Effect of actionable somatic mutations on racial/ethnic disparities in head and neck cancer prognosis

Head & Neck ◽  
2016 ◽  
Vol 38 (8) ◽  
pp. 1234-1241 ◽  
Author(s):  
Evan S. Wu ◽  
Jong Y. Park ◽  
Joseph A. Zeitouni ◽  
Carmen R. Gomez ◽  
Isildinha M. Reis ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Somatic Mutations ◽  
Ethnic Disparities ◽  
Cancer Prognosis ◽  
Racial Ethnic

Abstract 3876: Actionable somatic mutations in head and neck cancer prognosis

2015 ◽  
Author(s):  
Omar L. Nelson ◽  
Evan S. Wu ◽  
Jong Y. Park ◽  
Joseph Zeitouni ◽  
Carmen R. Gomez ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Somatic Mutations ◽  
Cancer Prognosis

Neutrophil-to-lymphocyte ratio in head and neck cancer prognosis: A systematic review and meta-analysis

Head & Neck ◽  
2018 ◽  
Vol 40 (5) ◽  
pp. 1091-1100 ◽  
Author(s):  
Marco A. Mascarella ◽  
Erin Mannard ◽  
Sabrina Daniela Silva ◽  
Anthony Zeitouni
Keyword(s):  
Systematic Review ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Meta Analysis ◽  
Neutrophil To Lymphocyte Ratio ◽  
Cancer Prognosis ◽  
Lymphocyte Ratio

scholarly journals Survival of dental implants and occurrence of osteoradionecrosis in irradiated head and neck cancer patients: a systematic review and meta-analysis

2021 ◽  
Author(s):  
Daniel Jan Toneatti ◽  
Ronny Roger Graf ◽  
John-Patrik Burkhard ◽  
Benoît Schaller
Keyword(s):  
Systematic Review ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Dental Implants ◽  
Neck Cancer ◽  
Meta Analysis ◽  
Cancer Prognosis ◽  
Implant Survival

Abstract Objectives This systematic review assesses dental implant survival, calculates the incidence rate of osteoradionecrosis, and evaluates risk factors in irradiated head and neck cancer patients. Materials and methods Various databases (e.g., Medline/Embase using Ovid) and gray literature platforms were searched using a combination of keywords and subject headings. When appropriate, meta-analysis was carried out using a random effects model. Otherwise, pooled analysis was applied. Results A total of 425 of the 660 included patients received radiotherapy. In total, 2602 dental implants were placed, and 1637 were placed in irradiated patients. Implant survival after an average follow-up of 37.7 months was 97% (5% confidence interval, CI 95.2%, 95% CI 98.3%) in nonirradiated patients and 91.9% (5% CI 87.7%, 95% CI: 95.3%) after an average follow-up of 39.8 months in irradiated patients. Osteoradionecrosis occurred in 11 cases, leading to an incidence of 3% (5% CI 1.6%, 95% CI 4.9%). The main factors impacting implant survival were radiation and grafting status, while factors influencing osteoradionecrosis could not be determined using meta-analysis. Conclusion Our data show that implant survival in irradiated patients is lower than in nonirradiated patients, and osteoradionecrosis is—while rare—a serious complication that any OMF surgeon should be prepared for. The key to success could be a standardized patient selection and therapy to improve the standard of care, reduce risks and shorten treatment time. Clinical relevance Our analysis provides further evidence that implant placement is a feasible treatment option in irradiated head and neck cancer patients with diminished oral function and good long-term cancer prognosis.

scholarly journals A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

2021 ◽  
Author(s):  
Benjamin Haibe-Kains ◽  
Michal Kazmierski ◽  
Mattea Welch ◽  
Sejin Kim ◽  
Chris McIntosh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Clinical Data ◽  
Neck Cancer ◽  
Evaluation Framework ◽  
Cancer Prognosis ◽  
Survival Prediction ◽  
Precision Oncology

Abstract Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider range of data, including imaging. Radiomics aims to extract quantitative predictive and prognostic biomarkers from routine medical imaging, but evidence for computed tomography radiomics for prognosis remains inconclusive. We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis. Using a large, retrospective dataset of 2,552 patients and a rigorous evaluation framework, we compared 12 different submissions using imaging and clinical data, separately or in combination. The winning approach used non-linear, multitask learning on clinical data and tumour volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction and outperforming models relying on clinical data only, engineered radiomics and deep learning. Combining all submissions in an ensemble model resulted in improved accuracy, with the highest gain from a image-based deep learning model. Our results show the potential of machine learning and simple, informative prognostic factors in combination with large datasets as a tool to guide personalized cancer care.

scholarly journals Association between PD-L1 expression and head and neck cancer prognosis: a meta-analysis

2019 ◽  
Author(s):  
Zhisen Shen ◽  
Linrong Wu ◽  
Xianlei Cai ◽  
Dong Ye ◽  
Gangjun Zhao
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Meta Analysis ◽  
Head And Neck Tumors ◽  
Cancer Prognosis ◽  
Cochrane Library ◽  
Prognostic Impact ◽  
Poor Prognostic Factor ◽  
Neck Tumors

Abstract Background: Programmed cell death ligand 1（PD-L1) plays an important role in tumor cell immune escape, and it has been extensively studied in head and neck cancer. However, its prognostic impact on patients with head and neck cancer remains controversial, so we sought to investigate this issue through a comprehensive meta-analysis. Methods: To assess the significance of PD-L1 on the survival of patients with head and neck cancer, we collected articles reported in PubMed, EMBASE, and Cochrane Library, until January 31, 2019. We also used the Newcastle Ottawa Scale (NOS) for literature quality evaluation. Results: The study included a total of 4551 patients affected by 6 different types of head and neck cancer reported in 26 articles. Our study found that the association between the expression of PD-L1 and the prognosis of head and neck tumors was highly heterogeneous (P < 0.00001, I2 = 80.0%); therefore, the random effects model was applied to combine the effect sizes. Based on the combined hazard ratios (HR)of 1.15 (95% CI: 0.88 to 1.50, P = 0.32), the expression of PD-L1 in head and neck tumors may not be a factor associated with poor prognosis. Conclusions: Our results suggest that PD-L1 expression cannot predict the overall survival of patients with oral, nasopharyngeal, or esophageal cancer. Through subgroup analysis, we found that the expression of PD-L1 may be a poor prognostic factor for some head and neck cancers.

Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT

2021 ◽  
pp. 59-68
Author(s):  
Pierre Fontaine ◽  
Vincent Andrearczyk ◽  
Valentin Oreiller ◽  
Joël Castelli ◽  
Mario Jreige ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Cancer Prognosis ◽  
Prognosis Prediction ◽  
Pet Ct ◽  
Fully Automatic

HPV status in unknown primary head and neck cancer: Prognosis and treatment outcomes

2018 ◽  
Vol 129 (3) ◽  
pp. 684-691 ◽  
Author(s):  
Shayan Cheraghlou ◽  
Sina J. Torabi ◽  
Zain A. Husain ◽  
Michael D. Otremba ◽  
Heather A. Osborn ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Treatment Outcomes ◽  
Neck Cancer ◽  
Cancer Prognosis ◽  
Unknown Primary ◽  
Hpv Status

scholarly journals Somatic mutations and promotor methylation of the ryanodine receptor 2 is a common event in the pathogenesis of head and neck cancer

2019 ◽  
Vol 145 (12) ◽  
pp. 3299-3310 ◽  
Author(s):  
Katrin Schmitt ◽  
Britta Molfenter ◽  
Natalia Koerich Laureano ◽  
Bouchra Tawk ◽  
Matthias Bieg ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Ryanodine Receptor ◽  
Neck Cancer ◽  
Somatic Mutations ◽  
Common Event
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