scholarly journals Australasian ACPGBI risk prediction model for 30‐day mortality after colorectal cancer surgery

BJS Open ◽  
2020 ◽  
Vol 4 (6) ◽  
pp. 1208-1216
Author(s):  
S. Wilkins ◽  
K. Oliva ◽  
E. Chowdhury ◽  
B. Ruggiero ◽  
A. Bennett ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Cancer Surgery ◽  
Risk Prediction Model ◽  
Colorectal Cancer Surgery

scholarly journals Prediction model and treatment of high-output ileostomy in colorectal cancer surgery

2017 ◽  
Vol 7 (3) ◽  
pp. 468-472 ◽  
Author(s):  
Shiki Fujino ◽  
Norikatsu Miyoshi ◽  
Masayuki Ohue ◽  
Yuske Takahashi ◽  
Masayoshi Yasui ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prediction Model ◽  
Cancer Surgery ◽  
Colorectal Cancer Surgery ◽  
High Output

scholarly journals Risk Prediction Model of Postoperative Venous Thrombosis of Ovarian Cancer

2021 ◽  
Author(s):  
Xue Wang ◽  
Xiao-hui Wang
Keyword(s):  
Ovarian Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Prediction Model ◽  
Risk Prediction ◽  
Roc Curve ◽  
Cancer Surgery ◽  
Risk Prediction Model ◽  
Prediction Ability ◽  
Figo Staging ◽  
D Dimer

Abstract Objective To investigate the influencing factors of venous thromboembolism (VTE) after ovarian cancer surgery, and construct its prediction model. Methods A total of 67 patients with ovarian cancer who developed VTE after surgery were selected from October 2008 to June 2020 in the Department of Obstetrics and Gynecology, First Hospital of Lanzhou University, and conducted a retrospective study with 100 patients without VTE after the operation who were confirmed by imaging during the same period. The clinical data of two groups of patients were analyzed and compared, and the risk prediction model was established. The ROC curve was drawn to evaluate the prediction effect of the model. Results Univariate analysis showed that there were statistically significant differences in age, menopausal status, hypertension, neoadjuvant chemotherapy, FIGO staging, lymph node metastasis, operation time, preoperative plasma FIB and D-dimer between the thrombosis group and the non-thrombosis group;The results of multivariate analysis showed that old age, neoadjuvant chemotherapy, late FIGO staging, high levels of plasma FIB and D-dimer before surgery are independent risk factors for VTE after ovarian cancer surgery. Construct a prediction model based on the results of multivariate regression analysis: Logit(P) = 0.053 × age + 0.917 × neoadjuvant chemotherapy + 0.956 × tumor FIGO staging + 0.398 × preoperative plasma FIB + 0.531 × preoperative D-dimer -7.679 ( Neoadjuvant chemotherapy, yes=1, no=0; tumor FIGO stage Ⅰ+Ⅱ=1, Ⅲ+Ⅳ=2; age, preoperative plasma FIB and D-dimer are actual values). The ROC curve analysis shows that the AUC value of the model is 0.773, the sensitivity is 74.6%, the specificity is 71.0%, and the total prediction accuracy rate is (78+39)/167=0.701. Conclusions Age, neoadjuvant chemotherapy, tumor FIGO staging, preoperative plasma FIB and D-dimer can be used as reliable indicators to predict the occurrence of postoperative VTE in patients with ovarian cancer. The constructed prediction model has good risk prediction ability, It has certain clinical application value.

scholarly journals The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program

2020 ◽  
Vol 4 (5) ◽  
Author(s):  
Sibel Saya ◽  
Jon D Emery ◽  
James G Dowty ◽  
Jennifer G McIntosh ◽  
Ingrid M Winship ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Family History ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Screening Colonoscopy ◽  
Crc Screening ◽  
Target Screening ◽  
The Impact ◽  
Risk Categories

Abstract Background In many countries, population colorectal cancer (CRC) screening is based on age and family history, though more precise risk prediction could better target screening. We examined the impact of a CRC risk prediction model (incorporating age, sex, lifestyle, genomic, and family history factors) to target screening under several feasible screening scenarios. Methods We estimated the model’s predicted CRC risk distribution in the Australian population. Predicted CRC risks were categorized into screening recommendations under 3 proposed scenarios to compare with current recommendations: 1) highly tailored, 2) 3 risk categories, and 3) 4 sex-specific risk categories. Under each scenario, for 35- to 74-year-olds, we calculated the number of CRC screens by immunochemical fecal occult blood testing (iFOBT) and colonoscopy and the proportion of predicted CRCs over 10 years in each screening group. Results Currently, 1.1% of 35- to 74-year-olds are recommended screening colonoscopy and 56.2% iFOBT, and 5.7% and 83.2% of CRCs over 10 years were predicted to occur in these groups, respectively. For the scenarios, 1) colonoscopy was recommended to 8.1% and iFOBT to 37.5%, with 36.1% and 50.1% of CRCs in each group; 2) colonoscopy was recommended to 2.4% and iFOBT to 56.0%, with 13.2% and 76.9% of cancers in each group; and 3) colonoscopy was recommended to 5.0% and iFOBT to 54.2%, with 24.5% and 66.5% of cancers in each group. Conclusions A highly tailored CRC screening scenario results in many fewer screens but more cancers in those unscreened. Category-based scenarios may provide a good balance between number of screens and cancers detected and are simpler to implement.

scholarly journals Risk Prediction Model for Colorectal Cancer: National Health Insurance Corporation Study, Korea

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88079 ◽  
Author(s):  
Aesun Shin ◽  
Jungnam Joo ◽  
Hye-Ryung Yang ◽  
Jeongin Bak ◽  
Yunjin Park ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Health Insurance ◽  
Prediction Model ◽  
Risk Prediction ◽  
National Health Insurance ◽  
National Health ◽  
Risk Prediction Model

scholarly journals Inclusion of a Genetic Risk Score into a Validated Risk Prediction Model for Colorectal Cancer in Japanese Men Improves Performance

2017 ◽  
Vol 10 (9) ◽  
pp. 535-541 ◽  
Author(s):  
Motoki Iwasaki ◽  
Sachiko Tanaka-Mizuno ◽  
Aya Kuchiba ◽  
Taiki Yamaji ◽  
Norie Sawada ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Risk Prediction Model ◽  
Japanese Men

A comparative study on feature selection for a risk prediction model for colorectal cancer

2019 ◽  
Vol 177 ◽  
pp. 219-229 ◽  
Author(s):  
Nahúm Cueto-López ◽  
Maria Teresa García-Ordás ◽  
Verónica Dávila-Batista ◽  
Víctor Moreno ◽  
Nuria Aragonés ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Feature Selection ◽  
Comparative Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Selection For
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