Artificial Intelligence and Advanced Melanoma: Treatment Management Implications

Abstract

Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by “intelligent” machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

“…Several studies have investigated the application of AI in the treatment and management of advanced melanoma. A review by Guerrisi et al has discussed the application significance and challenges of AI in melanoma treatment but failed to assess the risk of bias and accuracy of existing machine learning models (51). Valenti and colleagues have provided an overview of the application of multi-omics (genomics, transcriptomics, proteomics, metabolomics and radiomics) in evaluating the immunotherapy response of melanoma.…”

Development of Personalized Strategies for Precisely Battling Malignant Melanoma

“…In oncology, these technologies could allow clinicians to make precision-based predictions, diagnoses, and treatment decisions solely from analyzing patient data. Additionally, these technologies have the potential to improve accuracy, minimize patient sample volume collection, and detect melanoma and metastasis progression earlier [160,161]. For example, Marchetti et al [162] demonstrated the use of an AI algorithm (ADAE) to analyze dermatoscopy images of skin lesions and subsequently predict melanoma risk.…”

Role of the Microbiome in Immunotherapy of Melanoma

“…34,35 Others have applied machine learning methods in areas of genetic analysis of metastases, identification of markers associated with disease progression, and even analysis of histologic samples as potential markers for predicting response to therapy. [35][36][37][38] With similar methods, models could be developed to analyze a patient's microbiome composition before ICI therapy initiation. With this information, predictive algorithms could be generated based on the composition and abundance of specific microbial populations to predict whether patients will respond positively to therapy.…”

“…To attempt recommendations on treatments with high efficacy and low adverse effect frequency, the predictive algorithms incorporated information such as age, sex, melanoma type, spontaneous regression, number of invaded lymph nodes, extracapsular extension, mutational status, melanoma stage, number of metastases sites, lines of treatment, and so on. 34,35 Others have applied machine learning methods in areas of genetic analysis of metastases, identification of markers associated with disease progression, and even analysis of histologic samples as potential markers for predicting response to therapy. [35][36][37][38] With similar methods, models could be developed to analyze a patient's microbiome composition before ICI therapy initiation.…”

“…Goussault et al 34 have validated 4 machine learning models used for the prediction of response to immunotherapy in late-stage (IIIc or IV) melanoma patients. To attempt recommendations on treatments with high efficacy and low adverse effect frequency, the predictive algorithms incorporated information such as age, sex, melanoma type, spontaneous regression, number of invaded lymph nodes, extracapsular extension, mutational status, melanoma stage, number of metastases sites, lines of treatment, and so on 34,35 . Others have applied machine learning methods in areas of genetic analysis of metastases, identification of markers associated with disease progression, and even analysis of histologic samples as potential markers for predicting response to therapy 35–38 .…”