scholarly journals Risk Prediction of Second Primary Endometrial Cancer in Obese Women: A Hospital-Based Cancer Registry Study

2021 ◽  
Vol 18 (17) ◽  
pp. 8997
Author(s):  
Chi-Chang Chang ◽  
Chun-Chia Chen ◽  
Chalong Cheewakriangkrai ◽  
Ying Chen Chen ◽  
Shun-Fa Yang
Keyword(s):  
Endometrial Cancer ◽  
Risk Prediction ◽  
Cancer Registry ◽  
Prediction Models ◽  
Data Representation ◽  
Second Primary ◽  
Obese Women ◽  
Risk Prediction Models ◽  
Second Primary Cancers ◽  
Registry Database

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with endometrial cancer (EC). However, previous studies providing adequate evidence to support screening for SPCs in endometrial cancer are lacking. This study aimed to develop effective risk prediction models of second primary endometrial cancer (SPEC) in women with obesity (body mass index (BMI) > 25) and included datasets on the incidence of SPEC and the other risks of SPEC in 4480 primary cancer survivors from a hospital-based cancer registry database. We found that obesity plays a key role in SPEC. We used 10 independent variables as predicting variables, which correlated to obesity, and so should be monitored for the early detection of SPEC in endometrial cancer. Our proposed scheme is promising for SPEC prediction and demonstrates the important influence of obesity and clinical data representation in all cases following primary treatments. Our results suggest that obesity is still a crucial risk factor for SPEC in endometrial cancer.

scholarly journals Risk Prediction of Second Primary Endometrial Cancer in Obese Women: A Hospital-based Cancer Registry Study

2021 ◽  
Author(s):  
Chi-Chang Chang ◽  
Chun-Chia Chen ◽  
Chalong Cheewakriangkrai ◽  
Ying Chen Chen ◽  
Shun-Fa Yang
Keyword(s):  
Endometrial Cancer ◽  
Risk Prediction ◽  
Cancer Registry ◽  
Prediction Models ◽  
Second Primary ◽  
Obese Women ◽  
Risk Prediction Models ◽  
Second Primary Cancers ◽  
Data Representations ◽  
Registry Database

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with endometrial cancer (EC). However, there’s no previous literature mentioned about adequate evidence to support screening for SPCs in endometrial cancer. This study was aimed to develop effective risk prediction models of second primary endometrial cancer in women with obesity (Body-mass index; BMI > 25) and this study includes datasets of the incidence of SPCs and the other risks of SPCs in 4480 primary cancer survivors by a hospital-based cancer registry database. In our study, we found the obesity played a key role in SPCs. There’re 10 independent variables used as predicting variables, which corelated to obesity should be monitored for the early detection of SPCs in endometrial cancer. In conclusion, it is a promising SPCs prediction. The proposed scheme can support the important influence of obesity and clinical data representations in all cases after primary treatments. Our results suggested that obesity is still a crucial risk factor to SPCs in endometrial cancer.

Testing established endometrial cancer risk prediction models in women presenting with postmenopausal bleeding

2017 ◽  
Vol 145 ◽  
pp. 167-168
Author(s):  
B.J. Long ◽  
N. Wentzensen ◽  
A.D.M. Morillo ◽  
M.R. Hopkins ◽  
M.A. Lemens ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Postmenopausal Bleeding ◽  
Risk Prediction Models ◽  
Endometrial Cancer Risk

Risk prediction model for heart disease among endometrial cancer survivors.

2018 ◽  
Vol 36 (7_suppl) ◽  
pp. 120-120
Author(s):  
Mia Hashibe ◽  
Brenna Blackburn ◽  
Jihye Park ◽  
Kerry G. Rowe ◽  
John Snyder ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Prediction Model ◽  
Cancer Survivors ◽  
Risk Prediction ◽  
Prediction Models ◽  
Population Based ◽  
Risk Prediction Model ◽  
High Cholesterol ◽  
Risk Prediction Models ◽  
History Of

120 Background: There are an estimated 760,000 endometrial cancer survivors alive in the US today. We previously reported on increased heart disease (HD) risk among endometrial cancer survivors from our population-based cohort study. Although there are many risk prediction models for the risk of endometrial cancer, there are none to our knowledge for endometrial cancer survivors. Methods: We identified 2,994 endometrial cancer patients in the Utah Population Database, which links data from multiple statewide sources. We estimated hazard ratios with the Cox proportional hazards model for predictors of five-, ten- and fifteen-year risks. The Harrell’s C statistic was used to evaluate the model performance. We used 70% of the data randomly selected to develop the model and the rest of the data to validate the model. Results: A total of 1,591 patients were diagnosed with HD. Increased risks of HD among endometrial cancer patients were observed for older age, obesity at baseline, family history of HD, previous disease diagnosis (hypertension, diabetes, high cholesterol, COPD), distant stage, grade, histology, chemotherapy, and radiation therapy. The C-statistics for the risk prediction model were 0.69 for the hypothesized risk factors for HD, 0.56 for clinical factors, and 0.71 when statistically significant risk factors were included. With the final model selected, as one example, the absolute risks of HD were 17.6% at 5-years, 24.0% at 10-years and 32.0% at 15 years for a woman diagnosed with regional stage, grade I endometrial cancer in her fifties, was white, was obese at cancer diagnosis, had a family history of HD but no previous history of HD herself, had hypertension, but no history of diabetes or high cholesterol or COPD, and had radiation therapy treatment but no chemotherapy. The AUCs were 0.79 for the 5-year, 0.78 for the 10-year and 0.78 for the 15-year predictions. Conclusions: We developed the first risk prediction model for HD among endometrial cancer survivors within a population-based cohort study. Risk prediction models for cancer survivors are important in understanding long-term disease risks after cancer treatment is complete. Such models may contribute to management plans for treatment and individualized prevention efforts.

scholarly journals Rare Germline Pathogenic Variants Identified by Multigene Panel Testing and the Risk of Aggressive Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1495
Author(s):  
Tú Nguyen-Dumont ◽  
James G. Dowty ◽  
Robert J. MacInnis ◽  
Jason A. Steen ◽  
Moeen Riaz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Aggressive Prostate Cancer ◽  
Panel Testing ◽  
Pathogenic Variants ◽  
Increased Risk ◽  
Predisposition Genes ◽  
Rare Genetic Variants

While gene panel sequencing is becoming widely used for cancer risk prediction, its clinical utility with respect to predicting aggressive prostate cancer (PrCa) is limited by our current understanding of the genetic risk factors associated with predisposition to this potentially lethal disease phenotype. This study included 837 men diagnosed with aggressive PrCa and 7261 controls (unaffected men and men who did not meet criteria for aggressive PrCa). Rare germline pathogenic variants (including likely pathogenic variants) were identified by targeted sequencing of 26 known or putative cancer predisposition genes. We found that 85 (10%) men with aggressive PrCa and 265 (4%) controls carried a pathogenic variant (p < 0.0001). Aggressive PrCa odds ratios (ORs) were estimated using unconditional logistic regression. Increased risk of aggressive PrCa (OR (95% confidence interval)) was identified for pathogenic variants in BRCA2 (5.8 (2.7–12.4)), BRCA1 (5.5 (1.8–16.6)), and ATM (3.8 (1.6–9.1)). Our study provides further evidence that rare germline pathogenic variants in these genes are associated with increased risk of this aggressive, clinically relevant subset of PrCa. These rare genetic variants could be incorporated into risk prediction models to improve their precision to identify men at highest risk of aggressive prostate cancer and be used to identify men with newly diagnosed prostate cancer who require urgent treatment.

scholarly journals Risk Prediction of Barrett’s Esophagus in a Taiwanese Health Examination Center Based on Regression Models

2021 ◽  
Vol 18 (10) ◽  
pp. 5332
Author(s):  
Po-Hsiang Lin ◽  
Jer-Guang Hsieh ◽  
Hsien-Chung Yu ◽  
Jyh-Horng Jeng ◽  
Chiao-Lin Hsu ◽  
...  
Keyword(s):  
Barrett’S Esophagus ◽  
Risk Prediction ◽  
Barrett's Esophagus ◽  
Gastrointestinal Endoscopy ◽  
Prediction Models ◽  
Upper Gastrointestinal Endoscopy ◽  
Health Examination ◽  
Endoscopic Screening ◽  
Risk Prediction Models ◽  
Upper Gastrointestinal

Determining the target population for the screening of Barrett’s esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.

scholarly journals Evaluating risk prediction models for adults with heart failure: A systematic literature review

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0224135 ◽  
Author(s):  
Gian Luca Di Tanna ◽  
Heidi Wirtz ◽  
Karen L. Burrows ◽  
Gary Globe
Keyword(s):  
Heart Failure ◽  
Literature Review ◽  
Risk Prediction ◽  
Systematic Literature Review ◽  
Prediction Models ◽  
Risk Prediction Models
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