Purpose: Colorectal cancer is one of the most common cancers and the leading cause of cancer death in Iran. This study aimed to develop and validate a random survival forest (RSF) to identify important risk factors on mortality in colorectal patients based on their demographic and clinical-related variables.
Methods: In this retrospective cohort study, the information of 317 patients with colorectal cancer who were referred to Imam Khomeini Clinic of Hamadan during the years of 2002 to 2017 were examined. Patient survival was calculated from the time of diagnosis to death. In the present study, the RSF model was used to identify factors affecting patient survival. Also, the results of the RSF model were compared with the Cox model. The data were analyzed using R software (version 3.6.1) and survival packages.
Results: One-, 2-, 3-, 4-, 5-, and 10-year survival rates of included patients were 81.4%, 63%, 57%, 52%, 45%, and 34%, respectively, and the median survival was obtained to be 53 months. The number of 150 patients was died at this time period. The four most important predictors of survival included metastasis to other organs, WBC count, disease stage, and number of lymphomas involved. RSF method predicted survival better than the conventional Cox proportional hazard model.
Conclusion: We found that metastasis to other organs, WBC count, disease stage, and number of lymphomas involved were the most four most important predictors of low survival for colorectal cancer patients.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s12029-020-00544-3 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
PD15, a steroidal saponin, induces apoptosis of HCT116 colorectal cancer cells via suppressing the Akt/GSK3β pathway.
J Pharm Pharmacol
January 2025
Department of Pharmacy, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
Objectives: PD15, a novel natural steroidal saponin extracted from the rhizomes of Paris delavayi Franchet, has demonstrated a strong cytotoxic effect against HepG2 and U87MG cells. However, its therapeutic effects on colorectal cancer (CRC) and the underlying molecular mechanisms remain unclear.
Methods: MTT assay, clonogenic assay, Hoechst 33258 staining, flow cytometry, molecular docking, and western blot were used to investigate the mechanism of PD15 in HCT116 cell lines.
Dosimetry Assessment in Predicting Treatment Outcomes Following Yttrium-90 Transarterial Radioembolization of Hepatic Tumors.
Cancer Biother Radiopharm
January 2025
Department of Interventional Radiology, University of Kansas Medical Center, Kansas City, Kansas, USA.
To evaluate the use of yttrium-90 (Y90) dosimetry in predicting treatment outcomes when used following transarterial radioembolization with SIR-Spheres® (Resin Y90) in patients with hepatic tumors. This single institution retrospective analysis included 100 patients with hepatocellular carcinoma, colorectal carcinoma or other liver metastases who underwent transarterial radioembolization with resin Y90 and had imaging follow-up within one year of treatment. Mean tumor dose and mean dose to nontumor was calculated using voxel-based dosimetry software.
View Article and Find Full Text PDFPatient-derived Organoids and Xenografts Uncover Therapeutic Vulnerabilities in Colorectal Signet Ring Cell Carcinomas.
Clin Cancer Res
January 2025
ACTREC, Tata Memorial Centre, Navi Mumbai, Maharashtra, India.
Purpose: Identifying therapeutic targets for Signet Ring Cell Carcinoma (SRCC) of the colon and rectum is a clinical challenge due to the lack of Patient-Derived Organoids (PDO) or Xenografts (PDX). We present a robust method to establish PDO and PDX models to answer address this unmet need. We demonstrate that these models identify novel therapeutic strategies targeting therapy resistance and peritoneal metastasis.
View Article and Find Full Text PDFRevolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.
PLoS One
January 2025
Department of Computer Science, Faculty of Computing, Federal University of Lafia, Lafia, Nasarawa State, Nigeria.
Article Synopsis
- The emergence of Next Generation Sequencing (NGS) technology has transformed clinical diagnostics, providing extensive microbiome data for personalized medicine.
- Despite its potential, microbiome data's complexity and variability pose challenges for traditional statistical and machine learning approaches, including deep learning.
- The paper presents a novel feature engineering technique that combines two data feature sets, significantly improving the Deep Neural Network's performance in colorectal cancer detection, raising the Area Under the Curve (AUC) from 0.800 to 0.923, thus enhancing microbiome data analysis and disease detection capabilities.
Colorectal cancer (CRC) is the second leading cause of cancer-related mortality globally. While immunotherapeutic approaches are effective in a subset of CRC patients, the majority of CRC cases receive limited benefits from immunotherapy. This study developed an immune subtype classification system based on diverse immune cells and pathways.
View Article and Find Full Text PDFWant 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!