Harnessing retinoic acid metabolism-related genes to identify lung adenocarcinoma subtype and establish a risk model for predicting prognosis and drug therapy response.

Correlation study of nutritional status, mental health, sleep quality, and prognosis in patients with acute myeloid leukemia: Construction of early warning combined with mindfulness awareness countermeasures.

Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches.

A novel risk model incorporating MMP9, TLR8, and LILRB2 drives neutrophil extracellular trap formation and promotes immune evasion in glioma.

A multi-omics R-loop-linked risk program highlights CKS2-positive proliferative tumor cells as drivers of glioma growth.

Integration of various sources of information for prediction of disease progression is an unmet need in glaucoma diagnostics. We designed a deep learning-based prognostic model incorporating clinical and structural data for forecasting functional glaucoma progression and compared its performance to clinicians. Retrospective, comparative cohort study of prognostic accuracy. We included 1599 eyes (908 patients) with definite or suspected glaucoma with ≥5 24-2 visual fields (VF) and 3 or more years of follow-up. VF mean deviation (MD) rates of change were estimated with linear regression. Sequential MD rates of change were estimated with each series spanning only 5 years of follow-up. VF progression was declared when four sequential statistically significant negative MD slopes were observed, and slope for the entire follow-up was significant. A convolutional neural network pretrained on ImageNet was designed to predict VF progression using baseline clinical and demographic data, disc photographs, and optical coherence tomography-derived global and sectoral retinal nerve fiber layer and macular thickness measurements. In addition, average intraocular pressure and treatment information during follow-up were put into the model. The same data for a subset of patients was provided to two clinicians to independently predict future progression. The model was validated on a separate cohort of eyes in which optical coherence tomography imaging was done with a different device (291 eyes). Model's area under receiver operating characteristic curves (AUC), accuracy, and area under the precision and recall curves. Average (SD) baseline MD and number of VF exams were -3.5 (4.9) dB and 10.1 (4.7). 399 eyes (25%) deteriorated. The best-performing model incorporated baseline disc photographs, and retinal nerve fiber layer and macular thickness: AUC, 0.839 (0.771-0.906), accuracy, 76.0% (62.0%-85.0%), and area under the precision and recall curves, 0.558 (0.385-0.733). Deep learning model significantly outperformed clinical graders (AUC : 0.629 [0.531-0738], P < .001 and 0.680 [0.584-0.776], P = .001, for grader one and two, respectively). Model performance was similar on the validation cohort (AUC: 0.754 [0.671-0.837], and accuracy: 77% [71%-82%], respectively, P = .122). The model performed well when predicting fast-progression, defined as MD rate <-1.0 dB/y (AUC: 0.869 [0.792-0.947]). Our newly designed deep learning model can combine baseline demographic and clinical data with widely available structural measurements and provide clinically relevant information for the prediction of glaucoma progression.

Survival prediction in metastatic long-bone disease is essential for surgical decision-making, yet the performance of legacy prognostic models has declined in the era of immunotherapy and molecularly targeted agents. As these systemic therapies become routine, treatment patterns themselves may influence survival in ways not captured by earlier models. We, therefore, quantified preoperative systemic therapy use over the last decade and evaluated its association with postoperative overall survival in patients undergoing surgery for long-bone metastases. We conducted a retrospective cohort study of 975 consecutive adults who underwent surgery for long-bone metastases at two affiliated tertiary centers between January 2010 and June 2022. Preoperative systemic therapy was categorized into four groups: chemotherapy only, targeted therapy only, combined chemotherapy and targeted therapy, or neither. Postoperative was defined as time from surgery to death from any cause. We described temporal uptake of therapies by calendar year and assessed associations with postoperative survival using multivariable Cox regression adjusting for demographics, ECOG performance status, Katagiri primary tumor grouping, visceral/brain metastases, and laboratory variables. Adjusted survival over calendar time was summarized via marginal standardization at fixed horizons (1, 3, 12, and 24months). Use of targeted agents including checkpoint inhibitors rose markedly, from 2% in 2015 to 43% in 2022. In multivariable Cox models, preoperative chemotherapy alone was associated with worse survival versus no preoperative systemic therapy (HR 1.41, 95% CI 1.21-1.65; p<0.01), as was combined chemotherapy plus targeted therapy (HR 1.24, 95% CI 1.05-1.47; p=0.01). In contrast, targeted therapy alone before surgery was associated with better survival (HR 0.76, 95% CI 0.58-0.99; p=0.04). Adjusted survival appeared to increase modestly across calendar years, but the pre-specified joint Wald test for the calendar year spline terms was not significant (p=0.17), indicating a non-significant trend over time. This study found that preoperative systemic therapy patterns are strongly associated with post-operative survival in metastatic long-bone disease: targeted therapy alone correlates with improved survival whereas preoperative use of chemotherapy-alone or combined with targeted agents-was associated with worse survival. These findings support incorporating contemporary treatment exposure into prognostic models and suggest that model recalibration to the immunotherapy era may be warranted.

External validation of prognostic multivariable risk models for surgical site infections after open lower extremity revascularization for peripheral arterial disease.

ASO Visual Abstract: Deep Learning-Based Multimodal Clinico-Histology-Genomic Prognostic Model in Prostate Cancer.

Curve progression to surgical range in patients with adolescent idiopathic scoliosis (AIS) is strongly associated with the initial major curve angle and the degree of skeletal maturity as staged by the modified Proximal Humerus Ossification System (PHOS) and other systems. Our purpose was to (1) develop a prognostic model and risk score to estimate the probability of progression to surgical indications in untreated patients using the PHOS and (2) compare its performance to a model using the Risser stage. Patients from the BrAIST study and 2 other institutions were followed to either skeletal maturity (Risser 4+), a major curve angle of ≥45°, or spinal fusion. Candidate variables for the predictive models included age, sex, major curve angle, Scoliosis Research Society (SRS) curve classification, status of triradiate cartilage, Risser stage, and PHOS. Model calibration and discrimination were evaluated. A probability threshold was set, creating low- and high-risk groups to aid in clinical decision-making. Overall, 164 patients (77% female) were included. The mean age at presentation was 12.2±1.4 years (range: 10 to 16 years), and the mean maximum major curve angle was 24±9° (range: 10° to 43°). Fifty-six (34%) patients progressed to a surgical range or had a spinal fusion. The PHOS model included the major curve angle and the presence/absence of a thoracic apex. The model demonstrated strong discrimination (c-statistic = 0.89) and calibration (ICI = 0.02), performing similarly to one developed in this sample using the Risser stage. The sensitivity was 0.91, the specificity was 0.71, the PPV was 0.62, and the NPV was 0.94 at the probability cut-point of 0.22. This study derived a prognostic model estimating the baseline risk of progression to surgical indications in AIS patients using the PHOS. Estimates from this model can inform the shared decision-making process and motivate compliant bracing. Further validation in larger independent samples and exploration of the PHOS to predict bracing outcomes should be performed. Level II.

The Role of Interferon-γ-Induced Granzyme B in Enhancing Antitumour Immune Responses in Oral Squamous Cell Carcinoma.

Recovery or deterioration: a financial distress prognostic model

Artificial intelligence in emergency medicine: a narrative review.

Diosgenin targets ITGA11-driven angiogenesis in hepatocellular carcinoma: Prognostic and mechanistic insights.

Diagnostic and prognostic modeling of glacier dynamics and the driving factors in the Qilian Mountains, China

A critical review of remaining useful life prediction for water Electrolyzers: From degradation mechanisms to prognostic models

To investigate the influence of different feature aggregation and selection methods on the predictive performance of fluorine-18 fluorodeoxyglucose ( 18 F-FDG) PET radiomics in assessing survival outcomes in patients with lymphoma. This retrospective analysis included 80 patients with histologically confirmed lymphoma, each presenting with at least three lesions on baseline 18 F-FDG PET images. Metabolic tumor volumes were segmented using a standardized uptake value threshold of 4.0. From each lesion, 107 radiomic features were extracted. Of these, 30 features were preselected based on their robustness to variations in tracer uptake time, image reconstruction parameters, and respiratory motion. Six distinct feature aggregation approaches were evaluated in combination with six feature selection methods. Multivariable Cox proportional hazards regression was used to assess the predictive performance of each aggregation-selection strategy for progression-free survival (PFS) and overall survival (OS). All combinations of feature aggregation and selection methods produced statistically significant prognostic models for PFS and OS, with Harrell's concordance indices (C-index) ranging from 0.582 to 0.668 for PFS and from 0.597 to 0.721 for OS. The best predictive performance was achieved using median value aggregation across all individual lesions combined with feature selection via the least absolute shrinkage and selection operator. Integrating clinical variables with radiomic features further improved predictive performance. The prognostic value of 18 F-FDG PET radiomics remained consistent across different feature aggregation and selection strategies. The establishment of standardized analysis workflows is essential to facilitate its clinical implementation in personalized treatment planning for patients with lymphoma.

We previously developed a multi-outcome prognostic model for older adults admitted to skilled nursing facilities (SNFs) for short-term rehab using Medicare data. However, incorporating predictors from the Minimum Data Set (MDS), a mandated comprehensive assessment, may improve model performance. This study sought to develop an updated model with MDS elements for use on day 7 of SNF admission when clinical trajectories are more established. Retrospective cohort study. Twenty percent national sample of community-dwelling Medicare Fee-for-Service beneficiaries aged ≥66 admitted to an SNF for at least 7 days following a hospitalization between 2017 and 2019. We predicted 2 outcomes: 6-month mortality and successful community discharge (community discharge without rehospitalization or death in the subsequent 30 days). For model development, we started with predictors from our published Medicare-based model (age, sex, Medicaid status, discharge diagnosis, hospital length of stay, admission type, comorbidities, prior hospitalizations), used Least Absolute Shrinkage and Selection Operator (LASSO) on MDS elements for variable selection, and performed logistic regression to determine predictor coefficients. Model performance was assessed by concordance statistics (c-statistics), calibration plots, and decision curve analysis. The cohort included 426,680 individuals [mean age 81.3 years (SD = 8.3), 62.7% female, 7.9% Black]. Overall, 19.9% died within 6 months, and 57.6% experienced a successful community discharge. The updated MDS model, which included Medicare predictors and 6 MDS items (activities of daily living score, cognitive status, urinary incontinence, bowel incontinence, oxygen use, walking balance), showed improvements over the Medicare model in discrimination [bootstrapped optimism-corrected c-statistic of 0.789 (95% CI, 0.787-0.790) vs 0.747 (95% CI, 0.745-0.749) for 6-month mortality and 0.730 (95% CI, 0.728-0.731) vs 0.685 (95% CI, 0.683-0.687) for successful community discharge, respectively], net benefit, and fraction of new information. Models showed good calibration. Incorporating MDS data from the first 7 days of SNF admission improved the accuracy of predictions of 6-month mortality and successful community discharge.

Construction and validation of golgi apparatus-related genes as predictors of the immune microenvironment and prognosis in colorectal cancer.

Mask-aware foundational-model embeddings for 18F-FDG-PET/CT prognosis in multiple myeloma.