A Prediction Tool to Facilitate Risk-Stratified Screening for Squamous Cell Skin Cancer

Rapidly Growing and Aggressive Squamous Cell Carcinoma of the Forearm: A Report of Successful Treatment With Mohs Surgery and Complex Reconstruction With Rhombic Triple Z-Plasty

Reconstruction of complex post-surgical wounds requires functional and aesthetic considerations. We present a case of a complex radial-dorsal forearm defect in a patient who underwent Mohs surgery for an aggressive and rapidly growing squamous cell carcinoma. Following complete tumor excision, we utilized a modified rhombic flap for complete wound coverage with long-term conservation of extensor function. The rhombic flap modification included three Z-plasties at the flap base to add rotational components to the flap transposition. Long-term follow-up showed acceptable cosmesis, preserved extensor tendon function, and no evidence of tumor recurrence.

Patterns and cost of care according to keratinocyte cancer risk stratification in a volunteer population screening clinic: Real-world data from the TRoPICS study

BACKGROUND

Risk prediction tools have been developed for keratinocyte cancers (KCs) to effectively categorize individuals with different levels of skin cancer burden. Few have been clinically validated nor routinely used in clinical settings.

OBJECTIVES

To assess whether risk prediction tool categories associate with interventions including chemoprophylaxis for skin cancer, and health-care costs in a dermatologist-run screening clinic.

METHODS

Adult participants who presented to a walk-in screening facility were invited to participate. A self-completed KC risk prediction tool was used to classify participants into one of the five risk categories. Participants subsequently underwent full skin examination by a dermatologist. Dermatological interventions and skin cancer-related medical prescriptions were documented. Total health-care costs, both to the health-care system and patients were evaluated.

RESULTS

Of the 507 participants recruited, 5-fluorouracil cream and nicotinamide were more frequently prescribed in the higher risk groups as chemoprophylaxis (p < 0.005). A significant association with high predicted risk was also observed in the use of cryotherapy and curettage and cautery (p < 0.05). The average health-care costs associated with a skin check visit increased from $90 ± 37 (standard deviation) in the lowest risk group to $149 ± 97 in the highest risk group (p < 0.0001).

CONCLUSIONS

We observed a positive association between higher predicted risk of skin cancer and the prescription of chemoprophylaxis and health-care costs involved with opportunistic community skin cancer screening. A clinical use of risk stratification may be to provide an opportunity for clinicians to discuss skin cancer prevention and chemoprophylaxis with individual patients.

Ten-Year Follow-up of Persons With Sun-Damaged Skin Associated With Subsequent Development of Cutaneous Squamous Cell Carcinoma

Importance

Risk of cutaneous squamous cell carcinoma (cSCC) after the diagnosis of actinic keratosis (AK) has not been studied during long follow-up periods.

Objective

To estimate the risk up to 10 years and identify risk factors for cSCC development.

Design, Setting, and Participants

This longitudinal cohort study, performed from January 1, 2009, to February 29, 2020, examined Kaiser Permanente Northern California patients with AK and control patients matched 1:1 on age, sex, race/ethnicity, medical center, and date of the initial diagnosis plus 30 days in the patients with AK.

Exposures

Patients with AK and control participants were followed up for up to 10 years for incidence of cSCC.

Main Outcomes and Measures

Incident cSCC was obtained from pathologic data, and subdistribution hazard ratios (HRs) and 95% CIs were estimated using Cox proportional hazards regression analysis, accounting for competing risks, calendar year, demographic factors, and number of AKs.

Results

The study included 220 236 patients with AK and 220 236 matched control patients (mean [SD] age, 64.1 [12.2] years; 231 248 [52.5%] female). After losses to follow-up were accounted for, risk of cSCC increased with each year of follow-up by 1.92% (95% CI, 1.89%-1.95%) in patients with AK and 0.83% (95% CI, 0.81%-0.85%) in matched control patients (subdistribution HR, 1.90; 95% CI, 1.85-1.95). However, among patients 49 years or younger, those diagnosed with AK were nearly 7 times more likely to be diagnosed with cSCC than those without AK (HR, 6.77; 95% CI, 5.50-8.32). At 10 years, the cumulative incidence of cSCC reached 17.1% (95% CI, 16.9%-17.4%) in patients with AK and 5.7% (95% CI, 5.5%-5.9%) in control patients. Increased numbers of AKs were modestly associated with increased cSCC risk (≥15 AKs vs 1 AK: subdistribution HR, 1.89; 95% CI, 1.75-2.04). Older patients had much higher risk of cSCC than younger patients (compared with those ≤49 years of age at AK diagnosis; ≥80 years of age: subdistribution HR, 8.18; 95% CI, 7.62-8.78). Other than AK, risk factors for cSCC included older age, White race (a proxy for skin type), history of basal cell carcinoma, and male sex. Risk decreased between 2009 and 2019 (2018-2019 vs 2009-2010: subdistribution HR, 0.67; 95% CI, 0.63-0.72).

Conclusions and Relevance

The results of this longitudinal cohort study can be used to develop recommendations to increase early detection of cSCC. Additional research is needed to understand the effect of AK treatment on cSCC risk and outcomes of cSCC.

Risk Factors for Keratinocyte Carcinoma in Recipients of Allogeneic Hematopoietic Cell Transplants

Importance

Allogeneic hematopoietic cell transplant (alloHCT) is known to increase the risk for keratinocyte carcinoma. The extent to which host characteristics, including pigmentary phenotype and UV radiation exposure, contribute is unknown.

Objective

To identify and validate independent risk factors for keratinocyte carcinoma after alloHCT, including those associated with the transplant and the host.

Design, Setting, and Participants

This retrospective cohort study analyzed a consecutive sample of alloHCT recipients from January 1, 2000, to December 31, 2014, at the Mayo Clinic, Rochester, Minnesota (n = 872) and University Hospitals Cleveland Medical Center, Cleveland, Ohio (n = 147). Participants from the Mayo Clinic were randomly allocated (2:1) into discovery (n = 581) and validation (n = 291) cohorts. Time to first keratinocyte carcinoma and information about transplant- and host-associated risk factors were extracted. A multivariate keratinocyte carcinoma risk model was created using a stepwise Cox proportional hazards regression model with P ≤ .05 for entry that incorporated all covariates that were individually statistically significant at α = 0.05 in the discovery cohort. The risk model was first internally validated using the Mayo Clinic validation cohort and then externally validated using the independent cohort of alloHCT recipients at University Hospitals Cleveland Medical Center. Data were analyzed from March 13, 2018, to June 12, 2019.

Exposures

Allogeneic hematopoietic cell transplant.

Main Outcomes and Measures

The primary outcome was time to development of the first cutaneous keratinocyte carcinoma after alloHCT; secondary outcome, time to development of the first individual basal and/or squamous cell carcinoma after alloHCT.

Results

Of the 872 alloHCT recipients identified in the Mayo Clinic cohort (520 men [59.6%]; mean [SD] age, 48.3 [12.6] years), 95 (10.9%) developed keratinocyte carcinoma after alloHCT during 5349 person-years of follow-up. Of the 147 alloHCT recipients in the exernal validation cohort (86 men [58.5%]; mean [SD] age, 47.9 [17.5] years), 18 (12.2%) developed keratinocyte carcinoma after alloHCT in 880 person-years of follow up. Risk factors independently associated with keratinocyte carcinoma after alloHCT included age (hazard ratio [HR] per 10 years, 1.72; 95% CI, 1.21-2.42), chronic lymphocytic leukemia (HR, 2.47; 95% CI, 1.20-5.09), clinically photodamaged skin (HR, 3.47; 95% CI, 1.87-6.41), and history of cutaneous squamous cell carcinoma (HR, 2.60; 95% CI, 1.41-5.91). Harrell concordance statistics were 0.81 (95% CI, 0.72-0.90) and 0.86 (95% CI, 0.74-0.98) for internal and external validation of the keratinocyte carcinoma risk model, respectively.

Conclusions and Relevance

This study found validated independent risk factors for keratinocyte carcinoma after alloHCT that are enriched with host- compared with transplant-associated risk factors. These findings highlight the importance of assessing host-associated risk factors for keratinocyte carcinoma in patients eligible for alloHCT. Future studies should examine whether keratinocyte carcinoma risk stratification before alloHCT may inform long-term surveillance strategies.

Predicting keratinocyte carcinoma in patients with actinic keratosis: development and internal validation of a multivariable risk-prediction model

BACKGROUND

Patients with actinic keratosis (AK) are at increased risk for developing keratinocyte carcinoma (KC) but predictive factors and their risk rates are unknown.

OBJECTIVES

To develop and internally validate a prediction model to calculate the absolute risk of a first KC in patients with AK.

METHODS

The risk-prediction model was based on the prospective population-based Rotterdam Study cohort. We hereto analysed the data of participants with at least one AK lesion at cohort baseline using a multivariable Cox proportional hazards model and included 13 a priori defined candidate predictor variables considering phenotypic, genetic and lifestyle risk factors. KCs were identified by linkage of the data with the Dutch Pathology Registry.

RESULTS

Of the 1169 AK participants at baseline, 176 (15·1%) developed a KC after a median follow-up of 1·8 years. The final model with significant predictors was obtained after backward stepwise selection and comprised the presence of four to nine AKs [hazard ratio (HR) 1·68, 95% confidence interval (CI) 1·17-2·42], 10 or more AKs (HR 2·44, 95% CI 1·65-3·61), AK localization on the upper extremities (HR 0·75, 95% CI 0·52-1·08) or elsewhere except the head (HR 1·40, 95% CI 0·98-2·01) and coffee consumption (HR 0·92, 95% CI 0·84-1·01). Evaluation of the discriminative ability of the model showed a bootstrap validated concordance index (c-index) of 0·60.

CONCLUSIONS

We showed that the risk of KC in patients with AK can be calculated with the use of four easily assessable predictor variables. Given the c-index, extension of the model with additional, currently unknown predictor variables is desirable. Linked Comment: Kim et al. Br J Dermatol 2020; 183:415-416.

Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer

Importance

A prediction model for new-onset nonmelanoma skin cancer could enhance prevention measures, but few patient data-driven tools exist for more accurate prediction.

Objective

To use machine learning to develop a prediction model for incident nonmelanoma skin cancer based on large-scale, multidimensional, nonimaging medical information.

Design, Setting, and Participants

This study used a database comprising 2 million randomly sampled patients from the Taiwan National Health Insurance Research Database from January 1, 1999, to December 31, 2013. A total of 1829 patients with nonmelanoma skin cancer as their first diagnosed cancer and 7665 random controls without cancer were included in the analysis. A convolutional neural network, a deep learning approach, was used to develop a risk prediction model. This risk prediction model used 3-year clinical diagnostic information, medical records, and temporal-sequential information to predict the skin cancer risk of a given patient within the next year. Stepwise feature selection was also performed to investigate important and determining factors of the model. Statistical analysis was performed from November 1, 2016, to October 31, 2018.

Main Outcomes and Measures

Sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were used to evaluate the performance of the models.

Results

A total of 1829 patients (923 women [50.5%] and 906 men [49.5%]; mean [SD] age, 65.3 [15.7] years) with nonmelanoma skin cancer and 7665 random controls without cancer (3951 women [51.5%] and 3714 men [48.4%]; mean [SD] age, 47.5 [17.3] years) were included in the analysis. The 1-year incident nonmelanoma skin cancer risk prediction model using sequential diagnostic information and drug prescription information as a time-incorporated feature matrix could attain an AUROC of 0.89 (95% CI, 0.87-0.91), with a mean (SD) sensitivity of 83.1% (3.5%) and mean (SD) specificity of 82.3% (4.1%). Carcinoma in situ of skin (AUROC, 0.867; -2.80% loss) and other chronic comorbidities (eg, degenerative osteopathy [AUROC, 0.872; -2.32% loss], hypertension [AUROC, 0.879; -1.53% loss], and chronic kidney insufficiency [AUROC, 0.879; -1.52% loss]) served as more discriminative factors for the prediction. Medications such as trazodone, acarbose, systemic antifungal agents, statins, nonsteroidal anti-inflammatory drugs, and thiazide diuretics were the top-ranking discriminative features in the model; each led to more than a 1% decrease of the AUROC when eliminated individually (eg, trazodone AUROC, 0.868; -2.67% reduction; acarbose AUROC, 0.870; -2.50 reduction; and systemic antifungal agents AUROC, 0.875; -1.99 reduction).

Conclusions and Relevance

The findings of this study suggest that a risk prediction model may have potential predictive factors for nonmelanoma skin cancer. This model may help health care professionals target high-risk populations for more intensive skin cancer preventive methods.

Genome-wide association studies and polygenic risk scores for skin cancer: clinically useful yet?

BACKGROUND

Genome-wide association studies (GWAS) have identified thousands of susceptibility variants, although most have been associated with small individual risk estimates that offer little predictive value. However, combining multiple variants into polygenic risk scores (PRS) may be more informative. Multiple studies have developed PRS composed of GWAS-identified variants for cutaneous cancers. This review highlights data from these studies.

OBJECTIVES

To review published GWAS and PRS studies for melanoma, cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC), and discuss their potential clinical utility.

METHODS

We searched PubMed and the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue to identify relevant studies.

RESULTS

Results from 21 GWAS (11 melanoma, 3 cSCC, 7 BCC) and 11 PRS studies are summarized. Six loci in pigmentation genes overlap between these three cancers (ASIP/RALY, IRF4, MC1R, OCA2, SLC45A2 and TYR). Additional loci overlap for cSCC/BCC and BCC/melanoma, but no other loci are shared between cSCC and melanoma. PRS for melanoma show roughly two-to-threefold increases in risk and modest improvements in risk prediction (2-7% increases). PRS are associated with twofold and threefold increases in risk of cSCC and BCC, respectively, with small improvements (2% increase) in predictive ability.

CONCLUSIONS

Existing data indicate that PRS may offer small, but potentially meaningful, improvements to risk prediction. Additional research is needed to clarify the potential utility of PRS in cutaneous carcinomas. Clinical translation will require well-powered validation studies incorporating known risk factors to evaluate PRS as tools for screening. What's already known about this topic? Over 50 susceptibility loci for melanoma, basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) have been identified in genome-wide association studies (GWAS). Polygenic risk scores (PRS) using variants identified from GWAS have also been developed for melanoma, BCC and cSCC, and investigated with respect to clinical risk prediction. What does this study add? This review provides an overview of GWAS findings and the potential clinical utility of PRS for melanoma, BCC and cSCC.