This project contains the source code relevant for the study titled "Growth dynamics of untreated meningiomas" published in Neuro-Oncology Advances.
Background: Knowledge about meningioma growth characteristics is needed for
developing biologically rational follow-up routines. In this study of
untreated meningiomas followed with repeated MRIs, we studied growth
dynamics and explored potential factors associated with tumor growth.
Methods: In a single-center cohort study, we included 235 adult patients
with a radiologically suspected intracranial meningioma and at least three
MRI scans during follow-up. Tumors were segmented using an automatic
algorithm from contrast enhanced T1-series, and if needed manually
corrected. Potential meningioma growth curves were statistically compared;
linear, exponential, linear radial, or Gompertzian. Factors associated with
growth were explored.
Results: In 235 patients, 1394 MRI scans were carried out in the median
five-year observational period. Of the models tested, a Gompertzian growth
curve best described growth dynamics of meningiomas on group level. 59 % of
the tumors grew, 27 % remained stable, and 14 % shrunk. Only 13 patients(5%)
underwent surgery during the observational period and were excluded after
surgery. Tumor size at time of diagnosis, multifocality, and length of
follow-up were associated with tumor growth, whereas age, sex, presence of
peritumoral edema or hyperintense T2-signal were not significant factors.
Conclusion: Untreated meningiomas follow a Gompertzian growth curve,
indicating that increasing and potentially doubling of subsequent follow-up
intervals between MRIs seems biologically reasonable, instead of fixed time
intervals. Tumor size at diagnosis is the strongest predictor of future
growth, indicating a potential for longer follow up intervals for smaller
tumors. Although most untreated meningiomas grow, few require surgery.
The initial statistical analysis was performed in Python 3.7.9 on macOS (12.6 Monterey) using the following libraries:
The growth analysis was performed using Stata/MP 17 using the menl library.
The source code in this project expects some structure on the data, and was tailored for this application and not meant to generalize to new datasets and applications.
└── tumor-growth/
├── src/
│ ├── stata/
| | └── curve_fitting.do
│ └── python/
| ├── main.py
| └── utils.py
└── data/
├── cohort_personal_info.csv
├── cohort_volumes_quality-filtered.csv
├── T2_and_peritumorial_oedema.csv
├── scanners_info.csv
└── volumes.csv
Note that the CSV files under data/ are not provided as this dataset is not made public.
- Setup Python virtual environment and activate it:
virtualenv -ppython3 venv --clear
source venv/bin/activate
- Install Python dependencies:
pip install -r requirements.txt
- Given that the data lies in the
data/directory, generate summary statistics by:
python src/python/main.py --remove-missing --export-csv
- Finally, perform growth curve modelling in Stata using the DO-file that lies here.
Note that the main.py script support various arguments. Run python src/python/main.py --help to which arguments are available.
To activate the virtual environment on Windows, instead of source venv/bin/activate run ./venv/Scripts/activate.
The code in this repository is released under MIT license.
If you found our research article or this repository relevant in your research, consider citing our paper:
@article{10.1093/noajnl/vdad157,
title = {{Growth dynamics of untreated meningiomas}},
author = {Strand, Per Sveino and Wågø, Kathrine Jørgensen and Pedersen, André and Reinertsen, Ingerid and Nälsund, Olivia and Jakola, Asgeir Store and Bouget, David and Hosainey, Sayied Abdol Mohieb and Sagberg, Lisa Millgård and Vanel, Johanna and Solheim, Ole},
journal = {Neuro-Oncology Advances},
pages = {vdad157},
year = {2023},
month = {12},
issn = {2632-2498},
doi = {10.1093/noajnl/vdad157},
url = {https://doi.org/10.1093/noajnl/vdad157},
}