3D Total-Body Photography in Patients at High Risk for Melanoma: A Randomized Clinical Trial.

H Peter Soyer, Dilki Jayasinghe, Astrid J Rodriguez-Acevedo, Louisa G Collins, Liam J Caffery, David C Whiteman, Brigid Betz-Stablein, Sonya R Osborne, Anna Finnane, Caitlin Horsham, Clare Primiero, Leonard C Gray, Monika Janda
Author Information
  1. H Peter Soyer: Frazer Institute, The University of Queensland Dermatology Research Centre, Brisbane, Queensland, Australia.
  2. Dilki Jayasinghe: Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia.
  3. Astrid J Rodriguez-Acevedo: Frazer Institute, The University of Queensland Dermatology Research Centre, Brisbane, Queensland, Australia.
  4. Louisa G Collins: Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  5. Liam J Caffery: Centre for Online Health, The University of Queensland, Brisbane, Queensland, Australia.
  6. David C Whiteman: Cancer Control Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  7. Brigid Betz-Stablein: Frazer Institute, The University of Queensland Dermatology Research Centre, Brisbane, Queensland, Australia.
  8. Sonya R Osborne: School of Nursing and Midwifery, University of Southern Queensland, Ipswich, Queensland, Australia.
  9. Anna Finnane: School of Public Health, The University of Queensland, Brisbane, Queensland, Australia.
  10. Caitlin Horsham: Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia.
  11. Clare Primiero: Frazer Institute, The University of Queensland Dermatology Research Centre, Brisbane, Queensland, Australia.
  12. Leonard C Gray: Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia.
  13. Monika Janda: Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia.

Abstract

Importance: Three-dimensional (3D) total-body photography (TBP) can support clinicians in monitoring and identifying changes to skin lesions in patients at high risk of melanoma.
Objective: To assess clinical outcomes between patients at high risk of melanoma receiving usual clinical care compared with those receiving usual care plus 3D TBP and sequential digital dermoscopy imaging (SDDI) every 6 months via teledermatology.
Design, Setting, and Participants: This randomized clinical trial was conducted at a research hospital in Brisbane, Australia, from April 2018 to October 2021, with adult patients (≥18 years) at high risk of developing a primary or subsequent melanoma. Data analysis was conducted from March 2022 to June 2024.
Intervention: Usual care plus 3D-TBP in person and SDDI via teledermatology at baseline, 6, 12, 18, and 24 months. The control group continued usual care and completed online surveys every 6 months.
Main Outcome Measures: Number and rates of excisions and/or biopsies of lesions suggestive of melanoma, and results of histopathologic testing.
Results: The analysis included 314 participants (mean [SD] age, 51.6 [12.8] years; 194 females [62%]) who completed all of the study procedures (158 in the intervention and 156 in the control). In all, 1527 excisions (905 intervention and 622 in the control) were performed among 226 participants (122 intervention and 104 controls), with 67 (4%) histopathologically confirmed as melanoma and 402 (26%) as keratinocyte cancer (KC). The mean (SD) number of lesions of any type excised per person was significantly higher in the intervention (5.73 [6.77]; 95% CI, 4.66-6.79) compared to the control group (3.99 [5.72]; 95% CI, 3.08-4.89; P = .02). Fewer melanomas were detected among the intervention group compared with the control (24 [35%] vs 43 [64%], respectively), and therefore, a lower incidence rate: 2.03 (95% CI, 1.30-3.02) vs 3.62 (95% CI, 2.62-4.88), respectively. After 1 year of follow-up, the intervention had a lower, but not statistically significant, rate of melanoma per person: 0.08 (95% CI, 0.03-0.13) compared with 0.16 (95% CI, 0.08-0.25) in the control; an average of 0.86 (95% CI, 0.55-1.16) vs 0.42 (95% CI, 0.24-0.59) KCs per person; and 2.01 (95% CI, 1.50-2.51) vs 1.39 (95% CI, 0.98-1.82) excisions or biopsies per person, respectively.
Conclusions and Relevance: The results of this randomized clinical trial indicate that the addition of 3D-TPB and SDDI to usual care in a teledermatology setting without AI (artificial intelligence) increased the number and rate of skin excisions and biopsies performed. Further studies are required to compare teledermatology to usual care rather than adding it, and to study whether the use of AI can improve the teledermatology outcomes. Larger studies in multiple settings with a greater number of teledermatologists are needed. This study shows that conducting clinical trials in this setting is feasible.
Trial Registration: anzctr.org.au Identifier: ACTRN12618000267257.

References

  1. Int J Environ Res Public Health. 2022 Mar 08;19(6): [PMID: 35328865]
  2. JMIR Dermatol. 2023 May 17;6:e43395 [PMID: 37632914]
  3. BMJ Open. 2023 Sep 28;13(9):e072788 [PMID: 37770274]
  4. CA Cancer J Clin. 2021 May;71(3):209-249 [PMID: 33538338]
  5. CA Cancer J Clin. 2016 Nov 12;66(6):460-480 [PMID: 27232110]
  6. Med J Aust. 2023 May 15;218(9):426-431 [PMID: 37120760]
  7. Br J Dermatol. 2022 Oct;187(4):515-522 [PMID: 35531668]
  8. Appl Health Econ Health Policy. 2018 Apr;16(2):235-242 [PMID: 29305821]
  9. Int J Environ Res Public Health. 2021 Feb 10;18(4): [PMID: 33578996]
  10. BMJ Open. 2018 Sep 19;8(9):e025857 [PMID: 30232117]
  11. JMIR Dermatol. 2022 Jun 20;5(2):e37034 [PMID: 37632874]
  12. Front Med (Lausanne). 2022 Jan 17;8:818096 [PMID: 35111789]
  13. Aust N Z J Public Health. 2018 Feb;42(1):86-91 [PMID: 29168287]
  14. Epidemiology. 1996 Jan;7(1):29-33 [PMID: 8664397]
  15. Australas J Dermatol. 2023 Feb;64(1):e11-e20 [PMID: 36380357]
  16. BMJ Evid Based Med. 2024 Jan 19;29(1):17-28 [PMID: 37793786]
  17. JAMA Dermatol. 2015 Feb;151(2):178-86 [PMID: 25389712]
  18. Public Health Res Pract. 2022 Mar 10;32(1): [PMID: 35290997]
  19. BMJ Open. 2019 Nov 10;9(11):e032969 [PMID: 31712348]
  20. Stat Med. 2006 Oct 30;25(20):3518-33 [PMID: 16345026]
  21. J Cosmet Dermatol. 2022 Nov;21(11):5993-6004 [PMID: 36001057]
  22. J Clin Epidemiol. 2014 Mar;67(3):267-77 [PMID: 24275499]
  23. Cancer Epidemiol. 2021 Feb;70:101874 [PMID: 33341599]
  24. Nat Med. 2020 Aug;26(8):1229-1234 [PMID: 32572267]
  25. J Invest Dermatol. 2024 Jun;144(6):1200-1207 [PMID: 38231164]
  26. Dermatol Pract Concept. 2023 Oct 01;13(4): [PMID: 37992381]
  27. Med J Aust. 2022 Sep 19;217(6):275-278 [PMID: 36057953]
  28. Front Med (Lausanne). 2018 May 23;5:152 [PMID: 29911103]
  29. JAMA Dermatol. 2022 May 1;158(5):504-512 [PMID: 35385051]
  30. JAMA Dermatol. 2023 Apr 1;159(4):432-440 [PMID: 36857048]
  31. BMC Med Res Methodol. 2008 May 30;8:35 [PMID: 18513418]
  32. Med J Aust. 2023 Nov 6;219(9):402-404 [PMID: 37852608]
  33. Australas J Dermatol. 2021 Aug;62(3):300-309 [PMID: 33860932]
  34. JAMA. 2023 Apr 18;329(15):1290-1295 [PMID: 37071089]
  35. CA Cancer J Clin. 2017 Nov;67(6):472-492 [PMID: 29028110]
  36. Public Health Res Pract. 2019 Jul 31;29(2): [PMID: 31384886]
  37. Expert Rev Anticancer Ther. 2018 Aug;18(8):775-784 [PMID: 29923435]
  38. Int J Dermatol. 2023 Apr;62(4):524-533 [PMID: 36707877]
  39. JAMA Dermatol. 2019 Aug 01;155(8):914-921 [PMID: 31090868]
  40. J Prim Health Care. 2010 Dec 01;2(4):268-72 [PMID: 21125066]
  41. Australas J Dermatol. 2023 Feb;64(1):118-121 [PMID: 36349396]
  42. J Clin Oncol. 2017 Jan;35(1):63-71 [PMID: 28034073]
  43. Mediators Inflamm. 2022 Oct 14;2022:1734327 [PMID: 36274972]
  44. JAMA Dermatol. 2025 Mar 26;: [PMID: 40136266]
  45. BMC Cancer. 2022 Mar 1;22(1):223 [PMID: 35232405]
  46. JAMA Dermatol. 2021 Jul 1;157(7):871-874 [PMID: 34037674]
  47. Aust N Z J Public Health. 2020 Apr;44(2):111-115 [PMID: 32190955]

Word Cloud

Created with Highcharts 10.0.095%CI0melanomacarecontrolinterventionclinicalusualteledermatologycompared6personexcisionspervs13DlesionspatientshighriskSDDImonthsgroupbiopsiesstudynumber3respectively2TBPcanskinoutcomesreceivingpluseveryviarandomizedtrialconductedyearsanalysis24completedresultsparticipantsmean51performedamong02lowerrate16settingAIstudiesImportance:Three-dimensionaltotal-bodyphotographysupportcliniciansmonitoringidentifyingchangesObjective:assesssequentialdigitaldermoscopyimagingDesignSettingParticipants:researchhospitalBrisbaneAustraliaApril2018October2021adult≥18developingprimarysubsequentDataMarch2022June2024Intervention:Usual3D-TBPbaseline1218continuedonlinesurveysMainOutcomeMeasures:Numberratesand/orsuggestivehistopathologictestingResults:included314[SD]age[128]194females[62%]procedures1581561527905622226122104controls674%histopathologicallyconfirmed40226% askeratinocytecancerKCSDtypeexcisedsignificantlyhigher573[677]466-67999[572]08-489P = Fewermelanomasdetected[35%]43[64%]thereforeincidencerate:0330-36262-488yearfollow-upstatisticallysignificantperson:0803-01308-025average8655-14224-059KCs0150-23998-182ConclusionsRelevance:indicateaddition3D-TPBwithoutartificialintelligenceincreasedrequiredcompareratheraddingwhetheruseimproveLargermultiplesettingsgreaterteledermatologistsneededshowsconductingtrialsfeasibleTrialRegistration:anzctrorgauIdentifier:ACTRN12618000267257Total-BodyPhotographyPatientsHighRiskMelanoma:RandomizedClinicalTrial

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