Diagnostic and prognostic research最新文献

{"title":"Minimally acceptable criteria and required sample size for an accuracy study of non-invasive prenatal testing of sickle cell disease in screen positive women in England: results of a decision tree model.","authors":"Vittoria Vardanega, Ania Bobrowska, Benjamin Ruban-Fell, James A Doorbar, Silvia Lombardo, Farah Seedat, Yvonne Daniel, Amanda Hogan, Mariska M G Leeflang, Kathy Mann, Anna Schuh, John Marshall","doi":"10.1186/s41512-025-00192-w","DOIUrl":"10.1186/s41512-025-00192-w","url":null,"abstract":"<p><strong>Background: </strong>The screening pathway for sickle cell disease (SCD) in England starts with a carrier status blood test for the pregnant woman. Following a positive result, the test is offered to the biological father. Where both results are positive, further invasive testing is offered to assess the risk of SCD to the fetus. When the father is unavailable, the timely offer of further testing may be delayed and even missed, leaving the pregnant woman underinformed and limiting reproductive choice. Non-invasive prenatal testing (NIPT) presents an alternative to paternal testing and diagnostic accuracy studies of NIPT in SCD screening are required to inform utility and application. We explored the minimally acceptable sensitivity and specificity to inform such an accuracy study of NIPT.</p><p><strong>Methods: </strong>A decision tree model was produced to identify the minimally acceptable sensitivity and specificity of NIPT. Stakeholder engagement identified a positive predictive value (PPV) equal to the paternal carrier blood test and two sensitivity/specificity scenarios: Scenario 1 should result in < 10 false negative diagnoses per year; Scenario 2 should result in ≤ 2 false negative diagnoses per year. Subsequently, the minimally acceptable sensitivity and specificity were used to calculate the sample size for a hypothetical accuracy study of NIPT in each scenario.</p><p><strong>Results: </strong>Scenario 1 led to a minimally acceptable sensitivity of 96.0% and specificity of 88.5% and corresponding negative predictive value (NPV) of 99.81%, PPV of 25.51% and accuracy of 88.80%. Utilising an expected prevalence of SCD of 3.94%, the resulting sample size would include 315,824 total pregnancies. Scenario 2 led to a minimally acceptable sensitivity of 99.0% and specificity of 88.0% and corresponding NPV of 99.95%, PPV of 25.29% and accuracy of 88.43%. The resulting sample size would include 65,509 overall pregnancies.</p><p><strong>Conclusions: </strong>While the sample size calculated for each scenario is unfeasible if considering a prospective cohort study, approaches are presented to achieve realistic sample sizes. Increasing the sensitivity and specificity levels might be considered if this is likely to be achievable based on available studies, albeit limited in volume. Consideration could be given to study design mitigations that are consistent with guidance on accuracy studies in low prevalence settings.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148260762","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":"Modelling the cost-effectiveness of non-invasive prenatal testing in the English sickle cell and thalassaemia screening pathway.","authors":"Vittoria Vardanega, Ania Bobrowska, Benjamin Ruban-Fell, James A Doorbar, Silvia Lombardo, Farah Seedat, Amanda Hogan, Mariska M G Leeflang, Kathy Mann, Anna Schuh, John Marshall","doi":"10.1186/s41512-025-00212-9","DOIUrl":"10.1186/s41512-025-00212-9","url":null,"abstract":"<p><strong>Background: </strong>Sickle cell disease (SCD) and thalassaemia are inherited conditions causing chronic anaemia, increased infection risk and multi-organ failure. Standard of care (SoC) prenatal screening involves carrier blood testing for pregnant women and, if positive, carrier blood testing for biological fathers followed by invasive prenatal diagnosis for pregnancies at risk. Non-invasive prenatal testing (NIPT) presents an alternative pathway which may reduce diagnostic delays and improve equity for pregnant women when the biological father is unavailable by focusing invasive testing exclusively on fetuses shown to have a high risk of SCD by NIPT. This study compares the outcomes of SoC screening with a proposed NIPT pathway replacing the paternal blood testing stage.</p><p><strong>Methods: </strong>A deterministic decision tree model is used to identify the outcomes of the screening pathways, focusing on the SCD population, from the perspective of the National Health Service (NHS) England. Sensitivity and specificity inputs for NIPT are informed by a separately published minimally acceptable criteria study. Diagnostic outcomes include the number of performed and declined tests and true and false positive/negative diagnoses in each pathway. Economic outcomes include the testing cost of the pathway, the cost per case detected and per accurate diagnosis, and an incremental cost threshold analysis for NIPT. Additional scenario analyses are conducted for the SCD and thalassaemia combined population and for the thalassaemia populations.</p><p><strong>Results: </strong>When considering an overall cohort of 616,573 pregnancies, implementing the NIPT pathway for the screen-positive SCD population results in an incremental cost of £7,584,551. Of 276 prenatal diagnoses (PND) performed in the SoC arm, 76 show a true positive result for SCD, and 2 false positives are identified. In the NIPT arm, there are 6090 NIPTs and 543 PNDs performed, with 213 true positives and 235 false positives identified. The NIPT pathway costs £33,158 more per case detected, and £368 more per accurate diagnosis than SoC; to obtain no incremental cost per case detected versus the SoC, NIPT would need to cost £45.21.</p><p><strong>Conclusions: </strong>The presented exploratory analysis may gauge the potential cost-effectiveness of introducing NIPT into the screening pathway, pending further research on the technique's diagnostic efficacy.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148260576","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":"Urine and cerebrospinal fluid biomarkers for early glioma detection: a systematic review.","authors":"Craig Paterson, Jinyue Yu, Philippa Davies, Jack Read, Jennifer C Palmer, Sarah Dawson, Julian Pt Higgins, Kathreena M Kurian","doi":"10.1186/s41512-026-00230-1","DOIUrl":"10.1186/s41512-026-00230-1","url":null,"abstract":"<p><strong>Background: </strong>Liquid biopsies offer a minimally invasive approach for early glioma detection. However, potential non-blood biomarkers in urine and CSF are under-researched. While urine is easier to sample and has fewer proteins/cells, CSF may yield higher detection rates due to its proximity to the glioma. This systematic review evaluates non-blood liquid biopsy biomarkers (proteins, metabolites, cell-free DNA, and miRNA) in urine and CSF for early glioma detection.</p><p><strong>Methods: </strong>Three electronic databases (MEDLINE, Embase, and BIOSIS) were searched from inception to September 2024. Studies which assessed urine- or CSF-derived biomarkers as a means of identifying glioma patients were included. The primary outcomes of interest were measures of diagnostic test accuracy (specificity, sensitivity, positive predictive value, negative predictive value and area under the ROC curve). QUADAS-2 was used to assess risk of bias in included studies.</p><p><strong>Results: </strong>We extracted data from 20 studies (669 glioma patients and 597 controls) which met the inclusion criteria. Five of the included studies investigated urine-derived biomarkers, and 15 investigated CSF-derived biomarkers. Meta-analysis was not possible due to the heterogeneous nature of biomarkers collected, analysis methods, and differences in patient demographics.</p><p><strong>Conclusions: </strong>Conclusions are limited by the paucity of studies and small sample sizes. These limitations highlight the need for well-designed, adequately powered studies to evaluate the diagnostic utility of urine and CSF for glioma liquid biopsies.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13235107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148165796","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":"Methods for Evaluating Models, Tests And Biomarkers (MEMTAB): Abstracts from the 7^th International Conference, MEMTAB 2025.","authors":"","doi":"10.1186/s41512-026-00227-w","DOIUrl":"10.1186/s41512-026-00227-w","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 Suppl 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13191901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147990002","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":"Agreement between heuristic shrinkage factor and optimal shrinkage factors in logistic regression for risk prediction: a simulation study across different sample sizes and settings.","authors":"Alexander Pate, Glen P Martin, Richard D Riley","doi":"10.1186/s41512-026-00222-1","DOIUrl":"10.1186/s41512-026-00222-1","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13182129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147977613","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":"Variable selection for prediction models of Omicron infection: Insights from two population-based cohort studies.","authors":"Joanne Lacy, Dominik Menges, Tala Ballouz, Anja Frei, Céline Pellaton, Craig Fenwick, Giuseppe Pantaleo, Milo A Puhan","doi":"10.1186/s41512-026-00231-0","DOIUrl":"10.1186/s41512-026-00231-0","url":null,"abstract":"<p><strong>Background: </strong>During the early stages of the COVID-19 pandemic, prediction modelling was widely used to forecast infection rates while only few studies developed models to predict individual risk of infection for Omicron and subsequent variants. For such prediction models to perform well, it is important to carefully select predictors from a comprehensive set of potential factors, including prior infection and vaccination history, individual behaviors, and immunological markers.</p><p><strong>Methods: </strong>This exploratory analysis aimed to develop and compare prediction models for Omicron infection to provide an evidence base for identifying key predictors of SARS-CoV-2 infections which can be used to inform future derivation and validation of predictive models. We used data from 710 participants from two ongoing, prospective population-based cohorts: the Zurich SARS-CoV-2 Cohort and the Zurich SARS-CoV-2 Vaccine Cohort (Switzerland). Participants were recruited between 2020 and 2021 and provided demographic data, vaccination history, self-reported infections, and longitudinal serological data (anti-S IgG, anti-S IgA, anti-N IgG antibodies) collected at 6-month intervals. Our main outcome was SARS-CoV-2 infection during the first Omicron wave (01.01.2022-31.03.2022) based on self-reported positive tests or a doubling in any of anti-S IgG, anti-S IgA or anti-N IgG. We used logistic regression models with backward stepwise selection based on the Akaike Information Criterion (AIC) to evaluate predictors and identify the best-fitting models.</p><p><strong>Results: </strong>Only 17.3% of participants reported a positive SARS-CoV-2 test result during the Omicron wave. However, when including serological testing, 37.2% of participants had evidence of infection, indicating substantial underdiagnosis. The best-performing model had an AUC of 0.69 (95%CI 0.66, 0.73) and included the following predictors: age, sex, compliance with COVID-19 prevention guidelines, smoking status, comorbidities, prior anti-N IgG antibody levels, and the sequence of previous infections and vaccinations. We found that older age (≥ 65 years) was associated with a 50-60% lower odds of Omicron infection across all our models, while having fewer prior exposures (through infections or vaccinations) increased the odds of infection.</p><p><strong>Conclusion: </strong>This explorative study highlights the importance of integrating comprehensive immunological, clinical and behavioral data to predict SARS-CoV-2 infection risk. Our study lays the foundation to develop and validate future prediction models that identify individuals at risk, particularly through the novel use of infection and prior vaccination sequence as an important predictor.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13159202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147876679","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":"Traditional statistics and artificial intelligence-based prognostic models for predicting type 2 diabetes mellitus after gestational diabetes: a systematic review.","authors":"Nigus Bililign Yimer, Yitayeh Belsti, Beminate Lemma Seifu, Rebecca Goldstein, Helena Teede, Joanne Enticott","doi":"10.1186/s41512-026-00229-8","DOIUrl":"10.1186/s41512-026-00229-8","url":null,"abstract":"<p><p>BACKGROUND: Women with gestational diabetes (GDM) are at increased risk of developing type 2 diabetes (T2D). Prognostic models have been developed and evaluated, but their methodological quality and applicability remain inconclusive. Recent reviews with the latest search date of March 31, 2025, were conducted with major shortcomings, including failure to adhering best-practice guides and inappropriately pooling heterogeneous model performances. This systematic review aims to synthesise the methodological characteristics of existing prognostic models for T2D following GDM. METHODS: Five electronic databases were searched from inception to January 24, 2026. Prognostic models predicting T2D following GDM, regardless of study setting were included. Data extraction adhered to existing expert guidelines. Quality and applicability were assessed using the updated Prediction model Risk Of Bias ASsessment tool. Two reviewers independently screened and assessed quality, resolving disagreements through consensus and involvement of a third reviewer. RESULTS: Our updated review identified six more studies than the previous review. Our review identified 19 studies with 20 models, half from prospective cohorts (n = 18) mostly in hospital settings across North America, Europe, Australia, and Asia. Logistic regression model was most common (n = 9), followed by machine learning (n = 6), and Cox regression (n = 5). Internal and external validation were done in only 13 and 1 models, respectively. Discrimination was widely reported (Area Under the Curve (AUC) 0.67–0.92); while calibration, overall performance, and clinical utility measures were underreported. Only one study reported appropriate sample size determination. Maternal age, pregnancy fasting glucose, and BMI were common predictors. Risk of bias was generally low during development and evaluation phases, but applicability concerns were high in 60% of models. CONCLUSIONS: While several models demonstrated acceptable performance and low concern in selected quality domains, generalizability and clinical utility remain limited due to high concerns in applicability and inconsistent reporting. Adherence to best practice guides such as TRIPOD + AI, external validation of models, and exploration of novel prediction modelling techniques are recommended to advance the reporting and application of risk tools for personalised medicine post GDM. This review is the first to apply the PROBAST + AI, enabling a comprehensive evaluation of quality and applicability compared to previous works in the field. PROTOCOL REGISTRATION : PROSPERO CRD420251034657. </p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13094168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730843","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":"A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models - part 3: continuous outcomes.","authors":"Rebecca Whittle, Richard D Riley, Lucinda Archer, Gary S Collins, Amardeep Legha, Kym Ie Snell, Joie Ensor","doi":"10.1186/s41512-026-00228-9","DOIUrl":"10.1186/s41512-026-00228-9","url":null,"abstract":"<p><p>BACKGROUND: Clinical prediction models enable healthcare professionals to estimate individual outcomes using patient characteristics. Current sample size guidelines for developing or updating models with continuous outcomes aim to minimise overfitting and ensure accurate estimation of population-level parameters, but do not explicitly address the precision of predictions. This is a critical limitation, as wide confidence intervals around predictions can undermine clinical utility and fairness, particularly if precision varies across subgroups. METHODS: We propose methodology for calculating the sample size required to ensure precise and fair predictions when developing models with continuous outcomes using linear regression. Building on Fisher’s unit information matrix theory, our approach calculates how sample size impacts the epistemic (model-based) uncertainty of predictions and allows researchers to either (i) evaluate whether an existing dataset is sufficiently large, or (ii) determine the sample size needed to target a particular confidence interval width around predictions. The method requires real or synthetic data representing the target population. To assess fairness, the approach can evaluate prediction precision across subgroups. Extensions to prediction intervals are included to additionally address aleatoric uncertainty. RESULTS: We demonstrate the methodology by examining the sample size required to develop or update a model for predicting Forced Expiratory Volume (FEV) using age, height, and sex. Existing guidance suggests a minimum sample size of 237 participants for this setting. We show this corresponds to an anticipated mean confidence interval width of 0.206 L across all participants in the target population, and that widths may be considerably larger for some individuals. Our new approach calculates a minimum of 694 patients would be needed to ensure all anticipated interval widths were ≤ 0.3. Subgroup analysis showed comparable anticipated precision across sex subgroups. Sensitivity analysis assuming conditional independence among predictors yielded consistent results. For prediction intervals, the magnitude of the residual variance imposes a lower bound on interval width, even with very large samples. CONCLUSIONS: Our methodology provides a practical framework for examining required sample sizes when developing or updating prediction models with continuous outcomes, focusing on achieving precise and equitable predictions. It supports the development of more reliable and fair models, enhancing their clinical applicability and trustworthiness. </p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13072537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147679023","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 the development and validation of a risk score to predict risk of adverse events associated with systemic anti-cancer treatment in late-stage lung cancer: Lung Cancer Improved Decisions (LUCID).","authors":"Ofran Almossawi, Luke Steventon, Ruth Keogh, Karla Diaz-Ordaz, Zhe Wang, Andrew Challenger, David Dodwell, Martin Forster, Kenneth K C Man, Li Wei, Sebastian Masento, Adam Januszewski, Pinkie Chambers","doi":"10.1186/s41512-026-00225-y","DOIUrl":"10.1186/s41512-026-00225-y","url":null,"abstract":"<p><p>BACKGROUND: Less than 20% of patients diagnosed with advanced lung cancer will survive beyond five years and half of these will suffer a serious adverse event (SAE) caused by systemic anticancer therapy (SACT) that will result in a hospital attendance. As multiple different SACT treatments are available for patients, a risk score that predicts the likelihood of a SAE following each type of SACT treatment would improve both communication with the patient and shared decision making with all those involved in delivering care for patients. There are currently no risk scores available for use in those with advanced stage lung cancer. AIM: The overarching aim of this research is to develop and internally validate a risk score that will calculate the individualised risk of SAEs for different SACT treatments for patients with late stage lung cancer. METHODS: Utilising linked cancer registry data (National Cancer Registration and Analysis Service (NCRAS), England) for over 20,000 late stage lung cancer patients, a risk score will be developed using a multivariable logistic regression model to predict the risk of an acute admission within 30 days of SACT administration. Model performance will be summarised using calibration and discrimination. Internal validation will be used to quantify the degree of optimism due to overfitting, using re-sampling bootstrapping. Heterogeneity will be assessed, and the model will be fine-tuned. Fine-tuning and interrogation will be used to evaluate differences in performance between hospitals. The clinical utility will be assessed through calculating the net benefit in preventing SAEs. CONCLUSION: A developed risk score (under each treatment strategy) has real potential to support individualised treatment decisions and optimise management of SACT-induced SAEs for patients and reduce hospital attendances. </p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635243","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":"Raman spectroscopy as a diagnostic tool for glioma: protocol for a systematic review.","authors":"Corneliu Ovidiu Vrancianu, Marian Necula, Eduard Milea, Nicoleta Siminea, Roxana-Elena Cristian, Alice Stoica, Mariana Carmen Chifiriuc, Bogdan Oancea, Andrei Paun, Ion Petre, Mihaela Paun","doi":"10.1186/s41512-026-00226-x","DOIUrl":"10.1186/s41512-026-00226-x","url":null,"abstract":"<p><p>BACKGROUND: Glioma is the most common primary brain tumor, demanding prompt and accurate diagnosis to guide therapy decisions. Conventional diagnostic methods such as histopathology and neuroimaging are limited by their invasiveness, subjectivity, or lack of intraoperative precision. Raman spectroscopy is an emerging, label-free optical technique that detects the unique molecular \"fingerprint\" of tissues, enabling real-time differentiation between tumor-infiltrated and normal brains based on their biochemical composition. Despite a growing body of experimental and translational studies, a comprehensive synthesis of Raman spectroscopy's clinical applicability in detecting glioma micro-infiltration is lacking. METHODS: We will search four electronic databases (PubMed, Embase, Scopus, and Web of Science) for English-language studies published from 2015 to 2025. Eligible studies may use in vivo Raman (subjects with suspected or histologically confirmed glioma), ex vivo Raman (freshly excised human tissue), or in vitro analysis on archived human samples, provided the tissue originated from confirmed glioma cases. Primary outcomes will be diagnostic accuracy measures, including but not limited to sensitivity, specificity, accuracy, area under de curve (AUC), positive predictive value (PPV), negative predictive value (NPV), diagnostic odds ratio (DOR), and positive/negative likelihood ratios (LR+/LR−). Secondary outcomes include its role in intraoperative margin assessment, diagnostic time, and machine learning-assisted classification. Data traction and risk of bias assessment will be conducted independently by two reviewers. Meta-analyses will be performed using specific statistics where applicable. If a meta-analysis is not feasible, a structured narrative synthesis will be employed, following the SWiM (Synthesis Without Meta-analysis) guidelines. GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) will be applied to evaluate the certainty of evidence. The review will follow Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. DISCUSSION: This review will synthesize the emerging diagnostic landscape of Raman spectroscopy for glioma, positioning it as a transformative adjunct to traditional histopathology and intraoperative decision-making. Our findings will pave the way for a translational roadmap to integrate real-time Raman spectroscopy into neurosurgical workflows with machine learning support. SYSTEMATIC REVIEW REGISTRATION: PROSPERO: CRD420251025922. </p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13055004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635373","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}