Purpose: Retrospective study to investigate the impact of image derived biomarkers from [ 18 F]FDG PET/CT prior to surgical resection in patients with initial diagnosis of oral squamous cell carcinoma (OSCC), namely SUV max , SUV mean , metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary tumor to predict overall survival (OS). Materials and Methods: 127 subsequent patients with biopsy-proven OSCC were included who underwent [ 18 F]FDG PET/CT before surgery. SUV max , SUV mean , MTV and TLG of the primary tumor were measured. OS was estimated according to Kaplan-Meier and compared between median-splitted groups by the log-rank test. Prognostic parameters were analyzed by uni-/multivariate Cox-regression. Results: During follow-up 52 (41%) of the patients died. Median OS was longer for patients with lower MTV or lower TLG. SUV max and SUV mean failed to be significant predictors for OS. Univariate Cox-regression identified MTV, TLG, lymph node status and UICC stage as prognostic factors. By multivariate Cox-regression MTV and TLG turned out to be independent prognostic factors for OS. Conclusions: The pre-therapeutic [ 18 F]FDG PET/CT parameters MTV and TLG in the primary tumor are prognostic for OS of patients with an initial diagnosis of OSCC. TLG is the strongest independent prognostic factor for OS and outperforms established prognostic parameters in OSCC.

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Section: Introductionmentioning

confidence: 99%

Total lesion glycolysis in oral squamous cell carcinoma as a biomarker derived from pre-operative FDG PET/CT outperforms established prognostic factors in a newly developed multivariate prediction model

et al. 2021

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“…Since most head-and-neck cancers begin in the squamous cells of these organs, these cancers are named head-andneck squamous cell carcinoma (HNSCC). HNSCC is currently the sixth cause of cancer-related deaths in the world and accounts for about 3 percent of all cancers in the United States [1][2][3] . The main treatments for HNSCC include surgery, radiation therapy, chemotherapy, and immunotherapy, which are used singly or in combination for different stages of disease.…”

Section: Introductionmentioning

confidence: 99%

Inhibition of IKKβ/NF-κB signaling pathway to improve Dasatinib efficacy in suppression of cisplatin-resistant head and neck squamous cell carcinoma

et al. 2020

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Proto-oncogene tyrosine-protein kinase Src plays an important role in Head and Neck Squamous Cell Carcinoma (HNSCC). However, the FDA-approved SRC inhibitor Dasatinib shows very limited efficacy in HNSCC clinical trials, even though Dasatinib can completely inhibit SRC in the laboratory setting. These results suggest that SRC inhibition can cause compensatory up-regulation and/or activation of other survival pathways, which suggests that co-targeting of SRC and the potential signaling pathways may improve the Dasatinib efficacy. In this study, we investigated the role of IKKβ/NF-κB in regulation of the sensitivity of cisplatin-resistant HNSCC to Dasatinib. Additionally, we wished to determine whether inhibition of the IKKβ/NF-κB signaling pathway could enhance Dasatinib efficacy to inhibit cisplatin-resistant HNSCC without the use of cisplatin. Previous studies have shown that ETS-1 is a crucial SRC effector protein that regulates cancer cell proliferation, anti-apoptosis, and metastasis. We found that SRC kinase inhibition by Dasatinib decreased ETS-1 expression but caused elevation of IKKβ/NF-κB signaling in multiple cisplatin-resistant HNSCC. Interestingly, inhibition of IKKβ/NF-κB by CmpdA (Bay65-1942), a recently identified IKKβ inhibitor, also led to a decrease in ETS-1 levels. Moreover, the knockdown of IKK, but not NF-κB, dramatically decreased ETS-1 expression. In addition, IKKβ and ETS-1 interacted in cisplatin-resistant HNSCC. These data demonstrated cross-talk between SRC and IKK to regulate NF-κB and ETS-1. Furthermore, we found that simultaneous inhibition of SRC and IKKβ through a Dasatinib and CmpdA combination synergistically inhibited NF-κB activation and ETS-1expression, suppressed cell proliferation, and induced apoptosis. Taken together, our data indicate that SRC and IKKβ play crucial roles in cisplatinresistant HNSCCC and co-targeting SRC and IKKβ could be an effective strategy to treat cisplatin-resistant HNSCC.

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“…Pre-treatment staging is an important part of tumor diagnosis and a factor closely related to tumor prognosis. Studies have shown that T-stage of head and neck tumors, viral-related status, and lymph node status greatly influence the prognosis of cancer patients [60][61][62][63]. However, the current diagnostic methods focus on pre-treatment tissue biopsy, serological testing and traditional medical image diagnosis, which can determine tumor staging to a certain extent, but ultimately are local, qualitative and subjective.…”

Section: Pre-treatment Related Predictive Modelingmentioning

confidence: 99%

Application of radiomics and machine learning in head and neck cancers

Peng¹,

Wang²,

Wang³

et al. 2021

Int. J. Biol. Sci.

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With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.

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Imaging of Patients with Head and Neck Cancer

Cited by 36 publications

References 59 publications

Total lesion glycolysis in oral squamous cell carcinoma as a biomarker derived from pre-operative FDG PET/CT outperforms established prognostic factors in a newly developed multivariate prediction model

Total lesion glycolysis in oral squamous cell carcinoma as a biomarker derived from pre-operative FDG PET/CT outperforms established prognostic factors in a newly developed multivariate prediction model

Inhibition of IKKβ/NF-κB signaling pathway to improve Dasatinib efficacy in suppression of cisplatin-resistant head and neck squamous cell carcinoma

Application of radiomics and machine learning in head and neck cancers

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