Validation Of Prediction Model Research Articles

External Validation of the fullPIERS Risk Prediction Model in a U.S. Cohort of Individuals with Preeclampsia.

To externally validate the fullPIERS risk prediction model in a cohort of pregnant individuals with preeclampsia in the United States. This was a retrospective study of individuals with preeclampsia who delivered at 22 weeks or greater from January 1, 2010, to December 31, 2020. The primary outcome was a composite of maternal mortality or other serious complications of preeclampsia occurring within 48 hours of admission. We calculated the probability of the composite outcome using the fullPIERS prediction model based on data available within 12 hours of admission including, gestational age, chest pain or dyspnea, serum creatinine levels, platelet count, aspartate transaminase levels, and oxygen saturation. We assessed the model performance using the area under the curve (AUC) of the receiver operating characteristic curve. The optimal cutoff point was determined using Liu's method. A calibration plot was used to evaluate the model's goodness-of-fit. Among 1,510 individuals with preeclampsia, 82 (5.4%) experienced the composite outcome within 48 hours. The fullPIERS model achieved an AUC of 0.80 (95% confidence interval: 0.75-0.86). The predicted probability for individuals with the composite outcome (median: 18.8%; interquartile range: 2.9-59.1) was significantly higher than those without the outcome (median: 0.9%; interquartile range: 0.4-2.7). The optimal cutoff point of 5.5% yielded a sensitivity of 70.7% (95% CI: 59.6-80.3), a specificity of 85% (95% CI: 82.7-86.5), a positive likelihood ratio of 4.6 (95% CI: 3.8-5.5), and an odds ratio of 13.3 (95% CI: 8.1-21.8). The calibration plot indicated that the model underestimated risk when the predicted probability was below 1% and overestimated risk when the predicted probability exceeded 5%. The fullPIERS model demonstrated good discrimination in this U.S. cohort of individuals with preeclampsia, suggesting it may be a useful tool for healthcare providers to identify individuals at risk for severe complications.

Development and validation of prediction model for prolonged mechanical ventilation after total thoracoscopic valve replacement: a retrospective cohort study

Total thoracoscopic valve replacement (TTVR) is a minimally invasive alternative to traditional open-heart surgery. However, some patients undergoing TTVR experience prolonged mechanical ventilation (PMV). Predicting PMV risk is crucial for optimizing perioperative management and improving outcomes. We conducted a retrospective cohort study of 2,319 adult patients who underwent TTVR at a tertiary care center between January 2017 and May 2024. PMV was defined as mechanical ventilation exceeding 72 h post-surgery. A Fine-Gray competing risks regression model was developed and validated to identify predictors of PMV. Significant predictors of PMV included cardiopulmonary bypass time, ejection fraction, New York Heart Association grading, serum albumin, atelectasis, pulmonary infection, pulmonary edema, age, need for postoperative dialysis, hemoglobin levels, and PaO2/FiO2. The model demonstrated good discriminative ability, with areas under the receiver operating characteristic curves of 0.747 in the training set and 0.833 in the validation set. Calibration curves showed strong agreement between predicted and observed PMV probabilities. Decision curve analysis indicated clinical utility across a range of threshold probabilities. Our predictive model for PMV following TTVR demonstrates strong performance and clinical utility. It helps identify high-risk patients and tailor perioperative management to reduce PMV risk and improve outcomes. Further validation in diverse settings is recommended.

Predicting periprosthetic joint infection: external validation of preoperative prediction models

Abstract. Introduction: Prediction models for periprosthetic joint infections (PJIs) are gaining interest due to their potential to improve clinical decision-making. However, their external validity across various settings remains uncertain. This study aimed to externally validate promising preoperative PJI prediction models in a recent multinational European cohort. Methods: Three preoperative PJI prediction models – by Tan et al. (2018), Del Toro et al. (2019), and Bülow et al. (2022) – that have previously demonstrated high levels of accuracy were selected for validation. A retrospective observational analysis of patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) at centers in the Netherlands, Portugal, and Spain between January 2020 and December 2021 was conducted. Patient characteristics were compared between our cohort and those used to develop the models. Performance was assessed through discrimination and calibration. Results: The study included 2684 patients, 60 of whom developed a PJI (2.2 %). Our cohort differed from the models' original cohorts with respect to demographic variables, procedural variables, and comorbidity prevalence. The overall accuracies of the models, measured with the c statistic, were 0.72, 0.69, and 0.72 for the Tan, Del Toro, and Bülow models, respectively. Calibration was reasonable, but the PJI risk estimates were most accurate for predicted infection risks below 3 %–4 %. The Tan model overestimated PJI risk above 4 %, whereas the Del Toro model underestimated PJI risk above 3 %. Conclusions: The Tan, Del Toro, and Bülow PJI prediction models were externally validated in this multinational cohort, demonstrating potential for clinical application in identifying high-risk patients and enhancing preoperative counseling and prevention strategies.

Inflammatory Neuropathy Consortium base (INCbase): a protocol of a global prospective observational cohort study for the development of a prediction model for treatment response in chronic inflammatory demyelinating polyneuropathy

BackgroundINCbase is an international, multicenter prospective observational study using a customizable web-based modular registry to study the clinical, biological and electrophysiological variation and boundaries of chronic inflammatory demyelinating polyneuropathy (CIDP). The primary objective of INCbase is to develop and validate a clinical prediction model for treatment response.MethodsAll patients meeting clinical criteria for CIDP can be included in INCbase. Collected data include demographics, clinical history, diagnostics and various domains of clinical outcomes. Data is collected at a minimum of every 6 months for two years, and more frequently at the discretion of the investigational site to allow for assessment of unexpected changes in treatment response or clinical status. Participants can be enrolled in various sub-studies designed to capture data relevant to specific groups of interest. Data is entered directly into the web-based data entry system by local investigators and/or participants. Collection and local storage of biomaterial is optional. To develop a clinical prediction model for treatment response, newly diagnosed patients with active disease warranting start of first-line treatment will be included. The study population will be split into a development and validation cohort. Univariate and multivariate logistic regression analysis will be used to identify and combine predictors at start of treatment for treatment response at six months. Model performance will be assessed through discrimination and calibration in an external validation cohort. The externally validated prediction model will be made available to researchers and clinicians on the INCbase website.DiscussionWith this study, we aim to create a clinically relevant and implementable prediction model for treatment response to first line treatments in CIDP. INCbase enrollment started in April 2021, with 29 centers across 8 countries and 303 patients participating to date. This collaborative effort between academia, patient advocacy organizations and pharmaceutical industry will deepen our understanding of how to diagnose and treat CIDP.

Targeted Development and Validation of Clinical Prediction Models in Secondary Care Settings: Opportunities and Challenges for Electronic Health Record Data.

Before deploying a clinical prediction model (CPM) in clinical practice, its performance needs to be demonstrated in the population of intended use. This is also called "targeted validation." Many CPMs developed in tertiary settings may be most useful in secondary care, where the patient case mix is broad and practitioners need to triage patients efficiently. However, since structured or rich datasets of sufficient quality from secondary to assess the performance of a CPM are scarce, a validation gap exists that hampers the implementation of CPMs in secondary care settings. In this viewpoint, we highlight the importance of targeted validation and the use of CPMs in secondary care settings and discuss the potential and challenges of using electronic health record (EHR) data to overcome the existing validation gap. The introduction of software applications for text mining of EHRs allows the generation of structured "big" datasets, but the imperfection of EHRs as a research database requires careful validation of data quality. When using EHR data for the development and validation of CPMs, in addition to widely accepted checklists, we propose considering three additional practical steps: (1) involve a local EHR expert (clinician or nurse) in the data extraction process, (2) perform validity checks on the generated datasets, and (3) provide metadata on how variables were constructed from EHRs. These steps help to generate EHR datasets that are statistically powerful, of sufficient quality and replicable, and enable targeted development and validation of CPMs in secondary care settings. This approach can fill a major gap in prediction modeling research and appropriately advance CPMs into clinical practice.

Immune regulation and prognostic prediction model establishment and validation of PSMB6 in lung adenocarcinoma

Lung cancer is one of the most common malignant tumors, and patients are often diagnosed at an advanced stage, posing a substantial risk to human health, so it is crucial to establish a model to forecast the prognosis of patients with lung cancer. Recent research has indicated that proteasome 20S subunit 6 (PSMB6) may be closely associated with anti-apoptotic pathways, and proliferation transduction signals in tumor cells of different tumors. However, the precise role of PSMB6 in the immunoregulatory processes within lung adenocarcinoma (LUAD) is yet to be elucidated. By analyzing the TCGA database, we discovered a positive correlation between the expression of PSMB6 and tumor growth trends, and lung adenocarcinoma patients with elevated PSMB6 expression levels had a worse prognosis. Our findings suggest a close correlation between PSMB6 expression levels, immune cell infiltration and immune checkpoint gene expression, which suggests that PSMB6 may become a new independent prognostic indicator. In addition, we developed a prognostic model of PSMB6-regulated immune infiltration-associated genes by analyzing the link between PSMB6 and the immune microenvironment. This model can not only predict the prognosis of lung adenocarcinoma but also forecasts the patient’s reaction to immunotherapy. The validity of this research outcome has been confirmed by the GSE31210 and IMvigor210 cohorts. Analysis of the Kaplan-Meier Plotter database indicates that individuals with elevated levels of PSMB6 expression exhibit a poorer prognosis. Additionally, in vitro experiments demonstrated that knockdown of PSMB6 inhibits the proliferation, migration, and invasion of lung adenocarcinoma cells while promoting their apoptosis. Overall, our findings suggest that PSMB6 could remarkably influence the management and treatment of lung adenocarcinoma, opening new avenues for targeted immunotherapeutic strategies.

Development and validation of a machine learning-based nomogram for prediction of unplanned reoperation post-spinal surgery within 30 days

Unplanned reoperation post-spinal surgery (URPS) leads to prolonged hospital stays, higher costs, decreased patient satisfaction, and adversely affects postoperative rehabilitation. This study aimed to develop and validate prediction models (nomograms) for early URPS risk factors using machine learning (ML) methods, aiding spine surgeons in designing prevention strategies, promoting early recovery, reducing complications and improving patient satisfaction. Medical records of 639 patients who underwent reoperation post-spinal surgery (RPS) from the First Affiliated Hospital of Air Force Medical University (2018 - 2022) were collected, including baseline indicators, perioperative indicators, and laboratory indicators. After applying inclusion and exclusion criteria, 122 URPS and 155 non-URPS patients were identified and randomly divided into training (82 URPS, 111 non-URPS) and validation (40 URPS, 44 non-URPS) cohorts. Three ML methods (LASSO regression, Random Forest, and SVM-RFE) were used to select feature variables, and their intersection was used to develop the prediction model, tested on the validation cohort. Six factors-implant, postoperative suction drainage (PSD), gelatin sponge (GS), anticoagulants (ATG), antibiotics (ATB), and disease type (DT)-were identified to construct a nomogram diagnostic model. The area under the curve (AUC) of this nomogram was 0.829 (95% CI 0.771-0.886) in the training cohort and 0.854 (95% CI 0.775-0.933) in the validation cohort. Calibration curves demonstrated satisfactory agreement between predictions and actual probabilities. The decision curve indicated clinical usefulness with a threshold between 1% and 90%. The established model can effectively predict URPS in patients and can assist spine surgeons in devising personalized and rational clinical prevention strategies.

Predicting Admission to Neonatal Care Unit at Mid-Pregnancy and Delivery Using Data from a General Obstetric Population.

Development and validation of risk prediction models at mid-pregnancy and delivery to predict admission to the neonatal care unit. We used data from all singleton deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019. Admission to the neonatal care unit was assumed if length of stay in the unit was > 24h. Multivariable logistic regression with backward stepwise selection was used to develop the models. Discrimination was assessed using the ROC curve C-statistic, and internal validation was assessed using bootstrapping techniques. We conducted temporal external validation using data from all singleton deliveries at CUMH during 2020. Out of 6,077 women, 5,809 (95.6%) with complete data were included in the analyses. A total of 612 infants (10.54%) were admitted to the neonatal care unit for > 24 hours. Six variables were informative at mid-pregnancy: male infants, maternal smoking, advancing maternal age, maternal overweight/obesity, nulliparity and history of gestational diabetes (C-statistic: 0.600, 95% CI: 0.567, 0.614). Seven variables were informative at delivery: male infants, nulliparity, public antenatal care, gestational age < 39 weeks', non-spontaneous vaginal delivery, premature rupture of membranes and time of birth between 17:01-07.59h (C-statistic: 0.738, 95% CI: 0.715, 0.760). Using these predictors, we developed nomograms to calculate individualised risk of neonatal care unit admission. Bootstrapping indicated good internal performance and external validation suggested good reproducibility. Our nomograms allow the user to quickly estimate individualised risk of neonatal care unit admission. Future research should aim to improve accuracy in early pregnancy to better assist counselling of parents.

Development and validation of survival prediction models for patients with hepatocellular carcinoma treated with transcatheter arterial chemoembolization plus tyrosine kinase inhibitors.

Due to heterogeneity of molecular biology and microenvironment, therapeutic efficacy varies among hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) and tyrosine kinase inhibitors (TKIs). We examined combined models using clinicoradiological characteristics, mutational burden of signaling pathways, and radiomics features to predict survival prognosis. Two cohorts comprising 111 patients with HCC were used to build prognostic models. The training and test cohorts included 78 and 33 individuals, respectively. Mutational burden was calculated based on 17 cancer-associated signaling pathways. Radiomic features were extracted and selected from computed tomography images using a pyradiomics system. Models based on clinicoradiological indicators, mutational burden, and radiomics score (rad-score) were built to predict overall survival (OS) and progression-free survival (PFS). Eastern Cooperative Oncology Group performance status, Child-Pugh class, peritumoral enhancement, PI3K_AKT and hypoxia mutational burden, and rad-score were used to create a combined model predicting OS. C-indices were 0.805 (training cohort) and 0.768 (test cohort). The areas under the curve (AUCs) were 0.889, 0.900, and 0.917 for 1-year, 2-year, and 3-year OS, respectively. To predict PFS, alpha-fetoprotein level, tumor enhancement pattern, hypoxia and receptor tyrosine kinase mutational burden, and rad-score were used. C-indices were 0.782 (training cohort) and 0.766 (test cohort). AUCs were 0.885 and 0.925 for 6-month and 12-month PFS, respectively. Calibration and decision curve analyses supported the model's accuracy and clinical potential. The nomogram models are hopeful to predict OS and PFS in patients with intermediate-advanced HCC treated with TACE plus TKIs, offering a promising tool for treatment decisions and monitoring patient progress.

External validation of dementia prediction models in Black or African American and White older adults: A longitudinal population-based study in the United States.

Identifying people at high risk of Alzheimer's disease (AD) dementia allows for timely intervention, which, if successful, will result in preventing or delaying the onset of the disease. Utilizing data from the Chicago Health and Aging Project (CHAP; n=2130), we externally evaluated four risk-prediction models for AD dementia, including Cardiovascular Risk Factors, Aging, and Dementia (CAIDE), Australian National University Alzheimer's Disease Risk Index (ANU-ADRI), Brief Dementia Screening Indicator (BDSI), and Dementia Risk Score (DRS), in Black or African American and White adults. BDSI had the highest discriminate abilities for AD dementia (c-statistics of 0.79 in Black and 0.77 in White adults), followed by ANU-ADRI, within the age range and follow-up period of the original development cohort. CAIDE had the lowest discriminating power (c-statistic≤0.55). With increasing follow-up periods (i.e., 10-15 years), the discrimination abilities for all models declined. Because of racial disparities in AD dementia and longer preclinical and prodromal stages of disease development, race-specific models are needed to predict AD risk over 10 years. Utilizing risk-prediction models to identify individuals at higher risk of Alzheimer's disease (AD) dementia could benefit clinicians, patients, and policymakers. Clinicians could enroll high-risk individuals in clinical trials to test new risk-modifiable treatments or initiate lifestyle modifications, which, if successful, would slow cognitive decline and delay the onset of the disease. Current risk-prediction models had good discriminative power during the first 6 years of follow-up but decreased with longer follow-up time. Acknowledging the longer preclinical phase of AD dementia development and racial differences in dementia risk, there is a need to develop race-specific risk-prediction models that can predict 10 or 20 years of risk for AD and related dementias.

Establishment and validation of apnea risk prediction models in preterm infants: a retrospective case control study

BackgroundApnea is common in preterm infants and can be accompanied with severe hypoxic damage. Early assessment of apnea risk can impact the prognosis of preterm infants. We constructed a prediction model to assess apnea risk in premature infants for identifying high-risk groups.MethodsA total of 162 and 324 preterm infants with and without apnea who were admitted to the neonatal intensive care unit of Xiamen University between January 2018 and December 2021 were selected as the case and control groups, respectively. Demographic characteristics, laboratory indicators, complications of the patients, pregnancy-related factors, and perinatal risk factors of the mother were collected retrospectively. The participants were randomly divided into modeling (n = 388) and validation (n = 98) sets in an 8:2 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression analyses were used to independently filter variables from the modeling set and build a model. A nomogram was used to visualize models. The calibration and clinical utility of the model was evaluated using consistency index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve, and the model was verified using the validation set.ResultsResults of LASSO combined with multivariate logistic regression analysis showed that gestational age at birth, birth length, Apgar score, and neonatal respiratory distress syndrome were predictors of apnea development in preterm infants. The model was presented as a nomogram and the Hosmer-Lemeshow goodness of fit test showed a good model fit (χ2=5.192, df=8, P=0.737), with Nagelkerke R2 of 0.410 and C-index of 0.831. The area under the ROC curve and 95% CI were 0.831 (0.787-0.874) and 0.829 (0.722-0.935), respectively. Delong's test comparing the AUC of the two data sets showed no significant difference (P=0.976). The calibration curve showed good agreement between the predicted and actual observations. The decision curve results showed that the threshold probability range of the model was 0.07-1.00, the net benefit was high, and the constructed clinical prediction model had clinical utility.ConclusionsOur risk prediction model based on gestational age, birth length, Apgar score 10 min post-birth, and neonatal respiratory distress syndrome was validated in many aspects and had good predictive efficacy and clinical utility.

Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review

BackgroundPrognostic models that identify individuals with chronic kidney disease (CKD) at greatest risk of developing kidney failure help clinicians to make decisions and deliver precision medicine. It is recognised that people with CKD usually have multiple long-term health conditions (multimorbidity) and often experience frailty. We undertook a systematic review to evaluate the representation and consideration of multimorbidity and frailty within CKD cohorts used to develop and/or validate prognostic models assessing the risk of kidney failure.MethodsWe identified studies that described derivation, validation or update of kidney failure prognostic models in MEDLINE, CINAHL Plus and the Cochrane Library—CENTRAL. The primary outcome was representation of multimorbidity or frailty. The secondary outcome was predictive accuracy of identified models in relation to presence of multimorbidity or frailty.ResultsNinety-seven studies reporting 121 different kidney failure prognostic models were identified. Two studies reported prevalence of multimorbidity and a single study reported prevalence of frailty. The rates of specific comorbidities were reported in a greater proportion of studies: 67.0% reported baseline data on diabetes, 54.6% reported hypertension and 39.2% reported cardiovascular disease. No studies included frailty in model development, and only one study considered multimorbidity as a predictor variable. No studies assessed model performance in populations in relation to multimorbidity. A single study assessed associations between frailty and the risks of kidney failure and death.ConclusionsThere is a paucity of kidney failure risk prediction models that consider the impact of multimorbidity and/or frailty, resulting in a lack of clear evidence-based practice for multimorbid or frail individuals. These knowledge gaps should be explored to help clinicians know whether these models can be used for CKD patients who experience multimorbidity and/or frailty.Systematic review registrationThis review has been registered on PROSPERO (CRD42022347295).

Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses

Objectives Radiomics involves the extraction and analysis of numerous quantitative features of medical imaging which can add more information from radiological images often beyond initial comprehension of a clinician. Unlike deep learning, radiomics allows some understanding of identified quantitative features for clinical prediction. We sought to explore the current state of radiomics applications in the skull base literature. Methods A systematic review of studies evaluating radiomics in skull base was performed, including those with and without machine-learning approaches. Studies were summarized into thematic elements as well as specific pathologies. Results A total of 102 studies with 26,280 radiographic images were included. The earliest radiomic study was published in 2017 with exponential growth in research since then. Most studies focused on tumor diagnosis (40.8%), followed by tumor prognosis (31.1%), automated segmentation (16.5%), other applications (7.8%), and lastly prediction of intraoperative features (3.9%). Pituitary adenomas (41.7%) and vestibular schwannomas (18.4%) represented the most commonly evaluated pathologies; however, radiomics could be applied to a heterogeneous collection of skull base pathologies. The average study included 258677 cases (range 4; 6755). Conclusion Radiomics offers many functions in treating skull base pathology and will likely be an essential component of future clinical care. Larger sample sizes, validation of predictive models, and clinical application are needed. Further investigation into the strengths and weaknesses of radiomic applications in skull base treatments is warranted.

Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.

Several multivariate prognostic models have been published to predict outcomes in patients with first episode psychosis (FEP), but it remains unclear whether those predictions generalize to independent populations. Using a subset of demographic and clinical baseline predictors, we aimed to develop and externally validate different models predicting functional outcome after a FEP in the context of a schizophrenia-spectrum disorder (FES), based on a previously published cross-validation and machine learning pipeline. A crossover validation approach was adopted in two large, international cohorts (EUFEST, n = 338, and the PSYSCAN FES cohort, n = 226). Scores on the Global Assessment of Functioning scale (GAF) at 12 month follow-up were dichotomized to differentiate between poor (GAF current < 65) and good outcome (GAF current ≥ 65). Pooled non-linear support vector machine (SVM) classifiers trained on the separate cohorts identified patients with a poor outcome with cross-validated balanced accuracies (BAC) of 65-66%, but BAC dropped substantially when the models were applied to patients from a different FES cohort (BAC = 50-56%). A leave-site-out analysis on the merged sample yielded better performance (BAC = 72%), highlighting the effect of combining data from different study designs to overcome calibration issues and improve model transportability. In conclusion, our results indicate that validation of prediction models in an independent sample is essential in assessing the true value of the model. Future external validation studies, as well as attempts to harmonize data collection across studies, are recommended.

Validation of an Artificial Intelligence-Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma.

The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). Methods: In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUVpeak, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUVpeak Model performance was assessed using the area under the curve (AUC) and Kaplan-Meier curves. Results: The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 (P < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; P > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; P < 0.05). Conclusion: The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics.

Development and validation of machine learning models for intraoperative blood transfusion prediction in severe lumbar disc herniation patients

Development and validation of machine learning models for intraoperative blood transfusion prediction in severe lumbar disc herniation patients

Development and internal validation of prehospital prediction models for identifying intracerebral haemorrhage in suspected stroke patients

IntroductionDistinguishing patients with intracerebral haemorrhage (ICH) from other suspected stroke cases in the prehospital setting is crucial for determining the appropriate level of care and minimising the onset-to-treatment time, thereby...

Prospective external validation of radiomics-based predictive model of distant metastasis after dynamic tumor tracking stereotactic body radiation therapy in patients with non-small-cell lung cancer: A multi-institutional analysis.

This study aims to externally validate a predictive model for distant metastasis (DM) with computed tomography (CT)-based radiomics features in prospectively enrolled non-small-cell lung cancer patients undergoing dynamic tumor-tracking stereotactic body radiation therapy (DTT-SBRT). The study collected retrospective data from 567 patients across 11 institutions as the training dataset and prospectively enrolled 42 patients from four institutions asthe external test dataset. Four clinical features were collected, and 944 CT-based radiomic features were extracted from gross tumor volumes. After standardization and feature selection, DM predictive models were developed using fine and gray regression (FG) and random survival forest (RSF), incorporating clinical and radiomic features, and their combinations within the training dataset. Then, the model was applied to the test dataset, dividing patients into high- and low-risk groups based on medians of risk scores. Model performance was assessed using the concordance index (C-index), and the statistical significance between groups was evaluated using Gray's test. In the training dataset, 122 of 567 patients (21.5%) developed DM, compared to 9 of 42 patients (21.4%) in the test dataset. In the test dataset, the C-indices of the clinical, radiomics, and hybrid models with FG were 0.559, 0.544, and 0.560, respectively, whereas those with RSF were 0.576, 0.604, and 0.627, respectively. The hybrid model with RSF, which exhibited the best predictive performance of all models, identified 7 of 23 patients (30.4%) as high risk and 2 of 19 patients (10.5%) as low risk for DM incidence in the test dataset (p=0.116). Although predictive models for DM lack significance when applied to prospectively enrolled cases undergoing DTT-lung SBRT, the model with RSF exhibits a consistent capacity to effectively classify patients at a high risk of developing DM.

Discovery and Validation of a Kidney Rejection Prediction Model Using Urinary CXCL9 and CXCL10

Discovery and Validation of a Kidney Rejection Prediction Model Using Urinary CXCL9 and CXCL10

Validation of AKI Prediction Model as Clinical Decision Supporting System

Validation of AKI Prediction Model as Clinical Decision Supporting System