DOI: 10.18419/darus-4651
title: Onco* version 0.1.0

description: &lt;h1&gt;Onco* version 0.1.0&lt;/h1&gt;

&lt;p&gt;Onco* is a module based umbrella software project for numerical simulations of patient-specific cancer diseases, see following figure. From given input states of medical images the disease is modelled and its evolution is simulated giving possible predictions. In this way, a digital cancer patient is created, which could be used as a basis for further research, as a decision-making tool for doctors in diagnosis and treatment and as an additional illustrative demonstrator for enabling patients understand their individual disease. All parts resolve to an open-access framework, that is ment to be an accelerator for the digital cancer patient. Each module can be installed and run independently. The current state of development comprises the following modules:
&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;a href="https://github.com/masud-src/OncoFEM"&gt;OncoFEM&lt;/a&gt;&lt;/li&gt;
 &lt;li&gt;&lt;a href="https://github.com/masud-src/OncoGEN"&gt;OncoGEN&lt;/a&gt;&lt;/li&gt;
 &lt;li&gt;&lt;a href="https://github.com/masud-src/OncoTUM"&gt;OncoTUM&lt;/a&gt;&lt;/li&gt; 
 &lt;li&gt;&lt;a href="https://github.com/masud-src/OncoSTR"&gt;OncoSTR&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;Content&lt;/h2&gt;

&lt;p&gt;The uploaded virtual box is a virtual machine of a linux mint 21.2 cinnamon, 64 bit system and 8 GB RAM. The machine contains the pre-installed version of OncoFEM version 1.0, that corresponds to its related publication. The virtual machine need to be imported in Oracle VM VirtualBox.&lt;/p&gt;

&lt;p&gt;Username: onco&lt;/p&gt;
&lt;p&gt;password: 0000&lt;/p&gt;

&lt;p&gt;The pre-installed version of onco* is implemented in a conda environment that is activated with the terminal command&lt;/p&gt;

&lt;pre&gt;$conda activate oncofem&lt;/pre&gt;

&lt;p&gt;The Software can be found in /home/ with the sub-folder for the tutorials, that can be run with&lt;/p&gt;

&lt;pre&gt;$python3 oncofem_tut_01_quick_start&lt;/pre&gt;

&lt;p&gt;Of course, the tutorials will only run, if the system meets the necessary requirements. The tutorials (1, 2, 3, 4, 5, 6) where performed on a local machine (intel cpu i7-9700k with 3.6 GHz, 128 GB RAM). The tumor segmentation (tutorial 7, 8) have been tested on a different machine with a gpu (Nvidia a40, 48 GB VRAM, 32 core AMD epyc type 7452). For testing, the discretisation in space is decreased, compared to the results published in the respective paper.&lt;/p&gt;

&lt;p&gt;This is the software state at version 0.1.0 and the actual development can be found in the respective github.&lt;/p&gt;

&lt;ul&gt;
 &lt;li&gt;&lt;a href="https://github.com/masud-src/OncoFEM"&gt;OncoFEM&lt;/a&gt;&lt;/li&gt;
 &lt;li&gt;&lt;a href="https://github.com/masud-src/OncoGEN"&gt;OncoGEN&lt;/a&gt;&lt;/li&gt;
 &lt;li&gt;&lt;a href="https://github.com/masud-src/OncoTUM"&gt;OncoTUM&lt;/a&gt;&lt;/li&gt; 
 &lt;li&gt;&lt;a href="https://github.com/masud-src/OncoSTR"&gt;OncoSTR&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
description: The software provides a tutorial to learn the basic functionalities. More information can be found in the respective paper.

For usage, the user should be familiar in operating with unix based systems. All packages in Onco* are python-based software. Therefore, scripts, e. g. from the tutorial, can be created and executed with the python command:

<pre>$python3 *</pre>

 In order to run the tutorial please open a terminal and run the following commands:

<pre>$conda activate oncofem
$cd Tutorial
$python3 tut_01_oncofem_quickstart.py
$python3 tut_02_oncofem_academic.py</pre>

 There is a 'run_all.sh' script to run all tutorials. For the generalisation tutorial please activate the oncogen environment

<pre>$conda activate oncogen
$cd Tutorial
$python3 tut_05_oncogen_separated_full.py</pre>

 Be aware, that due to the limited power in a virtual box, it is recommended to work on academic test examples, rather than full brain geometries. Due to that, the first quickstart example runs with a reduced discretisation in space