Scientists find better way to predict prostate cancer growth

In a new study, researchers have discovered a new way to predict the aggressiveness and future behavior of prostate cancers.

The new method uses images from computed tomography (CT) scans that are routinely collected from all patients.

The images are then analyzed by a computer to extract hundreds of features, termed ‘radiomic features,” which have the potential to uncover disease characteristics that fail to be seen by the naked eye.

According to the researchers, this technique could complement traditional assessment methods and may help clinicians to make more informed personalized treatment decisions for men with prostate cancer.

In the long run, it may reduce or even replace the need for traditional invasive biopsies.

The research was conducted by a team from Queen’s University Belfast and elsewhere.

Prostate cancer is one of the most common forms of cancer, but the behavior of individual cancer is extremely variable.

While some tumors metastasize rapidly, others can remain harmlessly localized in the prostate gland for years.

To predict the risk represented by a given tumor, Gleason scores are typically assigned based on how a sample of the tumor appears under the microscope compared with normal prostate tissue.

Patients are then classified as low, medium, or high risk depending on their Gleason score, level of prostate specific antigen (PSA) in the blood, and on size of the tumor and whether it has spread to other parts of the body.

In the study, the team used CT scans for 342 prostate-cancer patients acquired as a routine care prior to radiotherapy treatment.

Focusing on the prostate gland, the researchers then extracted and analyzed over 500 radiomic features from each image.

These features, along with the Gleason score and risk group classification for each patient, were used to “train” a computer to be able to discriminate between patients in low- and high-risk groups, and between those with low and high Gleason score.

CT-based classification models proved able to discriminate between patients from different Gleason score and risk groups.

The system was especially competent at distinguishing between patients in low- and high-risk groups, and between those with low and high Gleason score.

This is the first CT-based radiomics investigation for this treatment site and it is showing very promising results.

This new work suggests that doctors may be able to use a CT scan to assess how aggressive cancer will be.

The next step will be to compare this to other imaging techniques and to assess how well it performs when biopsies are informed by multiparametric MRI scans, which is now standard practice.

The lead author of the study is Dr. Suneil Jain, a principal investigator.

The study is published in the International Journal of Radiation Oncology, Biology, and Physics.

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