Medical Imaging Through AI Models May Transform Cancer Care

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Through intense research, a new revolution is expected in how clinicians monitor and detect advanced prostate cancer. This breakthrough is possible by combining artificial intelligence with state-of-the-art MRI imaging. A new software incorporating Multiple AI models developed through research can automate the complex tasks involved in the whole body diffusion weighted MRI. This powerful imaging technique is useful in assessing cancer spread from its initial stage.

This new technique is equipped to support personalized care. It may also help in keeping the secondary tumor patients in better health for longer. This new research was made possible by The Institute of Cancer Research, London.

  • What are the Challenges of Whole Body MRI in Cancer Care?

A non-invasive, radiation-free technique, WB-DWI, may offer great insights into cancer spread. The cell density of cancer can be deciphered through this technique. If the values are low to intermediate, this indicates that cancer cells are more; hence the total amount of disease in the skeleton can be calculated. This imaging modality is crucial for advanced prostate cancer patients, as secondary cancer can be detected in the bone, and this may also help in evaluating treatment response.

When compared with WB-DWI, the conventional MRI techniques are less sensitive in terms of distinguishing between healthy and cancerous tissues. The conventional techniques cannot detect and track bone metastases, and these are insufficient in determining whether the treatment is working. On the other hand, expert radiologists are required while quantifying cancer responses from WB-DWI scans, as these experts can manually delineate anatomical structures and sites of disease. This may consume some time, but it is not feasible in routine practice. 

  • Benefits of Combining AI Models

To handle different tasks and to streamline the imaging process, researchers developed software that brings multiple AI models together. In less than 25 seconds, the model identifies and outlines the patient's skeleton. The other model standardizes the imaging process for comparison among patients and across scans. The third model can search for the probability of secondary cancer in the bone.

  • How AI May Improve Anatomical Mapping

In the beginning, anatomical segmentation was automated through a machine learning approach where researchers could employ more than 500 WB-DWI scans for training. There was no manual delineation from experts.

The key anatomical regions were easily identified. The spinal canal, the skeleton and internal organs, all areas highly susceptible to cancer spread, were identified. The accuracy isn’t affected while cutting, the processing time.

  • Lesion Detection Will Improve

The main focus of the research was on disease burden assessment, which could be a critical metric for guiding therapy. Potential metastatic lesions throughout the skeleton are detected through automated AI software, which can calculate total volume, along with providing the lesion count. The new AI software is different and more advanced than traditional radiologists, who estimate metastatic load through visual inspection of WB-DWI images. This process is prone to variability.

All these advanced technologies are beneficial for patients with advanced prostate cancer, which means better monitoring and improved outcomes.

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