Development of a metastatic risk model for cutaneous squamous cell carcinoma
Background: Cutaneous squamous cell carcinoma (cSCC) is the most common cancer capable of metastasis. Due to its high incidence and lack of inclusion in national databases it has been difficult to identify high-risk factors associate with metastasis. The development of a cSCC metastatic risk model would help physicians identify patients who are at risk for metastasis, and would allow for the initiation of early aggressive management to improve outcomes.
Aim: Explore different statistical approaches to develop a model to predict cSCC metastasis that is accurate and reflects routine clinical practice.
Methods: All cSCCs diagnosed and treated at Saint Louis University from January 2010 to March 2012 were included. Three statistical approaches were studied: multivariable logistic regression (MLR), pattern classification (PC) and sum score method (SSM). Two models using the SSM were created with a different number of factors used to merit assignment to the metastatic cohort: 2 factors (S2) or >2 factors (S2+). For each model, sensitivity (SN), specificity (SP) and positive predictive value (PPV) were calculated.
Results: SN, SP, and PPV for each model were: MLR: SN 4.3%, SP 97.4%, PPV 16.0%; S2: SN 78.3%, SP 83.7%, PPV 12.5%; S2+: SN 60.9%, SP 96.5% PPV 34.1%; PC: SN 73.9%, SP 95.9%, PPV 34.7%.Conclusions: The PC model was the most accurate. The S2+ model had a lower SN, but would be easier to implement as clinicians would only have to sum high-risk factors.
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