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Prostate cancer is the second most common cancer in men, and nearly 300,000 men are diagnosed with it each year in the US. To develop a consistent method of estimating the size of prostate cancer that can help clinicians make more accurate treatment decisions, Mass General Brigham researchers. trained and validated an AI model based on MRI scans from over 700 prostate cancer patients. The model was able to identify and delineate the margins of 85% of the most radiologically aggressive prostate lesions. Tumors with a larger volume, as estimated by the AI ​​model, were associated with a higher risk of treatment failure and metastasis, independent of other factors that are normally used to estimate this risk. Additionally, for patients who received radiation therapy, tumor volume performed better than traditional risk stratification for predicting metastases. The researchers believe the tool could be used to help clinicians understand a tumor’s aggressiveness, inform more personalized treatment plans and guide radiation therapy. The study is published in the journal Radiology.

“Tumor volume determined by Al has the potential to advance precision medicine for prostate cancer patients by improving our ability to understand the aggressiveness of a patient’s cancer and thereby recommend the most optimal treatment,” said the first author. David D. Yang, MDof the Department of Radio-oncology at Brigham and Women’s Hospitala founding member of Mass General Brigham Health System.

MRI has improved clinicians’ ability to diagnose prostate cancer and is a routine part of diagnosis and treatment. While human doctors can estimate tumor size based on MRI images, these estimates are somewhat subjective and can vary from person to person.

To develop a more consistent method of estimating tumor size, the researchers trained an AI model based on MRI images of prostate cancer tumors from 732 patients undergoing treatment at a single center. They then investigated whether estimated AI pattern dimensions were associated with treatment success 5 to 10 years after diagnosis.

They showed that the AI ​​model was able to locate and measure approximately 85% of prostate tumors that had a PI-RADS (Prostate Imaging Reporting and Data System) score of 5 within the patient cohort. The score indicates a very high risk of clinically significant prostate cancer. Estimated model sizes also showed potential as a prognostic marker: larger tumors were associated with a higher risk of prostate cancer recurrence as measured by blood levels of prostate-specific antigen (PSA) or metastasis, both for patients who were treated surgically. or with radiotherapy.

“Measuring AI itself can tell us something additional about patient outcomes,” said the lead author Martin King, MD, PhDof the Department of Radiation Oncology at Brigham. “For patients, this can really tell them something about the chances of a cure and the likelihood that their cancer will come back or metastasize in the future.”

In addition to helping clinicians and patients understand the aggressiveness of their cancer, the AI ​​model could also help guide radiation oncologists by identifying the focal region of the tumor for more targeted treatment. It’s also a much faster test compared to currently used methods of predicting the aggressiveness of prostate cancer, which typically take two weeks or more to produce results. AI-powered testing could mean patients can start treatment sooner.

Cancer research is a cornerstone of Brigham General’s patient care. Research, along with the strength of the system’s strengths in innovation, education and community engagement, enable Mass General Brigham Cancer to provide integrated cancer care for all, putting health equity at the center of that support. The vision is to provide a comprehensive, integrated and research-based approach to cancer care, helping patients navigate their entire care journey, from prevention and early detection to treatment and survivorship.

Looking ahead, the researchers plan to test their model with a larger, multi-institutional data set.

“We want to validate our findings using other institutions and patient cohorts with different disease characteristics to ensure that this approach is generalizable to all patients,” Yang said.

Reference: Yang DD, Lee LK, Tsui JMG, et al. AI-derived tumor volume from multiparametric MRI and outcomes in localized prostate cancer. Radiology. 2024. two: 10.1148/radiol.240041

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