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Copyright (c) 2019 OERCompBiomed

Precision medicine and quantitative imaging in prostate cancer - a multiscale approach

Description

Imagine that you are a group of established successful scientists that will team up to tackle an important biomedical and medical challenge. There is an open call for research proposals under a new umbrella program entitled “Artificial intelligence and computational (bio)medicine”, where your multidisciplinary group are aiming for a project on “Precision medicine and quantitative imaging in prostate cancer - a multiscale approach”.

The focus of the assignment is (i) description of relevant imaging technologies and modalities - at different scales, (ii) proposal of imaging-derived biomarkers for PCa, (iii) machine learning techniques for classification, treatment stratification and prediction, (iv) the novelty and impact of your approach, and (v) the evaluation of the ethics of your project - and not so much the scientific background of prostate cancer per se.

Radiomics image

Schema of a radiomic model for patients with PCa. Acquisition of pre-treatment PCa patient’s MR images; Regions of interest (i.e., subvolume $21 \times 21 \times 3$ voxels); Extraction of 41 radiomic features from ROIs; Feature significance analysis based on Spearman rank correlation and Kruskal-Wallis, and multivariate prediction of Gleason score groups using the random forest model. (Source: Chaddad A, Niazi T, Probst S, Bladou F, Anidjar M and Bahoric B (2018) Predicting Gleason Score of Prostate Cancer Patients Using Radiomic Analysis. Front. Oncol. 8:630. https://doi.org/10.3389/fonc.2018.00630 distributed under the terms of the Creative Commons Attribution License).

Organization of your report

Research plan

(3-5 pages incl. figures and bibliography)

  • A brief background to the field
  • Objectives and expected impact
  • Material and methods
  • Evaluation

Data management plan and ethical considerations

(1 1/2-2 1/2 pages incl. graphics / links)

  • Description of generated data and code
  • Sharing of data and code
  • Ethical consideration

Sources of information

Prostate cancer

Understanding Prostate Cancer - Coursera course (enroll for free, offered by Johns Hopkins University)
https://en.wikipedia.org/wiki/Prostate_cancer
https://www.cancer.org/cancer/prostate-cancer
https://prostatecancerfree.org/prostate-cancer-videos
http://pathology.jhu.edu/ProstateCancer
https://www.science.gov/topicpages/c/cancer+gleason+score
https://www.ncbi.nlm.nih.gov/pubmed/31092914

Imaging

https://prostatex.grand-challenge.org
https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges
https://promise12.grand-challenge.org
https://www.aapm.org/GrandChallenge/PROSTATEx-2
https://wiki.cancerimagingarchive.net/display/Public/Prostate+Fused-MRI-Pathology
https://onlinelibrary.wiley.com/doi/full/10.1002/jmri.25419
http://clincancerres.aacrjournals.org/content/22/11/2668.long
https://link.springer.com/article/10.1007%2Fs00330-019-06244-2
https://link.springer.com/article/10.1007%2Fs13246-019-00730-z
https://www.nature.com/articles/s41598-018-38381-x

Methods and code from the field of machine learning (incl radiomics, deep learning)

https://github.com/oercompbiomed/CBM101
https://github.com/eiriniar/gleason_CNN
https://github.com/mirzaevinom/prostate_segmentation
https://ai.googleblog.com/2018/11/improved-grading-of-prostate-cancer.html
https://github.com/SlicerProstate/SliceTracker
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703221
https://github.com/SlicerProstate/SliceTracker
https://github.com/anirudhtomer/prias
https://github.com/Wenyuan-Vincent-Li/Path_R_CNN
https://github.com/Radiomics/pyradiomics

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