Risk Prediction Models for Melanoma: A Systematic Review on the Heterogeneity in Model Development and Validation

Kaiser, Isabelle; Pfahlberg, Annette; Uter, Wolfgang; Heppt, Markus V.; Veierød, Marit B.; Gefeller, Olaf

doi:10.3390/ijerph17217919

Cited by 21 publications

(27 citation statements)

References 77 publications

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“…This procedure is more targeted, and the expected effects are larger because the prevalence of the screened population is significantly higher compared with the normal population. The latter procedure was used in the studies reviewed here, yet the definition of risk factors was fairly heterogeneous in keeping with previous reviews on risk prediction models [ 39 ]. In most studies, the presence of >4 to 5 clinically atypical nevi or a large number (>100) of common nevi was required for investigation by TBP.…”

Section: Discussionmentioning

confidence: 99%

The Value of Total Body Photography for the Early Detection of Melanoma: A Systematic Review

Hornung

Steeb

Wessely

et al. 2021

IJERPH

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Early detection of melanoma is critical to reduce the mortality and morbidity rates of this tumor. Total body photography (TBP) may aid in the early detection of melanoma. To summarize the current evidence on TBP for the early detection of melanoma, we performed a systematic literature search in Medline, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) for eligible records up to 6th August 2020. Outcomes of interest included melanoma incidence, incisional and excisional biopsy rates, as well as the Breslow’s index of detected tumors. Results from individual studies were described qualitatively. The risks of bias and applicability of the included studies was assessed using the QUADAS-2 checklist. In total, 14 studies published between 1997 and 2020 with an overall sample size of n = 12082 (range 100–4692) were included in the qualitative analysis. Individuals undergoing TBP showed a trend towards a lower Breslow’s thickness and a higher proportion of in situ melanomas compared to those without TBP. The number needed to excise one melanoma varied from 3:1 to 14.3:1 and was better for lesions that arose de novo than for tracked ones. The included studies were judged to be of unclear methodological concern with specific deficiencies in the domains “flow and timing” and “reference standard”. The use of TBP can improve the early detection of melanoma in high-risk populations. Future studies are warranted to reduce the heterogeneity of phenotypic risk factor definition and the technical implementation of TBP. Artificial intelligence-assisted analysis of images derived from 3-D TBP systems and digital dermoscopy may further improve the early detection of melanoma.

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Section: Discussionmentioning

confidence: 99%

The Value of Total Body Photography for the Early Detection of Melanoma: A Systematic Review

Hornung

Steeb

Wessely

et al. 2021

IJERPH

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“…In our TRIPOD rating we included all studies developing and validating models for predicting the individual risk of occurrence of cutaneous melanoma. As the basis for the set of studies to be assessed, we used a recent systematic review on melanoma prediction modeling [ 11 ] updating two earlier systematic reviews on the topic [ 12 , 13 ]. One eligibility criterion for that systematic review had been that all included studies provide either absolute risk or risk scores, or report mutually adjusted relative risks for primary cutaneous melanoma.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, the studies had to use a multivariable prediction model and a well-defined statistical method for the development of their models. More details on the search strategy are described in [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

Reporting Quality of Studies Developing and Validating Melanoma Prediction Models: An Assessment Based on the TRIPOD Statement

et al. 2022

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Transparent and accurate reporting is essential to evaluate the validity and applicability of risk prediction models. Our aim was to evaluate the reporting quality of studies developing and validating risk prediction models for melanoma according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis) checklist. We included studies that were identified by a recent systematic review and updated the literature search to ensure that our TRIPOD rating included all relevant studies. Six reviewers assessed compliance with all 37 TRIPOD components for each study using the published “TRIPOD Adherence Assessment Form”. We further examined a potential temporal effect of the reporting quality. Altogether 42 studies were assessed including 35 studies reporting the development of a prediction model and seven studies reporting both development and validation. The median adherence to TRIPOD was 57% (range 29% to 78%). Study components that were least likely to be fully reported were related to model specification, title and abstract. Although the reporting quality has slightly increased over the past 35 years, there is still much room for improvement. Adherence to reporting guidelines such as TRIPOD in the publication of study results must be adopted as a matter of course to achieve a sufficient level of reporting quality necessary to foster the use of the prediction models in applications.

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“…As healthcare knowledge continues to expand and outstrip human cognitive capacity, CRPM have gained popularity as they offer a scalable way to consolidate growing volumes of data and information complexity to support clinical decision-making [3]. Such CRPMs range from predicting hospital readmissions, to various types of cancers, and more recently COVID-19 [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Patient similarity analytics for explainable clinical risk prediction

Fang

Tan

et al. 2021

BMC Med Inform Decis Mak

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Background Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. Methods The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. Results The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. Conclusions Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice.

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Risk Prediction Models for Melanoma: A Systematic Review on the Heterogeneity in Model Development and Validation

Cited by 21 publications

References 77 publications

The Value of Total Body Photography for the Early Detection of Melanoma: A Systematic Review

The Value of Total Body Photography for the Early Detection of Melanoma: A Systematic Review

Reporting Quality of Studies Developing and Validating Melanoma Prediction Models: An Assessment Based on the TRIPOD Statement

Patient similarity analytics for explainable clinical risk prediction

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