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Principal Investigator
tian lin
Harbin Medical University
Position Title
About this CDAS Project
NLST (Learn more about this study)
Project ID
Initial CDAS Request Approval
Nov 2, 2023
Deep Learning Model Predicting Patient Prognosis based on Lung Cancer Pathological and Radiological Images
The aim of this project is to develop a deep learning model that predicts patient prognosis based on lung cancer pathological and radiological images. By analyzing and integrating data from both the pathological and radiological domains, we seek to build an accurate predictive model for patient prognosis, providing healthcare professionals with comprehensive clinical decision-making support. Leveraging expertise in the fields of pathology and radiology, this project will utilize deep learning algorithms to extract relevant features from image data and establish a predictive model that is correlated with patient prognosis.

Collect and curate a dataset consisting of lung cancer pathological slides and radiological images. This dataset may include histopathological slides of lung cancer tissues, as well as relevant radiological imaging such as CT scans
- Develop and train a deep learning model capable of extracting relevant features from both pathological and radiological images. Using convolutional neural networks (CNN) and other deep learning techniques, the model will learn to classify and predict based on different image features.
- Validate and evaluate the performance of the constructed deep learning model. By utilizing a held-out test set, evaluate and analyze the accuracy and stability of the model's predictions.
- Compare the predictions of the deep learning model with traditional prognosis assessment methods. Evaluate the advantages and effectiveness of the deep learning model in predicting lung cancer patient prognosis by comparing its results with traditional clinical assessment methods.
- Ultimately, establish a predictive model that can be used in clinical practice, providing accurate prognosis predictions to healthcare professionals to assist in clinical decision-making and improve patient treatment and management strategies.

By achieving these objectives, this project aims to provide a novel, deep learning-based approach to prognosis assessment in lung cancer, offering powerful support for personalized treatment and medical decision-making.


Chen Ziqiang School of Basic Medical Fudan University