Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

BACKGROUND: Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT). PURPOSE: This study tested an unsupervised generative adversarial network to mitigate ICD artifacts in balanced steady-state free precession (bSSFP) cine MRIs and improve image quality and tracking performance for MRgRT. METHODS: Fourteen healthy volunteers (Group A) were scanned on a 0.35 T MRI-Linac with and without an MR conditional ICD taped to their left pectoral to simulate an implanted ICD. bSSFP MRI data from 12 of the volunteers were used to train a CycleGAN model to reduce ICD artifacts. The data from the remaining two volunteers were used for testing. In addition, the dataset was reorganized three times using a Leave-One-Out scheme. Tracking metrics [Dice similarity coefficient (DSC), target registration error (TRE), and 95 percentile Hausdorff distance (95% HD)] were evaluated for whole-heart contours. Image quality metrics [normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), and multiscale structural similarity (MS-SSIM) scores] were evaluated. The technique was also tested qualitatively on three additional ICD datasets (Group B) including a patient with an implanted ICD. RESULTS: For the whole-heart contour with CycleGAN reconstruction: 1) Mean DSC rose from 0.910 to 0.935; 2) Mean TRE dropped from 4.488 to 2.877 mm; and 3) Mean 95% HD dropped from 10.236 to 7.700 mm. For the whole-body slice with CycleGAN reconstruction: 1) Mean nRMSE dropped from 0.644 to 0.420; 2) Mean MS-SSIM rose from 0.779 to 0.819; and 3) Mean PSNR rose from 18.744 to 22.368. The three Group B datasets evaluated qualitatively displayed a reduction in ICD artifacts in the heart. CONCLUSION: CycleGAN-generated reconstructions significantly improved both tracking and image quality metrics when used to mitigate artifacts from ICDs.

The First Reported Case of Treating the Ultra-Central Thorax With Cone Beam Computed Tomography-Guided Stereotactic Adaptive Radiotherapy (CT-STAR).

Stereotactic body radiotherapy (SBRT) to the central and ultra-central thorax is associated with infrequent but potentially serious adverse events. Adaptive SBRT, which provides more precise treatment planning and inter-fraction motion management, may allow the delivery of ablative doses to ultra-central tumors with effective local control and improved toxicity profiles. Herein, we describe the first reported case of cone beam computed tomography (CBCT)-guided stereotactic adaptive radiotherapy (CT-STAR) in the treatment of ultra-central non-small cell lung cancer (NSCLC) in a prospective clinical trial (NCT05785845). An 80-year-old man with radiographically diagnosed early-stage NSCLC presented for definitive management of an enlarging ultra-central lung nodule. He was prescribed 55 Gy in five fractions with CT-STAR. A simulation was performed using four-dimensional CT, and patients were planned for treatment at end-exhale breath-hold. Treatment plans were generated using a strict isotoxicity approach, which prioritized organ at risk (OAR) constraints over target coverage. During treatment, daily CBCTs were acquired and used to generate adapted contours and treatment plans based on the patient's anatomy-of-the-day, all while the patient was on the treatment table. The initial and adapted plans were compared using dose-volume histograms, and the superior plan was selected for treatment. The adapted plan was deemed superior and used for treatment in three out of five fractions. The adapted plan provided improved target coverage in two fractions and resolved an OAR hard constraint violation in one fraction. We report the successful treatment of a patient with ultra-central NSCLC utilizing CT-STAR. This case report builds on previously published in silico data to support the viability and dosimetric advantages of CT-STAR in the ablative treatment of this challenging tumor location. Further data are needed to confirm the toxicity and efficacy of this technique.

Inter-fractional portability of deep learning models for lung target tracking on cine imaging acquired in MRI-guided radiotherapy.

MRI-guided radiotherapy systems enable beam gating by tracking the target on planar, two-dimensional cine images acquired during treatment. This study aims to evaluate how deep-learning (DL) models for target tracking that are trained on data from one fraction can be translated to subsequent fractions. Cine images were acquired for six patients treated on an MRI-guided radiotherapy platform (MRIdian, Viewray Inc.) with an onboard 0.35 T MRI scanner. Three DL models (U-net, attention U-net and nested U-net) for target tracking were trained using two training strategies: (1) uniform training using data obtained only from the first fraction with testing performed on data from subsequent fractions and (2) adaptive training in which training was updated each fraction by adding 20 samples from the current fraction with testing performed on the remaining images from that fraction. Tracking performance was compared between algorithms, models and training strategies by evaluating the Dice similarity coefficient (DSC) and 95% Hausdorff Distance (HD95) between automatically generated and manually specified contours. The mean DSC for all six patients in comparing manual contours and contours generated by the onboard algorithm (OBT) were 0.68 ± 0.16. Compared to OBT, the DSC values improved 17.0 - 19.3% for the three DL models with uniform training, and 24.7 - 25.7% for the models based on adaptive training. The HD95 values improved 50.6 - 54.5% for the models based on adaptive training. DL-based techniques achieved better tracking performance than the onboard, registration-based tracking approach. DL-based tracking performance improved when implementing an adaptive strategy that augments training data fraction-by-fraction.

Stereotactic arrhythmia radioablation (STAR)-A systematic review and meta-analysis of prospective trials on behalf of the STOPSTORM consortium.

BACKGROUND: Stereotactic arrhythmia radioablation (STAR) is a noninvasive treatment of refractory ventricular tachycardia (VT). OBJECTIVES: This study aimed to systematically review prospective trials on STAR and pool harmonized outcome measures in a meta-analysis. METHODS: After registration in the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023439666), OVID Medline, OVID Embase, Web of Science Core Collection, the Cochrane Central Register of Controlled Trials, and Google Scholar search engine were searched on November 9, 2023, to identify reports describing results of prospective trials evaluating STAR for VT. Risk of bias was assessed using the Risk Of Bias In Non-randomized Studies of Interventions tool. Meta-analysis was performed using generalized linear mixed models. RESULTS: We identified 10 prospective trials in which 82 patients were treated with STAR between 2016 and 2022. The 90-day rate of treatment-related grade ≥3 adverse events was 0.10 (95% confidence interval [CI] 0.04-0.2). The proportions of patients achieving given VT burden reductions were 0.61 (95% CI 0.45-0.74) for ≥95%, 0.80 (95% CI 0.62-0.91) for ≥75%, and 0.9 (95% CI 0.77-0.96) for ≥50% in 63 evaluable patients. The 1-year overall survival rate was 0.73 (95% CI 0.61-0.83) in 81 patients, 1-year freedom from recurrence was 0.30 (95% CI 0.16-0.49) in 61 patients, and 1-year recurrence-free survival was 0.21 in 60 patients (95% CI 0.08-0.46). Limitations include methodological heterogeneity across studies and moderate to significant risk of bias. CONCLUSION: STAR is a promising treatment method, characterized by moderate toxicity. We observed 1-year mortality of ∼27% in this population of critically ill patients suffering from refractory VT. Most patients experience a significant reduction in VT burden; however, 1-year recurrence rates are high. STAR should still be considered an investigational approach and recommended to patients primarily within the context of prospective trials.

Dosimetric predictors of acute and late gastrointestinal toxicities in stereotactic online adaptive magnetic resonance-guided radiotherapy (SMART) for locally advanced pancreatic adenocarcinoma.

BACKGROUND AND PURPOSE: A retrospective evaluation of dosimetric predictors and leveraged dose-volume data for gastrointestinal (GI) toxicities for locally-advanced pancreatic cancer (LAPC) treated with daily stereotactic MRI-guided online-adaptive radiotherapy (SMART). MATERIALS AND METHODS: 147 patients with LAPC were treated with SMART at our institution between 2018 and 2021. Patients were evaluated using CTCAE V5.0 for RT-related acute (≤3 months) and late (>3 months) toxicities. Each organ at risk (OAR) was matched to a ≥ grade 2 (Gr2+) toxicity endpoint composite group. A least absolute shrinkage selector operator regression model was constructed by dose-volumes per OAR to account for OAR multicollinearity. A receiver operator curve (ROC) analysis was performed for the combined averages of significant toxicity groups to identify critical volumes per dose levels. RESULTS: 18 of 147 patients experienced Gr2+ GI toxicity. 17 Gr2+ duodenal toxicities were seen; the most significant predictor was a V33Gy odds ratio (OR) of 1.69 per cc (95 % CI 1.14-2.88). 17 Gr2+ small bowel (SB) toxicities were seen; the most significant predictor was a V33Gy OR of 1.60 per cc (95 % CI 1.01-2.53). The AUC was 0.72 for duodenum and SB. The optimal duodenal cut-point was 1.00 cc (true positive (TP): 17.8 %; true negative (TN); 94.9 %). The SB cut-point was 1.75 cc (TP: 16.7 %; TN: 94.3 %). No stomach or large bowel dose toxicity predictors were identified. CONCLUSIONS: For LAPC treated with SMART, the dose-volume threshold of V33Gy for duodenum and SB was associated with Gr2+ toxicities. These metrics can be utilized to guide future dose-volume constraints for patients undergoing upper abdominal SBRT.