Stereotactic body radiation therapy (SBRT) following Yttrium-90 (90Y) selective internal radiation therapy (SIRT): a feasibility planning study using90Y delivered dose

The Impact of Local Control on Overall Survival after Y-90 Selective Internal Radiotherapy of Liver Metastases in Oligometastatic Cancer: A Retrospective Analysis

Y-90 Selective Internal Radiotherapy (SIRT) is an ablative therapy used for inoperable liver metastasis. The purpose of this investigation was to examine the impact of local control after SIRT on overall survival (OS) in oligometastatic patients. A retrospective, single-institution study identified oligometastatic patients with ≤5 non-intracranial metastases receiving unilateral or bilateral lobar Y-90 SIRT from 2009 to 2021. The primary endpoint was OS defined from Y-90 SIRT completion to the date of death or last follow-up. Local failure was classified as a progressive disease at the target lesion(s) by RECIST v1.1 criteria starting at 3 months after SIRT. With a median follow-up of 15.7 months, 33 patients were identified who had a total of 79 oligometastatic lesions treated with SIRT, with the majority histology of colorectal adenocarcinoma (n = 22). In total, 94% of patients completed the Y-90 lobectomy. Of the 79 individual lesions treated, 22 (27.8%) failed. Thirteen patients received salvage liver-directed therapy following intrahepatic failure; ten received repeat SIRT. Median OS (mOS) was 20.1 months, and 12-month OS was 68.2%. Intralesional failure was associated with worse 1 y OS (52.3% vs. 86.2%, p = 0.004). These results suggest that intralesional failure following Y-90 may be associated with inferior OS, emphasizing the importance of disease control in low-metastatic-burden patients.

Immunotherapy and Radiation Therapy Combinatorial Approaches in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), a prevalent and often fatal liver cancer, presents significant treatment challenges, especially in its advanced stages. This article delves into the promising approach of combining immunotherapy, particularly immune checkpoint inhibitors, with radiation therapy, a cornerstone of HCC management. Our review synthesizes current preclinical and clinical research, highlighting the potential synergistic effects of this combinational treatment. Emerging evidence suggests that this synergy enhances tumor control and improves patient survival rates. The combination leverages the localized, tumor-targeting ability of radiation therapy and the systemic, immune-boosting effects of immunotherapy, potentially overcoming the limitations inherent in each treatment modality when used separately. This integrative approach is especially promising in addressing the complex tumor microenvironment of HCC. However, the treatment landscape is nuanced, with challenges such as patient-specific response variability and potential resistance to therapies. Future research directions should focus on refining these combination strategies, tailoring them to individual patient profiles, and understanding the underlying mechanisms that govern the interaction between immunotherapy and radiation therapy. Such advancements could significantly improve HCC management, setting new standards for patient care and treatment efficacy.

90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework

PURPOSE

90Y SPECT-based dosimetry following radioembolization (RE) in liver malignancies is challenging due to the inherent scatter and the poor spatial resolution of bremsstrahlung SPECT. This study explores a deep-learning-based absorbed dose-rate estimation method for 90Y that mitigates the impact of poor SPECT image quality on dosimetry and the accuracy-efficiency trade-off of Monte Carlo (MC)-based scatter estimation and voxel dosimetry methods.

METHODS

Our unified framework consists of three stages: convolutional neural network (CNN)-based bremsstrahlung scatter estimation, SPECT reconstruction with scatter correction (SC) and absorbed dose-rate map generation with a residual learning network (DblurDoseNet). The input to the framework is the measured SPECT projections and CT, and the output is the absorbed dose-rate map. For training and testing under realistic conditions, we generated a series of virtual patient phantom activity/density maps from post-therapy images of patients treated with 90Y-RE at our clinic. To train the scatter estimation network, we use the scatter projections for phantoms generated from MC simulation as the ground truth (GT). To train the dosimetry network, we use MC dose-rate maps generated directly from the activity/density maps of phantoms as the GT (Phantom + MC Dose). We compared performance of our framework (SPECT w/CNN SC + DblurDoseNet) and MC dosimetry (SPECT w/CNN SC + MC Dose) using normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) relative to GT.

RESULTS

When testing on virtual patient phantoms, our CNN predicted scatter projections had NRMSE of 4.0% ± 0.7% on average. For the SPECT reconstruction with CNN SC, we observed a significant improvement on NRMSE (9.2% ± 1.7%), compared to reconstructions with no SC (149.5% ± 31.2%). In terms of virtual patient dose-rate estimation, SPECT w/CNN SC + DblurDoseNet had a NMAE of 8.6% ± 5.7% and 5.4% ± 4.8% in lesions and healthy livers, respectively; compared to 24.0% ± 6.1% and 17.7% ± 2.1% for SPECT w/CNN SC + MC Dose. In patient dose-rate maps, though no GT was available, we observed sharper lesion boundaries and increased lesion-to-background ratios with our framework. For a typical patient data set, the trained networks took ~ 1 s to generate the scatter estimate and ~ 20 s to generate the dose-rate map (matrix size: 512 × 512 × 194) on a single GPU (NVIDIA V100).

CONCLUSION

Our deep learning framework, trained using true activity/density maps, has the potential to outperform non-learning voxel dosimetry methods such as MC that are dependent on SPECT image quality. Across comprehensive testing and evaluations on multiple targeted lesions and healthy livers in virtual patients, our proposed deep learning framework demonstrated higher (66% on average in terms of NMAE) estimation accuracy than the current "gold-standard" MC method. The enhanced computing speed with our framework without sacrificing accuracy is highly relevant for clinical dosimetry following 90Y-RE.