Implementing systematic melanoma risk assessment and risk-tailored surveillance in a skin cancer focussed dermatology clinic: A qualitative study of feasibility and acceptability to patients and clinic staff.

BACKGROUND: International bodies recommend that melanoma risk assessment should be integrated into skin cancer care provision, but evidence to support implementation is lacking. AIM: To explore the acceptability and feasibility of implementing personalised melanoma risk assessment and tailored patient education and skin surveillance within routine clinical care. METHODS: This prospective qualitative implementation study was informed by the Theoretical Framework of Acceptability (TFA). Personalised, systematic melanoma risk assessment was implemented in the dermatology clinic at the Melanoma Institute Australia, Sydney, Australia February-May 2021. Pre- and post-implementation observations and semi-structured interviews with patients and staff were conducted (September 2020-March 2021). Observational notes and interview transcript data were analysed thematically using the TFA as a classifying framework. RESULTS: A total of 37 h of observations were made, and 29 patients and 12 clinic staff were interviewed. We found that the delivery of personalised melanoma risk estimates did not impact on patient flow through the clinic. Dermatologists reported that the personalised risk information enhanced their confidence in assessing patient risk and recommending tailored surveillance schedules. Most patients reported that the risk assessment and tailored information were a beneficial addition to their care. Among patients whose risk deviated from their expectations, some reported feeling worried, confused or mistrust in the risk information, including those at lower risk who were recommended to decrease surveillance frequency. CONCLUSIONS: It is feasible and acceptable to patients and clinic staff to calculate and deliver personalised melanoma risk information and tailored surveillance as part of routine clinical care within dermatology clinics.

Health utilities for non-melanoma skin cancers and pre-cancerous lesions: A systematic review.

Background: Non-melanoma skin cancers (NMSCs) are common and consume many healthcare resources. A health utility is a single preference-based value for assessing health-related quality of life, which can be used in economic evaluations. There are scarce data on health utilities for NMSCs. Objectives: Using a systematic review approach, we synthesized the current data on NMSC-related health utilities. Methods: A systematic review of studies of NMSC-related health utilities was conducted in Medline, Embase, and Cochrane databases. Data were extracted based on the protocol and a quality assessment was performed for each study. Results: The protocol resulted in 16 studies, involving 121 621 participants. Mean utility values across the studies ranged from 0.56 to 1 for undifferentiated NMSC, 0.84 to 1 for actinic keratosis, 0.45 to 1 for squamous cell carcinoma, and 0.67 to 1 for basal cell carcinoma. There was considerable variability in utilities by type of cancer, stage of diagnosis, time to treatment, treatment modality, and quality of life instrument or method. Utility values were predominantly based on the EuroQol 5-dimension instrument and ranged from 0.45 to 0.96, while other measurement methods produced values ranging from 0.67 to 1. Lower utility values were observed for advanced cancers and for the time period during and immediately after treatment, after which values gradually returned to pre-treatment levels. Conclusions: Most utility values clustered around relatively high values of 0.8 to 1, suggesting small decrements in quality of life associated with most NMSCs and their precursors. Variability in utilities indicates that careful characterization is required for measures to be used in economic evaluations.

Risk factors for melanoma by anatomical site: an evaluation of aetiological heterogeneity.

BACKGROUND: Melanoma aetiology has been proposed to have two pathways, which are determined by naevi and type of sun exposure and related to the anatomical site where melanoma develops. OBJECTIVES: We examined associations with melanoma by anatomical site for a comprehensive set of risk factors including pigmentary and naevus phenotypes, ultraviolet radiation exposure and polygenic risk. METHODS: We analysed harmonized data from 2617 people with incident first invasive melanoma and 975 healthy controls recruited through two population-based case-control studies in Australia and the UK. Questionnaire data were collected by interview using a single protocol, and pathway-specific polygenic risk scores were derived from DNA samples. We estimated adjusted odds ratios using unconditional logistic regression that compared melanoma cases at each anatomical site with all controls. RESULTS: When cases were compared with control participants, there were stronger associations for many naevi vs. no naevi for melanomas on the trunk, and upper and lower limbs than on the head and neck (P-heterogeneity < 0·001). Very fair skin (vs. olive/brown skin) was more weakly related to melanoma on the trunk than to melanomas at other sites (P-heterogeneity = 0·04). There was no significant difference by anatomical site for polygenic risk. Increased weekday sun exposure was positively associated with melanoma on the head and neck but not on other sites. CONCLUSIONS: We found evidence of aetiological heterogeneity for melanoma, supporting the dual pathway hypothesis. These findings enhance understanding of risk factors for melanoma and can guide prevention and skin examination education and practices.

Sentinel node biopsy in patients with melanoma improves the accuracy of staging when added to clinicopathological features of the primary tumor.

BACKGROUND: It has been claimed, without supporting evidence, that knowledge of sentinel node (SN) status does not provide more accurate prognostic information than basic clinicopathological features of a primary cutaneous melanoma. We sought to investigate this claim and to quantify any additional value of SN status in predicting survival outcome. PATIENTS AND METHODS: Data for a Dutch population-based cohort of melanoma patients (n = 9272) and for a validation cohort from a large Australian melanoma treatment center (n = 5644) were analyzed. Patients were adults diagnosed between 2004 and 2014 with histologically-proven, primary invasive cutaneous melanoma who underwent SN biopsy. Multivariable Cox proportional hazards analyses were carried out in the Dutch cohort to assess recurrence-free survival (RFS), melanoma-specific survival (MSS) and overall survival (OS). The findings were validated using the Australian cohort. Discrimination (Harrell's C-statistic), net benefit using decision curve analysis and net reclassification index (NRI) were calculated. RESULTS: The Dutch cohort showed an improved C-statistic from 0.74 to 0.78 for OS and from 0.74 to 0.76 for RFS when SN status was included in the model with Breslow thickness, sex, age, site, mitoses, ulceration, regression and melanoma subtype. In the Australian cohort, the C-statistic increased from 0.70 to 0.73 for OS, 0.70 to 0.74 for RFS and 0.72 to 0.76 for MSS. Decision curve analyses showed that the 3-year and 5-year risk of death or recurrence were more accurately classified with a model that included SN status. At 3 years, sensitivity increased by 12% for both OS and RFS in the development cohort, and by 10% and 6% for OS and RFS, respectively, in the validation cohort. CONCLUSIONS: Knowledge of SN status significantly improved the predictive accuracy for RFS, MSS and OS when added to a comprehensive suite of established clinicopathological prognostic factors. However, clinicians and patients must consider the magnitude of the improvement when weighing up the advantages and disadvantages of SN biopsy for melanoma.

A risk prediction model for the development of subsequent primary melanoma in a population-based cohort.

BACKGROUND: Guidelines for follow-up of patients with melanoma are based on limited evidence. OBJECTIVES: To guide skin surveillance, we developed a risk prediction model for subsequent primary melanomas, using demographic, phenotypical, histopathological, sun exposure and genomic risk factors. METHODS: Using Cox regression frailty models, we analysed data for 2613 primary melanomas from 1266 patients recruited to the population-based Genes, Environment and Melanoma study in New South Wales, Australia, with a median of 14 years' follow-up via the cancer registry. Discrimination and calibration were assessed. RESULTS: The median time to diagnosis of a subsequent primary melanoma decreased with each new primary melanoma. The final model included 12 risk factors. Harrell's C-statistic was 0·73 [95% confidence interval (CI) 0·68-0·77], 0·65 (95% CI 0·62-0·68) and 0·65 (95% CI 0·61-0·69) for predicting second, third and fourth primary melanomas, respectively. The risk of a subsequent primary melanoma was 4·75 times higher (95% CI 3·87-5·82) for the highest vs. the lowest quintile of the risk score. The mean absolute risk of a subsequent primary melanoma within 5 years was 8·0 ± SD 4.1% after the first primary melanoma, and 46·8 ± 15·0% after the second, but varied substantially by risk score. CONCLUSIONS: The risk of developing a subsequent primary melanoma varies considerably between individuals and is particularly high for those with two or more primary melanomas. The risk prediction model and its associated nomograms enable estimation of the absolute risk of subsequent primary melanoma, on the basis of on an individual's risk factors, and can be used to tailor surveillance intensity, communicate risk and provide patient education. What's already known about this topic? Current guidelines for the frequency and length of follow-up to detect new primary melanomas in patients with one or more previous primary melanomas are based on limited evidence. People with one or more primary melanomas have, on average, a higher risk of developing another primary invasive melanoma, compared with the general population, but an accurate way of estimating individual risk is needed. What does this study add? We provide a comprehensive risk prediction model for subsequent primary melanomas, using data from 1266 participants with melanoma (2613 primary melanomas), over a median 14 years' follow-up. The model includes 12 risk factors comprising demographic, phenotypical, histopathological and genomic factors, and sun exposure. It enables estimation of the absolute risk of subsequent primary melanomas, and can be used to tailor surveillance intensity, communicate individual risk and provide patient education.

Benefits of a brief psychological intervention targeting fear of cancer recurrence in people at high risk of developing another melanoma: 12-month follow-up results of a randomized controlled trial.

BACKGROUND: People with melanoma want and need effective interventions for living with fear of cancer recurrence (FCR). OBJECTIVES: This study reports the 12-month outcomes of a brief, psychological intervention designed to reduce FCR in people at high risk of developing another primary melanoma compared with usual care. METHODS: In this two-arm randomized controlled trial, adults previously diagnosed with stage 0, I or II melanoma were randomly allocated to the intervention (n = 80) or control (usual care) arm (n = 84). The trial was registered with the Australian and New Zealand Clinical Trials Registry on 19 March 2013 (registration: ACTRN12613000304730). The intervention comprised a 76-page psychoeducational resource and three individually tailored, telephone-based sessions with a psychologist, scheduled at specific time points around participants' dermatological appointments. The primary outcome was the level of self-reported fear of new or recurrent melanoma assessed at 12 months postintervention using the severity subscale of the Fear of Cancer Recurrence Inventory. RESULTS: Compared with the control arm, the intervention group reported significantly lower FCR at 12 months postintervention; the between-group mean difference was -1·41 for FCR severity [95% confidence interval (CI) -2·6 to -0·2; P = 0·02] and -1·32 for FCR triggers (95% CI -2·6 to -0·02; P = 0·04). The odds ratio for FCR severity scores ≥13 (54% intervention, 63% control) was 0·59 (95% CI 0·30-1·14, P = 0·12). There were no differences between groups in secondary outcomes, such as anxiety, depression or health-related quality of life. CONCLUSIONS: The previously reported 6-month benefits of this brief, patient-centred psychological intervention in reducing FCR were found to continue 12 months postintervention, with no known adverse effects, supporting implementation as part of routine melanoma care.

Development and external validation study of a melanoma risk prediction model incorporating clinically assessed naevi and solar lentigines.

BACKGROUND: Melanoma risk prediction models could be useful for matching preventive interventions to patients' risk. OBJECTIVES: To develop and validate a model for incident first-primary cutaneous melanoma using clinically assessed risk factors. METHODS: We used unconditional logistic regression with backward selection from the Australian Melanoma Family Study (461 cases and 329 controls) in which age, sex and city of recruitment were kept in each step, and we externally validated it using the Leeds Melanoma Case-Control Study (960 cases and 513 controls). Candidate predictors included clinically assessed whole-body naevi and solar lentigines, and self-assessed pigmentation phenotype, sun exposure, family history and history of keratinocyte cancer. We evaluated the predictive strength and discrimination of the model risk factors using odds per age- and sex-adjusted SD (OPERA) and the area under curve (AUC), and calibration using the Hosmer-Lemeshow test. RESULTS: The final model included the number of naevi ≥ 2 mm in diameter on the whole body, solar lentigines on the upper back (a six-level scale), hair colour at age 18 years and personal history of keratinocyte cancer. Naevi was the strongest risk factor; the OPERA was 3·51 [95% confidence interval (CI) 2·71-4·54] in the Australian study and 2·56 (95% CI 2·23-2·95) in the Leeds study. The AUC was 0·79 (95% CI 0·76-0·83) in the Australian study and 0·73 (95% CI 0·70-0·75) in the Leeds study. The Hosmer-Lemeshow test P-value was 0·30 in the Australian study and < 0·001 in the Leeds study. CONCLUSIONS: This model had good discrimination and could be used by clinicians to stratify patients by melanoma risk for the targeting of preventive interventions. What's already known about this topic? Melanoma risk prediction models may be useful in prevention by tailoring interventions to personalized risk levels. For reasons of feasibility, time and cost many melanoma prediction models use self-assessed risk factors. However, individuals tend to underestimate their naevus numbers. What does this study add? We present a melanoma risk prediction model, which includes clinically-assessed whole-body naevi and solar lentigines, and self-assessed risk factors including pigmentation phenotype and history of keratinocyte cancer. This model performs well on discrimination, the model's ability to distinguish between individuals with and without melanoma, and may assist clinicians to stratify patients by melanoma risk for targeted preventive interventions.

Associations of pigmentary and naevus phenotype with melanoma risk in two populations with comparable ancestry but contrasting levels of ambient sun exposure.

BACKGROUND: People at high risk of developing melanoma are usually identified by pigmentary and naevus phenotypes. OBJECTIVE: We examined whether associations of these phenotypes with melanoma risk differed by ambient sun exposure or participant characteristics in two population-based, case-control studies with comparable ancestry but different ambient sun exposure. METHODS: Data were analysed from 616 cases and 496 controls from the Australian Melanoma Family Study and 2012 cases and 504 controls from the Leeds (UK) case-control study. Questionnaire, interview and dermatological skin examination data were collected using the same measurement protocols. Relative risks were estimated as odds ratios using unconditional logistic regression, adjusted for potential confounders. RESULTS: Hair and skin colour were the strongest pigmentary phenotype risk factors. All associations of pigmentary phenotype with melanoma risk were similar across countries. The median number of clinically assessed naevi was approximately three times higher in Australia than Leeds, but the relative risks for melanoma associated with each additional common or dysplastic naevus were higher for Leeds than Australia, especially for naevi on the upper and lower limbs. Higher naevus counts on the head and neck were associated with a stronger relative risk for melanoma for women than men. The two countries had similar relative risks for melanoma based on self-reported naevus density categories, but personal perceptions of naevus number differed by country. There was no consistent evidence of interactions between phenotypes on risk. CONCLUSIONS: Classifying people at high risk of melanoma based on their number of naevi should ideally take into account their country of residence, type of counts (clinical or self-reported), body site on which the naevus counts are measured and sex. The presence of naevi may be a stronger indicator of a genetic predisposition in the UK than in Australia based on less opportunity for sun exposure to influence naevus development.

Exploring the emotional and behavioural reactions to receiving personalized melanoma genomic risk information: a qualitative study.

BACKGROUND: There is a need for greater understanding of the spectrum of emotional and behavioural reactions that individuals in the general population may experience in response to genomic testing for melanoma risk. OBJECTIVES: To explore how individuals in the general population respond to receiving personalized genomic risk of melanoma. METHODS: Semistructured interviews were undertaken with 30 participants (aged 24-69 years, 50% female, 12 low risk, eight average risk, 10 high risk) recruited from a pilot trial in which they received personalized melanoma genomic risk information. We explored participants' emotional and behavioural responses to receiving their melanoma genomic risk information. The qualitative data were analysed thematically. RESULTS: Many participants reported a positive response to receiving their melanoma genomic risk, including feelings of happiness, reassurance and gaining new knowledge to help manage their melanoma risk. Some participants reported short-term negative emotional reactions that dissipated over time. Most individuals, particularly those who received average or high-risk results, reported making positive behaviour changes to reduce their melanoma risk. Emotional and behavioural responses were linked to participants' expectations for their risk result, their pre-existing perception of their own melanoma risk, their existing melanoma preventive behaviours and their genomic risk category. CONCLUSIONS: Personalized melanoma genomic risk information alongside education and lifestyle counselling is favourably received by people without a personal history and unselected for a family history of melanoma. Participants described increased knowledge and awareness around managing skin cancer risk and improved sun protection and skin examination behaviours. Any initial feelings of distress usually dissipated over time.

Does personalized melanoma genomic risk information trigger conversations about skin cancer prevention and skin examination with family, friends and health professionals?

BACKGROUND: Receiving information about genomic risk of melanoma might trigger conversations about skin cancer prevention and skin examinations. OBJECTIVES: To explore conversations prompted by receiving personalized genomic risk of melanoma with family, friends and health professionals. METHODS: We used a mixed-methods approach. Participants without a personal history and unselected for a family history of melanoma (n = 103, aged 21-69 years, 53% women) completed questionnaires 3 months after receiving a personalized melanoma genomic risk assessment. Semistructured interviews were undertaken with 30 participants in high, average and low genomic risk categories, and data were analysed thematically. RESULTS: From the questionnaires, 74% of participants communicated their genomic risk information with family, and 49% with friends. Communication with a health professional differed by risk level: 41%, 16% and 12% for high, average and low risk, respectively (P = 0·01). Qualitative analysis showed that perceived 'shared risk' and perceived interest of family and friends were motivations for discussing risk or prevention behaviours. The information prompted conversations with family and health professionals about sun protection and skin checks, and general conversations about melanoma risk with friends. Reasons for not discussing with family included existing personal or family health concerns, or existing high levels of sun protection behaviour among family members. CONCLUSIONS: Personalized melanoma genomic risk information can prompt risk-appropriate discussions about skin cancer prevention and skin examinations with family and health professionals. Sharing this information with others might increase its impact on melanoma prevention and skin examination behaviours, and this process could be used to encourage healthy behaviour change within families.