Pigmented Lesion Assay for Suspected Melanoma Lesions: A Health Technology Assessment.
Bibliographic record
Abstract
BACKGROUND: Early detection of melanoma is key, as survival rates are substantially better when the cancer is detected in its early stages. Currently, the standard of care is to biopsy any lesion suspected of melanoma for diagnostic confirmation by histopathology. As a result, most people who undergo biopsy receive negative melanoma results. If effective, a non-invasive alternative, such as pigmented lesion assay, could minimize the number of unnecessary biopsies performed. We conducted a health technology assessment of pigmented lesion assay for people with suspected melanoma lesions, which included an evaluation of diagnostic accuracy, clinical utility, the budget impact of publicly funding pigmented lesion assay, and the preferences and values of people who have undergone biopsy for suspected melanoma. METHODS: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and the Risk of Bias Assessment Tool for Non-randomized Studies (RoBANS). We assessed the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic literature search of the economic evidence. We also analyzed the budget impact of publicly funding pigmented lesion assay in adults with suspected melanoma in Ontario. To contextualize the potential value of pigmented lesion assay, we spoke with people who had undergone skin biopsy for melanoma. We also used the qualitative research synthesis from a report by the Canadian Agency for Drugs and Technologies in Health to provide context for the preferences and values of those with suspected melanoma. RESULTS: We included seven studies in the clinical evidence review. Pigmented lesion assay has a sensitivity of 79% (95% confidence interval [CI] 58%-93%) and a specificity of 80% (95% CI 73%-85%; GRADE: Low). We found one published cost-effectiveness study with potentially serious limitations. Therefore, the cost-effectiveness of pigmented lesion assay compared with the standard care pathway is currently uncertain. Assuming a very low uptake, we estimated that the budget impact of publicly funding pigmented lesion assay in Ontario over the next 5 years is about $3.44 million if the test is used exclusively by primary care providers, or about $2.56 million if it is used exclusively by specialists. The people with whom we spoke who had experienced biopsy for suspected melanoma responded positively to the potential benefits of pigmented lesion assay, emphasizing its ease-of-use, potential increase in early detection of melanoma, and reduction in physical and emotional burden of unnecessary biopsies. Participants also felt that the accuracy of this tool was essential to ensure minimal false negatives. CONCLUSIONS: There is uncertainty because of the low-quality evidence for the diagnostic accuracy of pigmented lesion assay. The cost-effectiveness of pigmented lesion assay compared with standard care is also uncertain. We estimated that publicly funding pigmented lesion assay in Ontario over the next 5 years would result in additional costs of $3.44 million (if used exclusively by primary care providers) or $2.56 million (if used exclusively by specialists). For people who had experienced biopsy for suspected melanoma, it was felt that pigmented lesion assay could represent an effective tool to increase early detection and avoid unnecessary biopsies, if the tool was accurate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.181 | 0.443 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".