Dataset Information

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.

ABSTRACT:

Introduction

Melanoma is Australia's fourth most common cancer. Early detection is fundamental in maximising health outcomes and minimising treatment costs. To date, population-based screening programmes have not been justified in health economic studies. However, a skin surveillance approach targeting high-risk individuals could improve the cost-benefit ratio.

Methods and analysis

This paper describes a 2-year longitudinal randomised controlled trial (RCT) to compare routine clinical care (control) with an intensive skin surveillance programme (intervention) consisting of novel three-dimensional (3D) total-body photography (TBP), sequential digital dermoscopy and melanoma-risk stratification, in a high-risk melanoma cohort. Primary outcomes will evaluate clinical, economic and consumer impact of the intervention. Clinical outcomes will evaluate differences in the rate of lesion excisions/biopsies per person, benign to malignant ratio for excisions and thickness of melanomas diagnosed. A health economic analysis using government data repositories will capture healthcare utilisation and costs relating to skin surveillance. Consumer questionnaires will examine intervention acceptability, the psychological impact, and attitudes towards melanoma risk and sun protective behaviour. Secondary outcomes include the development of a holistic risk algorithm incorporating clinical, phenotypic and genetic factors to facilitate the identification of those most likely to benefit from this surveillance approach. Furthermore, the feasibility of integrating the intervention with teledermatology to enhance specialist care in remote locations will be evaluated. This will be the first RCT to compare a targeted surveillance programme utilising new 3D TBP technology against current routine clinical care for individuals at high risk of melanoma.

Ethics and dissemination

This study has received Human Research Ethics Committee (HREC) approval from both Metro South Health HREC (HREC/17/QPAH/816) and The University of Queensland HREC (2018000074).

Trial registration number

ANZCTR12618000267257; Pre-results.

SUBMITTER: Primiero CA

PROVIDER: S-EPMC6858160 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

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.

Primiero Clare Amy CA McInerney-Leo Aideen M AM Betz-Stablein Brigid B Whiteman David C DC Gordon Louisa L Caffery Liam L Aitken Joanne F JF Eakin Elizabeth E Osborne Sonya S Gray Len L Smithers B Mark BM Janda Monika M Soyer H Peter HP Finnane Anna A

BMJ open 20191110 11

<h4>Introduction</h4>Melanoma is Australia's fourth most common cancer. Early detection is fundamental in maximising health outcomes and minimising treatment costs. To date, population-based screening programmes have not been justified in health economic studies. However, a skin surveillance approach targeting high-risk individuals could improve the cost-benefit ratio.<h4>Methods and analysis</h4>This paper describes a 2-year longitudinal randomised controlled trial (RCT) to compare routine clin ...[more]

PMID: 31712348

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Project description:ObjectivesEarly melanoma detection decreases morbidity and mortality. Early detection classically involves dermoscopy to identify suspicious lesions for which biopsy is indicated. Biopsy and histological examination then diagnose benign nevi, atypical nevi, or cancerous growths. With current methods, a considerable number of unnecessary biopsies are performed as only 11% of all biopsied, suspicious lesions are actually melanomas. Thus, there is a need for more advanced noninvasive diagnostics to guide the decision of whether or not to biopsy. Artificial intelligence can generate screening algorithms that transform a set of imaging biomarkers into a risk score that can be used to classify a lesion as a melanoma or a nevus by comparing the score to a classification threshold. Melanoma imaging biomarkers have been shown to be spectrally dependent in Red, Green, Blue (RGB) color channels, and hyperspectral imaging may further enhance diagnostic power. The purpose of this study was to use the same melanoma imaging biomarkers previously described, but over a wider range of wavelengths to determine if, in combination with machine learning algorithms, this could result in enhanced melanoma detection.MethodsWe used the melanoma advanced imaging dermatoscope (mAID) to image pigmented lesions assessed by dermatologists as requiring a biopsy. The mAID is a 21-wavelength imaging device in the 350-950 nm range. We then generated imaging biomarkers from these hyperspectral dermoscopy images, and, with the help of artificial intelligence algorithms, generated a melanoma Q-score for each lesion (0 = nevus, 1 = melanoma). The Q-score was then compared to the histopathologic diagnosis.ResultsThe overall sensitivity and specificity of hyperspectral dermoscopy in detecting melanoma when evaluated in a set of lesions selected by dermatologists as requiring biopsy was 100% and 36%, respectively.ConclusionWith widespread application, and if validated in larger clinical trials, this non-invasive methodology could decrease unnecessary biopsies and potentially increase life-saving early detection events. Lasers Surg. Med. 51:214-222, 2019. © 2019 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.

Project description:PurposeTotal and regional body composition are important indicators of health and mortality risk, but their measurement is usually restricted to controlled environments in clinical settings with expensive and specialized equipment. A method that approaches the accuracy of the current gold standard method, dual-energy x-ray absorptiometry (DXA), while only requiring input from widely available consumer grade equipment, would enable the measurement of these important biometrics in the wild, enabling data collection at a scale that would have previously been prohibitive in time and expense. We describe an algorithm for predicting three-dimensional (3D) body shape and composition from a single frontal 2-dimensional image acquired with a digital consumer camera.MethodsDuplicate 3D optical scans, two-dimensional (2D) optical images, and DXA whole-body scans were available for 183 men and 233 women from the Shape Up! Adults Study. A principal component analysis vector basis was fit to 3D point clouds of a training subset of 152 men and 194 women. The relationship between this vector space and DXA-derived body composition was modeled with linear regression. The principal component 3D shape was then fitted to match a silhouette extracted from a 2D photograph of a novel body. Body composition was predicted from the resulting 3D shape match using the linear mapping between the principal component parameters and the DXA metrics. Accuracy of body composition estimates from the silhouette method was evaluated against a simple model using height and weight as a baseline, and against DXA measurements as ground truth. Test-retest precision of the silhouette method was evaluated using the duplicate 2D optical images and compared against precision of the duplicate DXA scans. Paired t-tests were performed to detect significant differences between the sets.ResultsResults were reported on a held-out set. Body composition prediction achieved R2 s of 0.81 and 0.74 for percent fat prediction of males and females, respectively, on a held-out test set consisting of 31 males and 39 females. Precision estimates for fat mass were 2.31% and 2.06% for males and females, respectively, compared to 1.26% and 0.68% for DXA scans. The t-tests revealed no statistically significant differences between the silhouette method measurements and DXA measurements, or between retests.ConclusionTotal and regional body composition measures can be estimated from a single frontal photograph of a human body. Body composition prediction using consumer level photography can enable early screening and monitoring of possible physiological indicators of metabolic disease in regions where medical imagery or clinical assessment is inaccessible.

Dataset Information

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.

Introduction

Methods and analysis

Ethics and dissemination

Trial registration number

Publications

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.

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