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Project: CT image based prediction model in head and neck cancer: contribution to MHub #1102

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kateakhmad opened this issue May 28, 2024 · 4 comments · Fixed by #1227
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@kateakhmad
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kateakhmad commented May 28, 2024

Draft Status

Draft - team will hold off on page creation

Category

DICOM

Presenter Location

Online

Key Investigators

  • Kate Akhmad (???,???)

Project Description

Head and neck cancers account for nearly 4% of all cancers in the United States. Although there have been improvements in treatment and understanding of the disease, survival hasn't significantly improved in the last decades for the head neck cancer population in general. The CNN uses tumor delineations from the pre-treatment CT heard and neck scans to predict distant metastasis, loco-regional failure, and overall survival.

The model is peer-reviewed and was open-sourced published . While the model is open-sourced, it still requires some additional settings for its implementation. To make it easily available, it's interesting to add this model to the standardized I\O framework as MHub platform.

Model characteristics:

  • Model input: DICOM files of CT head and neck
  • Preprocessing steps: slice selection and cropping around the tumour region, transformation in png format.
  • Model output: prediction of loco-regional failure, overall survival and distant metastasis.
  • Metrics Table 3:
    The performance of our network varied for different outcomes:
    -- the 2-year distant metastasis prediction had the highest AUC, around 0.90, across the training, validation, and testing sets;
    -- 4-year overall survival AUC 0.78;
    -- 2-year loco-regional failure prediction AUC.

Objective

Objective A. To make the model easy available through a standardized I/O framework in MHub.
Objective B. To estimate reproducibility and quality of the model on external datasets (if it applicable).
Objective C. To be acquainted with the contribution pipeline in MHub.

Approach and Plan

  1. To go through the contribution pipeline of MHub platform
  2. To make it available through a standardized I/O framework.
  3. To test the excitability of the model in the framework

Progress and Next Steps

  1. Describe specific steps you have actually done.

Illustrations

No response

Background and References

@sjh26
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sjh26 commented Jun 17, 2024

Hi @kateakhmad , this is currently marked as a draft. If it is ready to go please let us know in a comment and we will get the page created

@sjh26
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sjh26 commented Jun 23, 2024

@kateakhmad ping

@kateakhmad
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@sjh26 hi! Thank you, you can publish it

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github-actions bot commented Jun 26, 2024

Project Page Pull Request Creation

COMPLETED: See #1227

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