Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application

Unveiling the Role of Protein Posttranslational Modifications in Glioma Prognosis

BACKGROUND

Gliomas represent the most aggressive malignancies of the central nervous system, with posttranslational modifications (PTMs) emerging as critical regulators of oncogenic processes through dynamic protein functional modulation. Despite their established role in tumor biology, the systematic characterization of PTM-mediated molecular mechanisms driving glioma progression remains unexplored. This study aims to uncover the molecular mechanisms of glioma, with a focus on the role of PTMs.

METHODS

We analyzed the PTM pathway to classify glioma patients into distinct clusters. Comprehensive analyses compared intercluster differences in clinical outcomes, mutational landscapes, and immune microenvironment profiles. Differentially expressed genes (DEGs) were identified to construct a robust prognostic prediction model with machine learning approaches. Among the genes included in the model, TOM1L1 (Target of Myb1 Like 1 Membrane Trafficking Protein) was selected for in vitro experimental validation to assess its role in glioma progression.

RESULTS

PTMs were found to influence glioma prognosis significantly. Dysregulation in specific pathways, such as glutathionylation and citrullination, was correlated with more aggressive clinical features. The prognostic model, comprising DEGs such as TOM1L1, demonstrated high predictive accuracy (c-index = 0.867)-the scores derived from the model strongly correlated with glioma progression indicators. In vitro experiments revealed that TOM1L1 facilitates malignant progression by modulating PTM pathways, confirming its functional role in glioma.

CONCLUSION

Our study establishes the first comprehensive PTM atlas in gliomas, revealing subtype-specific modification patterns with clinical and therapeutic implications. TOM1L1 emerges as a promising prognostic biomarker and a potential therapeutic intervention target. Targeting PTM pathways may offer novel strategies for glioma treatment, enhancing patient outcomes.

Predicting the Risk of HIV Infection and Sexually Transmitted Diseases Among Men Who Have Sex With Men: Cross-Sectional Study Using Multiple Machine Learning Approaches

BACKGROUND

Men who have sex with men (MSM) are at high risk for HIV infection and sexually transmitted diseases (STDs). However, there is a lack of accurate and convenient tools to assess this risk.

OBJECTIVE

This study aimed to develop machine learning models and tools to predict and assess the risk of HIV infection and STDs among MSM.

METHODS

We conducted a cross-sectional study that collected individual characteristics of 1999 MSM with negative or unknown HIV serostatus in Western China from 2013 to 2023. MSM self-reported their STD history and were tested for HIV. We compared the accuracy of 6 machine learning methods in predicting the risk of HIV infection and STDs using 7 parameters for a comprehensive assessment, ranking the methods according to their performance in each parameter. We selected data from the Sichuan MSM for external validation.

RESULTS

Of the 1999 MSM, 72 (3.6%) tested positive for HIV and 146 (7.3%) self-reported a history of previous STD infection. After taking the results of the intersection of the 3 feature screening methods, a total of 7 and 5 predictors were screened for predicting HIV infection and STDs, respectively, and multiple machine learning prediction models were constructed. Extreme gradient boost models performed optimally in predicting the risk of HIV infection and STDs, with area under the curve values of 0.777 (95% CI 0.639-0.915) and 0.637 (95% CI 0.541-0.732), respectively, demonstrating stable performance in both internal and external validation. The highest combined predictive performance scores of HIV and STD models were 33 and 39, respectively. Interpretability analysis showed that nonadherence to condom use, low HIV knowledge, multiple male partners, and internet dating were risk factors for HIV infection. Low degree of education, internet dating, and multiple male and female partners were risk factors for STDs. The risk stratification analysis showed that the optimal model effectively distinguished between high- and low-risk MSM. MSM were classified into HIV (predicted risk score <0.506 and ≥0.506) and STD (predicted risk score <0.479 and ≥0.479) risk groups. In total, 22.8% (114/500) were in the HIV high-risk group, and 43% (215/500) were in the STD high-risk group. HIV infection and STDs were significantly higher in the high-risk groups (P<.001 and P=.05, respectively), with higher predicted probabilities (P<.001 for both). The prediction results of the optimal model were displayed in web applications for probability estimation and interactive computation.

CONCLUSIONS

Machine learning methods have demonstrated strengths in predicting the risk of HIV infection and STDs among MSM. Risk stratification models and web applications can facilitate clinicians in accurately assessing the risk of infection in individuals with high risk, especially MSM with concealed behaviors, and help them to self-monitor their risk for targeted, timely diagnosis and interventions to reduce new infections.

Application of artificial intelligence in forecasting survival in high-grade glioma: systematic review and meta-analysis involving 79,638 participants

High-grade glioma (HGG) is an aggressive brain tumor with poor survival rates. Predicting survival outcomes is critical for personalized treatment planning. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) models, has emerged as a promising approach for enhancing prognostic accuracy in HGG but this study especially focused on the potential of AI in the recurrence of HGG. A systematic review and meta-analysis were conducted to assess the performance of AI-based models in predicting survival outcomes for HGG patients. Relevant studies were retrieved from PubMed, Embase, Scopus, and Web of Science until 2 Dec 2024, using predefined keywords ("High-Grade Glioma", "Survival" and "Machine Learning") without date or language restrictions. Data extraction and quality assessment were performed in accordance with PRISMA and PROBAST guidelines. In this study were included. The pooled diagnostic metric, the area under the curve (AUC), was analyzed using random-effects models. A total of 39 studies with 29 various algorithms and 79,638 patients were included, with 15 studies contributing to the meta-analysis. The most commonly used algorithms were random forest (RF) and logistic regression (LR), which demonstrated robust predictive accuracy. The pooled AUCs for one-year, two-year, three-year and overall survival predictions were 0.816, 0.854, 0.871 and 0.789 respectively. Subgroup analysis revealed that RSF achieved the highest predictive accuracy with an AUC of 0.91 (95% CI: 0.84-0.98), while LR followed with an AUC of 0.89 (95% CI: 0.82-0.96). Models integrating clinical, radiomics, and genetic features consistently outperformed single-data-type models. MRI was the most frequently utilized imaging modality. AI-based models, particularly ML and DL algorithms, show significant potential for improving survival prediction in HGG patients. By integrating multimodal data, these models offer valuable tools for personalized treatment planning, although further validation in prospective, multicenter studies is needed to ensure clinical applicability.

Assessing the Validity of Diffusion Weighted Imaging Models: A Study in Patients with Post-Surgical Lower-Grade Glioma

Background: Diffusion weighted imaging (DWI) is used for monitoring purposes for lower-grade glioma (LGG). While the apparent diffusion coefficient (ADC) is clinically used, various DWI models have been developed to better understand the micro-environment. However, the validity of these models and how they relate to each other is currently unknown. Therefore, this study assesses the validity and agreement of these models. Methods: Fourteen post-treatment LGG patients and six healthy controls (HC) underwent DWI MRI on a 3T MRI scanner. DWI processing included diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), white matter tract integrity (WMTI), neurite orientation dispersion and density imaging (NODDI), and fixel-based analysis (FBA). Validity was assessed by delineating surgical cavity, peri-surgical cavity, and normal-appearing white matter (NAWM) in LGG patients, and white matter (WM) in HC. Spearman correlation assessed the agreement between DWI parameters. Results: All obtained parameters differed significantly across tissue types. Remarkably, WMTI showed that intra-axonal diffusivity was high in the surgical cavity and low in NAWM and WM. Most DWI parameters correlated well with each other, except for WMTI-derived intra-axonal diffusivity. Conclusion: This study shows that all parameters relevant for tumour monitoring and DWI-derived parameters for axonal fibre-bundle integrity (except WMTI-IAS-Da) could be used interchangeably, enhancing inter-DWI model interpretability.

DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer

Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk profiles necessitates a deeper understanding of the interplay between different factors such as patient health, spatial tumor location and spread, and risk of subsequent toxicities that can not be adequately captured through simple heuristics. To support clinicians in better understanding tradeoffs when deciding on treatment courses, we developed DITTO, a digital-twin and visual computing system that allows clinicians to analyze detailed risk profiles for each patient, and decide on a treatment plan. DITTO relies on a sequential Deep Reinforcement Learning digital twin (DT) to deliver personalized risk of both long-term and short-term disease outcome and toxicity risk for HNC patients. Based on a participatory collaborative design alongside oncologists, we also implement several visual explainability methods to promote clinical trust and encourage healthy skepticism when using our system. We evaluate the efficacy of DITTO through quantitative evaluation of performance and case studies with qualitative feedback. Finally, we discuss design lessons for developing clinical visual XAI applications for clinical end users.

Retinal Microstructural Changes Reflecting Treatment-Associated Cognitive Dysfunction in Patients with Lower-Grade Gliomas

Purpose

To determine whether microstructural retinal changes, tumor features, and apolipoprotein E (APOE) ε4 polymorphism are correlated with clinically detectable treatment-associated cognitive dysfunction (TACD) in patients with lower-grade gliomas.

Design

Cohort study.

Participants and Controls

Sixteen patients with lower-grade glioma at a United States academic ophthalmology department between January 2021 and November 2023. Normal controls were recruited from convenient sampling.

Methods

Montreal Cognitive Assessment (MoCA) scores and retinal changes were assessed in 6-month intervals. Apolipoprotein E genotyping was performed, and tumor details were recorded. Partial least-squares discriminant (PLSD) model was established to evaluate the association between TACD with APOE genotype, ophthalmic, and tumor features.

Main Outcome Measures

The main outcome measure was cognitive status as measured by the MoCA score and analyzed in relation to ophthalmic measurements, tumor features, and APOE genotype.

Results

Median time to first eye examination was 34 months (2-266) from tumor diagnosis and 23 months (0-246) from radiation. Nine patients (56%) had abnormal cognition (MoCA <26/30). Montreal Cognitive Assessment scores were significantly worse in patients with temporal (22 ± 7.2) than frontal lobe tumors (26 ± 3.1, P = 0.02) and those with oligodendrogliomas (22 ± 4.1) than astrocytomas (26 ± 3.6, = 0.02). Patients with TACD had significant radial peripapillary capillary density loss (45% ± 4.6) compared with those with normal cognition (49% ± 2.6, P = 0.02). A PLSD model correlated MoCA scores with retinal nerve fiber thickness, intraocular pressure, foveal avascular zone, best-corrected visual acuity, months since first diagnosis, and tumor pathology (oligodendroglioma or not). Using these features, the model identified patients with TACD with 77% accuracy. Apolipoprotein E genotyping showed: 2 ε2/ε3 (13%), 10 ε3/ε3 (63%), and 1 ε3/ε4 (6%).

Conclusions

Retinal microstructural changes may serve as biomarkers for TACD in patients with lower-grade gliomas. Temporal lobe tumors and oligodendrogliomas may increase susceptibility to TACD. Utilization of retinal markers may enhance TACD diagnosis, progression monitoring, and inform management of lower-grade patients with glioma. A larger study with serial eye examinations is warranted to evaluate the role of APOE ε4 and develop a predictive model.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study

OBJECTIVE

The purpose of this study was to develop an individual survival prediction model based on multiple machine learning (ML) algorithms to predict survival probability for remnant gastric cancer (RGC).

METHODS

Clinicopathologic data of 286 patients with RGC undergoing operation (radical resection and palliative resection) from a multi-institution database were enrolled and analyzed retrospectively. These individuals were split into training (80%) and test cohort (20%) by using random allocation. Nine commonly used ML methods were employed to construct survival prediction models. Algorithm performance was estimated by analyzing accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), confusion matrices, five-fold cross-validation, decision curve analysis (DCA), and calibration curve. The best model was selected through appropriate verification and validation and was suitably explained by the SHapley Additive exPlanations (SHAP) approach.

RESULTS

Compared with the traditional methods, the RGC survival prediction models employing ML exhibited good performance. Except for the decision tree model, all other models performed well, with a mean ROC AUC above 0.7. The DCA findings suggest that the developed models have the potential to enhance clinical decision-making processes, thereby improving patient outcomes. The calibration curve reveals that all models except the decision tree model displayed commendable predictive performance. Through CatBoost-based modeling and SHAP analysis, the five-year survival probability is significantly influenced by several factors: the lymph node ratio (LNR), T stage, tumor size, resection margins, perineural invasion, and distant metastasis.

CONCLUSIONS

This study established predictive models for survival probability at five years in RGC patients based on ML algorithms which showed high accuracy and applicative value.

Synthetic minority over-sampling technique-enhanced machine learning models for predicting recurrence of postoperative chronic subdural hematoma

Objective

Chronic subdural hematoma (CSDH) is a neurological condition with high recurrence rates, primarily observed in the elderly population. Although several risk factors have been identified, predicting CSDH recurrence remains a challenge. Given the potential of machine learning (ML) to extract meaningful insights from complex data sets, our study aims to develop and validate ML models capable of accurately predicting postoperative CSDH recurrence.

Methods

Data from 447 CSDH patients treated with consecutive burr-hole irrigations at Wenzhou Medical University's First Affiliated Hospital (December 2014-April 2019) were studied. 312 patients formed the development cohort, while 135 comprised the test cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to select crucial features associated with recurrence. Eight machine learning algorithms were used to construct prediction models for hematoma recurrence, using demographic, laboratory, and radiological features. The Border-line Synthetic Minority Over-sampling Technique (SMOTE) was applied to address data imbalance, and Shapley Additive Explanation (SHAP) analysis was utilized to improve model visualization and interpretability. Model performance was assessed using metrics such as AUROC, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis (DCA).

Results

Our optimized ML models exhibited prediction accuracies ranging from 61.0% to 86.2% for hematoma recurrence in the validation set. Notably, the Random Forest (RF) model surpassed other algorithms, achieving an accuracy of 86.2%. SHAP analysis confirmed these results, highlighting key clinical predictors for CSDH recurrence risk, including age, alanine aminotransferase level, fibrinogen level, thrombin time, and maximum hematoma diameter. The RF model yielded an accuracy of 92.6% with an AUC value of 0.834 in the test dataset.

Conclusion

Our findings underscore the efficacy of machine learning algorithms, notably the integration of the RF model with SMOTE, in forecasting the recurrence of postoperative chronic subdural hematoma. Leveraging the RF model, we devised an online calculator that may serve as a pivotal instrument in tailoring therapeutic strategies and implementing timely preventive interventions for high-risk patients.