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Enregistrement W4376134258 · doi:10.36401/jipo-22-x6

Abstracts Presented at the 2022 Emirates Oncology Conference

2023· article· en· W4376134258 sur OpenAlexaboutno aff
Khalid Balaraj

Notice bibliographique

RevueJournal of Immunotherapy and Precision Oncology · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Cancer Incidence and Screening
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAbu dhabiMedicineHealth careBreast cancerFamily medicineLibrary scienceMedical educationPolitical scienceCancerInternal medicinePathology

Résumé

récupéré en direct d'OpenAlex

The Emirates Oncology Conference (EOC), now in its 10th year, continues to highlight important developments in the fields of cancer prevention and treatment. The Abu Dhabi Health Services - SEHA and Tawam Hospital hosted the conference. These organizations are dedicated to offering the UAE top-tier healthcare and ongoing medical education to keep the public informed and ensure that medical experts remain up to date in their fields.The EOC featured significant sessions covering a wide range of topics with globally renowned speakers who kept the audience interested with their illuminating lectures and visual presentations. Breast cancer, hematological malignancies, palliative care, lung cancer, radiation oncology, pediatric oncology, genitourinary, gastrointestinal, and neuro-oncology were some of the topics covered.Over the course of the 3 days, there were about 4000 attendees including physicians, surgeons, researchers, healthcare professionals, and industry representatives from over 40 nations, of which 65% were from the UAE and 35% traveled from the USA, Europe, Middle East, and GCC. Approximately 150 speakers were hosted by EOC; speakers came from the USA, UK, Italy, Spain, France, Germany, Belgium, India, Pakistan, Philippines, Malaysia, Canada, and many regional nations such as Jordan, KSA, Oman, Bahrain, Lebanon, Egypt, and the UAE. In addition, more than 2 dozen abstracts were presented as oral and poster presentations, and the top 14 were chosen to be published in a medical journal as a result of EOC 2022. Selected abstracts are included herein.We are grateful to all members of the scientific and medical community, our organizing team, speakers, delegates and sponsors for their time, efforts, and contributions to making EOC one of the most important scientific events of the year.INTRODUCTION: Cervical cancer is the fourth most frequently diagnosed cancer and the fourth leading cause of cancer death in women, with an estimated 604,000 new cases and 342,000 deaths worldwide in 2020. Globally, the average age at diagnosis of cervical cancer was 53 years, ranging from 44 years to 68 years. The global average age at death from cervical cancer was 59 years, ranging from 45 years to 76 years. Cervical cancer ranked in the top three cancers affecting women younger than 45 years in 146 (79%) of 185 countries assessed. Around 85% of the global burden occurs in the less-developed regions, where cervical cancer accounts for almost 12% of all female cancers. The anatomical location, the disease extension and survival outcomes are the key factors to define the staging of disease. The treatment of the patients depends on staging and prognostic groups. Disease bulk and lymph node involvement are key prognostic factors. SCC constitutes more than 75% of all diagnosed cervical cancers however lately the incidence of adenocarcinoma has been rising6. Various histological and clinical factors such as tumor size7 parametrial invasion, cervical stromal invasion depth, LVSI, FIGO Stage, tissue histology, tumor grade, and LN metastasis are associated with survival and prognosis in cervical cancer patients. AIM: This retrospective study aimed to determine the long-term clinical outcomes and to identify the prognostic factors that impact the survival of patients with cervical cancer in the United Arab Emirates (UAE). METHODS: All patients with cervical cancer treated with concurrent Cisplatin chemotherapy and Radiotherapy followed by High dose rate brachytherapy were identified from the Tawam hospital database. Patient demographics, staging investigations, treatment details and outcomes were compiled and analyzed. Kaplan-Meier curves were used to estimate disease-free survival (DFS) rates of treated survival cancer patients, while the log-rank test was used to compare differences in DFS across categories of selected clinical and demographic factors. Ethical approval was obtained from Tawam Human Research Ethics Committee. RESULTS: Between January 2009 and December 2021, we identified 275 patients with cervical cancer meeting the inclusion criteria for this study. Patients were staged with International Federation of Gynecology and Obstetrics (FIGO) system and detailed in Tables 1 and 2. The median age of the patients was 48 years (IQR=46-64). Majority of the patients (87.6%) were at stage IIB and above. Median follow-up for the entire group was 21.4 months (IQR=7.4-40.2). The overall 5-year DFS rate was 63%. The 5-year DFS rates for Stage II, III and IVA patients were 85%, 48% and 33%, respectively [P < 0.001] (Fig 1) which is similar to the previous findings in larger trials. In other factors lymph nodes was also found to be significantly associated with DFS rates (Figs 2 and 3). CONCLUSION: This is the largest study from UAE to assess outcomes and prognostic factors for cervical cancer patients receiving radical chemo-radiotherapy. Outcomes from our cohort were comparable to published international standards. Lymph node involvement and higher FIGO stage were independent poor prognostic factors for 5-year DFS. Our findings demonstrate that concurrent chemo-radiotherapy with cisplatin and HDR brachytherapy is safe and effective treatment as per International guidelines. However, although concurrent chemo-radiotherapy is a very effective regimen for cervical cancer, newer and more effective treatment strategies may be needed for patients with poorer prognoses such as those identified in this study. Since majority of our patients were at advanced stage at diagnosis, more robust screening and awareness programs are required.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,818
Score d'incertitude au seuil0,957

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,107
Tête enseignante GPT0,417
Écart entre enseignants0,310 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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