Search Results for: Linee guida sul melanoma cutaneo

Introduction
Diagnosing invasive cutaneous melanoma (CM) can be challenging due to subjectivity in distinguishing equivocal nevi, melanoma in situ and thin CMs. The underlying molecular mechanisms of progression from nevus to melanoma must be better understood. Identifying biomarkers for treatment response, diagnostics and prognostics is crucial. Using biomedical data from biobanks and population-based healthcare data, translational research can improve patient care by implementing evidence-based findings. The BioMEL biobank is a prospective, multicentre, large-scale biomedical database on equivocal nevi and all stages of primary melanoma to metastases. Its purpose is to serve as a translational resource, enabling researchers to uncover objective molecular, genotypic, phenotypic and structural differences in nevi and all stages of melanoma. The main objective is to leverage BioMEL to significantly improve diagnostics, prognostics and therapy outcomes of patients with melanoma.

Methods and analysis
The BioMEL biobank contains biological samples, epidemiological information and medical data from adult patients who receive routine care for melanoma. BioMEL is focused on primary and metastatic melanoma, but equivocal pigmented lesions such as clinically atypical nevi and melanoma in situ are also included. BioMEL data are gathered by questionnaires, blood sampling, tumour imaging, tissue sampling, medical records and histopathological reports.

Ethics and dissemination
The BioMEL biobank project is approved by the national Swedish Ethical Review Authority (Dnr. 2013/101, 2013/339, 2020/00469, 2021/01432 and 2022/02421-02). The datasets generated are not publicly available due to regulations related to the ethical review authority.

Trial registration number
NCT05446155.

Stroke, Volume 55, Issue Suppl_1, Page ATMP105-ATMP105, February 1, 2024. Introduction:Cancer is an emerging risk factor for ischemic stroke. The risk of stroke is highest during the first year after a new diagnosis of cancer, but no tools exist to identify which patients are at the highest risk..Methods:Using linked clinical and administrative health databases, we conducted a population-based retrospective cohort study of adults in Ontario, Canada with newly diagnosed cancer from 2010 – 2021 (excluding non-melanoma skin cancer & central nervous system malignancies). Patients were randomly selected for model derivation (60%) or validation (40%). The final model predicting stroke within 1 year following cancer diagnosis was derived using multivariable Fine-Gray regression with candidate predictors selected via backward elimination. Sub-distribution adjusted hazard ratios (aHR) and 95% confidence intervals (CI) were calculated, where all-cause mortality was treated as a competing event. Model performance of the validation cohort was assessed using the C-statistic & calibration plots for discrimination and calibration, respectively.Results:Of the 698,566 eligible patients, 418,911 were randomly allocated to derivation, and 279,576 to validation. The overall rate of stroke per 1000 person-years was 6.7 (6.4 – 6.9) for the derivation cohort. The final model included 22 predictors: age, sex, long-term care residency, history of heart failure, hypertension, dementia, asthma, atrial fibrillation, dyslipidemia, liver disease, ischemic stroke, transient ischemic attack, valvular disease, venous thromboembolism, hospitalization within the last 3 months, cancer type, cancer stage, cancer surgery or chemotherapy 3 months following diagnosis, and several 2-way interactions with age & cancer type. Discrimination was good, with a c-statistic of 0.73 in the validation cohort. The model was well calibrated, with points following the 45-degree line (Fig 1).Conclusion:We derived and validated a risk prediction model for ischemic stroke in patients with a new cancer diagnosis with good discrimination. Although our results require external validation, it has potential to identify individuals at highest risk for future randomized trials.

In the Navajo language, cancer is broadly described as łóód dóó nádzi híí, which translates directly as a “sore that does not heal.” Accurate determination of cancer incidence in a specified population is a critical first step toward addressing disease burden. Previous studies have shown that racial misclassification is a problem that hinders epidemiologic research in American Indian/Alaska Native populations and underestimates American Indian/Alaska Native cancer incidence. In this issue of JAMA Dermatology, Townsend et al use a method that corrects for racial misclassification among American Indian/Alaska Native patients with melanoma and show that the non-Hispanic American Indian/Alaska Native population has the second highest incidence of melanoma and a rising incidence of late-stage melanoma diagnoses. This melanoma incidence (10.7 per 100 000) is nearly double those previously published (4.5 to 5.5 per 100 000) behind non-Hispanic White patients (21.9 to 32.2 per 100 000). These findings suggest that previous studies may have overlooked American Indian/Alaska Native health disparities and underscore the importance of minimizing racial misclassification in this population.

This cross-sectional study examines invasive cutaneous melanoma incidence rates and trends over time among non-Hispanic American Indian/Alaska Native people.

Paziente è sottoposto alla sperimentazione di fase III

Paziente è sottoposto alla sperimentazione di fase III

Rappresenta le aziende a capitale europeo e nipponico

Sono 224mila i casi diagnosticati, ma si stimano circa 600mila

Sono 224mila i casi diagnosticati, ma si stimano circa 600mila

New England Journal of Medicine, Volume 390, Issue 3, Page 255-265, January 2024.