Dataset Information

RNA interference in head and neck oncology.

ABSTRACT: Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cause of cancer worldwide. The treatment of choice in case of head and neck cancer is surgery, followed by chemo- or/and radiotherapy. A potentially effective instrument to improve the outcome of numerous diseases, including viral infections, diabetes and cancer, is RNA interference (RNAi). It has been demonstrated that small interfering RNA and microRNA molecules are strongly involved in the regulation of various different pathological processes in cancer development. RNAi has become a valuable research tool allowing a better understanding of the mechanisms regulating cancer pathogenesis. Considering those advantages over other current therapeutics (including specificity and high efficacy), RNAi appears to be a potentially useful tool in cancer treatment. The present review discusses the current knowledge about the possibility of using RNAi in HNSCC therapy.

SUBMITTER: Sobecka A

PROVIDER: S-EPMC5103899 | biostudies-other | 2016 Nov

REPOSITORIES: biostudies-other

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RNA interference in head and neck oncology.

Sobecka Agnieszka A Barczak Wojciech W Suchorska Wiktoria Maria WM

Oncology letters 20160902 5

Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cause of cancer worldwide. The treatment of choice in case of head and neck cancer is surgery, followed by chemo- or/and radiotherapy. A potentially effective instrument to improve the outcome of numerous diseases, including viral infections, diabetes and cancer, is RNA interference (RNAi). It has been demonstrated that small interfering RNA and microRNA molecules are strongly involved in the regulation of various different patho ...[more]

PMID: 27899959

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Project description:ImportanceIn addition to their patient management value, multidisciplinary tumor boards have been recognized as effective learning tools. However, the value of using a virtual tumor board as a learning tool for head and neck surgical oncology fellows has not been studied.ObjectiveTo describe the structure and content of the American Head and Neck Society (AHNS) Virtual Tumor Board and assess its educational value as perceived by attendees.Design, setting, and participantsAll sessions of the AHNS Virtual Tumor Board from April 8, 2020, to June 1, 2022, were reviewed. Topics, presenters, participants, and viewership data were collected as of October 15, 2022, from session recordings posted to an online video sharing and social media platform. Additionally, an anonymous, 14-question online survey was designed to elicit feedback from head and neck surgery trainees on virtual tumor board engagement, strengths, and weaknesses. The survey was electronically distributed in June and July 2022 to the 101 fellows enrolled in AHNS-accredited programs between July 1, 2020, and June 30, 2022.Main outcomes and measuresThe primary aim was to tabulate online viewership of the sessions. The secondary aim was to qualitatively assess the experience of head and neck trainees with the AHNS Virtual Tumor Board using a survey.ResultsForty-two sessions of the virtual tumor board were held between April 8, 2020, and June 1, 2022. Almost all sessions (41 [98%]) were case based. One hundred and sixteen cases were presented, representing 2 to 3 cases per session, by 75 unique faculty members. Each session was viewed a mean of 217 times (range, 64-2216 views). In the 2021 to 2022 academic year, a mean of 60 viewers (range, 30-92 viewers) attended each live session. In all, 29 survey responses were collected from 101 fellows in AHNS-accredited programs (29% response rate). Most respondents felt the format allowed for excellent teaching (18 of 26 respondents [69%]) and discussion (19 of 26 respondents [73%]). Most respondents (22 of 29 respondents [76%]) believed that practicing head and neck surgeons would benefit from the sessions.Conclusions and relevanceThis survey study found that the AHNS Virtual Tumor Board was well-attended and well-reviewed by head and neck surgical oncology trainees. The virtual tumor board format could be used as model of remote learning for other organizations.

Project description:ObjectivesThere is a paucity of literature on what contributes to 30-day readmission rates from the patient's perspective in head and neck cancer patients. Post-operatively, these patients are often discharged with multiple surgical sites and home equipment requiring education and a large responsibility upon arriving home. Patients have a unique understanding of the discharge process and rationale behind presenting for care following initial discharge. Understanding the experience of the readmitted patient may be critical in finding effective methods to prevent unnecessary readmissions.MethodsThrough key-informant interviews, we assessed factors that impact readmission rates from the patient perspective. Patients were eligible if they were discharged within the last 30 days and subsequently readmitted following an admission for a head and neck cancer surgery. Inductive coding was used to analyze interview transcripts and develop themes.ResultsThirteen respondents were interviewed. 46% (n = 6) did not feel ready for discharge after the index admission. Out of these 6 patients, 83% (n = 5) felt their readmission could have been avoided. Patients often encountered unexpected events after returning home from the index admission and felt their readmissions were potentially avoidable with more information regarding what to expect at home, prior to discharge. Although medically stable upon discharge, these findings indicate some patients might be discharged sooner than they should be due to factors outside of medical indications.ConclusionsTo address the various biopsychosocial components of patient care in addition to medical aspects, an otolaryngology-specific, prior-to-discharge checklist could aid in lowering 30-day readmissions. Since the amount of education each patient requires to feel competent managing their health at home varies, more work is needed to craft post-education assessments of patient/caregiver competencies for home equipment, prior to discharge to reduce avoidable readmissions.

Project description:Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the "HPV" challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the "local recurrence" challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

Dataset Information

RNA interference in head and neck oncology.

Publications

RNA interference in head and neck oncology.

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