Development of a metastatic risk model for cutaneous squamous cell carcinoma
Main Article Content
Keywords
cutaneous squamous cell carcinoma, metastasis, model, high-risk, pattern classification, sum score method
Abstract
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.References
2. Breuninger H, Eigentler T, Bootz F, Hauschild A, et al. Brief S2k guidelines-Cutaneous squamous cell carcinoma. J Dtsch Dermatol Ges. 2013;11 Suppl 3:37-45, 39-47.
3. Czarnecki D, Staples M, Mar A, Giles G, Meehan C. Metastases from squamous cell carcinoma of the skin in southern Australia. Dermatology. 1994;189(1):52-54.
4. Karia PS, Schmults CD. Screening for nodal metastasis and its challenges: nodal needles in the SCC haystack. JAMA Dermatol. 2014;150(1):16-17.
5. Donaldson MR, Coldiron BM. No end in sight: the skin cancer epidemic continues. Semin Cutan Med Surg. 2011;30(1):3-5.
6. Ruiz ES, Karia PS, Morgan FC, Schmults CD. The positive impact of radiologic imaging on high-stage cutaneous squamous cell carcinoma management. J Am Acad Dermatol. 2017 Feb;76(2):217-225.
7. Chu MD, Slutsky JB, Dhandha MM, Beal BT et al. Evauation of the definitions of "high-risk" cutaneous squamous cell carcinoma using the America Joint Committee on Cancer staging criteria and National Comprehenisve Cancer Network guidelines. J Skin Cancer. 2014;154340.
8. Jambusaria-Pahlajani A, Kanetsky PA, Karia PS, Hwang WT et al. Evaluation of AJCC tumor staging for cutaneous squamous cell carcinoma and a proposed aternative tumor staing system. JAMA Dermatol. 2013 Apr;149(4):402-10.
9. Karia PS, Jambusaria-Pahlajani A, Harrington DP, Murphy GF, et al. Evaluation of American Joint Committee on Cancer, International Union Agaist Cancer, and Brigham and Women's Hospital tumor staging for cutaneous squamous cell carcinoma. J Clin Oncol. 2014 Feb 1;32(4):327-334.
10. Thompson AK, Kelley BF, Prokop LJ, Murad MH, Baum CL. Risk Factors for Cutaneous Squamous Cell Carcinoma Recurrence, Metastasis, and Disease-Specific Death: A Systematic Review and Meta-analysis. JAMA Dermatol. 2016;152(4):419-428.
11. Edge SB, Byrd DR, Compton CC, et al., editors. AJCC Cancer Staging Manual. 7th edition. New York, NY, USA: Springer; 2010. Cutaneous squamous cell carcinoma and other cutaneous carcinomas; pp. 301–314.
12. National Comprehensive Cancer Network. Squamous cell skin cancer (version 1.2017). Available from: URL: http://www.nccn.org/professionals/physician_gls/pdf/ squamous.pdf. Accessed April 2017.
13. Hidalgo B, Goodman M. Multivariate or multivariable regression? Am J Public Health. 2013;103(1):39-40.
14. DiStefano C ZM, Mindrila D. Understanding and Using Factor Scores: Considerations for the Applied Researcher Practical Assessment Research & Evaluation. 2014.
15. RO. D. Decision Trees. Pattern Classification New York, NY: Wiley & Sons; 2004:394-396.
16. Gage BF, Waterman AD, Shannon W, Boechler M, et al. Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA. 2001;285(22):2864-2870.
17. Peat B, Insull P, Ayers R. Risk stratification for metastasis from cutaneous squamous cell carcinoma of the head and neck. ANZ J Surg. 2012;82(4):230-233.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Creative Commons Attribution (CC BY): lets others distribute and copy the article, to create extracts, abstracts, and other revised versions, adaptations or derivative works of or from an article (such as a translation), to include in a collective work (such as an anthology), to text or data mine the article, even for commercial purposes, as long as they credit the author(s), do not represent the author as endorsing their adaptation of the article, and do not modify the article in such a way as to damage the author's honor or reputation.
Authors retain copyright in their work and license the publisher to publish the content. The publisher is the National Society for Cutaneous Medicine, with production and publishing support provided by OJS, Open Journal Systems,see https://pkp.sfu.
Prior to January 2022, authors transferred copyright to the National Society for Cutaneous Medicine. However, all articles are freely available to anyone in the world. There are no subscription fees, page charges, or article processing charges.