The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.

Documentation is available over at Read the Docs.

Clone the repo

git clone https://github.com/DOE-NCI-MOSSAIC/FrESCO.git

Load the working branch and pull in the subrepos

cd fresco
git checkout main

Setup the conda environment (the default name for the environment is "ms39", this can be edited in the ms39.yml file)

conda env create -n fresco python=3.9
conda activate fresco

Install the FrESCO library and dependencies

pip install fresco .

For further PyTorch instructions or more details for pytorch, head over to the PyTorch docs.

We have supplied example notebooks for each of the sample datasets contained in this repository showing model training on each dataset. We have also supplied model_args files for each of the datasets contained within the repo to speedup the time to ge up and running with the codebase.

We have supplied three different datasets as examples, each must be unzipped before any model training via the tar -xf dataset.tar.gz command from the data directory. The three datasets are:

• imdb: binary sentiment classification with the imdb dataset,
• P3B3: benchmark multi-task classification task, and
• clc: case-level context multi-task classfication data.

We have prepared model_args files for each dataset within the configs/ directory. To run with your own data, the following instructions explain the requirements for the training data.

Add the path to the desired dataset in the the data_path argument in the configs/model_args.yml file. The required data files are: - data_fold0.csv: Pandas dataframe with columns: - X: list of input values, of int type - task_n: output for task n, a string type (these are the y-values) - split: one of train, test, or val - id2labels_fold0.json: index to label dictionary mapping for each of the string representations of the outputs to an integer value, dict keys must match the y-values label - word_embeds_fold0.npy: word embedding matrix for the vocabulary, dimensions are words x embedding_dim. If no word embedding exists, the model will use randomly generated embeddings.

For the P3B3 and clc datasets we have provided the required files. To create these files for the imdb dataset, you may run the data_setup.py script within the scripts folder to create these necessary files for model training.

You will also need to set the tasks, these must correspond to the task columns names in the data_fold0.csv file and keys in the id2labels_fold0.json dictionary. For example, in the P3B3 data, the task columns are task_n, n = 1,2,3,4. Whereas the imdb data has the sentiment task. If using any sort of class weighting scheme, the keyword class_weights must be either a pickle file or dictionary with keys corresponding to the task and value with a corresponding list, or numpy array, of weights for that task.
If the class_weights keyword is blank, corresponding to None, no class weighting scheme will be used during training nor inference.

If working with hierarchical data, and the case-level context model is the desired output, then the dataframe in the corresponding data_fold0.csv must contain an additional integer-valued column group where the values describe the hierarchy present within the data. For example, all rows where group = 17 are associated for the purpose of training a case-level context model.

The model_args.yaml file controls the settings for model training. Edit this file as desired based on your requirements and desired outcome. The "save_name" entry controls the name used for model checkpoints and prediction outputs; if left empty, a datetimestamp will be used.

The following commands allow setting your GPUs, if enabled, before training your model.

nvidia-smi                             #check GPU utilization
export CUDA_VISIBLE_DEVICES=0,1        #replace 0,1 with the GPUs you want to use
echo $CUDA_VISIBLE_DEVICES             #check which GPUs you have chosen

To train the model for any information extraction task, multi-task calssification, simply run

python train_model.py -m ie -args ../configs/model_args.yml

from the scripts directory.

We have supplied test data for each of the model types provided. Information extraction models may be created with either P3B3 or imdb data.

If you're wanting a case-level context model, there is a two-step process. See notebooks/clc_example.ipynb for a fully worked example.

Step 1: Create an information extraction model specifying the data/clc data directory in the configs/model_args.yml. Then run

python train_model.py -m ie -args ../configs/clc_step1.yml

from the scripts/ directory.

Step 2: To train a clc model, set the model_path keyword arg to the path of the trained model trained from step 1 step in the configs/clc_step2.yml file. Then run

python train_model.py -m clc -args ../configs/clc_step2.yml

from the scripts/ directory to train a case-level context model.

Note that the case level context model requires a pre-trained information extraction model to be specified in the configs/clc_args.yml file. The default setting, if the -m argument is omitted, is information extraction, and which task is specified in the configs/model_args.yml file.

Both information extraction and case-level context models have the ability to incorporate abstention or Ntask. The deep abstaining classifier (DAC) from the CANDLE repository, allows the model to not make a prediction on a sample if the softmax score does not meet a predetermined threshold specified in the min_acc keyword of the model_args.yml file. It can be tuned to meet a threshold of accuracy, minimum number of samples abstained, or both through adapting the values of alpha through the training process. This adapation is automated in the code, only requires the user to specify the initial values, tuning mode, and scaling.

Ntask is useful for multi-task learning. It creates and additional 'task' that predicts if the softmax scores from all of the tasks do not meet a specified threshold within the model_args file . It has its own parameters that are tuned during the training process to obtain a minimum of abstained samples and maximum accuracy on the predicted samples. Ntask may not be enabled without abstention being enabled as well. The code will throw an exception and halt if such configuration is passed.

Training a model with the supplied default args, we see convergence within 50-60 epochs with 0.80-0.85 accuracy on the imdb data set and in excess of 0.90 accuracy across all tasks for the P3B3 dataset within 60 epochs or so. NOTE: P3B3 has a known issue training with mixed precision and with DAC and NTask enabled. Ensure these keywords are all False for all runs with the P3B3 dataset.

Get in touch if you would like to help in writing code, example notebooks, and documentation are essential aspects of the project. To contribute please fork the project, make your proposed changes and submit a pull request. We will do our best to sort out any issues and get your contributions merged into the main branch.

If you found a bug, have questions, or are just having trouble with the library, please open an issue in our issue tracker and we'll try to help resolve it.

If you use our software in your work please cite this repository. The bibtex entry is:

@misc{osti_1958817,
title = {FrESCO},
author = {Spannaus, Adam and Gounley, John and Hanson, Heidi and Chandra Shekar, Mayanka and Schaefferkoetter, Noah and Mohd-Yusof, Jamaludin and Fox, Zach and USDOE},
abstractNote = {The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.},
url = {https://www.osti.gov//servlets/purl/1958817},
doi = {10.11578/dc.20230227.2},
url = {https://www.osti.gov/biblio/1958817}, year = {2023},
month = {3},
note =
}