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MSINet: Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

method_outline

This repository contains the code for predicting microsatellite status in colorectal cancer from H&E-stained FFPE histopathology slides.

Software Requirements

This code was developed and tested in the following settings.

OS

  • Ubuntu 18.04

GPU

  • Nvidia GeForce RTX 2080 Ti

Dependencies

  • python (3.6.10)
  • numpy: (1.18.1)
  • pandas (0.25.3)
  • pillow (7.0.0)
  • matplotlib (3.1.0)
  • scikit-learn (0.21.3)
  • scikit-image (0.15.0)
  • opencv-python (4.1.2.30)
  • openslide-python (1.1.1)
  • staintools (2.1.2)
  • h5py (2.9.0)
  • pytables (3.5.1)
  • pytorch (1.4.0)
  • torchvision (0.5.0)
  • fastai (1.0.55)

Installation

  • Install Miniconda on your machine (download the distribution that comes with python3).

  • After setting up Miniconda, install OpenSlide (3.4.1):

apt-get install openslide-tools
  • Create a conda environment with environment.yml:
conda env create -f environment.yml
  • Activate the environment:
conda activate msinet

Demo

data collection

gdc-client download -m gdc_manifest_tcga_coadread.txt
  • Download NCT-CRC-HE-100K and CRC-VAL-HE-7K datasets (generated by Kather et al.) from here to train and test tissue type classifier.

modify NCT-CRC-HE-100K and CRC-VAL-HE-7K datasets and train tissue type classfier

python tissue_type_classifier.py

prepare dataset for tissue type inference

python svs2tile.py  
python svs_tile2hdf.py

apply tissue type classifier to generate tissue maps

python tissue_type_inference.py

prepare datasets (Stanford-CRC and TCGA-CRC) for MSI prediction

python tmap2tile.py  
python tmap_tile2hdf.py

train MSI predictor on Stanford-CRC

python msi_predictor.py

evaluate MSI predictor on TCGA-CRC

python msi_inference.py

Note: please edit paths in each .py file.

Citation

Lancet Oncology 2021;22(1):132–41

@ARTICLE{Yamashita2021deep,
  title = "Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study",
  author = "Yamashita, Rikiya and Long, Jin and Longacre, Teri and Peng, Lan and Berry, Gerald and Martin, Brock and Higgins, John and Rubin, Daniel L and Shen, Jeanne",
  journal  = "Lancet Oncol.",
  volume   =  22,
  number   =  1,
  pages    = "132--141",
  month    =  jan,
  year     =  2021,
  language = "en"
  }