Cost-Effectiveness Analysis of 3D Total-Body Photography for People at High Risk of Melanoma

Daniel Lindsay; H Peter Soyer; Monika Janda; David C Whiteman; Sonya Osborne; Anna Finnane; Liam J Caffery; Louisa G Collins

doi:10.1001/jamadermatol.2025.0219

. 2025 Mar 26;161(5):482–489. doi: 10.1001/jamadermatol.2025.0219

Cost-Effectiveness Analysis of 3D Total-Body Photography for People at High Risk of Melanoma

Daniel Lindsay ^1,^2,^✉, H Peter Soyer ³, Monika Janda ⁴, David C Whiteman ^1,², Sonya Osborne ^5,⁶, Anna Finnane ², Liam J Caffery ^4,⁷, Louisa G Collins ^1,^2,⁸

¹Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

²School of Public Health, The University of Queensland, Brisbane, Queensland, Australia

³Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia

⁴Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia

⁵School of Nursing and Midwifery, University of Southern Queensland, Ipswich, Queensland, Australia

⁶Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia

⁷Centre for Online Health, The University of Queensland, Brisbane, Queensland, Australia

⁸School of Nursing and Cancer and Palliative Care Outcomes Centre, Queensland University of Technology, Brisbane, Queensland, Australia

Accepted for Publication: November 12, 2024.

Published Online: March 26, 2025. doi:10.1001/jamadermatol.2025.0219

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Corresponding Author: Daniel Lindsay, PhD, Population Health, QIMR Berghofer Medical Research Institute, 300 Herston Rd, Herston, QLD 4006, Australia (daniel.lindsay@qimrberghofer.edu.au).

Author Contributions: Dr Lindsay had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Lindsay, Soyer, Janda, Osborne, Finnane, Caffery, Collins.

Acquisition, analysis, or interpretation of data: Lindsay, Soyer, Janda, Whiteman.

Drafting of the manuscript: Lindsay.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Lindsay, Osborne.

Obtained funding: Soyer, Janda, Osborne, Finnane, Caffery.

Administrative, technical, or material support: Janda, Finnane.

Supervision: Soyer, Janda, Collins.

Conflict of Interest Disclosures: Dr Soyer reported grants from the National Health and Medical Research Council (NHMRC) Centre for Research Excellence in Skin Imaging and Precision Diagnosis and consulting fees from Canfield Scientific during the conduct of the study as well as being a shareholder in MoleMap NZ and e-derm-consult GmbH and a medical advisor for First Derm outside the submitted work. Dr Janda reported grants from NHMRC, Queensland Genomics, the Princess Alexandra Research Foundation Translational Research Innovation Award, Medical Research Future Fund (MRFF) Rapid Applied Research Translation Program, and Queensland Government during the conduct of the study. Dr Osborne reported grants from NHMRC, Queensland Genomics, Queensland Government, Princess Alexandra Research Foundation Translational Research Innovation Award, and MRFF Rapid Applied Research Translation Program during the conduct of the study. No other disclosures were reported.

Funding/Support: This research was conducted with the support of the following: NHMRC Partnership Grant (APP1153046), NHMRC Synergy grant (2009923), Queensland Genomics, Queensland Government (M1215057), Princess Alexandra Research Foundation Translational Research Innovation Award, Brisbane Diamantina Health Partners Health System Improvement Ideas grant funded from the MRFF Rapid Applied Research Translation Programme. Dr Whiteman is supported by a Research Fellowship (APP1155413) from the NHMRC.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank the following research staff: Joanne Aitken, PhD, Cancer Council Queensland; Brigid Betz-Stablein, PhD, Elizabeth Eakin, PhD, Leonard Gray, PhD, Caitlin Horsham, MSc, Dilki Jayasinghe, PhD, Uyen Koh, PhD, Aideen McInerney-Leo, PhD, Kaitlin Nufer, MPhil (University of Queensland); Montana O’Hara, BHSc, QIMR Berghofer; Elizabeth Payne, BSc, Clare Primero, PhD, Astrid Rodriguez-Acevedo, PhD, and Chantal Rutjes, BSc (University of Queensland). None of these individuals were compensated for their contributions.

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Corresponding author.

PMCID: PMC11947968 PMID: 40136266

Key Points

Question

What is the cost-effectiveness of usual care with or without 3-dimensional (3D) total-body photography (TBP) for improved early melanoma detection?

Findings

In this prespecified cost-effectiveness analysis based on a randomized clinical trial of 309 adults with a high risk of developing melanoma, 3D TBP was associated with similar numbers of malignant excisions, similar quality-adjusted life-years, and higher health care costs than usual care. The model assessed a junior clinician–led 3D TBP surveillance approach for patients with high-risk melanoma, with a teledermatologist providing the final assessment of identified lesions.

Meaning

The study results suggest that, in the short term, 3D TBP in addition to usual care was not cost-effective for patients with high-risk melanoma vs usual care only.

Abstract

Importance

Greater use of novel digital technologies could be associated with improved health outcomes and save health care costs by detecting smaller melanomas earlier (needing less treatment) or benign tumors (needing no treatment).

Objective

To compare costs and health effects of 3-dimensional (3D) total-body photography (TBP) and sequential digital dermoscopy imaging (SDDI) vs usual care for early detection of melanoma.

Design, Setting, and Participants

This prespecified cost-effectiveness analysis using randomized clinical trial (n = 309) data with 2 years of follow-up was conducted at a research hospital in Brisbane, Australia, and took a health system perspective. It included adults 18 years or older at high risk of developing a primary or subsequent melanoma.

Intervention

The intervention group received usual care plus clinical skin examinations by junior clinicians at baseline and 6, 12, 18, and 24 months with 3D TBP-SDDI reviewed by a teledermatologist. The control group continued to receive usual care and completed online surveys every 6 months.

Main Outcomes and Measures

Government health care costs, patient out-of-pocket costs, numbers of benign and malignant skin tumor excisions, and quality-adjusted life-years. Skin biopsy, excisions, pathology, and their costs were collected using administrative claims data. Quality of life was collected using the EuroQol-5D-5L.

Results

The trial included 314 participants (mean [SD] age, 51.6 [12.8] years; 194 female individuals [62%]) who completed all of the study procedures (158 in the intervention and 156 in the control groups). Compared with controls, intervention group participants had fewer melanoma excisions, more keratinocyte carcinomas and benign excisions, and more biopsy specimens. Over 24 months, mean per-person costs (analyzed in Australian dollars and converted to US$) for the intervention group were $1708 (95% CI, $1455-$1961) vs $763 (95% CI, $655-$870) for controls, an incremental cost of $945 (95% CI, $738-$1157) to provide the intervention. Total quality-adjusted life-years per person were similar for the intervention (1.84; 95% CI, 1.82-1.86) and control groups (1.84; 95% CI, 1.83-1.86). The incremental cost per additional malignant skin tumor excised was $40 (95% CI, $34-$48).

Conclusions and Relevance

Over 2 years of the trial, the 3D TBP-SDDI model by junior clinicians and teledermatologist review generated higher costs and detected similar numbers of malignant tumors than usual care in a high-risk melanoma cohort. Cost-effectiveness is a necessary but not sufficient consideration for implementation. Other benefits of 3D TBP-SDDI may arise once artificial intelligence clinician support systems are integrated, and more research is needed to understand factors associated with costs and whether there are other benefits of 3D TBP-SDDI.

This cost-effectiveness analysis compares costs and health effects of 3-dimensional total-body photography and sequential digital dermoscopy imaging vs usual care for early detection of melanoma.

Introduction

Incidence of cutaneous melanoma of the skin in susceptible populations has increased during the past few decades, with an estimated 325 000 cases globally in 2020.¹ Increasing melanoma incidence and the services required for effectively treating patients contribute to high-cost burdens to health care systems and wider society.^2,3,4,5 As late-stage melanomas have a greater mortality risk and require more advanced and expensive treatment than early-stage melanomas,^6,7,8,9 early diagnosis is critical for curative intent. Advances in early detection methods include sequential digital dermoscopic imaging (SDDI), teledermatology, and total-body photography (TBP), with their combined use contributing to increased detection of early-stage melanomas.^10,11

An approach to TBP that includes automated 3-dimensional (3D) imaging allows for imaging of skin lesions, including anatomical localization on a created 3D avatar.^12,13 Expected benefits include detecting suspicious changes in a tumor over time to help identify malignant melanoma and greater imaging precision to reduce excisions of benign tumors. While advances in melanoma detection have been associated with a reduced benign to malignant excision ratio,^14,15 reported ratios remain high (9.7:1).¹⁶ Using 3D TBP in high-risk melanoma cohorts may be associated with reduced excisions of benign skin tumors, benefiting patients and improving skin medicine efficiency.¹⁷

Cost-effectiveness analyses inform decision-makers on whether a new technology should be publicly funded and implemented in health systems. While there is evidence for the cost-effectiveness of early detection methods in high-risk melanoma cohorts,^18,19 further evidence is required. We undertook a cost-effectiveness analysis of adding 3D TBP-SDDI and teledermatologist review to detect melanoma in a high-risk melanoma cohort, compared with usual care.

Methods

Overview

We conducted a prespecified cost-effectiveness analysis alongside a 24-month randomized clinical trial (ACTRN12618000267257) that compared usual clinical care plus sequential 3D TBP-SDDI imaging and teledermatologist review (intervention group) with usual clinical care only (control group).²⁰ Data included participant surveys and administrative health care claims. We adopted a health system perspective that combined federal government and patient-incurred costs. In Australia, skin excisions are typically performed by primary care clinicians in private practices within community settings and are funded by the Australian Government through the national Medicare Benefits Scheme (MBS) and patient copayment contributions. The primary outcomes for this analysis were incremental cost per malignant skin tumor excised and incremental cost per quality-adjusted life-year (QALY) gained.

We obtained ethical approval from the human research ethics committees of Metro South and QIMR Berghofer Medical Research Institute, and all patients provided written consent before trial participation. This analysis conformed to a health economic analysis plan (eAppendix 1 in Supplement 1),²⁰ recommended guidelines for cost-effectiveness analyses alongside clinical trials,²¹ and the Consolidated Health Economic Evaluation Reporting Standards checklist (eTable 1 in Supplement 1).²²

Study Population and Setting

Eligible participants were adults deemed at high risk of developing melanoma as they had a personal or strong family history of melanoma and/or dysplastic nevus syndrome.²⁰ Full study population and setting details are described in a companion article.²³ We followed up participants for 24 months, with clinical and survey data captured every 6 months.²⁰

Intervention and Comparator

Intervention group participants underwent clinical skin examinations by 1 of 6 junior clinicians and 3D TBP-SDDI using the VECTRA WB360 (Canfield Scientific) every 6 months for 2 years (5 visits total) in addition to seeing their usual medical professional. A teledermatologist (H.P.S.) reviewed images of suspicious lesions that were brought to their attention by junior clinicians, with the final decision to refer to the participant’s usual clinician for further management at the discretion of the teledermatologist.

Control group participants were advised to continue attending their regular skin examination appointments with their usual clinician. Any lesions identified in these appointments were managed at the discretion of participant’s usual clinician. Further information on study procedures is included in a companion article.²³

Measuring Skin Tumor Services and Valuing Costs

Only services associated with skin tumor management were analyzed (eTable 2 in Supplement 1). We collated all incidence and associated health care costs of excisions for melanoma, keratinocyte carcinomas (KCs), or nonmalignant tumors, biopsies, skin flap repairs/grafts, and other skin-related procedures, as well as pathology services occurring within 14 days following a biopsy, melanoma, or KC excision for participants. Individual-level MBS data included the cost to the government and out-of-pocket payments by individuals. The MBS covers all medical services performed outside of public hospitals, such as biopsies and skin excisions. We excluded items for primary care clinician visits due to the inability to determine if they were services for a skin tumor.

The number of excisions presented in this article and the companion article²³ differ due to different data sources. This analysis used MBS data for any skin tumor service and may have included re-excisions and wider excisions following an initial biopsy on the same tumor, whereas the companion article reported diagnoses and excisions confirmed through histopathological reports.²³

Per-person costs of the 3D TBP-SDDI technology under conditions of wider implementation are unknown. We benchmarked the costs against other medical imaging services and estimated the costs of 3D TBP-SDDI at $158 (250 Australian dollars) per session for 5 sessions ($788 [1250 Australian dollars] per person), hypothetically incurred by the government. This imaging cost included the costs of time for junior clinicians to consult with patients and for a senior clinician to review images. We valued all resource use in 2023 Australian dollars (converted to US$), with inflation adjustments made when necessary using the health factor of the Australian Consumer Price Index.²⁴

Measuring and Valuing Benefits

We captured the incidence of malignant skin tumor excisions over 24 months (ie, invasive melanomas and all KC). We calculated QALYs to estimate the benefit of 3D TBP-SDDI. We assessed quality of life using the EurolQol-5D 5-level version (EQ-5D-5L)²⁵ at baseline and every 6 months for 24 months (ie, 5 assessments). We converted EQ-5D-5L responses to utilities using Australian norms,²⁶ with the area-under-the-curve method used to estimate QALYs.²⁷

Handling Missing Data

Five participants (1 from the intervention group, 4 from the control group) did not consent to providing their MBS data and were removed from analyses. We assumed that if the individual had no MBS data for a skin tumor service, they had 0 occurrence and cost. Missing health utility data were imputed 1000 times using multiple imputation by chained equations with predictive mean matching (eAppendix in Supplement 1).²⁸

Data Analysis

We summarized data on a tumor-basis and person-basis, as an individual could have multiple tumors and excisions. The main outcomes of incremental cost per additional malignant cancer excised or QALYs gained were person-based. We compared the differences in (1) number of services, (2) costs per person, and (3) number of malignant tumor excisions between groups using t tests. Because MBS items for melanomas do not differentiate services for in situ and invasive tumors, we calculated the estimated number of invasive melanoma excisions by multiplying the proportion of histopathologically confirmed invasive melanomas identified in clinical reports (ie, 0.17 and 0.21 in the intervention and control groups, respectively²³).

We estimated the adjusted differences in outcomes by study group using generalized linear models (GLMs) with the Gaussian family and identity link function (QALYs), the γ family and log link function (costs), or the negative binomial and log link function (malignant excisions). Covariates included in GLMs were those significantly associated with total cost and baseline health utilities in univariate analyses.

We calculated incremental cost-effective ratios (ICERs) by dividing the mean difference in costs between study groups by the mean difference in malignant tumor excisions or QALYs. We used a resampling method using single imputation–nested bootstrapping to assess uncertainty around ICER values.²⁹ 95% CIs around ICERs were generated through the bootstrap percentile method using 1000 bootstrap replications.³⁰ We used a willingness-to-pay threshold of $31 500 per QALY gained to interpret the ICER for QALYs, with costs less than this generally considered cost-effective in Australia.³¹ We did not compare the incremental cost per malignant skin tumor excised with a willingness-to-pay threshold.

Sensitivity analyses examined the effect of alternative per-person costs for 3D TBP-SDDI: $95, $126, $158 (base case), and $189. Additional sensitivity analyses adjusted the proportion of histopathologically confirmed invasive melanomas in each study group by 10% or more. All costs and QALYs from the first year of the trial were not discounted, while those from the second year were discounted by 5% in base analyses, then 3% and 7% in sensitivity analyses. We performed all analyses in Stata, version 17 (StataCorp). Significance for all analyses was set at P < .05.

Results

Demographic Characteristics

From 315 participants randomized into study groups, 2-year follow-up data were available for 314 (99%). Removing the 5 participants who did not consent to release their MBS data, the final sample for analysis included 157 intervention and 152 control group participants (n = 309). Baseline characteristics of study groups were similar (eTable 3 in Supplement 1). Nineteen participants had no record of any MBS services used during the study period and were included as $0 cost in analyses. Those with MBS services recorded were more likely to be on home duties, retired or unemployed, or had undergone skin checks more frequently than those participants with no MBS services recorded (eTable 4 in Supplement 1).

Skin Biopsy and Excision Outcomes

Intervention group participants had fewer melanoma excisions, more KC and benign excisions, and more biopsy specimens and pathology services than controls (eTable 5 in Supplement 1). The mean number of skin tumor services per person across study groups was similar (eTable 5 in Supplement 1). MBS data indicated 107 melanoma excisions for all participants,²³ with a ratio of MBS melanoma excisions to pathology-confirmed melanomas of 1.6:1 for both study groups. There was no clear indication that excised melanomas were smaller in the intervention group than the control group (eTable 6 in Supplement 1). The proportion of in situ melanomas excised in the intervention (20 [83%]) and control (34 [79%]) groups was similar.²³ After adjusting for the estimated number of excisions for melanoma in situ, there were 150 (95% CI, 129-168) excisions of malignant tumors in the intervention group and 125 (95% CI, 108-140) in the control group (Table 1). The mean number of excisions for malignant tumors in the intervention group (2.6; 95% CI, 1.9-3.2) was similar to the control group (2.2; 95% CI, 1.7-2.8).

Table 1. Results of Bootstrapped Costs and Quality-Adjusted Life-Years (QALYs) Over 24 Months.

Cost outcomes	Estimated costs, mean (95% CI), $^a		Mean difference (95% CI)	Cost ratio (95% CI)^b
Cost outcomes	Intervention (n = 157)	Control (n = 152)	Mean difference (95% CI)	Cost ratio (95% CI)^b
Cost to government
Including TBP costs	1462 (1255 to 1697)	525 (454 to 595)	937 (730 to 1106)	2.8 (2.3 to 3.4)
Excluding TBP costs	570 (463 to 677)	551 (449 to 654)	18 (−52 to 62)	1.0 (0.8 to 1.3)
Cost to individual	243 (177 to 309)	253 (182 to 324)	−10 (−69 to 59)	0.9 (0.7 to 1.4)
Total cost (including TBP costs)	1708 (1455 to 1961)	763 (655 to 870)	945 (738 to $1157)	2.2 (1.8 to 2.7)
QALYs	Mean (95% CI)		Mean difference (95% CI)	Ratio of QALY gains (95% CI)
With MI data	1.83 (1.82 to 1.84)	1.85 (1.84 to 1.86)	0.02 (−0.001 to 0.03)	−0.02 (−0.03 to 0.002)
Base case	1.84 (1.82 to 1.85)	1.85 (1.84 to 1.87)	0.02 (−0.002 to 0.04)	0.98 (0.96 to 1.01)
Malignant excisions^c	Mean (95% CI) number of malignant excisions^d		NA	Coefficient (95% CI)
Malignant excisions^c	150 (129 to 168)	126 (108 to 140)	24 (19 to 31)	1.1 (0.7 to 1.6)

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Abbreviations: MI, multiply imputed; NA, not applicable; TBP, total-body photography.

^{^a}

Estimated marginal means and the ratio of the difference in the mean cost for the intervention and control groups estimated using a generalized linear model (family = γ, link = log) adjusted for study group, sex, insurance status, yearly household income, and comorbidity group.

^{^b}

The adjusted rate ratio produced by the generalized linear model predicting costs, interpreted as a cost ratio, or the difference in costs between study groups (intervention to control).

^{^c}

Number of melanoma excisions was adjusted by the proportion of histopathologically confirmed invasive melanomas (intervention group = 0.17; control group = 0.21).

^{^d}

Estimated marginal mean and the ratio of the difference in the mean number of malignant excisions for the intervention and control groups estimated using a generalized linear model (family = negative binomial, link = log) adjusted for study group, sex, insurance status, yearly household income, and comorbidity group.

Costs

Over 2 years, mean costs per person to the government for skin tumor services were similar between the intervention ($555; 95% CI, $463-$648) and control groups ($617; 95% CI, $495-$739) (Table 2). Intervention group participants also had similar mean out-of-pocket costs ($333; 95% CI, $271-$395) as controls ($352; 95% CI, $277-$428) (Table 2).

Table 2. Unadjusted Mean Costs to the Government and Individuals for Skin Tumor Services (Person-Based) by Study Group.

Health care service	No. using service (% of group)		Mean (95% CI), $^a
	Intervention (n = 157)	Control (n = 152)	Costs to government		Costs to individual
	Intervention (n = 157)	Control (n = 152)	Intervention	Control	Intervention	Control
Melanoma excision	27 (17)	34 (22)	212 (163-261)	277 (194-359)	202 (118-285)	135 (100-171)
KC excision	44 (28)	40 (26)	296 (227-365)	234 (189-280)	132 (88-176)	164 (101-182)
Benign tumor excision	100 (64)	94 (62)	137 (115-159)	145 (110-180)	134 (105-163)	134 (101-167)
Biopsy	110 (70)	97 (64)	94 (79-109)	89 (72-105)	138 (113-164)	134 (107-161)
Flap repair, skin graft, wedge excision	14 (9)	15 (10)	265 (195-335)	374 (161-587)	282 (207-356)	395 (217-573)
Other MBS^b	8 (5)	6 (4)	142 (76-207)	170 (41-299)	117 (64-169)	244 (−40 to 530)
Pathology^c	138 (88)	122 (80)	219 (185-252)	237 (193-280)	79 (47-112)	67 (44-91)
Total MBS	140 (89)	124 (82)	555 (463-648)	617 (495-739)	333 (271-395)	352 (277-428)

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Abbreviations: KC, keratinocyte carcinoma; MBS, Medicare benefits scheme.

^{^a}

Mean costs were per person affected ($0 costs removed).

^{^b}

Other MBS services for skin tumor services included tumor/cyst/ulcer/scar procedures, Mohs surgery, other skin surgery, and large lesion procedures.

^{^c}

Costs for pathology services within 14 days following a biopsy, KC, or melanoma excision. Pathology services occurring outside this period were not included as they were assumed not to be a skin tumor service.

Adjusted GLMs showed significantly greater total costs for the intervention group ($1708; 95% CI, $1455-$1961) compared with controls ($763; 95% CI, $655-$870; P < .01) (Table 1). This difference was largely associated with costs to the government, which, after adjusting for various factors, were 2.8 (95% CI, 2.3-3.4) times greater for the intervention group compared with controls (Table 1). Excluding 3D TBP-SDDI intervention costs, costs to the government were similar between study groups. Out-of-pocket costs were also similar between study groups (Table 1). Over 2 years, mean QALYs were similar for the intervention (1.84; 95% CI, 1.82-1.86) and control groups (1.84; 95% CI, 1.83-1.86; P = .32) (Table 1). The mean incremental cost per malignant excision was $40 (95% CI, $34-$48).

For each cost perspective, 3D TBP-SDDI produced higher costs and similar QALYs than usual care. There was a 0% likelihood that 3D TBP-SDDI was cost-effective when looking at total and government costs, and a 4.4% likelihood when looking at individual costs (Figure; eFigure 1 in Supplement 1). When intervention costs were removed from total cost calculations, the likelihood of 3D TBP-SDDI being cost-effective remained low (3.9%; eFigure 2 in Supplement 1).

Figure. — Each dot represents an incremental cost and QALY pairing, selected randomly during 1000 iterations. The ellipse represents the 95% CI of joint cost and effects pairs from the bootstrapping replications. Dots falling below the diagonal line (the willingness-to-pay [WTP] threshold of AU$50 000 per QALY) and to the right of the vertical dashed line are considered cost-effective. The 3D TBP-SDDI intervention had a 0% likelihood of being cost-effective at the willingness-to-pay threshold of AU$50 000 per QALY when total costs were analyzed. There was a 95.4% likelihood that the 3D TBP-SDDI intervention was more costly and less effective than usual care when looking at total costs. One Australian dollar is equal to US$0.63.

Sensitivity analyses found that 3D TBP-SDDI produced higher total costs than usual care, regardless of intervention cost (Table 3). Adjusting the proportion of histopathologically confirmed invasive melanomas in each study group by plus or minus 10% or the discount rate used did not materially affect the results.

Table 3. Results of Bootstrapped Costs Over 24 Months for Sensitivity Analyses.

Input variable	Estimated costs over 24 mo, mean (95% CI), $^a		Incremental cost (95% CI), $	Cost ratio (95% CI)^b
Input variable	Intervention (n = 157)	Control (n = 152)	Incremental cost (95% CI), $	Cost ratio (95% CI)^b
3D TBP cost (base case $250 per session)	1461 (1230-1691)	543 (462-624)	917 (669-1166)	2.7 (2.2-3.4)
$150	1108 (926-1288)		564 (363-765)	2.0 (1.6-2.5)
$200	1284 (1079-1489)		741 (517-965)	2.4 (1.9-2.9)
$300	1637 (1381-1893)		1094 (820-1367)	3.0 (2.4-3.7)
Input variable	No. of malignant excisions, mean (95% CI)		Incremental cost (95% CI), $
Proportion of invasive melanomas as % of all (base case = 0.17 intervention, 0.21 control)	150 (141-158)	126 (119-134)	40 (34-48)
Low value (0.153 intervention, 0.189 control)	150 (143-157)	125 (120-129)	38 (33-45)
High value (0.187 intervention, 0.231 control)	151 (143-158)	128 (122-133)	41 (35-50)

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Abbreviations: 3D, 3-dimensional; TBP, total-body photography.

^{^a}

^{^b}

The adjusted rate ratio produced by the generalized linear model predicting costs, interpreted as a cost ratio, or the difference in costs between treatment groups (intervention to control).

Discussion

In this cost-effectiveness analysis of an intervention trial, adding 3D TBP-SDDI by junior clinicians and teledermatologist review to usual care was associated with mostly equivalent skin tumor excision outcomes and similar treatment costs but higher overall costs from the anticipated payments for 3D TBP-SDDI services. Unexpectedly, the intervention group had fewer melanomas excised, a similar number of malignant skin tumors excised and had undergone more biopsies than the control group. In this specific setting, 3D TBP-SDDI in addition to usual care was not cost-effective for a high-risk melanoma cohort. Without knowing exactly what the cost of 3D TBP-SDDI would be if implemented (eg, cost to purchase the imaging equipment and software, as well as maintenance and upgrades) and who would pay, we estimated that costs could be 3-fold higher in the intervention group compared with usual care. Other than technology costs, no significant differences were found between the intervention and control groups in mean costs per person to the government or individual.

The clinical findings for 3D TBP-SDDI may have arisen because the short surveillance period limited the opportunity to observe potential long-term benefits of removing severely dysplastic lesions before they become malignant. The study by Watts et al¹⁸ found that TBP and digital dermoscopy was cost-effective compared with standard care in a high-risk melanoma population over 10 years. Study group differences were associated with lower treatment costs from earlier-stage melanoma detection and a lower annual mean excision rate for suspicious lesions in the specialized surveillance group,¹⁸ suggesting that TBP and digital dermoscopy for high-risk populations might be more cost-effective in the long term. As we looked at health care over 2 years, we were unable to quantify the long-term effects of early melanoma detection.

From an economic perspective, it is necessary to include the costs of treating KCs and benign tumors that are detected through this intensive screening program. While early detection methods are designed to detect early-stage, treatable skin cancers, screening interventions, at least initially, will detect high numbers of in situ and thin melanomas and KCs that require medical intervention. This adds to the overall economic burden and lowers cost-effectiveness at the start, but might also remove lesions that present later at an advanced stage, thereby producing potential long-term benefits.^32,33,34

The unfavorable cost-effectiveness result found in this study might, in part, have been due to implementation and clinical factors of our specific intervention tested in addition to usual care. Skin checks were performed by junior clinicians, who subsequently presented the identified suspicious lesions to a teledermatologist for final assessment. As a result, the accuracy of the diagnostics was constrained by the skill levels and experience of the junior clinicians. Ideally, 3D TBP-SDDI would reduce the number needed to treat through an improved benign to malignant excision ratio. This study was conducted in Queensland, Australia, which is often called the “skin cancer capital of the world.”³⁵ Queensland clinicians may be more cautious by excising more tumors that are suggestive of melanoma, particularly in high-risk melanoma cohorts. Fear of medical malpractice litigation resulting from missed melanomas, as well as patient pressure and anxiety, may contribute to an increased likelihood of excising a suspicious tumor.^36,37 These pressures may be lower in countries with lower skin cancer prevalence, with beneficial flow-on effects for cost-effectiveness. Intervention group participants receiving usual care as well as 3D TBP-SDDI meant that they received additional skin tumor services compared with controls. These results contrast with growing evidence that digital health technologies are cost-effective^38,39 and highlight that assessing the cost-effectiveness of medical technologies, such as 3D TBP-SDDI, is difficult, given their complex and dynamic nature, often resulting in improved precision over time.⁴⁰

Further developments, and the integration of strategies, such as artificial intelligence (AI) clinician support tools, may further improve the efficiency and effectiveness of the 3D TBP-SDDI intervention. AI-assisted classification of skin tumors has rapidly increased during the past decade,⁴¹ with AI-assisted clinician reports now demonstrating higher accuracy in tumor classification than AI or clinician decisions alone.^42,43 AI integration into 3D TBP-SDDI may enhance the accuracy of malignant tumor identification, potentially reducing the number of unnecessary excisions and enhancing the cost-effectiveness profile of this intervention.⁴⁴ Integration of other technologies within 3D TBP-SDDI aligns with a paradigm shift in dermatology toward a more precision-based model of disease prevention, potentially improving patient outcomes and optimizing resource use in resource-constrained health care systems.⁴⁵

By using routinely captured administrative data to quantify health care costs and validated measures to assess quality of life within a randomized clinical trial, we provided a comprehensive estimate of intervention costs and clinical outcomes. We found no significant differences in QALYs between the study groups, with both groups reporting high quality of life. Given the relatively minimal effect of treatment for a skin cancer or melanoma diagnosis on health utility scores,^46,47,48 intervention costs and resource use may be more important outcomes for cost-effective analyses in skin cancer than QALYs. The use of malignant skin tumors excised as an alternative outcome for cost-effectiveness was a strength of this study and may be a useful measure of benefit in future cost-effectiveness analyses of melanoma screening programs.

Limitations

The limitations of this study included being a relatively small single-center study from a major metropolitan area in Australia. This limited the representativeness of the sample, as it was predominately highly educated individuals with low socioeconomic disadvantage. Very few individuals living in remote areas were able to attend the clinic where the 3D TBP-SDDI system was located. As individuals from remote areas are less likely to access specialist services and more likely to receive a diagnosis of later-stage skin cancers than those living in metropolitan areas,^49,50 these individuals may benefit most from an early detection intervention. As we did not include primary care physician visits and only captured out-of-pocket costs for MBS medical services, we have underestimated service use and associated costs in this analysis. The 3D TBP-SDDI cost was estimated and benchmarked on existing imaging MBS items, potentially overestimating or underestimating the actual costs involved. New technologies are difficult to cost due to factors such as propriety or commercial pricing agreements and the degree of economies of scale. If this technology is to be widely implemented in routine clinical care, further research is required to accurately quantify its cost for individuals and the Australian health care system.

Conclusions

The results of the cost-effectiveness analysis suggest that, under the specific conditions tested, a 3D TBP-SDDI by junior clinicians with teledermatologist review intervention over an initial 2-year surveillance window was not cost-effective. There may be other benefits of 3D TBP-SDDI once AI clinician support systems have been integrated.

Supplement 1.

eTable 1. CHEERS Statement Checklist

eTable 2. MBS items used for grouping of skin tumor services

eTable 3. Demographic and clinical characteristics of intervention and control group participants

eTable 4. Demographic and clinical characteristics of participants with and without MBS data

eTable 5. Health service use by study group

eTable 6. Number of MBS items for excisions of malignant melanoma, by study group

eFigure 1a. Incremental cost-effectiveness for costs to the government, 3D TBP-SDDI versus usual care

eFigure 1b. Incremental cost-effectiveness for costs to the individual, 3D TBP-SDDI versus usual care

eFigure 2. Incremental cost-effectiveness for total costs (intervention costs not included), 3D TBP-SDDI versus usual care

eAppendix. Additional information on missing data

jamadermatol-e250219-s001.pdf^{(459.7KB, pdf)}

Supplement 2.

Data sharing statement

jamadermatol-e250219-s002.pdf^{(16.3KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eTable 1. CHEERS Statement Checklist

eTable 2. MBS items used for grouping of skin tumor services

eTable 3. Demographic and clinical characteristics of intervention and control group participants

eTable 4. Demographic and clinical characteristics of participants with and without MBS data

eTable 5. Health service use by study group

eTable 6. Number of MBS items for excisions of malignant melanoma, by study group

eFigure 1a. Incremental cost-effectiveness for costs to the government, 3D TBP-SDDI versus usual care

eFigure 1b. Incremental cost-effectiveness for costs to the individual, 3D TBP-SDDI versus usual care

eFigure 2. Incremental cost-effectiveness for total costs (intervention costs not included), 3D TBP-SDDI versus usual care

eAppendix. Additional information on missing data

jamadermatol-e250219-s001.pdf^{(459.7KB, pdf)}

Supplement 2.

Data sharing statement

jamadermatol-e250219-s002.pdf^{(16.3KB, pdf)}

[doi250007r1] 1.Arnold M, Singh D, Laversanne M, et al. Global burden of cutaneous melanoma in 2020 and projections to 2040. JAMA Dermatol. 2022;158(5):495-503. doi: 10.1001/jamadermatol.2022.0160 [DOI] [PMC free article] [PubMed] [Google Scholar]

[doi250007r2] 2.Olsen CM, Green AC, Pandeya N, Whiteman DC. Trends in melanoma incidence rates in eight susceptible populations through 2015. J Invest Dermatol. 2019;139(6):1392-1395. doi: 10.1016/j.jid.2018.12.006 [DOI] [PubMed] [Google Scholar]

[doi250007r3] 3.Gordon LG, Leung W, Johns R, et al. Estimated healthcare costs of melanoma and keratinocyte skin cancers in Australia and Aotearoa New Zealand in 2021. Int J Environ Res Public Health. 2022;19(6):3178. doi: 10.3390/ijerph19063178 [DOI] [PMC free article] [PubMed] [Google Scholar]

[doi250007r4] 4.Kao SZ, Ekwueme DU, Holman DM, Rim SH, Thomas CC, Saraiya M. Economic burden of skin cancer treatment in the USA: an analysis of the Medical Expenditure Panel Survey data, 2012-2018. Cancer Causes Control. 2023;34(3):205-212. doi: 10.1007/s10552-022-01644-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[doi250007r5] 5.Vallejo-Torres L, Morris S, Kinge JM, Poirier V, Verne J. Measuring current and future cost of skin cancer in England. J Public Health (Oxf). 2014;36(1):140-148. doi: 10.1093/pubmed/fdt032 [DOI] [PubMed] [Google Scholar]

[doi250007r6] 6.Cancer Australia National Cancer Control Indicators . Relative survival by stage at diagnosis (melanoma). Accessed February 4, 2024. https://ncci.canceraustralia.gov.au/outcomes/relative-survival-rate/5-year-relative-survival-diagnosis

[doi250007r7] 7.American Cancer Society . 2023. Survival rates for melanoma skin cancer. Accessed February 4, 2024. https://www.cancer.org/cancer/types/melanoma-skin-cancer/detection-diagnosis-staging/survival-rates-for-melanoma-skin-cancer-by-stage.html

[doi250007r8] 8.Elliott TM, Whiteman DC, Olsen CM, Gordon LG. Estimated healthcare costs of melanoma in Australia over 3 years post-diagnosis. Appl Health Econ Health Policy. 2017;15(6):805-816. doi: 10.1007/s40258-017-0341-y [DOI] [PubMed] [Google Scholar]

[doi250007r9] 9.Grange F, Mohr P, Harries M, et al. Economic burden of advanced melanoma in France, Germany and the UK: a retrospective observational study (Melanoma Burden-of-Illness Study). Melanoma Res. 2017;27(6):607-618. doi: 10.1097/CMR.0000000000000372 [DOI] [PubMed] [Google Scholar]

[doi250007r10] 10.Salerni G, Carrera C, Lovatto L, et al. Benefits of total body photography and digital dermatoscopy (“two-step method of digital follow-up”) in the early diagnosis of melanoma in patients at high risk for melanoma. J Am Acad Dermatol. 2012;67(1):e17-e27. doi: 10.1016/j.jaad.2011.04.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[doi250007r11] 11.Moloney FJ, Guitera P, Coates E, et al. Detection of primary melanoma in individuals at extreme high risk: a prospective 5-year follow-up study. JAMA Dermatol. 2014;150(8):819-827. doi: 10.1001/jamadermatol.2014.514 [DOI] [PubMed] [Google Scholar]

[doi250007r12] 12.Janda M, Soyer HP. Using advances in skin imaging technology and genomics for the early detection and prevention of melanoma. Dermatology. 2019;235(1):1-3. doi: 10.1159/000493260 [DOI] [PubMed] [Google Scholar]

[doi250007r13] 13.Rayner JE, Laino AM, Nufer KL, et al. Clinical perspective of 3D total body photography for early detection and screening of melanoma. Front Med (Lausanne). 2018;5:152. doi: 10.3389/fmed.2018.00152 [DOI] [PMC free article] [PubMed] [Google Scholar]

[doi250007r14] 14.Carli P, De Giorgi V, Crocetti E, et al. Improvement of malignant/benign ratio in excised melanocytic lesions in the ‘dermoscopy era’: a retrospective study 1997-2001. Br J Dermatol. 2004;150(4):687-692. doi: 10.1111/j.0007-0963.2004.05860.x [DOI] [PubMed] [Google Scholar]

[doi250007r15] 15.Guitera P, Menzies SW, Coates E, et al. Efficiency of detecting new primary melanoma among individuals treated in a high-risk clinic for skin surveillance. JAMA Dermatol. 2021;157(5):521-530. doi: 10.1001/jamadermatol.2020.5651 [DOI] [PMC free article] [PubMed] [Google Scholar]

[doi250007r16] 16.Petty AJ, Ackerson B, Garza R, et al. Meta-analysis of number needed to treat for diagnosis of melanoma by clinical setting. J Am Acad Dermatol. 2020;82(5):1158-1165. doi: 10.1016/j.jaad.2019.12.063 [DOI] [PMC free article] [PubMed] [Google Scholar]

[doi250007r17] 17.Adler NR, Kelly JW, Guitera P, et al. Methods of melanoma detection and of skin monitoring for individuals at high risk of melanoma: new Australian clinical practice. Med J Aust. 2019;210(1):41-47. doi: 10.5694/mja2.12033 [DOI] [PubMed] [Google Scholar]

[doi250007r18] 18.Watts CG, Cust AE, Menzies SW, Mann GJ, Morton RL. Cost-effectiveness of skin surveillance through a specialized clinic for patients at high risk of melanoma. J Clin Oncol. 2017;35(1):63-71. doi: 10.1200/JCO.2016.68.4308 [DOI] [PubMed] [Google Scholar]

[doi250007r19] 19.Wilson ECF, Usher-Smith JA, Emery J, Corrie P, Walter FM. A modeling study of the cost-effectiveness of a risk-stratified surveillance program for melanoma in the United Kingdom. Value Health. 2018;21(6):658-668. doi: 10.1016/j.jval.2017.11.009 [DOI] [PubMed] [Google Scholar]

[doi250007r20] 20.Primiero CA, McInerney-Leo AM, Betz-Stablein B, et al. Evaluation of the efficacy of 3D total-body photography with sequential digital dermoscopy in a high-risk melanoma cohort: protocol for a randomised controlled trial. BMJ Open. 2019;9(11):e032969. doi: 10.1136/bmjopen-2019-032969 [DOI] [PMC free article] [PubMed] [Google Scholar]

[doi250007r21] 21.Ramsey SD, Willke RJ, Glick H, et al. Cost-effectiveness analysis alongside clinical trials II-An ISPOR Good Research Practices Task Force report. Value Health. 2015;18(2):161-172. doi: 10.1016/j.jval.2015.02.001 [DOI] [PubMed] [Google Scholar]

[doi250007r22] 22.Husereau D, Drummond M, Augustovski F, et al. ; CHEERS 2022 ISPOR Good Research Practices Task Force . Consolidated health economic evaluation reporting standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. Int J Technol Assess Health Care. 2022;38(1):e13. doi: 10.1017/S0266462321001732 [DOI] [PubMed] [Google Scholar]

[doi250007r23] 23.Soyer HP, Jayasinghe D, Rodriguez-Acevedo AJ, et al. Efficacy of 3D total-body photography in high-risk melanoma patients: a randomized clinical trial. JAMA Dermatol. Published online March 26, 2025. doi:10.1001/jamadermatol.2025.0211 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Cost-Effectiveness Analysis of 3D Total-Body Photography for People at High Risk of Melanoma

Daniel Lindsay, PhD

H Peter Soyer, MD

Monika Janda, PhD

David C Whiteman, PhD

Sonya Osborne, PhD

Anna Finnane, PhD

Liam J Caffery, PhD

Louisa G Collins, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Intervention

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Overview

Study Population and Setting

Intervention and Comparator

Measuring Skin Tumor Services and Valuing Costs

Measuring and Valuing Benefits

Handling Missing Data

Data Analysis

Results

Demographic Characteristics

Skin Biopsy and Excision Outcomes

Table 1. Results of Bootstrapped Costs and Quality-Adjusted Life-Years (QALYs) Over 24 Months.

Costs

Table 2. Unadjusted Mean Costs to the Government and Individuals for Skin Tumor Services (Person-Based) by Study Group.

Figure. Incremental Total Costs and Quality-Adjusted Life-Years (QALYs) of 3-Dimensional (3D) Total-Body Photography (TBP)–Sequential Digital Dermoscopy Imaging (SDDI) vs Usual Care.

Table 3. Results of Bootstrapped Costs Over 24 Months for Sensitivity Analyses.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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