Diagnostic and prognostic research最新文献

{"title":"Diagnostic accuracy for colorectal cancer of a quantitative faecal immunochemical test in symptomatic primary care patients: a study protocol.","authors":"Anna Lööv,&nbsp;Cecilia Högberg,&nbsp;Mikael Lilja,&nbsp;Elvar Theodorsson,&nbsp;Per Hellström,&nbsp;Alexandra Metsini,&nbsp;Louise Olsson","doi":"10.1186/s41512-022-00129-7","DOIUrl":"https://doi.org/10.1186/s41512-022-00129-7","url":null,"abstract":"<p><strong>Background: </strong>There is increasing evidence supporting the use of faecal immunochemical tests (FIT) in patients reporting symptoms associated with colorectal cancer (CRC), but most studies until now have focused on selected subjects already referred for investigation. We therefore set out to determine the accuracy and predictive values of FIT in a primary care population.</p><p><strong>Method: </strong>A prospective, multicentre, single-gated comparative diagnostic study on quantitative FIT in patients aged 40 years and above presenting in primary care with symptoms associated with CRC will be conducted. Patients representing the whole spectrum of severity of such symptoms met with in primary care will be eligible and identified by GPs. Participants will answer a short form on symptoms during the last month. They will provide two faecal samples from two separate days. Analyses will be performed within 5 days (QuikRead go®, Aidian Oy). The analytical working range is 10-200 μg Hb/g faeces. Reference test will be linked to the Swedish Colorectal Cancer Registry up to 2 years after inclusion. Accuracy, area under ROC curves, and predictive values will be calculated for one FIT compared to the highest value of two FIT and at cutoff < 10, 10-14.9, 15-19.9 and ≥ 20 μg Hb/g faeces. Subgroup analyses will be conducted for patients with anaemia and those reporting rectal bleeding. A model-based cost-effectiveness analysis based on the clinical accuracy study will be performed. Based on previous literature, we hypothesized that the sensitivity of the highest value of two FIT at cutoff 10 μg Hb/g faeces will be 95% (95% CI + / - 15%). The prevalence of CRC in the study population was estimated to be 2%, and the rate of non-responders to be 1/6. In all, 3000 patients will be invited at 30 primary care centres.</p><p><strong>Discussion: </strong>This study will generate important clinical real-life structured data on accuracy and predictive values of FIT in the most critical population for work-up of CRC, i.e. patients presenting with at times ambiguous symptoms in primary care. It will help establish the role of FIT in this large group.</p><p><strong>Trial registration: </strong>NCT05156307 . Registered on 14 December 2021-retrospectively registered.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40621197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study.","authors":"Stelios Theophanous, Per-Ivar Lønne, Ananya Choudhury, Maaike Berbee, Andre Dekker, Kristopher Dennis, Alice Dewdney, Maria Antonietta Gambacorta, Alexandra Gilbert, Marianne Grønlie Guren, Lois Holloway, Rashmi Jadon, Rohit Kochhar, Ahmed Allam Mohamed, Rebecca Muirhead, Oriol Parés, Lukasz Raszewski, Rajarshi Roy, Andrew Scarsbrook, David Sebag-Montefiore, Emiliano Spezi, Karen-Lise Garm Spindler, Baukelien van Triest, Vassilios Vassiliou, Eirik Malinen, Leonard Wee, Ane L Appelt","doi":"10.1186/s41512-022-00128-8","DOIUrl":"10.1186/s41512-022-00128-8","url":null,"abstract":"<p><strong>Background: </strong>Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven challenging. Developing and validating prognostic models using routinely collected data may provide new insights for treatment development and selection. However, due to the rarity of the cancer, it can be difficult to obtain sufficient data, especially from single centres, to develop and validate robust models. Moreover, multi-centre model development is hampered by ethical barriers and data protection regulations that often limit accessibility to patient data. Distributed (or federated) learning allows models to be developed using data from multiple centres without any individual-level patient data leaving the originating centre, therefore preserving patient data privacy. This work builds on the proof-of-concept three-centre atomCAT1 study and describes the protocol for the multi-centre atomCAT2 study, which aims to develop and validate robust prognostic models for three clinically important outcomes in anal cancer following chemoradiotherapy.</p><p><strong>Methods: </strong>This is a retrospective multi-centre cohort study, investigating overall survival, locoregional control and freedom from distant metastasis after primary chemoradiotherapy for anal squamous cell carcinoma. Patient data will be extracted and organised at each participating radiotherapy centre (n = 18). Candidate prognostic factors have been identified through literature review and expert opinion. Summary statistics will be calculated and exchanged between centres prior to modelling. The primary analysis will involve developing and validating Cox proportional hazards models across centres for each outcome through distributed learning. Outcomes at specific timepoints of interest and factor effect estimates will be reported, allowing for outcome prediction for future patients.</p><p><strong>Discussion: </strong>The atomCAT2 study will analyse one of the largest available cross-institutional cohorts of patients with anal cancer treated with chemoradiotherapy. The analysis aims to provide information on current international clinical practice outcomes and may aid the personalisation and design of future anal cancer clinical trials through contributing to a better understanding of patient risk stratification.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40579094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protocol for a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence for grading of ophthalmology imaging modalities.","authors":"Jessica Cao,&nbsp;Brittany Chang-Kit,&nbsp;Glen Katsnelson,&nbsp;Parsa Merhraban Far,&nbsp;Elizabeth Uleryk,&nbsp;Adeteju Ogunbameru,&nbsp;Rafael N Miranda,&nbsp;Tina Felfeli","doi":"10.1186/s41512-022-00127-9","DOIUrl":"https://doi.org/10.1186/s41512-022-00127-9","url":null,"abstract":"<p><strong>Background: </strong>With the rise of artificial intelligence (AI) in ophthalmology, the need to define its diagnostic accuracy is increasingly important. The review aims to elucidate the diagnostic accuracy of AI algorithms in screening for all ophthalmic conditions in patient care settings that involve digital imaging modalities, using the reference standard of human graders.</p><p><strong>Methods: </strong>This is a systematic review and meta-analysis. A literature search will be conducted on Ovid MEDLINE, Ovid EMBASE, and Wiley Cochrane CENTRAL from January 1, 2000, to December 20, 2021. Studies will be selected via screening the titles and abstracts, followed by full-text screening. Articles that compare the results of AI-graded ophthalmic images with results from human graders as a reference standard will be included; articles that do not will be excluded. The systematic review software DistillerSR will be used to automate part of the screening process as an adjunct to human reviewers. After the full-text screening, data will be extracted from each study via the categories of study characteristics, patient information, AI methods, intervention, and outcomes. Risk of bias will be scored using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) by two trained independent reviewers. Disagreements at any step will be addressed by a third adjudicator. The study results will include summary receiver operating characteristic (sROC) curve plots as well as pooled sensitivity and specificity of artificial intelligence for detection of any ophthalmic conditions based on imaging modalities compared to the reference standard. Statistics will be calculated in the R statistical software.</p><p><strong>Discussion: </strong>This study will provide novel insights into the diagnostic accuracy of AI in new domains of ophthalmology that have not been previously studied. The protocol also outlines the use of an AI-based software to assist in article screening, which may serve as a reference for improving the efficiency and accuracy of future large systematic reviews.</p><p><strong>Trial registration: </strong>PROSPERO, CRD42021274441.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40591217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk of bias of prognostic models developed using machine learning: a systematic review in oncology.","authors":"Paula Dhiman, Jie Ma, Constanza L Andaur Navarro, Benjamin Speich, Garrett Bullock, Johanna A A Damen, Lotty Hooft, Shona Kirtley, Richard D Riley, Ben Van Calster, Karel G M Moons, Gary S Collins","doi":"10.1186/s41512-022-00126-w","DOIUrl":"10.1186/s41512-022-00126-w","url":null,"abstract":"<p><strong>Background: </strong>Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain.</p><p><strong>Methods: </strong>We conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published between 01/01/2019 and 05/09/2019. The primary outcome was risk of bias, judged using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We described risk of bias overall and for each domain, by development and validation analyses separately.</p><p><strong>Results: </strong>We included 62 publications (48 development-only; 14 development with validation). 152 models were developed across all publications and 37 models were validated. 84% (95% CI: 77 to 89) of developed models and 51% (95% CI: 35 to 67) of validated models were at overall high risk of bias. Bias introduced in the analysis was the largest contributor to the overall risk of bias judgement for model development and validation. 123 (81%, 95% CI: 73.8 to 86.4) developed models and 19 (51%, 95% CI: 35.1 to 67.3) validated models were at high risk of bias due to their analysis, mostly due to shortcomings in the analysis including insufficient sample size and split-sample internal validation.</p><p><strong>Conclusions: </strong>The quality of machine learning based prognostic models in the oncology domain is poor and most models have a high risk of bias, contraindicating their use in clinical practice. Adherence to better standards is urgently needed, with a focus on sample size estimation and analysis methods, to improve the quality of these models.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40475678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian latent class analysis produced diagnostic accuracy estimates that were more interpretable than composite reference standards for extrapulmonary tuberculosis tests","authors":"E. MacLean, Mikashmi Kohli, Lisa Köppel, Ian Schiller, Surendra K Sharma, M. Pai, C. Denkinger, N. Dendukuri","doi":"10.1186/s41512-022-00125-x","DOIUrl":"https://doi.org/10.1186/s41512-022-00125-x","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49635825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A scoping methodological review of simulation studies comparing statistical and machine learning approaches to risk prediction for time-to-event data.","authors":"Hayley Smith, Michael Sweeting, Tim Morris, Michael J Crowther","doi":"10.1186/s41512-022-00124-y","DOIUrl":"10.1186/s41512-022-00124-y","url":null,"abstract":"<p><strong>Background: </strong>There is substantial interest in the adaptation and application of so-called machine learning approaches to prognostic modelling of censored time-to-event data. These methods must be compared and evaluated against existing methods in a variety of scenarios to determine their predictive performance. A scoping review of how machine learning methods have been compared to traditional survival models is important to identify the comparisons that have been made and issues where they are lacking, biased towards one approach or misleading.</p><p><strong>Methods: </strong>We conducted a scoping review of research articles published between 1 January 2000 and 2 December 2020 using PubMed. Eligible articles were those that used simulation studies to compare statistical and machine learning methods for risk prediction with a time-to-event outcome in a medical/healthcare setting. We focus on data-generating mechanisms (DGMs), the methods that have been compared, the estimands of the simulation studies, and the performance measures used to evaluate them.</p><p><strong>Results: </strong>A total of ten articles were identified as eligible for the review. Six of the articles evaluated a method that was developed by the authors, four of which were machine learning methods, and the results almost always stated that this developed method's performance was equivalent to or better than the other methods compared. Comparisons were often biased towards the novel approach, with the majority only comparing against a basic Cox proportional hazards model, and in scenarios where it is clear it would not perform well. In many of the articles reviewed, key information was unclear, such as the number of simulation repetitions and how performance measures were calculated.</p><p><strong>Conclusion: </strong>It is vital that method comparisons are unbiased and comprehensive, and this should be the goal even if realising it is difficult. Fully assessing how newly developed methods perform and how they compare to a variety of traditional statistical methods for prognostic modelling is imperative as these methods are already being applied in clinical contexts. Evaluations of the performance and usefulness of recently developed methods for risk prediction should be continued and reporting standards improved as these methods become increasingly popular.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45749533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality and transparency of reporting derivation and validation prognostic studies of recurrent stroke in patients with TIA and minor stroke: a systematic review","authors":"K. Abdulaziz, J. Perry, K. Yadav, D. Dowlatshahi, I. Stiell, G. Wells, M. Taljaard","doi":"10.1186/s41512-022-00123-z","DOIUrl":"https://doi.org/10.1186/s41512-022-00123-z","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42169045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury","authors":"I. Helmrich, A. Mikolić, D. Kent, H. Lingsma, L. Wynants, E. Steyerberg, D. van Klaveren","doi":"10.1186/s41512-022-00122-0","DOIUrl":"https://doi.org/10.1186/s41512-022-00122-0","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49067042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the effect of evaluation sample size on the sensitivity and specificity of COVID-19 diagnostic tests in practice: a simulation study","authors":"C. Sammut-Powell, C. Reynard, Joy A Allen, J. McDermott, Julian Braybrook, R. Parisi, D. Lasserson, R. Body, Richard Gail Joy Julian Peter Paul Kerrie Eloise Adam Anna Body Hayward Allen Braybrook Buckle Dark Davis Coo, R. Body, G. Hayward, Joy A Allen, J. Braybrook, P. Buckle, P. Dark, Kerrie Davis, Eloïse Cook, A. Gordon, Anna Halstead, D. Lasserson, A. Lewington, Brian Nicholson, R. Perera-Salazar, J. Simpson, Philip Turner, Graham Prestwich, C. Reynard, Be Riley, Valerie Tate, Mark A. Wilcox","doi":"10.1186/s41512-021-00116-4","DOIUrl":"https://doi.org/10.1186/s41512-021-00116-4","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45429790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative prediction error analysis to investigate predictive performance under predictor measurement heterogeneity at model implementation","authors":"K. Luijken, Jiaolei Song, R. Groenwold","doi":"10.1186/s41512-022-00121-1","DOIUrl":"https://doi.org/10.1186/s41512-022-00121-1","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48348478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}