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# 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.

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).

### 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

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

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