Diagnostic and prognostic research最新文献

{"title":"Accurate assessment of surgical HER2-low status based on core needle biopsy data of breast cancer.","authors":"Wen Li, Zixi Chen, Junyao Li, Tao Ding, Haike Lei, Rui Chen, Jialu Li","doi":"10.1186/s41512-026-00241-y","DOIUrl":"10.1186/s41512-026-00241-y","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer is a leading cause of cancer-related mortality among women globally. Accurate identification of HER2 status, particularly the HER2-low category, is crucial for effective treatment strategies, given the emergence of HER2-targeted therapies for this subgroup. However, discrepancies often arise between core needle biopsy (CNB) and surgical resection tissue (SRT) regarding more refined HER2 status.</p><p><strong>Methods: </strong>This retrospective study included female patients with invasive breast cancer who underwent surgical resection between January 1, 2020, and December 31, 2024, at a single cancer center in China. We assessed the concordance of HER2 status, alongside other clinicopathological features on both CNB and SRT samples. We then developed a supervised binary modeling framework to predict SRT HER2 status (with metastasis) based on CNB data.</p><p><strong>Results: </strong>Among 1,919 patients analyzed, the concordance rate for HER2 classification was 97.0% in a binary system but dropped to 75.9% when using a 3-category system, with discordance noted particularly between HER2-zero and HER2-low categories. Logistic regression identified significant factors impacting concordance. The predictive model utilizing CNB HER2 classification achieved a mean AUC of 0.824, indicating substantial predictive capability for SRT HER2-low status. Integrating CNB clinicopathological variables further improved prediction accuracy (mean AUC from 0.691 to 0.771) for SRT HER2-low with metastasis in clinically relevant regions.</p><p><strong>Conclusion: </strong>While CNB effectively determines binary HER2 status, establishing HER2-low categorization reveals discrepancies. Our findings highlight the importance of accurate preoperative HER2 assessment, supported by AI-based prediction models that enhance clinical decision-making in breast cancer management targeted toward HER2-low patients.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13651143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149702","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":"ACCEPT 3.0: Recalibrating ACute COPD Exacerbation Prediction Tool (ACCEPT) for global use.","authors":"Laura Huey Mien Lim, Jeenat Mehareen, Amin Adibi, Christer Janson, Pierre-Régis Burgel, Tae Yoon Lee, Hana Müllerová, Della Varghese, Jonathan Marshall, Surya P Bhatt, Don Sin, Wenjia Chen, Mohsen Sadatsafavi","doi":"10.1186/s41512-026-00242-x","DOIUrl":"10.1186/s41512-026-00242-x","url":null,"abstract":"<p><strong>Background: </strong>The ACute COPD Exacerbation Prediction Tool (ACCEPT) is an algorithm for predicting 12-month risk of chronic obstructive pulmonary disease (COPD) exacerbations. To make it suitable for international use, we externally validated and recalibrated ACCEPT 2.0 to incorporate country-specific background exacerbation risk, using multi-country data from NOVELTY (a NOVEL observational longiTudinal studY).</p><p><strong>Methods: </strong>Our study cohort included patients aged [Formula: see text]40 years with physician-diagnosed COPD and [Formula: see text]30 days of follow-up. We predicted the occurrence of any moderate-to-severe (primary endpoint) and severe (secondary) exacerbations at 12 months, using country-level random effects to account for between-country differences in background exacerbation risks unexplained by predictor effects. We assessed model performance via discrimination, calibration, and net benefit.</p><p><strong>Results: </strong>The analysis sample comprised 5,181 patients (59.4% male, mean age at baseline 66.5 years) from 18 countries, with a mean follow-up of 357 days. 12-month risks ranged between 0.11 and 0.46 (mean = 0.26) for moderate-to-severe and 0.03-0.21 (mean = 0.08) for severe exacerbations. Without country-level adjustment, ACCEPT 2.0's area under the receiver operating characteristic curve (AUROC) was 0.73 (95% CI 0.72-0.75) and 0.76 (95% CI 0.73-0.78) for moderate-to-severe and severe exacerbations, respectively. However, ACCEPT 2.0 consistently overpredicted risks (observed-to-expected [O/E] risk ratio = 0.57 [95% CI 0.54-0.60] and 0.51 [95% CI 0.46-0.56]). After country-level recalibration, ACCEPT 3.0 showed similar AUROCs (0.75 and 0.76) but substantially improved calibration (O/E ratios: 0.99 [95% CI 0.94-1.05] and 0.99 [95% CI 0.90-1.09]). The net benefit of ACCEPT 3.0 exceeded ACCEPT 2.0 across treatment thresholds of 5%-20%.</p><p><strong>Conclusion: </strong>Recalibrating ACCEPT 2.0 with country-level effects significantly improved calibration of COPD exacerbation risks. The resulting ACCEPT 3.0 is expected to provide higher clinical utility across a range of treatment thresholds.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13651374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139698","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":"Use of qualitative and mixed methods research in diagnostic technology evaluation and implementation: insights from a systematic review.","authors":"Caity Roleston, Umasha Ukwatte, Nia Roberts, Gail N Hayward, Philip J Turner, Margaret Glogowska","doi":"10.1186/s41512-026-00240-z","DOIUrl":"10.1186/s41512-026-00240-z","url":null,"abstract":"<p><strong>Background: </strong>Development of new diagnostic health technologies is increasing, alongside interest in how they can be implemented into health services. Evaluation of new diagnostic tests has typically consisted of technical and diagnostic accuracy, place in the clinical pathway, effectiveness and cost-effectiveness. However, there are other considerations which are important in determining their ultimate adoption. These include the usability and acceptability of new tests - how clinicians and patients respond to and interact with them - as well as how applicable they are to individual settings and contexts. Such issues are more likely to be explored and understood using qualitative and mixed methods research. The purpose of this systematic review was to discern in the literature which qualitative and mixed methods approaches have been used in evaluating and implementing diagnostic technologies and tests and to identify how future use of these methods can be supported in this rapidly developing field.</p><p><strong>Methods: </strong>We searched Medline, Embase and CINAHL from inception to September 2025 for evaluations of diagnostic technology and tests including qualitative and mixed methods research. The content and quality of the retrieved studies was examined using critical appraisal frameworks appropriate for the type of study, Spencer et al (2003) for qualitative studies and Mixed Methods Appraisal Tool (MMAT) (2018) for mixed methods studies.</p><p><strong>Results: </strong>Our search yielded 11331 results, with 28 studies included in the review. All the studies were evaluations of point of care technology or tests for diagnosis. The studies were relevant to seven different medical conditions, across 18 countries. Of the 28 studies, 18 used a qualitative research design only and 10 a mixed methods design. Most studies used semi-structured interviews with health care professionals as either the sole or principal data collection method. There were issues around quality and reporting, including engagement with the methodological literature especially with regard to data analysis, idiosyncratic use of terminology and guiding frameworks, and lack of exploration of patient perspectives.</p><p><strong>Conclusions: </strong>The review suggests that guidance to support the use of qualitative and mixed methods approaches in this field would be timely and helpful.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129508","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":"Scoping review of methodology for aiding generalisability and transportability of clinical prediction models.","authors":"Kritchavat Ploddi, Glen P Martin, Maurice O'Connell, Matthew Sperrin","doi":"10.1186/s41512-026-00237-8","DOIUrl":"https://doi.org/10.1186/s41512-026-00237-8","url":null,"abstract":"<p><strong>Background: </strong>Generalisability and transportability of clinical prediction models (CPMs) refer to their ability to maintain predictive performance when applied to new populations. While CPMs may show good generalisability or transportability to a specific new population, it is rare for a CPM to be developed using methods that prioritise good generalisability or transportability. There is an emerging literature of such techniques; therefore, this scoping review aims to summarise the main methodological approaches, assumptions, advantages, disadvantages and future development of methodology aiding the generalisability and transportability of CPMs.</p><p><strong>Methods: </strong>Relevant articles were systematically searched from MEDLINE, Embase, medRxiv, arxiv databases until August 2023 using a predefined set of search terms. Extracted information included methodology description, assumptions, applied examples, advantages and disadvantages.</p><p><strong>Results: </strong>The searches found 1,761 articles; 172 were retained for full text screening; 18 were finally included. We categorised the methodologies based on whether they are data-driven or knowledge-driven, and whether they are generalisable or transportable to specific target population. Data-driven approaches range from data augmentation to ensemble methods and density ratio weighting, while knowledge-driven strategies rely on causal methodology.</p><p><strong>Conclusions: </strong>Future research could focus on comparing these methodologies across simulated and real datasets to identify their strengths and weaknesses in broad applications, as well as synthesising these approaches for enhancing their practical usefulness.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13629139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119569","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":"The use of blood test trends in cancer detection: a scoping review.","authors":"Sufen Zhu, Claire Friedemann Smith, Kiana K Collins, Sheba E Ziyenge, Jacqueline Murphy, Isabella de Vere Hunt, Zach Brubert, Nia Roberts, Eva J A Morris, Richard Hobbs, Brian D Nicholson, Pradeep S Virdee","doi":"10.1186/s41512-026-00236-9","DOIUrl":"10.1186/s41512-026-00236-9","url":null,"abstract":"<p><strong>Background: </strong>Monitoring trends in repeated blood tests might offer opportunities to improve cancer detection. This review aimed to systematically map the existing literature on blood test trends in cancer detection, characterise how these trends have been used in cancer detection approaches, and identify research gaps.</p><p><strong>Methods: </strong>MEDLINE, Embase, ProQuest Dissertations & Theses Global, and Overton were searched from inception to April 2025 to identify published and unpublished studies investigating pre-diagnostic blood test trends for cancer detection. Screening was performed by four reviewers. Data extraction was conducted independently by two reviewers, with discrepancies resolved through consensus. Data were narratively synthesised to summarise study and patient characteristics, and the blood tests and cancers investigated. Major themes were identified to categorise key research areas and describe how blood test trends have been used in cancer detection.</p><p><strong>Results: </strong>Ninety studies were included. Colorectal and pancreatic cancers were the most commonly studied cancers, with haemoglobin and HbA1c the most frequently investigated blood tests for each, respectively. Studies primarily focused on either the association between blood test trends and cancer (n = 64) or on prediction models incorporating blood test trends to predict cancer risk (n = 26). Additional thematic areas covered by the included studies were: combining trends with other risk factors for cancer (n = 62), implication for practice (n = 58), blood tests trends versus abnormality (n = 40), combination of trends in different blood tests (n = 39), and bias due to informative observation (n = 12). However, evidence for many other cancer types was scarce, and important gaps remain in understanding how blood test trends can be further used for cancer detection, including their predictive value and implementation strategies.</p><p><strong>Conclusion: </strong>Evidence to date indicates that blood test trends have been increasingly studied in the context of cancer detection, although knowledge remains fragmented and largely exploratory. Future research should expand to under-studied cancers and examine these trends across a broader range of contexts to better understand their role in cancer detection.</p><p><strong>Registration: </strong>Open Science Framework https://osf.io/ue8s4 .</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13573397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972363","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":"Evaluating discrimination of prediction models for recurrent clinical events.","authors":"Thomas J Spain, Alexandra Hunt, Maria Sudell, Jane L Hutton, Hein Putter, Victoria Watson, John Blakey, Anthony Marson, Laura Jayne Bonnett","doi":"10.1186/s41512-026-00238-7","DOIUrl":"10.1186/s41512-026-00238-7","url":null,"abstract":"<p><strong>Background: </strong>Prediction models for clinical conditions involving recurrent events require performance metrics tailored to repeated outcomes. Although we and others have developed methods to evaluate calibration and related aspects of performance, approaches for assessing discrimination remain limited. Recent contributions, including concordance-based methods and Brier-type accuracy measures, address parts of this gap, but no widely adopted, flexible framework for discrimination exists. As discrimination is a core TRIPOD+AI-recommended metric, accessible methodology and software for evaluating how well recurrent event models separate higher- and lower-risk individuals are still needed.</p><p><strong>Methods: </strong>We propose adapted discrimination statistics based on comparing predicted and observed cumulative event counts, addressing limitations of conventional concordance approaches. Statistical uncertainty is evaluated using several resampling strategies, including delete-d jackknifing, with user-friendly R code provided. We illustrate the approach using four tie-handling methods (c statistic, Kendall's τₐ, Somers' D, Goodman-Kruskal's γ), four recurrent event models (negative binomial, zero-inflated negative binomial, Andersen-Gill, and Prentice-Williams-Peterson Total Time), and two clinical datasets: the SANAD epilepsy trial and an OPCRD asthma cohort.</p><p><strong>Results: </strong>Discrimination varied substantially across models. In the asthma data, the Prentice-Williams-Peterson model showed the strongest discrimination (C = 0.939, 95% CI 0.935-0.944), with rank-based metrics (τₐ = 0.603; Somers' D and γ = 0.879) also indicating very strong discrimination. The Andersen-Gill model performed moderately well (C = 0.823, 95% CI 0.815-0.830), while the negative binomial (C = 0.573) and zero-inflated negative binomial models (C = 0.586) showed weak discrimination. In the epilepsy data, the Prentice-Williams-Peterson model again performed best (C = 0.943, 95% CI 0.912-0.974), with all rank-based metrics above 0.85. The negative binomial, zero-inflated negative binomial and Andersen-Gill models showed moderate discrimination.</p><p><strong>Conclusion: </strong>This study provides practical methods and open-source code for evaluating the discrimination of prediction models for recurrent event data. Used alongside existing calibration tools, these methods support comprehensive performance evaluation and help standardise validation and reporting for prediction models developed for recurrent clinical events as recommended by the TRIPOD + AI guidelines.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551822/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908230","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":"Impact of a diagnostic strategy for appendicitis in children with acute abdominal pain in primary care: study protocol for a hybrid type 1 cluster randomised controlled trial.","authors":"Esmee M Hogervorst, Roderick P Venekamp, Grietje E Knol-de Vries, Tamara N Platteel, Roeland M Watjer, Daan P Hurkmans, Mattijs E Numans, Guus C G H Blok, Eline J A Q Naber, Michiel R de Boer, Karin M Vermeulen, Meefa Hogenes, Eric Hiddink, P Chris Tromp, Ramon R Gorter, Albert Holvast, Jan B F Hulscher, Chantal M C R Everts-Panman, Gera A Welker, Sven J G Geelen, Arne R M van der Bilt, Elyne Deuring, Marjolein Y Berger, Esen Doganer, Huibert Burger, Gea A Holtman","doi":"10.1186/s41512-026-00234-x","DOIUrl":"https://doi.org/10.1186/s41512-026-00234-x","url":null,"abstract":"<p><strong>Background: </strong>Children with acute abdominal pain pose a diagnostic challenge for general practitioners (GPs), as it can be difficult to distinguish appendicitis from self-limiting conditions due to overlapping symptoms. To support GPs, a diagnostic strategy for appendicitis was developed that integrates an externally validated prediction rule with C-reactive protein point-of-care testing (CRP POCT) and risk-based management advice. This study will compare the impact of this diagnostic strategy to usual care in children with acute abdominal pain in primary care in terms of: (i) clinical effectiveness, (ii) cost-effectiveness, and (iii) implementation potential.</p><p><strong>Methods: </strong>We will conduct a pragmatic, hybrid type 1 effectiveness-implementation, cluster randomised controlled trial in Dutch general practice. Children aged 4-18 years with acute (≤ 7 days) abdominal pain will be included. GP practices will be randomly allocated to either: (i) use of the diagnostic strategy for appendicitis, or (ii) usual care. In the intervention group, children are stratified into low, medium, or high risk for appendicitis based on a seven-item prediction rule including symptoms and signs. Management advice is tailored to appendicitis risk: safety netting for low-risk, immediate referral for high-risk, or CRP POCT for medium-risk (CRP < 10 mg/L: safety netting; CRP 10-50 mg/L: reassessment or immediate referral (based on GPs' clinical judgement and parent/child preference); CRP ≥ 50 mg/L: immediate referral). The primary outcome is referral efficiency (proportion of non-referrals amongst patients with no appendicitis during 30 days follow-up). Secondary outcomes include safety, proportion of children with CRP POCT, proportion of children with planned reassessment, child anxiety, patient satisfaction, quality of life, and costs. A parallel process evaluation will assess implementation outcomes, including the reach, adoption, implementation, and maintenance of the diagnostic strategy. We aim to include 566 children without appendicitis to determine an improvement of efficiency from 88% to 95%.</p><p><strong>Discussion: </strong>We hypothesise that use of the diagnostic strategy will reduce referrals without increasing delayed diagnoses of appendicitis, compared to usual care in children presenting with acute abdominal pain in primary care. This may ultimately result in better patient outcomes, lower costs, and reduced health care resource use.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov: NCT06762275 (registration date: 2024/12/03).</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13474870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148765748","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":"Diagnostic accuracy of deep learning-assisted low-dose CT screening for lung cancer detection: a systematic review and meta-analysis.","authors":"Dagem Gebremichael, Zaki Abate Zimme, Lulit Y Mengesha, Nathan Jibat, Abiye Berihun, Asnakew Achaw Ayele, Saron Assefa Ashenafi, Henok Getachew Tegegn, Kalab Yigermal Gete","doi":"10.1186/s41512-026-00235-w","DOIUrl":"10.1186/s41512-026-00235-w","url":null,"abstract":"<p><strong>Background: </strong>Deep learning (DL)-assisted low-dose computed tomography (LDCT) may improve lung cancer screening, but the available evidence is heterogeneous and patient-level diagnostic accuracy remains uncertain.</p><p><strong>Methods: </strong>MEDLINE, Embase, and Web of Science were searched from January 2010 to December 2025 for studies evaluating DL-assisted LDCT in lung cancer screening or screening-relevant populations. Two reviewers independently screened studies, extracted data, and assessed risk of bias using QUADAS-2. AI-specific reporting completeness was assessed descriptively using items adapted from CLAIM and STARD-AI. Studies with complete or reconstructible patient-level 2 × 2 data at a defined threshold were pooled using bivariate random-effects and hierarchical summary receiver operating characteristic models. Studies without sufficient threshold-specific data were synthesised narratively.</p><p><strong>Results: </strong>Eleven studies met the inclusion criteria. Five studies, comprising 2,220 participants, 232 lung cancer cases, and 1,988 non-cases, provided usable threshold-specific 2 × 2 data and were included in the meta-analysis. Six studies were synthesised narratively because patient-level TP, FP, FN, and TN values were unavailable or not reconstructible. Pooled sensitivity was 85.4% (95% CI, 79.1-90.0), and pooled specificity was 83.5% (95% CI, 73.5-90.2). The positive likelihood ratio was 5.17, the negative likelihood ratio was 0.18, and the diagnostic odds ratio was 29.53. No study was at low risk of bias across all QUADAS-2 domains, and AI-specific reporting gaps were common. Subgroup, meta-regression, and sensitivity analyses were not feasible because only five studies were quantitatively eligible. No statistical evidence of small-study effects was detected, although this assessment was inconclusive because only five studies were pooled.</p><p><strong>Conclusions: </strong>DL-assisted LDCT shows promising but preliminary diagnostic accuracy for lung cancer screening. However, the small, heterogeneous, and methodologically limited evidence base does not support autonomous clinical use. DL is currently best considered a decision-support tool within radiologist-led screening pathways, pending prospective external validation and workflow-based evaluation.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708595","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":"Treatment effect estimation by comparing observed and predicted outcomes: conditions for valid inference and practical illustration.","authors":"Lotta M Meijerink, Artuur M Leeuwenberg, Jungyeon Choi, Bas B L Penning de Vries, Johannes A Langendijk, Judith G M van Loon, Remi A Nout, Karel G M Moons, Ewoud Schuit","doi":"10.1186/s41512-026-00233-y","DOIUrl":"10.1186/s41512-026-00233-y","url":null,"abstract":"<p><strong>Background: </strong>One non-randomized approach to estimate the average effect of a newly introduced treatment is to compare observed outcomes under the new treatment with predicted outcomes under the standard treatment. These counterfactual predictions are made using a model developed before the new treatment was introduced, using patient characteristics and individualized treatment information. Although the approach has been intuitively applied (e.g., as model-based clinical evaluation in radiotherapy) and can be recognized as a specific case of standardization, a method of virtual controls or a g-method, the theory and conditions required for unbiased treatment effect estimation have not been formally described. The objective of this paper is to formalize the approach and clarify these conditions.</p><p><strong>Methods: </strong>We formalize the approach within the potential outcomes framework for causal inference. We explain the methodology, its necessary conditions, and approaches for assessing their validity. These conditions are furthermore illustrated through a case study from radiotherapy, estimating the benefit of proton therapy compared to photon therapy on dysphagia in patients with head and neck cancer.</p><p><strong>Results: </strong>We describe a set of five sufficient conditions, including examples of violations: transportability, ignorability of treatment assignment, consistency, positivity, and correct model specification. While these conditions are largely untestable, we describe how empirical evidence, such as comparing predicted and observed outcomes in related samples, can increase confidence in their plausibility.</p><p><strong>Conclusion: </strong>When the prediction model predicts well in relevant (sub)populations, the approach can yield unbiased treatment effect estimates. However, there are many possible sources of bias. Therefore, we recommend systematic consideration of all required conditions, informed by domain expertise, and the use of empirical evidence whenever possible to support their plausibility.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13397745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586115","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":"Correction: 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/s41512-026-00232-z","DOIUrl":"10.1186/s41512-026-00232-z","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13339456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148407086","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}