Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response

Artificial intelligence for nonmelanoma skin cancer

Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Avelumab for advanced Merkel cell carcinoma in the Netherlands: a real-world cohort

BACKGROUND

Merkel cell carcinoma (MCC) is associated with high recurrence rates and poor survival when metastatic disease is present. The immune checkpoint inhibitor avelumab has shown high response rates (RRs) and durable responses in patients with advanced MCC (aMCC) in clinical trials. To date, only results from clinical trials, patients treated in an expanded access program and very small numbers of patients have been reported. In this study, detailed real-world efficacy and toxicity data of avelumab in patients with aMCC are reported.

METHODS

Patients with aMCC treated in four dedicated referral centers in the Netherlands were analyzed from February 2017 until December 2019. Patients were included if they had received at least one administration of avelumab, regardless of previous lines of therapy. Patient data were collected retrospectively from patient records. Primary endpoints were response rate (RR) and duration of response (DOR). Secondary endpoints were progression-free survival (PFS), overall survival (OS), and toxicity.

RESULTS

Fifty-four patients received avelumab. Eight (15%) patients had locally advanced disease (laMCC). In 40 (74%) patients, avelumab was first-line treatment, these included all patients with laMCC. The median follow-up was 8.9 (range 0.5-35.9) months. RR was 57% (n=31) with 24% (n=13) of patients achieving a complete response. The median DOR was 8.4 (range 1.3-22.1) months and 23 (43%) patients had an ongoing response at the end of the study. The median PFS was 8.6 (95% CI 1.6-15.5) months, and the median OS was 25.8 (95% CI 9.1-42.4) months. Six (11%) patients experienced grade 3 toxicity. No grade 4-5 toxicity was seen.

CONCLUSIONS

In this real-world cohort, clinical efficacy and toxicity outcomes in clinical practice were in line with results from clinical trials and showed relatively high RRs and durable responses in patients with aMCC.