Accurate Prediction Of Survival Research Articles

GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.

Multimodal information provides valuable resources for cancer prognosis and survival prediction. However, the computational integration of this heterogeneous data information poses significant challenges due to the complex interactions between molecules from different biological modalities and the limited sample size. Here, we introduce GD-Net, a Graph Deep learning algorithm to enhance the accuracy of survival prediction with an average accuracy of 72% by early fusing of multimodal information, which includes an interpretable and lightweight XGBoost module to efficiently extract informative features. First, we applied GD-Net to eight cancer datasets and achieved superior performance compared to benchmarking methods, with an average 7.9% higher C-index value. The ablation experiments strongly supported that multi-modal integration could significantly improve accuracy over the single-modality model. In the deep case study of liver cancer, 319 differential genes, 15 differential miRNAs and 155 methylated differential genes based on the predicted risk subgroups are identified as the informative features, and then we have statistically and biologically validated the efficacy of these key molecules in internal and external test datasets. The comprehensive independent validations demonstrated that GD-Net is accurate and competitive in predicting different cancer outcomes in real-time, and it is an effective tool for identifying new multimodal prognosis biomarkers.

Clinical significance and expression of SLC35F6 in bladder urothelial carcinoma

BackgroundSLC35F6 negatively regulates outer mitochondrial membrane permeability and positively regulates apoptotic signaling pathways and cell population proliferation. The biological function of SLC35F6 in bladder cancer (BC) remains inadequately established. This study evaluates the expression and clinical significance of SLC35F6 in BC, assesses its prognostic value and explores its relationship with key immune-related molecules in the tumor microenvironment.MethodsCombining bioinformatics tools and immunohistochemistry (IHC) analysis, the expression of SLC35F6 was analyzed through IHC in the tissues of 145 BC patients treated at the Affiliated Hospital of Nantong University from 2004 to 2009. The relationship between SLC35F6 expression levels and significant clinicopathological factors was examined using the chi-square test. Prognostic values were analyzed using the COX regression model and the Kaplan-Meier survival curve. Analysis of the receiver operating characteristic curve was conducted to assess the predictive performance of SLC35F6 in BC patients.ResultsThe expression levels of both SLC35F6 mRNA and protein were elevated in BC tissue relative to benign tissue. Kaplan-Meier analysis indicated that patients exhibiting elevated SLC35F6 protein expression had a worse prognosis. Multivariate Cox regression analysis confirmed that SLC35F6, TNM stage and grade are independent risk factors for bladder cancer. SLC35F6, when analyzed alongside clinical pathological factors, enhances the accuracy of survival predictions for Bladder Urothelial Carcinoma (BLCA) patients.ConclusionSLC35F6 is upregulated in BC patients compared to normal individuals and is linked to a worse prognosis. SLC35F6 analyzed alongside clinical pathological factors can enhance the accuracy of survival predictions for BLCA patients, suggesting its potential value as a prognostic and predictive biomarker.

Deep weighted survival neural networks to survival risk prediction

Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a challenge in building an efficient method to improve the accuracy of cancer-specific survival risk prediction, such as data noise problem. In order to solve the above problem, we propose a diversity reweighted deep survival neural network method with grid optimization (DRGONet) to improve the accuracy of cancer-specific survival risk prediction. Specifically, reweighting can be employed to adjust the weights assigned to each data point in the dataset based on their importance or relevance, thereby mitigating the impact of noisy or irrelevant data and improving model performance. Incorporating diversity into the goal of multiple learning models can help minimize bias and improve learning outcomes. Furthermore, hyperparameters can be optimized with grid optimization. Experimental results have demonstrated that our proposed approach has significant advantages (improved about 5%) in real-world medical scenarios, outperforming state-of-the-art comparison methods by a large margin. Our study highlights the significance of using DRGONet to overcome the limitations of building accurate survival prediction models. By implementing our technique in cancer research, we hope to reduce the suffering experienced by cancer patients and improve the effectiveness of treatment.

Prognostic significance of right atrial volume and interval changes in patients with idiopathic pulmonary arterial hypertension

IntroductionIdiopathic pulmonary arterial hypertension (IPAH) is a severe condition characterized by a poor prognosis, rapid deterioration, and high mortality in the absence of lung transplantation. However, non-invasive prognostic markers in assessing IPAH remain uncertain. We aimed to investigate the prognostic significance of chest CT and alterations in heart contours on chest radiography (CXR) in predicting adverse outcomes in IPAH patients. MethodsA retrospective study analyzed medical records of IPAH patients who underwent right heart catheterization and chest CT at a tertiary center between 2001 and 2023. Clinical, hemodynamic, and CT findings, and changes (Δ) in heart contours on CXR were assessed. Adverse events were defined as IPAH-related death or lung transplantation. Cox regression models evaluated the predictive power of these parameters. ResultsIn 80 patients with IPAH (mean age 42.6 years; 75 % females), the 3- and 5-year survival rates were 65.8 % and 55.1 %, respectively. Adverse events were associated with a history of cardiac arrest (Hazard ratio [HR], 11.67, p = 0.02), mean pulmonary artery pressure (HR, 1.04, p = 0.001), creatinine (HR, 4.12, p < 0.001), 6-min walk distance (HR, 0.997, p = 0.03), CT-derived right atrial (RA) volume index (HR, 1.01, p = 0.02), and ΔRA contour on CXR (HR, 1.27, p < 0.001). Combined clinical, CT, and CXR findings showed a 3-year event-free survival predictive accuracy of 82.2 % (95 % CI, 69.8–94.7), outperforming clinical factors alone. ConclusionRapid increases in RA contour on CXR and CT-derived RA volume index were associated with adverse outcomes in IPAH. Assessing these parameters may be helpful in identifying patients who require proactive treatments.

Artificial intelligence-enhanced electrocardiography predicts 10-year risk of atherosclerotic cardiovascular disease

Abstract Background The ACC/AHA pooled cohort equations (PCE) calculates the 10-year primary risk of atherosclerotic cardiovascular disease (ASCVD), an indication to initiate primary prevention statin therapy in international guidelines. The validity of PCE is limited by its overestimation of risk in high-risk cohorts and an ethnic heterogeneity. Electrocardiography (ECG) has not been incorporated in current ASCVD risk estimation tools. Artificial Intelligence (AI)-based models are capable to capture patterns that are informative for cardiovascular prognosis. Purpose We explored the predictive value of a discrete-time survival model in 10-year ASCVD risk in comparison to PCE. Methods We developed an AI-ECG model to predict the 10-year ASCVD risk in an unselected secondary care population from the USA comprised of 1,163,401 ECGs from 189,539 patients. Data was split into training/validation and hold-out test sets by patient ID. Our AI-ECG model uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to output subject-specific survival curves, accounting for both time to event and censorship (i.e., loss to follow up). We compared the performance of out AI-ECG model using a subset of outpatient data (n=4590) of the BIDMC dataset to PCE and ASCVD risk factors including systolic blood pressure, total cholesterol, HDL cholesterol, hypertension, smoking history, diabetes mellitus, and ethnicity. Performances were evaluated using cox survival analysis. Our AI-ECG model was externally validated in the UKB Biobank (UKB), where we compared the performance of our AI-ECG model to predictions of existing AI-ECG models such as the Stanford Estimator of Electrocardiogram Risk (SEER) model. Results Our AI-ECG successfully predicted future ASCVD events in individuals (C-index 0.721 (0.719-0.723), 5-year AUC 0.761 (0.758-0.763), and differentiated between high and low risk group with an adjusted HR of 2.33 (2.26-2.41). Upon internal validation our AI-ECG model showed a superior performance (C-index 0.679 (0.651-0.708)) than PCE (0.605 (0.577-0.634)) and all risk factors combined (C index 0.642 (0.613-0.672)). When combining AI-ECG predictions with all risk factors, the model showed additional predictive value (0.686 (0.657-0.715)) (Figure 1). Our AI-ECG model showed modest predictive value within the UKB dataset (C-index 0.655 (0.637-0.673)), but significantly outperformed the SEER model (C-index 0.547 (0.527-0.567), p &amp;lt;0.0001 for comparison with AIRE). Conclusion We have shown the potential of AI-ECG models in predicting future ASCVD risks. AI-ECG models demonstrated accurate discrete time survival prediction and good classification performance superior to that of the SEER model, as well as PCE and ASCVD risk factors. . Accurate risk assessment of ASCVD may help prevent adverse events in high risk subjects and save unnecessary pharmacological interventios in low risk subjects.Figure 1

A Risk Model Developed based on Homologous Recombination Deficiency Genes for Evaluating the Drug Sensitivity and Prognostic Prediction of Lung Adenocarcinoma.

This study was designed to construct a risk model based on homologous recombination deficiency (HRD) to evaluate the prognosis and drug sensitivity for patients with lung adenocarcinoma (LUAD). LUAD is a subtype of lung cancer with unfavorable overall survival (OS) and prognosis. HRD has been widely studied in various tumors, but its role in LUAD has not been fully understood. We aimed to construct an HRD-related risk model for predicting the prognosis and drug sensitivity of patients with LUAD. Gene expression data of the LUAD samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We extracted HRD genes from previous literature and performed univariate COX analysis to select those closely associated with LUAD prognosis. ConsensusClusterPlus was employed to stratify the samples in the TCGA-LUAD cohort into different subtypes. A RiskScore model was established applying random forest method. Furthermore, immunotherapy response and drug sensitivity were predicted using Tumor Immune Dysfunction and Exclusion (TIDE) software and pRRophytic R package, respectively. Finally, the clinical features between High- and Low- RiskScore groups were compared. A total of 16 HRD genes relevant to LUAD prognosis were selected and used to classify 3 LUAD clusters (C1, C2, and C3). Specifically, C1, with a lower TIDE score displayed higher immune infiltration and immunotherapy benefit and the optimal OS, while C2 was closely correlated with tumor-relevant pathways and had the worst OS. Finally, 4 HRD genes (RAD51AP1, BRCA1, H2AFX, and FANCL) were determined to develop a RiskScore signature. It was found that a higher RiskScore was related to more advanced stages, worse OS, and tumor development pathways. Additionally, the High-RiskScore group with a higher TIDE score was sensitive to 44 traditional chemotherapy drugs. A nomogram combined with RiskScore exhibited an accurate survival prediction ability. The HRD-based RiskScore played a crucial role in LUAD development, showing a strong potential to serve as a prognostic indicator for LUAD. Our findings contributed to the diagnosis of LUAD and its treatment.

AI-Assisted Survival Prediction in Colorectal Cancer: A Clinical Decision Support Tool

Purpose: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Accurate survival prediction is crucial for advanced-stage patients to optimize treatment strategies and improve clinical outcomes. This study aimed to develop an artificial intelligence-assisted clinical decision support system (CDSS) for survival prediction in CRC patients using clinical and genomic data from the Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD) dataset. Methods: Machine learning algorithms, including C4.5 Decision Tree, Support Vector Machines (SVM), Random Forest, and Naive Bayes, were employed to create survival prediction models. Clinical parameters and genomic data from key pathways, such as glycolysis/gluconeogenesis and mTORC1, were integrated into the models. The models were evaluated based on accuracy and performance. Results: The Random Forest algorithm achieved the highest accuracy (82.3%) when only clinical parameters were used. When clinical data were combined with gene expression data, the model’s accuracy increased further. The resulting models were incorporated into a user-friendly web interface, SurvCOCA, for clinical use. Conclusions: This study demonstrates the potential of AI-based tools to improve prognosis predictions in CRC patients. Further research is needed, with larger datasets and additional machine learning algorithms, to enhance clinical decision-making and optimize treatment strategies.

Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage.

The objective of this study was to analyze the incidence and overall survival (OS) of osteosarcoma (OSC) and Ewing's sarcoma (EWS) in a pediatric and adolescent population, employing machine learning (ML) and deep learning (DL) models to predict the likelihood of metastasis. Involving 2465 OSC and 1373 EWS patients aged 0-19 years, from 2004 to 2020. ML techniques-Lasso, Ridge Regression, Elastic Net, and Random Forest-were used alongside a deep learning model based on TensorFlow and Keras, to construct predictive models for metastasis. These models were optimized using grid search with cross-validation and evaluated on their performance metrics, including AUC, sensitivity, and accuracy. The variables' importance in metastasis prediction was determined using SHAP values. Statistical analysis was performed using R software, and an online nomogram was developed for clinical use. The age-adjusted incidence of OSC and EWS from 2004 to 2020 showed a significant uptrend. The deep learning model, iterated 50 times, outperformed the Random Forest model in both loss and accuracy stabilization. The nomogram created demonstrated accurate survival predictions, as evidenced by its calibration curves and the distinction between high and low-risk groups. The increasing trend in age-adjusted incidence of OSC and EWS highlights the need for continued research and improved therapeutic strategies in this domain. The study employed ML and DL models to predict distant metastasis in pediatric and adolescent patients with OSC and EWS, providing a valuable tool for prognosis. The online nomogram developed as a part of this research enhances the models' clinical utility, offering an accessible means for clinicians to predict survival outcomes effectively.

New prognostic model for liver transplantation outcomes in hepatocellular carcinoma

Background. Liver transplantation remains a priority treatment option for hepatocellular carcinoma in the presence of liver cirrhosis; yet precise outcome prediction post-operation continues to be a complex challenge. Existing prognostic model often overlook patient age and donor type. Enhanced models that incorporate these parameters can improve prediction accuracy and treatment efficacy, which is critically important in the dynamically evolving field of transplantation.Objective. The aim of this study is to develop a prognostic model for liver transplantation outcomes in patients with hepatocellular carcinoma and liver cirrhosis.Material and methods. This retrospective study included 69 patients with hepatocellular carcinoma on the background of liver cirrhosis who underwent liver transplantation between May 2010 and December 2022. Of these, 42 patients (61%) received organs from living donors, and 27 (39%) from deceased donors. The study involved analysis of alpha-fetoprotein levels in blood, as well as assessment of radiological (maximum tumor nodule size, number of nodules) and histological parameters (maximum tumor nodule size, number of nodules, presence of vascular invasion). Cox regression model was used to predict recurrence-free survival, and the results for five-year recurrence-free survival, recipient age, and donor type were reused in the Cox model to predict overall survival.Results. Four models for predicting recurrence-free survival and overall survival based on histological and radiological data were developed, demonstrating high prognostic value with C-indexes on training/test data of 0.76/1; 0.73/1; 0.78/0.8; 0.6/0.8 respectively. All models showed recurrence-free survival prediction accuracy comparable to the Milan criteria. The model outcomes are available as a calculator on the website https://nadit.ru/calculate_HCC.Conclusion. The developed prognostic models are vital tools for personalized outcome prediction after liver transplantation for hepatocellular carcinoma. To enhance the accuracy of these models, further amalgamation and validation of data from various medical centers, as well as open scientific collaboration, are necessary.

Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer.

Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated. The TACSPR and TACSDFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P < 0.001). The TACSPR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACSDFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACSPR and low TACSDFS, or high TACSPR and low TACSDFS, or low TACSPR and high TACSDFS, but had no impact on patients with high TACSPR and high TACSDFS. The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.

Radiomics-Based Computed Tomography Urogram Approach for the Prediction of Survival and Recurrence in Upper Urinary Tract Urothelial Carcinoma.

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with a poor prognosis. The accurate prediction of survival and recurrence in UTUC is crucial for effective risk stratification and guiding therapeutic decisions. Models combining radiomics and clinicopathological data features derived from computed tomographic urograms (CTUs) can be a way to predict survival and recurrence in UTUC. Thus, preoperative CTUs and clinical data were analyzed from 106 UTUC patients who underwent radical nephroureterectomy. Radiomics features were extracted from segmented tumors, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select the most relevant features. Multivariable Cox models combining radiomics features and clinical factors were developed to predict the survival and recurrence. Harrell's concordance index (C-index) was applied to evaluate the performance and survival distribution analyses were assessed by a Kaplan-Meier analysis. The significant outcome predictors were identified by multivariable Cox models. The combined model achieved a superior predictive accuracy (C-index: 0.73) and higher recurrence prediction (C-index: 0.84). The Kaplan-Meier analysis showed significant differences in the survival (p < 0.0001) and recurrence (p < 0.002) probabilities for the combined datasets. The CTU-based radiomics models effectively predicted survival and recurrence in the UTUC patients, and enhanced the prognostic performance by combining radiomics features with clinical factors.

Rethinking Surgical Margins: A New Approach to Predict Outcomes in Oral Squamous Cell Carcinoma.

The traditional categorical division of surgical margins using a 5 mm cutoff in oral cavity squamous cell carcinoma (OCSCC) is controversial. The primary aim of this study was to investigate the presence of an optimal cutoff point or, alternatively, assess the potential improvement in predictive value by considering the surgical margins as a continuum. Retrospective analysis of OCSCC patients at a tertiary medical center in 1995-2020. Clinical, pathological, and surgical data were evaluated for effect on survivability by regression analyses. The cohort included 266 patients (48.1% male, mean age 65.4 ± 17.7). Patient stratification by categorical margin status yielded no significant between-group differences in survival (p = 0.54). Significance was achieved when margin distance was reevaluated as a continuous variable (p = 0.0018). Similar results were shown in local control (categorical p = 0.59 vs. continuous p = 0.06). Multivariate model excluded possible confounders. A predictive model was created to provide a more accurate prediction of survival. The continuum spectrum of margin distance better predicts survival outcomes and locoregional control in OCSCC. 3 Laryngoscope, 2024.

Data Tells the Truth: A Knowledge Distillation Method for Genomic Survival Analysis by Handling Censoring

Survival analysis is a critical tool for cancer research, yet handling censored data remains challenging due to supervision bias and inaccurate hazard estimates. To address these issues, we propose a simple but effective method termed KD, which employs knowledge distillation using uncensored data to rectify the supervision bias in censored data. This approach leverages the combined power of both rectified censored data and uncensored data to improve survival prediction accuracy. Remarkably, our KD method not only effectively harnesses censored data but also better reflects clinical reality, demonstrating its immense value in survival analysis. We applied our KD method to 19 target cancer sites using The Cancer Genome Atlas (TCGA) dataset. Our results consistently outperform traditional machine learning and deep learning-based methods across both target cancer sites and independent cancer cohorts. More importantly, our data-driven approach enables the model to extract hidden information from censored data, leading to conclusions that align more closely with clinical knowledge and scenarios. This validation of our KD method’s effectiveness highlights the substantial value of rational censored data usage, providing valuable insights for cancer research and clinical decisions. All data and codes are freely available at: https://datatellstruth.github.io/.

Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer

BackgroundThis study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs. MethodsUtilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group. ResultsIn our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy. ConclusionA prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.

Constructing a Nomogram for Predicting Pelvic Osteosarcoma: A Retrospective Study Based on the SEER Database and a Chinese Cohort.

Aims/Background Generally, pelvic osteosarcoma has a worse prognosis compared with limb osteosarcoma. This study aims to create and validate a new nomogram for predicting the prognosis of pelvic osteosarcoma. Methods Clinical data of 62 patients derived from the Surveillance, Epidemiology, and End Results (SEER) database and 31 Chinese patients diagnosed with pelvic osteosarcoma were gathered. Kaplan-Meier survival analysis was utilized to calculate the median survival time for all variables. Univariate and multivariate Cox regression models were employed to identify the prognostic factors of pelvic osteosarcoma. A nomogram was constructed using data gleaned from the SEER cohort and verified using the receiver operating characteristic (ROC) curve and calibration plot in the Chinese cohort. Results Kaplan-Meier analysis revealed that individuals of other races (Asians) (hazard ratio (HR) = 0.24, 95% confidence interval (CI): 0.1-0.57, p = 0.001), aged ≤51 years old (HR = 0.4, 95% CI: 0.22-0.73, p = 0.003), and with tumor size ≤160 mm (HR = 0.37, 95% CI: 0.2-0.71, p = 0.03) had better survival outcomes. Conversely, factors such as no primary surgery (HR = 3.6, 95% CI: 1.81-7.15, p < 0.001), lung metastasis (HR = 1.96, 95% CI: 1.17-3.28, p = 0.010), and radiotherapy (HR = 1.89, 95% CI: 1.10-3.25, p = 0.021) were associated with poorer survival. Multivariate Cox analysis indicated that lung metastasis (HR = 2.57, 95% CI: 1.29-5.13, p = 0.008), other races (Asians) (HR = 0.23, 95% CI: 0.07-0.75, p = 0.015), tumor size (HR = 0.28, 95% CI: 0.13-0.62, p = 0.001) and age (HR = 0.3, 95% CI: 0.16-0.59, p < 0.001) were independent prognostic factors for pelvic osteosarcoma. Univariate and multivariate Cox regression models identified three independent variables in the training cohort: age, lung metastasis, and tumor size. A predictive nomogram was developed based on the data from the SEER cohort and validated in the Chinese cohort. The areas under the curves (AUCs) that are used to predict 1-year, 2-year, and 3-year survival rates were 0.81 (95% CI: 0.68-0.94), 0.75 (95% CI: 0.63-0.86), and 0.80 (95% CI: 0.70-0.89) in the training cohort, and 0.67 (95% CI: 0.30-1.04), 0.66 (95% CI: 0.43-0.90) and 0.71 (95% CI: 0.50-0.93) in the validation cohort. Conclusion The predictive nomogram constructed in this study facilitates accurate and effective prediction of the overall survival of patients with pelvic osteosarcoma and helps enhance the clinical decision-making process.

Prediction of disease-free survival using strain elastography and diffuse optical tomography in patients with T1 breast cancer: a 10-year follow-up study

BackgroundEarly-stage breast cancer (BC) presents a certain risk of recurrence, leading to variable prognoses and complicating individualized management. Yet, preoperative noninvasive tools for accurate prediction of disease-free survival (DFS) are lacking. This study assessed the potential of strain elastography (SE) and diffuse optical tomography (DOT) for non-invasive preoperative prediction of recurrence in T1 BC and developed a prediction model for estimating the probability of DFS.MethodsA total of 565 eligible patients with T1 invasive BC were enrolled prospectively and followed to investigate the recurrence. The associations between imaging features and DFS were evaluated and a best-prediction model for DFS was developed and validated.ResultsDuring the median follow-up period of 10.8 years, 77 patients (13.6%) developed recurrences. The fully adjusted Cox proportional hazards model showed a significant trend between an increasing strain ratio (SR) (P < 0.001 for trend) and the total hemoglobin concentration (TTHC) (P = 0.001 for trend) and DFS. In the subgroup analysis, an intensified association between SR and DFS was observed among women who were progesterone receptor (PR)-positive, lower Ki-67 expression, HER2 negative, and without adjuvant chemotherapy and without Herceptin treatment (all P < 0.05 for interaction). Significant interactions between TTHC status and the lymphovascular invasion, estrogen receptor (ER) status, PR status, HER2 status, and Herceptin treatment were found for DFS(P < 0.05).The imaging–clinical combined model (TTHC + SR + clinicopathological variables) proved to be the best prediction model (AUC = 0.829, 95% CI = 0.786–0.872) and was identified as a potential risk stratification tool to discriminate the risk probability of recurrence.ConclusionThe combined imaging-clinical model we developed outperformed traditional clinical prognostic indicators, providing a non-invasive, reliable tool for preoperative DFS risk stratification and personalized therapeutic strategies in T1 BC. These findings underscore the importance of integrating advanced imaging techniques into clinical practice and offer support for future research to validate and expand on these predictive methodologies.

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non–small-cell lung cancer

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical aspects of clinical study design and biomarker discovery but also ensures that the ‘right patient’ receives the ‘right treatment’. However, survival prediction is a highly complex task, given the large number of ‘omics; and clinical features, as well as the high degree of freedom that drive patient survival. Prior knowledge could play a critical role in uncovering the complexity of a disease and understanding the driving factors affecting a patient’s survival. We introduce a methodology for incorporating prior knowledge into machine learning–based models for prediction of patient survival through Knowledge Graphs, demonstrating the advantage of such an approach for NSCLC patients. Using data from patients treated with immuno-oncologic therapies in the POPLAR (NCT01903993) and OAK (NCT02008227) clinical trials, we found that the use of knowledge graphs yielded significantly improved hazard ratios, including in the POPLAR cohort, for models based on biomarker tumor mutation burden compared with those based on knowledge graphs. Use of a model-defined mutational 10-gene signature led to significant overall survival differentiation for both trials. We provide parameterized code for incorporating knowledge graphs into survival analyses for use by the wider scientific community.

Advancing precision medicine in immunoglobulin light-chain amyloidosis: a novel prognostic model incorporating multi-organ indicators.

To develop a more accurate prognostic model that incorporates indicators of multi-organ involvement for immunoglobulin light-chain (AL) Amyloidosis patients. Biopsy-proven AL amyloidosis patients between January 1, 2012, and February 28, 2023, were enrolled and randomly divided into a training set and a test set at a ratio of 7:3. Prognostic indicators that comprehensively cover cardiac, renal, and hepatic involvement were identified in the training set by random survival forest (RSF). Then, RSF and Cox models were established. The Concordance index (C-index) and integrated brier scores (IBS) were applied to evaluate the models' performance in the test set. Besides, the net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated. A total of 173 eligible patients were included. After a median follow-up of 25.9 (9.2, 50.3) months, 48 (27.7%) patients died. Creatine kinase-MB, estimated glomerular filtration rate ≤ 50mL/min/1.73m2, interventricular septum ≥ 15mm, ejection fraction, alanine aminotransferase and Live involved were selected to develop prediction models. The RSF model based on the above indicators achieved C-index and IBS values of 0.834 (95% CI 0.725-0.915) and 0.151 (95% CI 0.1402-0.181), respectively. At last, the NRI and IDI of the RSF model were 0.301 (95% CI 0.048-0.546, P = 0.012) and 0.157 (95% CI 0.041-0.269, P < 0.001) at 5-year by comparing the RSF model with the Cox model which is based on the Mayo 2012 staging system. The RSF model that incorporates indicators of multi-organ involvement had a great performance, which may be helpful for physicians' decision-making and more accurate overall survival prediction.

Tab-Cox: An Interpretable Deep Survival Analysis Model for Patients With Nasopharyngeal Carcinoma Based on TabNet.

The nutritional status of cancer patients is closely associated with the clinical progression of the disease. A survival analysis model combined with a neural network can predict future disease trends in patients, facilitating early prevention and assisting physicians in making diagnoses. However, the complexity of neural networks and their incompatibility with medical tabular data can reduce the interpretability of the model. To address this issue, thr paper propose a novel survival analysis model called Tab-Cox, which combines TabNet and Cox models. This model is specifically designed to predict the survival outcomes of patients with nasopharyngeal carcinoma. The model utilizes TabNet's sequential attention mechanism to extract more interpretable features, providing an interpretable method for identifying disease risk factors. Consequently, the model ensures accurate survival prediction while also making the results more comprehensible for both patients and doctors. The paper tested the efficacy of the model by conducting experiments on various diverse datasets in comparison with other commonly used survival models. The results showed that the proposed model delivered the highest or second-highest accuracy across all datasets. Furthermore, the paper conducted a comparative interpretability analysis against the classical Cox model. In addition and compare the interpretability of the Tab-Cox model with the classical Cox model and discuss the advantages and disadvantages of its interpretability. This demonstrates that Tab-Cox can assist doctors in identifying risk factors that are challenging to capture using artificial methods.