Evaluation and enhancement of the prognostic ability of the eighth edition of TNM staging in cutaneous malignant melanoma: a population-based study, using machine learning, of 111 871 patients

Clinical Snapshot

Key Findings

• P Value: Not specifically reported for primary discrimination measures

• Effect Size: C-indices for enhanced models: 0.84-0.85 (OS) and 0.89-0.92 (MSS)

• Primary Outcome: Overall survival and melanoma-specific survival discrimination

• Nnt Or Sensitivity: C-index improvement from 0.67-0.72 to 0.84-0.85 for OS; 0.82-0.87 to 0.89-0.92 for MSS

• Confidence Interval: C-index ranges provided but specific confidence intervals not reported

Clinical Application

Requires access to comprehensive clinical data and computational tools; may be challenging in routine practice without decision support systems Relevant to Australian melanoma management given high incidence rates; could inform Cancer Council guidelines and specialist referral pathways under Medicare Adults with cutaneous malignant melanoma requiring prognostic assessment for treatment and surveillance planning

Abstract

BACKGROUND: Melanoma is the fifth most common cancer in the USA and the UK, with global incidence on the rise. The TNM staging system guides treatment decisions and predicts patients' outcomes. OBJECTIVES: To evaluate the prognostic ability of the eighth edition TNM (TNM-8) staging system to predict overall (OS) and melanoma-specific survival (MSS) in cutaneous malignant melanoma (CMM), and to explore the potential of machine learning (ML) methods to enhance melanoma prognostication. METHODS: The records of adult patients diagnosed with CMM from 2018 to 2022 were extracted from the Surveillance, Epidemiology, and End Results (SEER) Program database. TNM-8 was evaluated using Kaplan-Meier plots and OS and MSS probabilities. Fine-Gray competing risk and accelerated failure time (AFT) models evaluated factors affecting MSS and OS, respectively. ML models were used to evaluate the predictive ability of TNM stages for OS and MSS, compared with that of multiple clinically important prognostic variables. RESULTS: Altogether, 111 871 patients with CMM were included in the analysis, with OS and MSS as survival endpoints. Most T (tumour), N (node), M (metastasis) and TNM-8 stage categories had distinct MSS and OS probabilities, except in the N1 and N2 stages at 12 months and in OS for TNM-8 stages 2 and 3 at 12, 36, 48 and 59 months. Fine-Gray and AFT models showed that age, sex, race/origin, tumour site, histological type, ulceration, Breslow thickness, mitotic rate, number of positive lymph nodes and M stage were important prognostic variables for MSS and OS. ML models showed that TNM had a higher predictive prognostic ability for MSS than OS and that including clinically important prognostic variables in addition to TNM stage has higher discriminative prognostic ability for OS and MSS (testing C-indices range 0.84-0.85 and 0.89-0.92, respectively) than using TNM staging alone (testing C-indices range 0.67-0.72 and 0.82-0.87, respectively). CONCLUSIONS: This proof-of-concept study demonstrated that although TNM staging retains prognostic value, adding important prognostic variables and using ML could improve prognostication for patients with melanoma. ML could be used to develop interactive clinical decision-support tools to improve the prognostication of individuals with melanoma. Cutaneous malignant melanoma is a common type of skin cancer. A guide called the ‘TNM’ is often used to decide what treatment patients should get. It also helps doctors decide how long patients need specialist care for. The guide is also used to help determine what the outlook for a patient might be. Lots of factors can affect the outcome for a person with the disease. These include a person’s age, sex and race. They also include factors related to the tumour, whether lymph glands are involved and whether the cancer has spread to other organs. Not all these factors are included in the current TNM guide. An accurate staging system is important. It will ensure that patients are followed-up and treated appropriately. This will help avoid unnecessary treatment or surveillance. In this study, we used a type of artificial intelligence (AI) to test how well the latest edition of the TNM can determine what the outlook for patients with melanoma is. We also used it to see if adding other important factors could improve its ability to predict the outlook for these patients. We included over 100,000 patients with melanoma from a large database called SEER. We found that using AI in addition to the current TNM guide could enhance the prediction of outcomes for patients with melanoma. The findings suggest that AI could be useful in helping health professionals predict outcomes for patients with melanoma.

References

1. 1.Mortagy, M., Cliff-Patel, N., Askary, R., Bologan, A. M., Abdelhameed, A., Burns, D., Ramage, J., & Akhras, V. (2026). Evaluation and enhancement of the prognostic ability of the eighth edition of TNM staging in cutaneous malignant melanoma: a population-based study, using machine learning, of 111 871 patients. British Journal of Dermatology. https://doi.org/10.1093/bjd/ljag018