Hybrid descriptive‐inferential method for key feature selection in prostate cancer radiomics

2021 ◽  
Author(s):  
Stefano Barone ◽  
Roberto Cannella ◽  
Albert Comelli ◽  
Arianna Pellegrino ◽  
Giuseppe Salvaggio ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Feature Selection ◽  
Inferential Method

Prostate Cancer Classification Based on Best First Search and Taguchi Feature Selection Method

2019 ◽  
pp. 325-336
Author(s):  
Md Akizur Rahman ◽  
Priyanka Singh ◽  
Ravie Chandren Muniyandi ◽  
Domingo Mery ◽  
Mukesh Prasad
Keyword(s):  
Prostate Cancer ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Cancer Classification ◽  
Best First Search ◽  
Prostate Cancer Classification

scholarly journals Predicting discontinuation of docetaxel treatment for metastatic castration-resistant prostate cancer (mCRPC) with random forest

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2673 ◽  
Author(s):  
Daniel Kristiyanto ◽  
Kevin E. Anderson ◽  
Ling-Hong Hung ◽  
Ka Yee Yeung
Keyword(s):  
Prostate Cancer ◽  
Feature Selection ◽  
Random Forest ◽  
Developed Countries ◽  
Hill Climbing ◽  
Support Vector ◽  
Castration Resistant Prostate Cancer ◽  
K Nearest Neighbor ◽  
Missing Data Imputation ◽  
Docetaxel Treatment

Prostate cancer is the most common cancer among men in developed countries. Androgen deprivation therapy (ADT) is the standard treatment for prostate cancer. However, approximately one third of all patients with metastatic disease treated with ADT develop resistance to ADT. This condition is called metastatic castrate-resistant prostate cancer (mCRPC). Patients who do not respond to hormone therapy are often treated with a chemotherapy drug called docetaxel. Sub-challenge 2 of the Prostate Cancer DREAM Challenge aims to improve the prediction of whether a patient with mCRPC would discontinue docetaxel treatment due to adverse effects. Specifically, a dataset containing three distinct clinical studies of patients with mCRPC treated with docetaxel was provided. We  applied the k-nearest neighbor method for missing data imputation, the hill climbing algorithm and random forest importance for feature selection, and the random forest algorithm for classification. We also empirically studied the performance of many classification algorithms, including support vector machines and neural networks. Additionally, we found using random forest importance for feature selection provided slightly better results than the more computationally expensive method of hill climbing.

scholarly journals A New Combinatorial Optimization Approach for Integrated Feature Selection Using Different Datasets: A Prostate Cancer Transcriptomic Study

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0127702 ◽  
Author(s):  
Nisha Puthiyedth ◽  
Carlos Riveros ◽  
Regina Berretta ◽  
Pablo Moscato
Keyword(s):  
Prostate Cancer ◽  
Feature Selection ◽  
Combinatorial Optimization ◽  
Optimization Approach

scholarly journals Long Non-Coding RNA Landscape in Prostate Cancer Molecular Subtypes: A Feature Selection Approach

2021 ◽  
Vol 22 (4) ◽  
pp. 2227
Author(s):  
Simona De Summa ◽  
Antonio Palazzo ◽  
Mariapia Caputo ◽  
Rosa Maria Iacobazzi ◽  
Brunella Pilato ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Feature Selection ◽  
Molecular Subtypes ◽  
Genomic Alteration ◽  
Low Grade ◽  
Non Coding Rna ◽  
Selection Approach ◽  
Tcga Dataset ◽  
Feature Selection Approach ◽  
Long Non Coding Rna

Prostate cancer is one of the most common malignancies in men. It is characterized by a high molecular genomic heterogeneity and, thus, molecular subtypes, that, to date, have not been used in clinical practice. In the present paper, we aimed to better stratify prostate cancer patients through the selection of robust long non-coding RNAs. To fulfill the purpose of the study, a bioinformatic approach focused on feature selection applied to a TCGA dataset was used. In such a way, LINC00668 and long non-coding(lnc)-SAYSD1-1, able to discriminate ERG/not-ERG subtypes, were demonstrated to be positive prognostic biomarkers in ERG-positive patients. Furthermore, we performed a comparison between mutated prostate cancer, identified as “classified”, and a group of patients with no peculiar genomic alteration, named “not-classified”. Moreover, LINC00920 lncRNA overexpression has been linked to a better outcome of the hormone regimen. Through the feature selection approach, it was found that the overexpression of lnc-ZMAT3-3 is related to low-grade patients, and three lncRNAs: lnc-SNX10-87, lnc-AP1S2-2, and ADPGK-AS1 showed, through a co-expression analysis, significant correlation values with potentially druggable pathways. In conclusion, the data mining of publicly available data and robust bioinformatic analyses are able to explore the unknown biology of malignancies.

scholarly journals Two-step feature selection for predicting survival time of patients with metastatic castrate resistant prostate cancer

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2678 ◽  
Author(s):  
Motoki Shiga
Keyword(s):  
Prostate Cancer ◽  
Feature Selection ◽  
Survival Time ◽  
Clinical Decision Making ◽  
Statistical Hypothesis ◽  
Global Risk ◽  
Clinical Variables ◽  
Castrate Resistant Prostate Cancer ◽  
Selection For ◽  
Castrate Resistant

Metastatic castrate resistant prostate cancer (mCRPC) is the major cause of death in prostate cancer patients. Even though some options for treatment of mCRPC have been developed, the most effective therapies remain unclear. Thus finding key patient clinical variables related with mCRPC is an important issue for understanding the disease progression mechanism of mCRPC and clinical decision making for these patients. The Prostate Cancer DREAM Challenge is a crowd-based competition to tackle this essential challenge using new large clinical datasets. This paper proposes an effective procedure for predicting global risks and survival times of these patients, aimed at sub-challenge 1a and 1b of the Prostate Cancer DREAM challenge. The procedure implements a two-step feature selection procedure, which first implements sparse feature selection for numerical clinical variables and statistical hypothesis testing of differences between survival curves caused by categorical clinical variables, and then implements a forward feature selection to narrow the list of informative features. Using Cox’s proportional hazards model with these selected features, this method predicted global risk and survival time of patients using a linear model whose input is a median time computed from the hazard model. The challenge results demonstrated that the proposed procedure outperforms the state of the art model by correctly selecting more informative features on both the global risk prediction and the survival time prediction.

scholarly journals Feature Selection using Random Forest Classifier for Predicting Prostate Cancer

2019 ◽  
Vol 546 ◽  
pp. 052031 ◽  
Author(s):  
Mia Huljanah ◽  
Zuherman Rustam ◽  
Suarsih Utama ◽  
Titin Siswantining
Keyword(s):  
Prostate Cancer ◽  
Feature Selection ◽  
Random Forest ◽  
Random Forest Classifier

Predictive Deconvolution and Hybrid Feature Selection for Computer-Aided Detection of Prostate Cancer

2010 ◽  
Vol 29 (2) ◽  
pp. 455-464 ◽  
Author(s):  
Simona Maggio ◽  
Alessandro Palladini ◽  
Luca De Marchi ◽  
Martino Alessandrini ◽  
Nicolò Speciale ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Feature Selection ◽  
Computer Aided Detection ◽  
Predictive Deconvolution ◽  
Computer Aided ◽  
Selection For
Sign in / Sign up

Export Citation Format

Share Document