We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Algorithm Predicts Prostate Cancer Recurrence in Patients Treated by Radiation Therapy

By MedImaging International staff writers
Posted on 02 Jan 2024
Print article
Image: The algorithm predicts prostate cancer recurrence 15 months faster in patients treated by radiation therapy (Photo courtesy of 123RF)
Image: The algorithm predicts prostate cancer recurrence 15 months faster in patients treated by radiation therapy (Photo courtesy of 123RF)

Radiation therapy is a prevalent treatment option for prostate cancer patients across various risk levels. Despite its effectiveness, 20% to 30% of patients will experience cancer recurrence within five years after treatment. Detecting this recurrence typically involves monitoring prostate-specific antigen (PSA) levels in the serum. However, this method can be slow, often delaying additional necessary treatments for those with returning tumors. Addressing this challenge, an international team of researchers has introduced a new patent-pending methodology and algorithm designed to predict the likelihood of prostate cancer recurrence post-radiation therapy.

Developed by specialists at Purdue University (West Lafayette, IN, USA), this innovative algorithm leverages data collected through routine patient monitoring. It is based on a patient-specific mechanistic model, which is continuously informed by periodic PSA measurements, a standard part of post-radiation monitoring for prostate cancer patients. The effectiveness of this approach was tested on historical data from 166 patients. For each patient, the algorithm's prediction of recurrence was compared with the actual occurrence as identified by traditional clinical practices. The findings were promising, indicating that the model-based predictors could flag potential relapses, on average, 14.8 months earlier than conventional methods. The researchers are now focusing on refining the model to benefit a broader range of patients.

“The PSA data is used in conjunction with the model to obtain patient-specific parameters that determine the PSA dynamics and serve as classifiers for recurrence,” said Hector Gomez, a professor at Purdue University. “In addition to recurrence identification, our model can be used for designing personalized PSA monitoring strategies. It can tell physicians the right time to investigate tumor recurrences and maximize the window of curability.”

“Our current model can be used only for patients who do not receive any treatment additional to radiation,” Gomez added. “Some patients receive radiation and hormone therapy simultaneously and cannot benefit from our method right now. We plan to extend the method to make it applicable also to patients who receive radiation and hormone therapy simultaneously.”

Related Links:
Purdue University

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Ultrasound System
P20 Elite
New
1.5T MRI Scanner
MAGNETOM Amira
Fetal Monitor
Avante Compact II

Print article
Radcal

Channels

Radiography

view channel
Image: 3D cinematic renderings of the control and diseased heart in anatomic orientation (Photo courtesy of ESRF)

Innovative X-Ray Technique Captures Human Heart with Unprecedented Detail

Cardiovascular disease remains the leading cause of death globally. In 2019, ischemic heart disease, which weakens the heart due to reduced blood supply, accounted for approximately 8.9 million or 16%... Read more

MRI

view channel
Image: SubtleSYNTH creates synthetic STIR images with zero acquisition time that are interchangeable with conventionally acquired STIR images (Photo courtesy of Subtle Medical)

AI-Powered Synthetic Imaging Software to Further Redefine Speed and Quality of Accelerated MRI

The development of innovative solutions is not only redefining the landscape of artificial intelligence (AI)-based diagnostic imaging but also simplifying the ever-increasing complexity of workflows faced... Read more

Ultrasound

view channel
Image: The new FDA-cleared AI-enabled applications have been integrated into the EPIQ CVx and Affiniti CVx ultrasound systems (Photo courtesy of Royal Philips)

Next-Gen AI-Enabled Cardiovascular Ultrasound Platform Speeds Up Analysis

Heart failure is a significant global health challenge, affecting approximately 64 million individuals worldwide. It is associated with high mortality rates and poor quality of life, placing a considerable... Read more

General/Advanced Imaging

view channel
Image: HeartFlow Plaque Analysis leverages cutting-edge AI for assessment of plaque quantity and composition (Photo courtesy of HeartFlow, Inc.)

Next Gen Interactive Plaque Analysis Platform Assesses Patient Risk in Suspected Coronary Artery Disease

A first-of-its-kind plaque analysis tool to be fully integrated with FFRCT (when FFRCT is performed) provides impactful insights that enhance clinical decision-making and enable personalized patient treatment... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more

Industry News

view channel
Image: The new collaborations aim to further advance AI foundation models for medical imaging (Photo courtesy of Microsoft)

Microsoft collaborates with Leading Academic Medical Systems to Advance AI in Medical Imaging

Medical imaging is a critical component of healthcare, with health systems spending roughly USD 65 billion annually on imaging alone, and about 80% of all hospital and health system visits involve at least... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.