A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 197

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1075
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3195
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 317
Function: require_once

Predicting Colon Cancer-Specific Survival for the Asian Population Using National Cancer Registry Data from Taiwan. | LitMetric

Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Purpose: Colon cancer is the third most incident and life-threatening cancer in Taiwan. A comprehensive survival prediction system would greatly benefit clinical practice in this area. This study was designed to develop an accurate prognostic model for colon cancer patients by using clinicopathological variables obtained from the Taiwan Cancer Registry database.

Methods: We analyzed 20,218 colon cancer patients from the Taiwan Cancer Registry database, who were diagnosed between 2007 and 2015, were followed up until December 31, 2017, and had undergone curative surgery. We proposed two prognostic models, with different combinations of predictors. The first model used only traditional clinical features. The second model included several colon cancer site-specific factors (circumferential resection margin, perineural invasion, obstruction, and perforation), in addition to the traditional features. Both prediction models were developed by using a Cox proportional hazards model. Furthermore, we investigated whether race is a significant predictor of survival in colon cancer patients by using Model 1 on the Surveillance, Epidemiology, and End Results (SEER) cancer registry dataset.

Results: The proposed models displayed a robust prediction performance (all Harrell's c-index >0.8). For both the calibration and validation steps, the differences between the predicted and observed mortality were mostly less than 5%.

Conclusions: The prediction model (Model 1) is an effective predictor of survival regardless of the ethnic background of patients and can potentially help to provide better prediction of colon cancer-specific survival outcomes, thus allowing physicians to improve treatment plans.

Download full-text PDF

Source
http://dx.doi.org/10.1245/s10434-021-10646-2DOI Listing

Publication Analysis

Top Keywords

colon cancer
20
cancer registry
16
cancer patients
12
cancer
10
colon cancer-specific
8
cancer-specific survival
8
taiwan cancer
8
predictor survival
8
model
7
colon
6

Similar Publications