license: cc-by-4.0
Survival prediction using PORPOISE (TCGA KIRP)
This model predicts a patient's overall survival using an H&E-stained digital pathology image of KIRP. It was trained by Jakub Kaczmarzyk using PORPOISE. It is an attempt to reproduce the PORPOISE manuscript.
Original journal article: https://doi.org/10.1016/j.ccell.2022.07.004
If you find this model useful, please make sure you cite the original publication.
Inputs: Bag of patches with 128um edge length, embedded with CTransPath.
Output classes: logits of hazards at four timepoints
To calculate the arbitrary risk score given the model outputs logits, use the following:
hazards = torch.sigmoid(logits)
S = torch.cumprod(1 - hazards, dim=1)
risk = -torch.sum(S, dim=1)
Data
TCGA-KIRP was used to train the model. The whole slide images were tiled into 128x128um patches, and each patch was encoded using CTransPath (this produces 768-dimensional embeddings).
The training and validation splits were provided by the original PORPOISE code. Here, we report the model in fold 1, because it had the highest c-index of the folds.
Samples sizes:
- Train: 232 slides (208 patients)
- Validation: 52 slides (52 patients)
Reusing this model
To use this model on the command line, see WSInfer-MIL.
Alternatively, you may use PyTorch on ONNX to run the model. First, embed 128um x 128um patches using CTransPath. Then pass the bag of embeddings to the model.
import onnxruntime as ort
import numpy as np
embedding = np.ones((1_000, 768), dtype="float32")
ort_sess = ort.InferenceSession("model.onnx")
logits, attention = ort_sess.run(["logits", "attention"], {'input': embedding})
# To get the risk score, implement the following:
# hazards = sigmoid(logits)
# S = cumprod(1 - hazards, dim=1)
# risk = -sum(S, dim=1)
The median risk score was -2.84, and this value was used to split patients into low risk and high risk.
Model performance
The model achieves a c-index of 0.81 in the validation set.
Intended uses
This model is ONLY intended for research purposes.
This model may not be used for clinical purposes. This model is distributed without warranties, either express or implied.