Health
Utilizing AI to visualize lesions in prostate cancer PET/CT imaging diagnosis
New editorial published Oncoscience (Volume 11) was published on May 20, 2024, titled “Deep Learning-Assisted Lesion Segmentation in PET/CT Imaging: A Feasibility Study for Salvage Radiotherapy in Prostate Cancer.”
In this new editorial, the researchers Richard LJ Chiu, Chi-Wei Chan, and Yang Xiaofeng Emory University researchers discuss prostate cancer, the most commonly diagnosed malignancy in men after skin cancer. Although there have been great advances in treatment outcomes over the past half century, progression and recurrence after initial treatments such as prostate removal and radiation therapy remain a challenge for some patients.
“In these situations, patients are often offered salvage radiation therapy as a treatment option. Planning salvage radiation therapy requires imaging to detect and localize recurrent disease.”
Traditional imaging modalities used after prostatectomy, such as CT, bone scan, MRI, and 18F-FDG PET, often fall short in accurately detecting and quantifying recurrent disease, which is crucial for salvage radiotherapy planning. However, the introduction of 18F-fluciclovine (anti-1-amino-3-18F-fluorocyclobutane-1-carboxylic acid) PET/CT has significantly advanced salvage disease management. Recent studies, including the Emory Molecular Prostate Imaging Radiotherapy Enhancement (EMPIRE-1), a phase 2/3 randomized controlled trial, have demonstrated that incorporating 18F-fluciclovine PET/CT into post-prostatectomy radiotherapy planning improves biochemical recurrence or recurrence-free survival.
One of the key steps in salvage radiotherapy planning is the delineation of lesions on 18F-fluciclovine PET/CT images, a task that is currently performed manually by physicians. This task is meticulous, labor-intensive, and prone to inter- and intra-observer variability. The use of artificial intelligence (AI) algorithms in medical image processing has recently surged, and automated segmentation of lesions using deep learning (DL)-based lesion delineation methods, as opposed to manual contouring, shows promising potential to improve the quality of treatment.
“In this editorial, we review the research study by Wang et al. and present the feasibility of DL models in lesion segmentation of 18F-fluciclovine PET/CT images.”
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Journal References:
Richard L.J. and others. (2024) Deep learning-assisted lesion segmentation in PET/CT imaging: a feasibility study for salvage radiotherapy in prostate cancer. Oncoscience. Oncoscience.
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Sources 2/ https://www.news-medical.net/news/20240628/AI-powered-lesion-delineation-in-prostate-cancer-PETCT-imaging.aspx The mention sources can contact us to remove/changing this article |
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