A new editorial paper published in Oncoscience discusses the use of deep learning segmentation methods in 18F-fluciclovine PET/CT imaging for precision medicine in salvage prostate cancer care. Prostate cancer remains a challenge for some patients even after initial treatments like prostatectomy or radiation therapy, leading to the need for salvage radiation therapy. Traditional imaging modalities often fall short in accurately detecting and determining the volume of recurrent disease, making treatment planning difficult. The introduction of 18F-fluciclovine PET/CT has shown promise in improving salvage disease management. The editorial explores the feasibility of using deep learning models for lesion segmentation on 18F-fluciclovine PET/CT images, which could improve treatment quality compared to manual contouring. The potential of AI algorithms in medical image processing for automatic lesion segmentation is highlighted, with a focus on a study showcasing the feasibility of DL models in this context. This research direction represents an important advancement in precision medicine for salvage prostate cancer care.

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