scholarly journals Development and validation of a machine learning-based predictive model to improve the prediction of inguinal status of anal cancer patients: A preliminary report

Oncotarget ◽  
2016 ◽  
Vol 8 (65) ◽  
pp. 108509-108521 ◽  
Author(s):  
Berardino De Bari ◽  
Mauro Vallati ◽  
Roberto Gatta ◽  
Laëtitia Lestrade ◽  
Stefania Manfrida ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Cancer Patients ◽  
Anal Cancer ◽  
Preliminary Report ◽  
Development And Validation

scholarly journals Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
José M. Lezcano-Valverde ◽  
Fernando Salazar ◽  
Leticia León ◽  
Esther Toledano ◽  
Juan A. Jover ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Predictive Model ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Development And Validation

scholarly journals Risk Assessment of Bone Metastasis for Cervical Cancer Patients by Multiple Models: A Large Population Based Real-World Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Yun Han ◽  
Bo Wang ◽  
Jinjin Zhang ◽  
Su Zhou ◽  
Jun Dai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Cervical Cancer ◽  
Bone Metastasis ◽  
Predictive Model ◽  
Cancer Patients ◽  
Roc Curve ◽  
Predictive Models ◽  
Population Based ◽  
Machine Learning Algorithms

Background: Population-based data on the risk assessment of newly diagnosed cervical cancer patients' bone metastasis (CCBM) are lacking. This study aimed to develop various predictive models to assess the risk of bone metastasis via machine learning algorithms.Materials and Methods: We retrospectively reviewed the CCBM patients from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute to risk factors of the presence of bone metastasis. Clinical usefulness was assessed by Akaike information criteria (AIC) and multiple machine learning algorithms based predictive models. Concordance index (C-index) and receiver operating characteristic (ROC) curve were used to define the predictive and discriminatory capacity of predictive models.Results: A total of 16 candidate variables were included to develop predictive models for bone metastasis by machine learning. The areas under the ROC curve (AUCs) of the random forest model (RF), generalized linear model (GL), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), artificial neutral network (ANN), decision tree (DT), and naive bayesian model (NBM) ranged from 0.85 to 0.93. The RF model with 10 variables was developed as the optimal predictive model. The weight of variables indicated the top seven factors were organ-site metastasis (liver, brain, and lung), TNM stage and age.Conclusions: Multiple machine learning based predictive models were developed to identify risk of bone metastasis in cervical cancer patients. By incorporating clinical characteristics and other candidate variables showed robust risk stratification for CCBM patients, and the RF predictive model performed best among these predictive models.

Anti b-Glucan Antibody in Cancer Patients (Preliminary Report)

2004 ◽  
Vol 6 (1) ◽  
pp. 8 ◽  
Author(s):  
Masuro Motoi ◽  
Ken-Ichi Ishibashi ◽  
Osamu Mizukami ◽  
Noriko N. Miura ◽  
Yoshiyuki Adachi ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Preliminary Report

scholarly journals UHPLC-ESI-MS/MS Quantification of Relevant Substrates and Metabolites of the Kynurenine Pathway Present in Serum and Peritoneal Fluid from Gastric Cancer Patients—Method Development and Validation

2021 ◽  
Vol 22 (13) ◽  
pp. 6972
Author(s):  
Ilona Sadok ◽  
Katarzyna Jędruchniewicz ◽  
Karol Rawicz-Pruszyński ◽  
Magdalena Staniszewska
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Peritoneal Fluid ◽  
Method Development ◽  
Solid Phase ◽  
Kynurenine Pathway ◽  
Serum Samples ◽  
Esi Ms ◽  
Gastric Cancer Patients ◽  
Development And Validation

Metabolites and enzymes involved in the kynurenine pathway (KP) are highly promising targets for cancer treatment, including gastrointestinal tract diseases. Thus, accurate quantification of these compounds in body fluids becomes increasingly important. The aim of this study was the development and validation of the UHPLC-ESI-MS/MS methods for targeted quantification of biologically important KP substrates (tryptophan and nicotinamide) and metabolites(kynurenines) in samples of serum and peritoneal fluid from gastric cancer patients. The serum samples were simply pretreated with trichloroacetic acid to precipitate proteins. The peritoneal fluid was purified by solid-phase extraction before analysis. Validation was carried out for both matrices independently. Analysis of the samples from gastric cancer patients showed different accumulations of tryptophan and its metabolites in different biofluids of the same patient. The protocols will be used for the evaluation of tryptophan and kynurenines in blood and peritoneal fluid to determine correlation with the clinicopathological status of gastric cancer or the disease’s prognosis.

Sign in / Sign up

Export Citation Format

Share Document