Background: We developed a prognostic model to evaluate the overall survival (OS) and progression-free survival (PFS) of patients with unresectable hepatocellular carcinoma (u-HCC) treated with Hepatic arterial infusion chemotherapy of infusion oxaliplatin, fluorouracil and leucovorin (FOLFOX-HAIC).

Methods: This model was based on a retrospective study of u-HCC patients treated with the FOLFOX-HAIC (oxaliplatin 130 mg/m, leucovorin 400 mg/m, fluorouracil bolus 400 mg/m on day 1, and fluorouracil infusion 2,400 mg/m for 23-46 h, once every 3-4 weeks). We divided the patients into a training cohort and a validation cohort, used LASSO regression construct prognostic models, predict patient's OS and PFS based on nomograms of models. Patients were divided into high-risk, medium-risk, and low-risk groups according to their respective model risk scores. Kaplan-Meier survival analysis was used to assess the survival time between the three patient cohorts.

Results: A total of 333 patients were enrolled in the study and divided into a training cohort and a verification cohort at a ratio of 7:3 (233 in the training cohort and 100 in the validation cohort). The prognostic model we established contained nine prognostic variables. The results of concordance index (C-index) of the OS and PFS prognostic model was 0.75 and 0.71, respectively, higher than that of the TNM staging (0.57 and 0.55, p < 0.001), time-dependent ROC (td-ROC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) also showed that the model was better than the TNM staging for u-HCC predicting OS and PFS. Subsequently, the model was used to develop a nomogram to predict the individualized prognosis of patients with u-HCC treated with the FOLFOX-HAIC, with a higher net benefit than the TMN staging. According to the risk score, patients were divided into a low-risk group (risk score ≤ 0.458), the medium-risk group (risk score: 0.458-0.799) and the high-risk group (risk score > 0.799). There were significant differences in the OS and PFS between the three groups.

Conclusions: The model developed by our team enables risk stratification and personalized prognosis assessment for u-HCC patients undergoing FOLFOX-HAIC treatment, exhibiting superior predictive accuracy and discriminative capability compared to TNM staging.

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12885-024-13390-4DOI Listing

Publication Analysis

Top Keywords

prognostic model
16
training cohort
12
survival progression-free
8
progression-free survival
8
unresectable hepatocellular
8
hepatocellular carcinoma
8
treated folfox-haic
8
u-hcc patients
8
400 mg/m
8
validation cohort
8

Similar Publications

Aim: Many combinations of inflammation-based markers have been reported their prognostic ability. The prognostic value of albumin-to-gama-glutamyltransferase ratio (AGR), an inflammation-related index, has been identified for several cancers. However, the predictive value of AGR for high-grade glioma patients remains unclear.

View Article and Find Full Text PDF

Non-small cell lung cancer (NSCLC) frequently metastasizes to the brain, significantly worsened prognoses. This study aimed to develop an interpretable model for predicting survival in NSCLC patients with brain metastases (BM) integrating radiomic features and RNA sequencing data. 292 samples are collected and analyzed utilizing T1/T2 MRIs.

View Article and Find Full Text PDF

An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer.

J Oral Microbiol

January 2025

Integrative Microecology Clinical Center, Shenzhen Clinical Research Center for Digestive Disease, Shenzhen Technology Research Center of Gut Microbiota Transplantation, The Clinical Innovation & Research Center, Shenzhen Key Laboratory of Viral Oncology, Department of Clinical Nutrition, Shenzhen Hospital, Southern Medical University, Shenzhen, China.

Background: This study aims to develop an oral microbiota-based model for gastric cancer (GC) risk stratification and prognosis prediction.

Methods: Oral microbial markers for GC prognosis and risk stratification were identified from 99 GC patients, and their predictive potential was validated on an external dataset of 111 GC patients. The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis.

View Article and Find Full Text PDF

Background: Although there is a known correlation between obesity and revision risk following total knee arthroplasty (TKA), there is an ongoing debate regarding the appropriateness of denying TKA solely based on the body mass index (BMI) of a patient. Our aim was to determine whether a patient's American Society of Anesthesiologists (ASA) class predicts their risks of early all-cause revision and revision for periprosthetic joint infection (PJI) following primary TKA, independent of their BMI.

Methods: Data from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) were obtained regarding all patients who underwent primary TKA for osteoarthritis in Australia from January 1, 2015, to December 31, 2022.

View Article and Find Full Text PDF

Objective: This study aims to evaluate the association between the white blood cell-to-platelet ratio (WPR) and 28-day all-cause mortality among patients experiencing cardiac arrest.

Methods: Utilizing data from 748 cardiac arrest patients in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 database, machine learning algorithms, including the Boruta feature selection method, random forest modeling, and SHAP value analysis, were applied to identify significant prognostic biomarkers.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!