Background: Multiple perioperative inflammatory markers are considered important factors affecting the long-term survival of esophageal cancer (EC) patients. Hematological parameters, whether single or combined, have high predictive value.
Aim: To investigate the inflammatory status of patients with preoperative EC using blood inflammatory markers, and to establish and validate competing risk nomogram prediction models for overall survival (OS) and progression-free survival (PFS) in EC patients.
Methods: A total of 508 EC patients who received radical surgery (RS) treatment in The First Affiliated Hospital of Zhengzhou University from August 5, 2013, to May 1, 2019, were enrolled and randomly divided into a training cohort (356 cases) and a validation cohort (152 cases). We performed least absolute shrinkage and selection operator (LASSO)-univariate Cox- multivariate Cox regression analyses to establish nomogram models. The index of concordance (C-index), time-dependent receiver operating characteristic (ROC) curves, time-dependent area under curve (AUC) and calibration curves were used to evaluate the discrimination and calibration of the nomograms, and decision curve analysis (DCA) was used to evaluate the net benefit of the nomograms. The relative integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to evaluate the improvement in predictive accuracy of our new model compared with the AJCC staging system and another traditional model. Finally, the relationship between systemic inflammatory response markers and prognostic survival was explored according to risk plot, time-dependent AUC, Kaplan-Meier and restricted cubic spline (RCS).
Results: Based on the multivariate analysis for overall survival (OS) in the training cohort, nomograms with 10 variables, including the aggregate index of systemic inflammation (AISI) and lymphocyte-to-monocyte ratio (LMR), were established. Time-dependent ROC, time-dependent AUC, calibration curves, and DCA showed that the 1-, 3-, and 5 year OS and PFS probabilities predicted by the nomograms were consistent with the actual observations. The C-index, NRI, and IDI of the nomograms showed better performance than the AJCC staging system and another prediction model. Moreover, risk plot, time-dependent AUC, and Kaplan-Meier showed that higher AISI scores and lower LMR were associated with poorer prognosis, and there was a nonlinear relationship between them and survival risk.
Conclusion: AISI and LMR are easy to obtain, reproducible and minimally invasive prognostic tools that can be used as markers to guide the clinical treatment and prognosis of patients with EC.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881346 | PMC |
http://dx.doi.org/10.1186/s12935-023-02856-3 | DOI Listing |
J Cardiovasc Dev Dis
December 2024
Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.
: Fatal coronary heart disease (FCHD) affects ~650,000 people yearly in the US. Electrocardiographic artificial intelligence (ECG-AI) models can predict adverse coronary events, yet their application to FCHD is understudied. : The study aimed to develop ECG-AI models predicting FCHD risk from ECGs.
View Article and Find Full Text PDFBMC Cancer
December 2024
Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Background: Accurate prediction of pathological complete response (pCR) and disease-free survival (DFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (NCRT) is essential for formulating effective treatment plans. This study aimed to construct and validate the machine learning (ML) models to predict pCR and DFS using pathomics.
Method: A retrospective analysis was conducted on 294 patients who received NCRT from two independent institutions.
Int J Med Inform
December 2024
Department of Oncology Medicine, Fujian Medical University Union Hospital, Fuzhou, 350001, PR China. Electronic address:
Objective: Accurate predictive models for second primary non-small cell lung cancer (SP-NSCLC) are limited. This study aimed to develop and validate overall survival (OS) prediction models for SP-NSCLC patients using time-dependent interpretable survival machine learning algorithms.
Methods: This study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database, encompassing 8 and 12 registries, to extract data on patients aged 20-89 diagnosed with SP-NSCLC between 1988 and 2020.
Cancer Immunol Res
December 2024
Sun Yat-sen Memorial Hospital, Guangzhou, China.
CD8+ T-cell abundance is insufficient to assess antitumor immunity and shows poor performance in predicting breast cancer prognosis and immunotherapy response, presumably owing to the complexity of CD8+ T-cell functionalities. While single-cell RNA sequencing (scRNA-seq) can dissect the multifaceted functions of CD8+ T cells for better immune assessment, its clinical application is limited. Herein, we developed bulk RNA-seq-based FuncDimen models from integrative analysis of scRNA-seq and matched bulk RNA-seq data, to evaluate CD8+ T-cell functionalities across 5 dimensions: tumor reactivity, cytotoxicity, IFN-γ secretion, proliferation, and apoptosis.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
December 2024
Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, Sichuan, 610041, China.
Purpose: Extranodal natural killer/T-cell lymphoma (ENKTCL) is an hematologic malignancy with prognostic heterogeneity. We aimed to develop and validate DeepENKTCL, an interpretable deep learning prediction system for prognosis risk stratification in ENKTCL.
Methods: A total of 562 patients from four centers were divided into the training cohort, validation cohort and test cohort.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!