Publications by authors named "E Dika"
Article Synopsis
- - Melanoma cases are on the rise, leading to frequent updates in guidelines for diagnosis, staging, and treatment; recent approvals now include adjuvant therapy for stage IIb/c melanoma.
- - A study involving 92 melanoma patients revealed that those with stage IIb/c have a later age of diagnosis and a higher occurrence of specific characteristics like ulceration and angiotropism compared to stage IIIa patients.
- - While not statistically significant, stage IIb/c patients showed a higher rate of metastasis over a 5-year period (15%) compared to those with stage IIIa melanoma (4%), highlighting the potential benefits of adjuvant immunotherapy for stage IIb/c patients.
Exploring the Common Mutational Landscape in Cutaneous Melanoma and Pancreatic Cancer.
Pigment Cell Melanoma Res
January 2025
Cutaneous melanoma (CM) and pancreatic cancer are aggressive tumors whose incidences are rapidly increasing in the last years. This review aims to provide a complete and update description about mutational landscape in CM and pancreatic cancer, focusing on similarities of these two apparently so different tumors in terms of site, type of cell involved, and embryonic origin. The familial forms of CM and pancreatic cancers are often characterized by a common mutated gene, namely CDKN2A.
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- Advanced melanoma treatments have evolved with targeted therapies and immunotherapies, which significantly impact management but also lead to increased skin toxicities during treatment.
- A study in Bologna identified that 37.5% of 202 patients treated for melanoma developed cutaneous adverse events, primarily linked to drugs like ipilimumab and nivolumab.
- Most skin reactions were low-grade and manageable, but some resulted in severe conditions, highlighting the importance of recognizing and treating these toxicities to ensure patient quality of life.
Article Synopsis
- Diagnosing atypical pigmented facial lesions (aPFLs) is difficult for dermatologists and crucial for patient care, as incorrect diagnoses can lead to mismanagement and delays in treatment.
- The study compared machine learning and deep learning models to improve diagnostic accuracy of aPFLs using 1197 dermoscopic images classified into seven categories, focusing on the potential role of AI in supporting clinicians.
- Results showed that while dermatologists were 71.2% accurate in identifying malignant versus benign lesions, their accuracy dropped to 42.9% when distinguishing among specific lesions, highlighting the complexity of aPFL evaluations.