AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans

Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Patients Using a Longitudinally-Aware Segmentation Network

Purpose

Automatic quantification of longitudinal changes in PET scans for lymphoma patients has proven challenging, as residual disease in interim-therapy scans is often subtle and difficult to detect. Our goal was to develop a longitudinally-aware segmentation network (LAS-Net) that can quantify serial PET/CT images for pediatric Hodgkin lymphoma patients.

Materials and Methods

This retrospective study included baseline (PET1) and interim (PET2) PET/CT images from 297 patients enrolled in two Children's Oncology Group clinical trials (AHOD1331 and AHOD0831). LAS-Net incorporates longitudinal cross-attention, allowing relevant features from PET1 to inform the analysis of PET2. Model performance was evaluated using Dice coefficients for PET1 and detection F1 scores for PET2. Additionally, we extracted and compared quantitative PET metrics, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in PET1, as well as qPET and ΔSUVmax in PET2, against physician measurements. We quantified their agreement using Spearman's ρ correlations and employed bootstrap resampling for statistical analysis.

Results

LAS-Net detected residual lymphoma in PET2 with an F1 score of 0.606 (precision/recall: 0.615/0.600), outperforming all comparator methods (P<0.01). For baseline segmentation, LAS-Net achieved a mean Dice score of 0.772. In PET quantification, LAS-Net's measurements of qPET, ΔSUVmax, MTV and TLG were strongly correlated with physician measurements, with Spearman's ρ of 0.78, 0.80, 0.93 and 0.96, respectively. The performance remained high, with a slight decrease, in an external testing cohort.

Conclusion

LAS-Net achieved high performance in quantifying PET metrics across serial scans, highlighting the value of longitudinal awareness in evaluating multi-time-point imaging datasets.

Deep learning-based PET image denoising and reconstruction: a review

This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.

Radiation-targeted immunotherapy: A new perspective in cancer radiotherapy

In contemporary oncology, radiation therapy and immunotherapy stand as critical treatments, each with distinct mechanisms and outcomes. Radiation therapy, a key player in cancer management, targets cancer cells by damaging their DNA with ionizing radiation. Its effectiveness is heightened when used alongside other treatments like surgery and chemotherapy. Employing varied radiation types like X-rays, gamma rays, and proton beams, this approach aims to minimize damage to healthy tissue. However, it is not without risks, including potential damage to surrounding normal cells and side effects ranging from skin inflammation to serious long-term complications. Conversely, immunotherapy marks a revolutionary step in cancer treatment, leveraging the body's immune system to target and destroy cancer cells. It manipulates the immune system's specificity and memory, offering a versatile approach either alone or in combination with other treatments. Immunotherapy is known for its targeted action, long-lasting responses, and fewer side effects compared to traditional therapies. The interaction between radiation therapy and immunotherapy is intricate, with potential for both synergistic and antagonistic effects. Their combined use can be more effective than either treatment alone, but careful consideration of timing and sequence is essential. This review explores the impact of various radiation therapy regimens on immunotherapy, focusing on changes in the immune microenvironment, immune protein expression, and epigenetic factors, emphasizing the need for personalized treatment strategies and ongoing research to enhance the efficacy of these combined therapies in cancer care.