Big Data in Prostate Cancer

Big data as the name would refer to a subsequently large quantity of data which is being processed. With the advent of social media the data presently available is text, images, audio video. In order to process this data belonging to variety of format led to the concept of Big Data processing. To overcome these challenges of data, big data techniques evolved. Various tools are available for the big data naming MAP Reduce, etc. But to get the taste of Cloud based tool we would be working with the Microsoft Azure. Microsoft Azure is an integrated environment for the Big data analytics along with the SaaS Cloud platform. For the purpose of experiment, the Prostate cancer data is used to perform the predictive analysis for the Cancer growth in the gland. An experiment depending on the segmentation results of Prostate MRI scans is used for the predictive analytics using the SVM. Performance analysis with the ROC, Accuracy and Confusion matrix gives the resultant analysis with the visual artifacts. With the trained model, the proposed experiment can statistically predict the cancer growth.

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Big Data Predictive Analysis for Detection of Prostate Cancer on Cloud-Based Platform

Privacy and Security Policies in Big Data - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-2486-1.ch012 ◽

2017 ◽

pp. 259-278

Author(s):

Ritesh Anilkumar Gangwal ◽

Ratnadeep R. Deshmukh ◽

M. Emmanuel

Keyword(s):

Prostate Cancer ◽

Big Data ◽

Predictive Analytics ◽

Confusion Matrix ◽

Big Data Analytics ◽

Cancer Growth ◽

Predictive Analysis ◽

Mri Scans ◽

Microsoft Azure ◽

Visual Artifacts

Big data as the name would refer to a subsequently large quantity of data which is being processed. With the advent of social media the data presently available is text, images, audio video. In order to process this data belonging to variety of format led to the concept of Big Data processing. To overcome these challenges of data, big data techniques evolved. Various tools are available for the big data naming MAP Reduce, etc. But to get the taste of Cloud based tool we would be working with the Microsoft Azure. Microsoft Azure is an integrated environment for the Big data analytics along with the SaaS Cloud platform. For the purpose of experiment, the Prostate cancer data is used to perform the predictive analysis for the Cancer growth in the gland. An experiment depending on the segmentation results of Prostate MRI scans is used for the predictive analytics using the SVM. Performance analysis with the ROC, Accuracy and Confusion matrix gives the resultant analysis with the visual artifacts. With the trained model, the proposed experiment can statistically predict the cancer growth.

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PIONEER: European Commission Innovative Medicines Initiative funded BD4BO (Big data for better outcome) project on prostate cancer

European Urology Open Science ◽

10.1016/s2666-1683(20)33808-8 ◽

2020 ◽

Vol 19 ◽

pp. e1798-e1799

Author(s):

S. Evans Axelsson ◽

T. Abbott ◽

L. Fullwood ◽

M.I. Omar ◽

M. Cavelaars ◽

...

Keyword(s):

Prostate Cancer ◽

Big Data ◽

European Commission

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Radiomics in prostate cancer: basic concepts and current state-of-the-art

Chinese Journal of Academic Radiology ◽

10.1007/s42058-019-00020-3 ◽

2019 ◽

Vol 2 (3-4) ◽

pp. 47-55 ◽

Cited By ~ 3

Author(s):

Shan Yao ◽

Hanyu Jiang ◽

Bin Song

Keyword(s):

Prostate Cancer ◽

Big Data ◽

State Of The Art ◽

Common Type ◽

Major Contributor ◽

Future Directions ◽

Mortality And Morbidity ◽

Current State ◽

Basic Concepts ◽

Related Mortality

Abstract Prostate cancer (PCa) is the second most common type of cancer among males and the fifth major contributor to cancer-related mortality and morbidity worldwide. Radiomics, as a superior method of mining big data in medical imaging, has enormous potential to assess PCa from diagnosis to prognosis to treatment response, empowering clinical medical strategies accurately, reliably, and effectively. Hence, this article reviews the basic concepts of radiomics and its current state-of-the-art in PCa as well as put forwards the prospects of future directions.

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Author Correction: Introducing PIONEER: a project to harness big data in prostate cancer research

Nature Reviews Urology ◽

10.1038/s41585-020-0355-3 ◽

2020 ◽

Vol 17 (8) ◽

pp. 482-482

Author(s):

Muhammad Imran Omar ◽

◽

Monique J. Roobol ◽

Maria J. Ribal ◽

Thomas Abbott ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Research ◽

Big Data ◽

Prostate Cancer Research

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Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

Frontiers in Oncology ◽

10.3389/fonc.2021.763381 ◽

2021 ◽

Vol 11 ◽

Author(s):

Liwei Wei ◽

Yongdi Huang ◽

Zheng Chen ◽

Hongyu Lei ◽

Xiaoping Qin ◽

...

Keyword(s):

Prostate Cancer ◽

Big Data ◽

Lymph Node ◽

False Negative ◽

Specific Antigen ◽

Lymph Node Involvement ◽

Net Benefit ◽

Cutoff Value ◽

Model Based ◽

Node Involvement

BackgroundA more accurate preoperative prediction of lymph node involvement (LNI) in prostate cancer (PCa) would improve clinical treatment and follow-up strategies of this disease. We developed a predictive model based on machine learning (ML) combined with big data to achieve this.MethodsClinicopathological characteristics of 2,884 PCa patients who underwent extended pelvic lymph node dissection (ePLND) were collected from the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Eight variables were included to establish an ML model. Model performance was evaluated by the receiver operating characteristic (ROC) curves and calibration plots for predictive accuracy. Decision curve analysis (DCA) and cutoff values were obtained to estimate its clinical utility.ResultsThree hundred and forty-four (11.9%) patients were identified with LNI. The five most important factors were the Gleason score, T stage of disease, percentage of positive cores, tumor size, and prostate-specific antigen levels with 158, 137, 128, 113, and 88 points, respectively. The XGBoost (XGB) model showed the best predictive performance and had the highest net benefit when compared with the other algorithms, achieving an area under the curve of 0.883. With a 5%~20% cutoff value, the XGB model performed best in reducing omissions and avoiding overtreatment of patients when dealing with LNI. This model also had a lower false-negative rate and a higher percentage of ePLND was avoided. In addition, DCA showed it has the highest net benefit across the whole range of threshold probabilities.ConclusionsWe established an ML model based on big data for predicting LNI in PCa, and it could lead to a reduction of approximately 50% of ePLND cases. In addition, only ≤3% of patients were misdiagnosed with a cutoff value ranging from 5% to 20%. This promising study warrants further validation by using a larger prospective dataset.

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Research Protocol for an Observational Health Data Analysis to Assess the Long-term Outcomes of Prostate Cancer Patients Undergoing Non-Interventional Management (i.e., Watchful Waiting) and the Impact of Comorbidities and Life Expectancy – PIONEER IMI’s “Big Data for Better Outcomes” program

10.21203/rs.3.pex-1468/v1 ◽

2021 ◽

Author(s):

Giorgio Gandaglia ◽

Kees van Bochove ◽

Anders Bjartell ◽

Alberto Briganti ◽

Phil Conford ◽

...

Keyword(s):

Prostate Cancer ◽

Big Data ◽

Life Expectancy ◽

Watchful Waiting ◽

Health Data ◽

Long Term Outcomes ◽

Disease Characteristics ◽

The Impact

Abstract This is a study protocol for an observational health data analysis, submitted as a preprint to facilitate transparency and open science. Watchful waiting (WW) represents a deferred treatment option for prostate cancer (PCa) patients when curative treatment seems overtreatment right from the outset. Patients are ‘watched’ for the development of local or systemic progression with disease-related symptoms, at which stage they are then treated palliatively according to their symptoms, in order to maintain quality of life. When choosing WW, it is important to adequately assess life expectancy of patients. Although previous studies reported the outcomes of PCa patients managed with WW, which is the impact of individual patient characteristics and comorbidities on long-term outcomes is still largely unknown. The PIONEER, which is a novel project of the Innovative Medicine Initiative’s (IMI’s) “Big Data for Better Outcomes” program with the mission to transform PCa care with particular focus on improving cancer-related outcomes, health system efficiency and the quality of health and social care across Europe, aims at assessing which are the long-term outcomes of PCa patients undergoing WW overall and after stratification according to disease characteristics, comorbidities and life expectancy. Of note, this topic emerged as the second one with the highest agreement score among different stakeholders after an international consensus to identify and prioritize the most important questions in the field of PCa. This study aims to describe demographics, clinical characteristics and estimate outcomes of PCa patients under delayed treatment (WW) across a network of databases in the overall population and subgroups of patients identified by individual disease characteristics, demographics and comorbidities. The study will rely on large observational data, namely population-based registries, electronic health records and insurance claims data. The study will be an observational cohort study based on routinely collected health care data which has been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).

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