-
Notifications
You must be signed in to change notification settings - Fork 1
/
README.Rmd
64 lines (37 loc) · 2.98 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# AIPAL: Artificial Intelligence-based Prediction of Acute Leukemia
**A machine-learning model based on simple laboratory parameters for the prediction of acute leukaemia subtypes: a multicentre model development and validation study**
*Vincent Alcazer, Grégoire Le Meur, Marie Roccon, Sabrina Barriere, Baptiste Le Calvez, Bouchra Badaoui, Agathe Spaeth, Olivier Kosmider, Nicolas Freynet, Marion Eveillard5, Carolyne Croizier, Simon Chevalier, Pierre Sujobert*
**Methods**
We conducted a multicentre, model development, and validation study based on 19 routine laboratory parameters collected at disease onset in 1410 acute leukaemia patients from six independent French University Hospitals. Using training (n=679) and external validation (n=731) cohorts, several machine learning models were evaluated with a custom sensitivity analysis for variable selection. An additional prospective cohort of n=66 patients was also used for further validation.
Based on ten routine laboratory parameters, our final eXtreme Gradient Boosting (XGB)-model showed an AUC [95%CI] of 0.97 [0.95-0.99], 0.90 [0.83-0.97], and 0.89 [0.82-0.95] for APL, ALL, and AML diagnoses, respectively. Optimal cutoffs to guide clinical decisions were then set, leading to an accuracy of 99.7/99.5/98.8% for confident predictions and 96.1/87.9/86.3% for overall predictions of APL, ALL, and AML, respectively. These results were confirmed in the prospective cohort. The final model was integrated into a web-app with a user-friendly graphical interface, AI-PAL.
# Getting started
AI-PAL is a free and open-source software package built in R, with a user-friendly interface provided via Shiny, that enables clinical hematologists and biologists to diagnose the three main subtypes of acute leukemia based solely on routine biological parameters.
## Online version
AI-PAL has a ready-to-use online version available at [https://alcazerv.shinyapps.io/AIPAL/](https://alcazerv.shinyapps.io/AIPAL/).
## Local version
You can install a local version from [GitHub](https://github.com/VincentAlcazer/AIPAL) either by cloning the repository or directly by downloading the package in R:
You'll need to have R (>= 4.1.0) and the remotes package installed.
```{r Github install, eval = F }
install.packages("remotes")
remotes::install_github("VincentAlcazer/AIPAL")
AIPAL::run_app()
```
The AI-PAL Shiny app will open in your default web browser.
# Citing AIPAL
/!\ This work is currently not published and is available for personal use or review only. /!\
# Bug report
If you encounter any problem with the software or find a bug, please report it on GitHub:
- Create a [new issue](https://github.com/VincentAlcazer/AIPAL/issues) on the Github page
- Try to describe the problem/bug with reproductible steps