scholarly journals Metabolite Analysis and Histology on the Exact Same Tissue: Comprehensive Metabolomic Profiling and Metabolic Classification of Prostate Cancer

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Tao Huan ◽  
Dean A. Troyer ◽  
Liang Li
Keyword(s):  
Prostate Cancer ◽  
Metabolite Analysis ◽  
Metabolomic Profiling

Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer

2021 ◽  
Author(s):  
Mamta Juneja ◽  
Sumindar Kaur Saini ◽  
Jatin Gupta ◽  
Poojita Garg ◽  
Niharika Thakur ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Preoperative Staging ◽  
Threshold Value ◽  
Logical Function ◽  
Adequate Treatment ◽  
Monotonic Functions ◽  
Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

Improving intermediate risk classification of prostate cancer with radiomics features

2021 ◽  
Vol 79 ◽  
pp. S1309
Author(s):  
R. Thenault ◽  
L. Beuzit ◽  
N. Rioux-Leclercq ◽  
Z-E. Khene ◽  
A. Gasmi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Classification ◽  
Intermediate Risk

Efficacy of estramustine phosphate according to risk classification of castration-resistant prostate cancer

2012 ◽  
Vol 29 (4) ◽  
pp. 2895-2900 ◽  
Author(s):  
Akinori Minato ◽  
Naohiro Fujimoto ◽  
Tatsuhiko Kubo ◽  
Shuji Harada ◽  
Soichiro Akasaka ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Classification ◽  
Estramustine Phosphate ◽  
Castration Resistant Prostate Cancer ◽  
Castration Resistant

scholarly journals Prospective serum metabolomic profiling of lethal prostate cancer

2019 ◽  
Vol 145 (12) ◽  
pp. 3231-3243 ◽  
Author(s):  
Jiaqi Huang ◽  
Alison M. Mondul ◽  
Stephanie J. Weinstein ◽  
Andriy Derkach ◽  
Steven C. Moore ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Metabolomic Profiling ◽  
Lethal Prostate Cancer

scholarly journals Interfocal heterogeneity challenges the clinical usefulness of molecular classification of primary prostate cancer

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kristina Totland Carm ◽  
Andreas M. Hoff ◽  
Anne Cathrine Bakken ◽  
Ulrika Axcrona ◽  
Karol Axcrona ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Molecular Classification ◽  
Primary Diagnosis ◽  
The Cancer Genome Atlas ◽  
Clinical Usefulness ◽  
Whole Exome ◽  
Primary Prostate Cancer ◽  
Cancer Genome Atlas ◽  
Prostate Cancers

Abstract Prostate cancer is a highly heterogeneous disease and typically multiple distinct cancer foci are present at primary diagnosis. Molecular classification of prostate cancer can potentially aid the precision of diagnosis and treatment. A promising genomic classifier was published by The Cancer Genome Atlas (TCGA), successfully classifying 74% of primary prostate cancers into seven groups based on one cancer sample per patient. Here, we explore the clinical usefulness of this classification by testing the classifier’s performance in a multifocal context. We analyzed 106 cancer samples from 85 distinct cancer foci within 39 patients. By somatic mutation data from whole-exome sequencing and targeted qualitative and quantitative gene expression assays, 31% of the patients were uniquely classified into one of the seven TCGA classes. Further, different samples from the same focus had conflicting classification in 12% of the foci. In conclusion, the level of both intra- and interfocal heterogeneity is extensive and must be taken into consideration in the development of clinically useful molecular classification of primary prostate cancer.

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