A Prostate cancer CDSS system with multi Model Approach with Image And Number data
The key innovation lies in the meta-classifier, a model designed to combine predictions from both the text-based and image-based models. This integration offers a more holistic approach to cancer classification, as it captures the complexity of both clinical features and medical imaging. By using multiple data sources, the system aims to provide more accurate and robust predictions compared to traditional methods.
Building on previous work with the PLCO (Prostate, Lung, Colorectal, and Ovarian) Cancer Screening Trial dataset, which focused on general numeric and textual data, this project takes it a step further by incorporating image data specifically for prostate cancer. This extension will enable the CDSS to assess prostate cancer more effectively by utilizing both structured clinical data and detailed imaging to improve accuracy in diagnosing and staging cancer.
Ultimately, this system will provide healthcare professionals with a more advanced tool for diagnosing prostate cancer, aiding in personalized treatment planning and potentially improving patient outcomes.
Specific Aims
Develop a Clinical Decision Support System (CDSS): Create an advanced system that integrates multiple data sources (numeric, text, and image data) to assist healthcare providers in predicting prostate cancer stage and type.
Leverage Independent Models for Classification: Implement two separate models: one for numeric and text-based clinical records and another for medical images (e.g., MRI scans) to capture different dimensions of cancer data.
Meta-Classifier for Enhanced Prediction: Develop a meta-classifier that synthesizes predictions from the numeric/text-based model and the image-based model, allowing for a comprehensive and integrated prediction of prostate cancer stage and type
Build on PLCO Data Experience: Expand on the prior success with the PLCO dataset by incorporating medical images into the CDSS, targeting more accurate classification of prostate cancer.
Enhance Diagnostic Accuracy and Personalization: Provide healthcare professionals with a more robust tool that improves the accuracy of prostate cancer diagnosis, enabling personalized treatment plans based on a combination of clinical and imaging data.
Focus on Prostate Cancer Classification: Specifically target the classification of prostate cancer types and stages, with the potential to extend this approach to other cancers in the future.
Create a Scalable Framework: Design the system to be scalable and adaptable, enabling future integration of additional data types or expansion to other disease domains.
Support Personalized Medicine: Enable the CDSS to assist in personalized treatment planning, ultimately improving patient outcomes by offering more detailed, data-driven insights.
u1911029@student.cuet.ac.bd
salman_karim_khan@yahoo.com