Evaluating the quality of reporting of melanoma prediction models

Jiang, Matthew Y.; Dragnev, Nathalie C.; Wong, Sandra L.

doi:10.1016/j.surg.2020.04.016

Cited by 9 publications

(15 citation statements)

References 11 publications

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“…Even though this analysis related to a special segment of prediction studies, our finding of inadequate reporting in the field of melanoma risk prediction is comparable to other studies [ 64 , 65 , 66 , 67 ]. Heus et al [ 65 ] included 146 publications across 37 clinical domains and reported a median TRIPOD adherence of 44%, which is even lower than the median of the studies included in our analysis (57%).…”

Section: Discussionsupporting

confidence: 82%

“…One previous study [ 66 ] focused already on melanoma prediction studies and claimed to assess TRIPOD adherence in this area. However, due to a less sensitive search strategy combined with broad eligibility criteria this study is not comparable to ours.…”

Section: Discussionmentioning

confidence: 99%

“…However, due to a less sensitive search strategy combined with broad eligibility criteria this study is not comparable to ours. The set of studies assessed by Yiang et al [ 66 ] comprised a mixed bag of investigations devoted to predicting (i) occurrence of melanoma in population settings, (ii) progression of melanoma in clinical settings, (iii) survival of melanoma patients, (iv) lymph node positivity of melanoma patients, and (v) correct identification of melanoma in diagnostic settings. Due to the broader definition of melanoma prediction, Yiang et al [ 66 ] should have found many more publications for their TRIPOD assessment than we, but in fact they identified only 27 studies in their literature search.…”

Section: Discussionmentioning

confidence: 99%

“…The set of studies assessed by Yiang et al [ 66 ] comprised a mixed bag of investigations devoted to predicting (i) occurrence of melanoma in population settings, (ii) progression of melanoma in clinical settings, (iii) survival of melanoma patients, (iv) lymph node positivity of melanoma patients, and (v) correct identification of melanoma in diagnostic settings. Due to the broader definition of melanoma prediction, Yiang et al [ 66 ] should have found many more publications for their TRIPOD assessment than we, but in fact they identified only 27 studies in their literature search. Altogether 34 of the 42 studies in our evaluation were not included in their set of studies, while 19 of their studies were intentionally not covered by us as they did not address prediction models for melanoma occurrence.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…Oncology is a key area where prediction models are needed where they can help diagnose cancers, assess patient prognosis and guide patient treatment plans [4] . However, numerous studies have observed poor reporting of prediction models [5] , [6] , [7] , particularly in the field of oncology [8] , [9] , [10] , [11] , [12] , [13] .…”

Section: Introductionmentioning

confidence: 99%

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved

Dhiman

Navarro

et al. 2021

Journal of Clinical Epidemiology

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Objective: Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology.Study design and setting: We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD.Results: Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n = 12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]). Conclusion:Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste.

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Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review

Hudda¹,

Archer²,

Smeden³

et al. 2023

Journal of Clinical Epidemiology

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Evaluating the quality of reporting of melanoma prediction models

Cited by 9 publications

References 11 publications

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

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

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved

Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review

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