3D Total Body Photography as a Promising Innovation for Early Skin Cancer Detection: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Skin cancer is a global health concern due to its high and still increasing incidence and associated healthcare cost. Belgium is no exception as one in five people are diagnosed with skin cancer before the age of 75. A promising innovation, the VECTRA WB360, a three-dimensional total body photography system allows clinicians to objectively compare the totality of the skin on a macroscopic level on further appointments. The integrated lesion visualisation software allows automated detection, counts and assessment of skin lesions. And detailed comparison of individual lesions is possible through the attached digital dermatoscope. OBJECTIVE: This study aims to review available literature on the use of the Vectra in research and clinical settings, and to summarise the clinical utility, advantages and limitations reported for this system. METHODS: An electronic literature search was conducted on PubMed from December 2023 - March 2024 using a combination of following search terms: 3D imaging, VECTRA WB360, melanoma, non-melanoma skin cancer, their synonyms and associated entry terms. Publications that used a device other than the Vectra WB360 were excluded, as were articles reporting on new technology without further research or without added cases. After thorough screening of the articles and removal of duplicates, 11 articles remained. RESULTS: Our literature search yielded 11 relevant papers, which included 2 case studies, 6 prospective and 3 retrospective studies. According to multiple studies, the VECTRA WB360 images were of a high enough quality to allow on-screen diagnosis of some melanoma and nonmelanoma skin cancers by dermatologists. Sensitivity compared to face to face examination for melanoma is unknown. The integrated lesion visualisation software is capable of detecting and counting naevi and distinguishing melanoma from other skin lesions with high accuracy, with convolutional neural network (CNN) integration further enhancing its sensitivity and specificity. However, it is important to note that no comparison to the usual standard of care was made. Also, dermatologists achieved greater specificity and thus remained superior to machine and artificial intelligence. CONCLUSIONS: While the VECTRA 3D TBP holds substantial promise for the early detection and monitoring of skin cancer, its application cannot yet replace the expertise of trained clinicians. Although the lesion visualizer and DEXI score offer potential enhancements, they also pose risks, including a significant increase in unnecessary excisions due to lower specificity. Despite these promising results, expert overview is still recommended and superior, since there is not enough evidence yet that 3D TBP and/or AI is reliable on its own or beneficial as a support tool Given the small samples and lack of blinded trials, further studies are needed to explore and improve the diagnostic capacities of 3D TBP and the possible integration of CNN's or other AI extensions. It will also be important to examine the Vectra 360WB compared to the usual standard of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".