Impact of modern systemic therapies and clinical markers on treatment outcome for metastatic melanoma in a real-world setting

Progressive Observation of Covid-19 Vaccination Effects on Skin-Cellular Structures by Use of Intelligent Laser Speckle Classification (ILSC) Technique

The recent pandemic showed that the current global research strategies on vaccine development in an emergency period necessitates more optimized supplementary techniques to observe instant progressive vaccines' subtle effects on human metabolisms to make better and speedy evolutionary health assessments. To fill this gap, we have followed a multi-disciplinary approach exploiting AI, laser-optics, and specific imaging methods. The proposed technique can make progressive observations on Covid-19 Astra Zeneca vaccination effects on skin cellular network by use of the well-established technique-Intelligent Laser Speckle Classification (ILSC), as Covid-19 is a skin-affecting systemic disease. The method also managed to distinguish between three different subject groups via their laser speckle skin image samplings, grouped as early-vaccinated, late-vaccinated and non-vaccinated participants. The results have proven that the ILSC technique, in association with the parametrically optimised Bayesian network, can classify hidden skin changes of vaccinated and non-vaccinated individuals up to 90% accuracy and is also capable of detecting instant progressive developments pertaining to skin cellular properties. The proposed method has also proven that the continuous Covid-19 vaccine effect on the sub-skin layers can be observable by high frequency and speedy non-invasive data collection in real-time with high reliability.

Long-term survival trends in solid cancers in the Nordic countries marking timing of improvements

Survival studies are an important indicator of the success of cancer control. We analyzed the 5-year relative survival in 23 solid cancers in Denmark, Finland, Norway and Sweden over a 50-year period (1970-2019) at the NORDCAN database accessed from the International Agency for Research on Cancer website. We plotted survival curves in 5-year periods and showed 5-year periodic survival. The survival results were summarized in four groups: (1) cancers with historically good survival (>50% in 1970-1974) which include melanoma and breast, endometrial and thyroid cancers; (2) cancers which constantly improved survival at least 20% units over the 50 year period, including cancers of the stomach, colon, rectum, kidney, brain and ovary; (3) cancer with increase in survival >20% units with changes taking place in a narrow time window, including oral, oropharyngeal, testicular and prostate cancers; (4) the remaining cancers with <20% unit improvement in survival including lung, esophageal, liver, pancreatic, bladder, soft tissue, penile, cervical and vulvar cancers. For cancers in groups 1 and 2, the constant development implied multiple improvements in therapy, diagnosis and patient care. Cancers in group 3 included testicular cancers with known therapeutic improvements but for the others large incidence changes probably implied that cancer stage (prostate) or etiology (oropharynx) changed into a more tractable form. Group 4 cancers included those with dismal survival 50 years ago but a clear tendency upwards. In 17 cancers 5-year survival reached between 50% and 100% while in only six cancers it remained at below 50%.

SURVIVAL IN MELANOMA IN THE NORDIC COUNTRIES INTO THE ERA OF TARGETED AND IMMUNOLOGICAL THERAPIES

OBJECTIVES

Survival in melanoma has been increasing and the most recent interest is to observe the population-level impact of novel targeted therapies and immunotherapy. We analysed survival in melanoma from Denmark (DK), Finland (FI), Norway (NO) and Sweden (SE) over a 50-years period (1971-2020).

METHODS

Relative 1-5/1- and 5-year survival data were obtained from the NORDCAN database for the years 1971-2020. We estimated annual changes in survival rates and determined significant breaking points for trends.

RESULTS

Survival in melanoma has reached the point where 1-year survival is approaching 100% (men 97.5-98.6%, women 98.4-99.3%, depending on the country) and 5-year survival is 93% for men (91.5-95.2%) and 96% for women (95.3-97.2%). The highest survival figures were for DK. Significant increases in both 1- and 5-year survival were observed in most countries even towards the end of the follow-up (from 2006 to 2010-2011-2015 and further to 2016-2020).

CONCLUSIONS

The main increase in melanoma survival took place up to year 1990, which was probably largely achieved through successful population campaigns for sun protection and programmes for early detection of lesions. Survival increased again after year 2000 up to the last period 2016-2020. This late development coincided with the introduction of targeted therapies using BRAF and BRAF/MEK inhibitors, and towards the end of the time period availability of checkpoint inhibitors. The success of melanoma treatment in DK was mostly likely due to the efficient use of modern therapies and to the centralised treatment for metastatic disease.

Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy

The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale" of Napoli, Italy (INT-NA). To compare patients' clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm-survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.