RESUMO
Background: Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer, affecting more than 2 million people worldwide yearly and metastasising in 2-5% of patients. However, current clinical staging systems do not provide estimates of absolute metastatic risk, hence missing the opportunity for more personalised treatment advice. We aimed to develop a clinico-pathological model that predicts the probability of metastasis in patients with cSCC. Methods: Nationwide cohorts from (1) all patients with a first primary cSCC in The Netherlands in 2007-2008 and (2) all patients with a cSCC in 2013-2015 in England were used to derive nested case-control cohorts. Pathology records of primary cSCCs that originated a loco-regional or distant metastasis were identified, and these cSCCs were matched to primary cSCCs of controls without metastasis (1:1 ratio). The model was developed on the Dutch cohort (n = 390) using a weighted Cox regression model with backward selection and validated on the English cohort (n = 696). Model performance was assessed using weighted versions of the C-index, calibration metrics, and decision curve analysis; and compared to the Brigham and Women's Hospital (BWH) and the American Joint Committee on Cancer (AJCC) staging systems. Members of the multidisciplinary Skin Cancer Outcomes (SCOUT) consortium were surveyed to interpret metastatic risk cutoffs in a clinical context. Findings: Eight out of eleven clinico-pathological variables were selected. The model showed good discriminative ability, with an optimism-corrected C-index of 0.80 (95% Confidence interval (CI) 0.75-0.85) in the development cohort and a C-index of 0.84 (95% CI 0.81-0.87) in the validation cohort. Model predictions were well-calibrated: the calibration slope was 0.96 (95% CI 0.76-1.16) in the validation cohort. Decision curve analysis showed improved net benefit compared to current staging systems, particularly for thresholds relevant for decisions on follow-up and adjuvant treatment. The model is available as an online web-based calculator (https://emc-dermatology.shinyapps.io/cscc-abs-met-risk/). Interpretation: This validated model assigns personalised metastatic risk predictions to patients with cSCC, using routinely reported histological and patient-specific risk factors. The model can empower clinicians and healthcare systems in identifying patients with high-risk cSCC and offering personalised care/treatment and follow-up. Use of the model for clinical decision-making in different patient populations must be further investigated. Funding: PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships.
RESUMO
BACKGROUND: Risk factors for cutaneous squamous cell carcinoma (cSCC) metastasis have been investigated only in relatively small data sets. OBJECTIVE: To analyze and replicate risk factors for metastatic cSCC. METHODS: From English and Dutch nationwide cancer registry cohorts, metastatic cases were selected and 1:1 matched to controls. The variables were extracted from pathology reports from the National Disease Registration Service in England. In the Netherlands, histopathologic slides from the Dutch Pathology Registry were revised by a dermatopathologist. Model building was performed in the English data set using backward conditional logistic regression, whereas replication was performed using the Dutch data set. RESULTS: In addition to diameter and thickness, the following variables were significant risk factors for metastatic cSCC in the English data set (n = 1774): poor differentiation (odds ratio [OR], 4.56; 95% CI, 2.99-6.94), invasion in (OR, 1.69; 95% CI, 1.05-2.71)/beyond (OR, 4.43; 95% CI, 1.98-9.90) subcutaneous fat, male sex (OR, 2.59; 95% CI, 1.70-3.96), perineural/lymphovascular invasion (OR, 2.12; 95% CI, 1.21-3.71), and facial localization (OR, 1.57; 95% CI, 1.02-2.41). Diameter and thickness showed significant nonlinear relationships with metastasis. Similar ORs were observed in the Dutch data set (n = 434 cSCCs). LIMITATIONS: Retrospective use of pathology reports in the English data set. CONCLUSION: cSCC staging systems can be improved by including differentiation, clinical characteristics such as sex and tumor location, and nonlinear relationships for diameter and thickness.