MelRisk: Using the Neutrophil-to-Lymphocyte Ratio to Improve Risk Prediction Models for Metastatic Cutaneous Melanoma in the Sentinel Lymph Node

Author(s):  
Ryckie G Wade ◽  
Samuel Bailey ◽  
Alyss V Robinson ◽  
Michelle C I Lo ◽  
Howard Peach ◽  
...  
2014 ◽  
Vol 150 (4) ◽  
pp. 434 ◽  
Author(s):  
Kylie Vuong ◽  
Kevin McGeechan ◽  
Bruce K. Armstrong ◽  
Anne E. Cust

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1495
Author(s):  
Tú Nguyen-Dumont ◽  
James G. Dowty ◽  
Robert J. MacInnis ◽  
Jason A. Steen ◽  
Moeen Riaz ◽  
...  

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.


Author(s):  
Po-Hsiang Lin ◽  
Jer-Guang Hsieh ◽  
Hsien-Chung Yu ◽  
Jyh-Horng Jeng ◽  
Chiao-Lin Hsu ◽  
...  

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.


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