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Guide to ID at-risk patients


A recent study explored varying statistical approaches to a metastatic risk model for identifying SCC tumors at high risk of metastasis. Researchers identified six risk factors.

A recent study put forth the first metastatic risk model for cutaneous squamous cell carcinoma (cSCC), a utility that could significantly help clinicians better identify which patients may be at risk for metastasis, enabling the initiation of timely aggressive management to improve outcomes. 

Cutaneous squamous cell carcinoma is one of the most common cancers capable of metastasis. Invasive cSCC refers to cancer cells that have grown beyond the epidermis. Cutaneous squamous cell carcinoma is the second most common cancer diagnosed, and there has been an alarming increase in global incidence. The rate of metastasis is relatively rare. However, in cases where metastasis occurs, the disease course has significant morbidity. 

For the majority of patients diagnosed with cSCC, the disease is largely curable using techniques such as Mohs surgery or wide local excision. However, in the high-risk subgroup, an increasing number of people are requiring very extensive and potentially disfiguring surgery, which could negatively impact quality of life. 

“It would be ideal if we could identify these higher-risk patients earlier, recognize the potential for a given SCC to metastasize, and accordingly choose appropriate aggressive therapies to optimally treat these tumors in a timely fashion,” says Brandon T. Beal, M.D., a dermatologist in the department of dermatology at Cleveland Clinic. Dr. Beal is a co-author of the study which was published in the first issue of Skin: The journal of cutaneous medicine (July 2017).


Due to its high incidence and lack of inclusion in national databases, Dr. Beal says it has been very challenging to identify high-risk factors for cSCC associated with metastasis. He and colleagues recently conducted a study to explore a variety of different statistical approaches for developing a model to predict cSCC metastasis accurately, and that reflects routine clinical practice. 

The study analysis included all of the cSCCs (n=800) diagnosed and treated at Saint Louis University from January 2010 to March 2012. Dermatology diagnosed, managed, and/or treated 93.4% of the tumors. 

Researchers identified six risk factors associated with metastatic cSCC, including poorly or moderately differentiated histology (OR: 5.88), anatomic location (OR: 4.11), size in context of location (OR: 4.01), rapidly growing (OR: 3.03), recurrent (OR: 2.71), and perineural invasion (OR: 2.03).

“This is an affirmation of what is being done in centers that treat high-risk skin cancer. This metastatic risk model offers clinicians a novel approach to calculate the risk of metastatic disease in their patients with cSCC, and could assist them in identifying high-risk patients early,” Dr. Beal says. 

The three statistical approaches that were studied included: 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: Two factors (S2) or greater than two factors (S2+). For each model, sensitivity (SN), specificity (SP), and positive predictive value (PPV) were calculated. 

Results showed that the PC model was the most accurate predicting metastasis. The SN, SP, and PPV for each model were: MLR: SN 4.3%, SP 97.4%, PPV 16%; 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%, and PPV 34.7%, respectively.

NEXT: Guidelines




Clinicians are already taking the suggestions and guidelines from the National Comprehensive Cancer Network (NCCN) and the American Joint Committee on Cancer (AJCC) into account, Dr. Beal says. The NCCN may be more comprehensive, but both bodies agree on histology and size to be high-risk factors as well as anatomical location. According to Dr. Beal, most clinicians intuitively think in the pattern classification model when processing the characteristics of the tumor in question, which is formatted as a decision tree or twenty questions. However, as far as risk stratification, the S2+ model or even the sum score method might be the better fit. Also for staging purposes, these models can help in trying to figure out what combination of the high risk factors are more important in a given scenario. 

“In my opinion, the S2+ model is likely the one that you would go to as far as the ease in which one can pull the variables of the tumor together in a brief clinical note. This can assist the clinician in assessing the status of the SCC under scrutiny and can help in deciding on the appropriate therapy according to the level of metastatic risk,” Dr. Beal says. 

Although the data showed that the S2+ model had a lower SN, Dr. Beal says that the model would be easier to implement as clinicians would only have to sum the high-risk factors. The S2+ model may be the best method to use for conceptualization and utility in a fast-paced dermatology clinic, he suggests. 

“Much research has been done on high risk cSCC and this study is part of the larger effort. However, if we really want to improve the management of high risk cSCC patients, we are going to need more multicenter collaboration, the same way it is already done with other cancers. As a basic rule, if you have more than two to three high risk factors when assessing an SCC, you should probably take a closer look at that patient and just slow down and think more carefully about appropriate management,” Dr. Beal says. 



1. Fosko SW, Chu MB, Beal BT, et al. Development of a metastatic risk model for cutaneous squamous cell carcinoma. Skin: The journal of cutaneous medicine. 2017; 1(1): 1-7


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