Abstract
Clinical and pathological features that impact melanoma patient survival have been studied extensively for decades at major melanoma centers around the world. With the aid of powerful statistical techniques and computational methods, remarkable progress has been made in the identification of dominant factors that are linked to the natural history of melanoma and associated clinical outcome. A wide array of clinical prediction tools have been promulgated, primarily focused on forecasting survival outcomes across the melanoma continuum, with the exception of distant metastatic (Stage IV) melanoma. Recent changes in melanoma clinical practice resulting from the availability of new targeted and immune therapies that are effective in both metastatic and adjuvant settings, as well as level I evidence demonstrating no survival benefit for completion lymph node dissection after a positive sentinel lymph node biopsy, have together changed the melanoma landscape and will no doubt impact on approaches to outcome prediction. Against this contemporary and ever-evolving backdrop, we present clinical applications, criteria, challenges, and opportunities for interpreting and building tools for predicting melanoma outcomes.
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References
Alba AC, Agoritsas T, Walsh M et al (2017) Discrimination and calibration of clinical prediction models users’ guides to the medical literature. JAMA 318(14):1377–1384
Balch CM, Buzaid AC, Soong SJ et al (2001a) Final version of the American joint committee on Cancer staging system for cutaneous melanoma. J Clin Oncol 19(16):3635–3648
Balch CM, Soong SJ, Gershenwald JE et al (2001b) Prognostic factors analysis of 17,600 melanoma patients: validation of the American Joint Committee on cancer melanoma staging system. J Clin Oncol 19(16):3622–3634
Berry SD, Ngo L, Samelson EJ et al (2010) Competing risk of death: an important consideration in studies of older adults. J Am Geriatr Soc 58(4):783–787
Cadili A, Dabbs K, Scolyer RA et al (2010) Re-evaluation of a scoring system to predict nonsentinel-node metastasis and prognosis in melanoma patients. J Am Coll Surg 211(4):522–525
Callender GG, Gershenwald JE, Egger ME et al (2012) A novel and accurate computer model of melanoma prognosis for patients staged by sentinel lymph node biopsy: comparison with the American Joint Committee on Cancer model. J Am Coll Surg 214(4):608–617
Cochran AJ, Elashoff D, Morton DL et al (2000) Individualized prognosis for melanoma patients. Hum Pathol 31(3):327–331
Collett D (2015) Modelling survival data in medical research, 3rd edn. CRC Press, Boca Raton
Collins GS, Reitsma JB, Altman DG et al (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. J Clin Epidemiol 68(2):134–143
Cox DR (1972) Regression models and life tables (with discussion). J R Stat Soc B 34:187
Faries MB, Thompson JF, Cochran AJ et al (2017) Completion dissection or observation for sentinel-node metastasis in melanoma. N Engl J Med 376(23):2211–2222
Gershenwald JE, Scolyer RA, Hess KR et al (2017a) Melanoma of the skin. In: Amin M, Edge SB, Greene FL et al (eds) AJCC cancer staging manual, 8th edn. Springer, Cham, pp 563–585
Gershenwald JE, Scolyer RA, Hess KR et al (2017b) Melanoma staging: evidence-based changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin 67(6):472–492
Gimotty PA, Elder DE, Fraker DL et al (2007) Identification of high-risk patients among those diagnosed with thin cutaneous melanoma. J Clin Oncol 25(9):1129–1134
Gourgou-Bourgade S, Cameron D, Poortmans P et al (2015) Guidelines for time-to-event end point definitions in breast cancer trials: results of the DATECAN initiative (definition for the assessment of time-to-event endpoints in CANcer trials). Ann Oncol 26(5):873–879
Harrell FE, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15(4):361–387
Haydu LE, Scolyer RA, Lo S et al (2017) Conditional survival: an assessment of the prognosis of patients at time points after initial diagnosis and treatment of locoregional melanoma metastasis. J Clin Oncol 35(15):1721–1731
Hess KR (1995) Graphical methods for assessing violations of the proportional hazards assumption in Cox regression. Stat Med 14(15):1707–1723
Hieke S, Kleber M, Konig C et al (2015) Conditional survival: a useful concept to provide information on how prognosis evolves over time. Clin Cancer Res 21(7):1530–1537
Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New York
Hosmer DW, Lemeshow S, May S (2007) Applied survival analysis regression modeling of time-to-event data, 2nd edn. Wiley, Hoboken
Kattan MW, Hess KR, Amin MB et al (2016) American joint committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine. CA Cancer J Clin 66(5):370–374
Khosrotehrani K, van der Ploeg AP, Siskind V et al (2014) Nomograms to predict recurrence and survival in stage IIIB and IIIC melanoma after therapeutic lymphadenectomy. Eur J Cancer 50(7):1301–1309
Kim HT (2007) Cumulative incidence in competing risks data and competing risks regression analysis. Clin Cancer Res 13(2):559–565
Leiter U, Stadler R, Mauch C et al (2016) Complete lymph node dissection versus no dissection in patients with sentinel lymph node biopsy positive melanoma (DeCOG-SLT): a multicentre, randomised, phase 3 trial. Lancet Oncol 17(6):757–767
Lyth J, Hansson J, Ingvar C et al (2013) Prognostic subclassifications of T1 cutaneous melanomas based on ulceration, tumour thickness and Clark's level of invasion: results of a population-based study from the Swedish melanoma register. Br J Dermatol 168(4):779–786
Mahar AL, Compton C, Halabi S et al (2016) Critical assessment of clinical prognostic tools in melanoma. Ann Surg Oncol 23(9):2753–2761
Maurichi A, Miceli R, Camerini T et al (2014) Prediction of survival in patients with thin melanoma: results from a multi-institution study. J Clin Oncol 32(23):2479–2485
Michaelson JS (2011) Melanoma outcome calculator. http://lifemath.net/cancer/melanoma/outcome/index.php
Molenberghs G, Kenward MG (2007) Missing data in clinical studies. Wiley, West Sussex
Morton DL, Wen DR, Wong JH et al (1992) Technical details of intraoperative lymphatic mapping for early stage melanoma. Arch Surg 127:392–399
Murali R, Sesilva C, Thompson JF, Scolyer RA (2010) Non-sentinel node risk score (N-SNORE): a scoring system for accurately stratifying risk of non-sentinel node positivity in patients with cutaneous melanoma with positive sentinel lymph nodes. J Clin Oncol 28(29):4441–4449
National Cancer Institute of the National Institutes of Health (2018.) https://www.cancer.gov/publications/dictionaries/cancer-terms/def/personalized-medicine. Accessed 05 Nov 2018
Pintilie M (2006) Competing risks a practical perspective. Wiley, West Sussex
Soong SJ (1985) A computerized mathematical model and scoring system for predicting outcome in melanoma patients. In: Balch CM, Milton GW (eds) Cutaneous melanoma: clinical management and treatment results worldwide. JB Lippincott, Philadelphia, p 353
Soong SJ (1992) A computerized mathematical model and scoring system for predicting outcome in patients with localized melanoma. In: Balch CM, Houghton AN, Milton GW et al (eds) Cutaneous melanoma, 2nd edn. JB Lippincott, Philadelphia, p 200
Soong SJ, Shaw HM, Balch CM et al (1992) Predicting survival and recurrence in localized melanoma: a multivariate approach. World J Surg 16(2):191–195
Soong SJ, Zhang Y, Desmond R (2003) Models for predicting outcome. In: Balch CM, Houghton AN, Sober A, Soong SJ (eds) Cutaneous melanoma, 4th edn. Quality Medical Publishing, St. Louis, p 77
Soong SJ, Ding S, Coit DG et al (2009) Models for predicting melanoma outcome. In: Balch CM, Houghton AN, Sober AJ et al (eds) Cutaneous melanoma, 5th edn. Quality Medical Publishing, St. Louis, pp 87–104
Soong SJ, Ding S, Coit D et al (2010) Predicting survival outcome of localized melanoma: an electronic prediction tool based on the AJCC melanoma database. Ann Surg Oncol 17(8):2006–2014
U.S. Department of Health and Human Services, Food and Drug Administration (2007) Guidance for industry clinical trial endpoints for the approval of cancer drugs and biologics. https://www.fda.gov/downloads/drugsGuidanceComplianceRegulatoyInformation/Guidance/UCM071590.pdf. Accessed 7 Oct 2018
Zabor EC, Coit DG, Gershenwald JE et al (2018) Variability in predictions from online tools: a demonstration using internet-based melanoma predictors. Ann Surg Oncol 25(8):2172–2177
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Haydu, L.E., Gimotty, P.A., Coit, D.G., Thompson, J.F., Gershenwald, J.E. (2020). Models for Predicting Melanoma Outcome. In: Balch, C., et al. Cutaneous Melanoma. Springer, Cham. https://doi.org/10.1007/978-3-030-05070-2_5
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DOI: https://doi.org/10.1007/978-3-030-05070-2_5
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