Sio, Terence T.

Abstract PURPOSE 18F-DOPA is sensitive/specific for identifying areas of biologically aggressive disease for glioblastoma. We report the impact of 18F-DOPA on radiation treatment planning from a phase II study. METHODS Gross target volume (GTV) was defined by the boolean of high-risk PET (tumor/normal brain SUV > 2.0) and MRI T1 contrast enhancement (MRI-CE) including surgical cavity. The clinical target volume (CTV) included the GTV with 1cm margin. Using proton beam therapy, GTV ≤ 65 cc was treated with 25 (CTV), 30 (MRI-CE), and 35 (PET) GyE over 5 fractions with a simultaneous integrated boost. GTV > 65 cc received 30 (CTV), 35 (MRI-CE), and 40 (PET) GyE over 10 fractions. Radiation field design was compared to traditional 1.5cm CTV margin on MRI-CE. RESULTS Between May 2019 and October 2021, 39 patients were treated. The median MRI-CE tumor diameter was 5.2cm. The median PET volume was 7.9cc (range 0–64.9cc). The median MRI-CE volume was 35.1cc (range 8.6–114cc). High-risk PET was contained within: MRI-CE 18/39 (46%), MRI-CE + T2W FLAIR 13/39 (33%), MRI-CE + T2W FLAIR + outside GTV 4/39 (10%), outside GTV 2/39 (5%) and no uptake 2/39 (5%). Utilizing this planning technique, there was a 26% volume reduction in CTV to traditional 1.5 cm margin on MRI-CE. PET imaging in treatment planning allowed inclusion of high-risk disease that would have been excluded on traditional CTV in 23% of cases (range 1.6-33.8cc). PET was more predictive [HR 1.03 (95% CI 1.01, 1.05) p = 0.002] than MRI [HR 1.01 (95% CI 1.00, 1.03) p = 0.02] for death with each cc increasing risk of death by 3.1%. CONCLUSIONS The incorporation of 18F-DOPA in treatment planning yields a more targeted approach with reductions in both normal brain treated and risk of geographic miss compared to traditional CTV margins.

AbstractBackgroundAccurate and efficient dose calculation is essential for on‐line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL‐based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities.PurposeTo develop a DL‐based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on‐line adaptive proton therapy clinical decision and subsequent replanning.MethodsPBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in‐house developed Monte‐Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs‐at‐risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D‐Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso‐dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency.ResultsCompared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s.ConclusionsAn accurate and efficient deep learning‐augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.

Abstract Coronavirus disease 2019 (COVID‐19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), can manifest itself in several ways, including coagulopathy and thrombosis. These complications can be the first and sometimes only manifestations of SARS‐CoV‐2 infection and can occur early or late in the course of the disease. However, these symptoms are more prevalent in hospitalized patients with venous thromboembolism, particularly those admitted to intensive care units. Moreover, various forms of arterial and venous thrombosis, or micro‐ or macro‐vasculature embolisms, have been reported during the current pandemic. They have led to harmful consequences, such as neurological and cardiac events, nearly all resulting from the hypercoagulable state caused by this viral infection. The severe hypercoagulability observed in patients with COVID‐19 accounts for most cases of the disease that become critical. Therefore, anticoagulants seem to be one of the most vital therapeutics for treating this potentially life‐threatening condition. In the current paper, we present a thorough review of the pathophysiology of COVID‐19‐induced hypercoagulable state and the use of anticoagulants to treat SARS‐CoV‐2 infections in different patient groups, as well as their pros and cons.

AbstractBackgroundDeformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application.PurposeA deep‐learning‐based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc.MethodsA deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2 weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre‐trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per‐voxel absolute CT‐number‐difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3 mm/3%/10% and 2 mm/2%/10%, respectively.ResultsThe two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190 ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per‐voxel absolute CT‐number‐difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5, within ±0.06% for total lung V5, ≤1.5cGy[RBE] for heart and esophagus Dmean, and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (> 96% for 3 mm/3%/10% and > 94% for 2 mm/2%/10%, respectively) were also observed.ConclusionA deep neural network‐based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.

2002 Background: Elderly patients with glioblastoma have a poor prognosis with phase III trials reporting median overall survival (OS) and global time to deterioration of 6–9 and 1.2 months respectively. 18F-DOPA (3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine) positron emission tomography (PET) is sensitive and specific for identifying regions of high density and biologically aggressive glioblastoma. Proton beam therapy (PBT) can spare more healthy tissue that may improve quality of life for this patient population. With improved targeting of disease along with dosimetric advantages of PBT, this phase II trial tested whether hypofractionated PBT can offer both improved survival and quality of life. Methods: Patients with newly diagnosed glioblastoma aged ≥65 years without contraindications to 18F-DOPA were eligible. Target volumes were defined by PET (tumor/normal brain SUV ≥2.0) and MRI areas of contrast enhancement including surgical cavity. Patients received dose escalated hypofractionated PBT of 35-40 GyE to PET/MRI over 5-10 treatments with 10 mm margin. Patients received concurrent/adjuvant temozolomide. With an alpha of 0.1, 18 of 39 patients would be required to be alive at 12 months for the study to be considered a success. Kaplan-Meier/Cox regression analysis was performed for progression free survival (PFS) and OS. Toxicities were evaluated with CTCAE v4.0. Patients completed EORTC BN20, QLQ-C30, and MMSE questionnaires. Time to deterioration, defined as a 10-point decrease in the score of the function domain or 10-point increase in score of symptom domain, was evaluated. Results: Between 5/2019-6/2021, 43 patients were enrolled, with 4 patients withdrawing due to progression/insurance denial prior to beginning protocol therapy. The median age was 70.2 yrs. and median follow-up was 12.5 months. MGMT methylation was present in 13 (33%). Tumors were multifocal in 10 (26%) and unifocal in 29 (74%). The primary endpoint was met with median PFS/OS was 6.5/12.9 months for all patients and for MGMT methylated patients 11.9/29.8 months respectively. Grade 3 baseline adjusted treatment related adverse events included 1 (3%) confusion, 2 (5%) fatigue, and 1 (3%) seizure. There were no grade 4/5 events. Multivariate analysis revealed MGMT unmethylated, ECOG 2 and biopsy only patients had worse survival. PET volume was more predictive than MRI volume for OS with PET volume>26 cc having a hazard ratio of 3.7. The median time to global deterioration per QLC-C30 was 6.3 months. Conclusions: This phase II trial incorporating 18F-DOPA PET-guided dose escalated hypofractionated proton beam treatment for elderly patients with glioblastoma met its primary endpoint for improved OS and patient reported quality of life compared to historical controls. Further investigation is warranted. Clinical trial information: NCT03778294 .

Abstract: Remdesivir has appeared to be the most effective medication against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and is broadly administered to coronavirus disease 2019 (COVID-19) patients around the world. Remdesivir is an RNA polymerase inhibitor with a broad spectrum of antiviral activities against RNA viruses in in-vitro and in-vivo models of SARSCoV, the Middle East respiratory syndrome (MERS), and SARS-CoV-2. Remdesivir is the first Food and Drug Administration (FDA) approved anti-SARS-CoV-2 treatment for adult and pediatric patients and has been used for not hospitalized and have mild-to-moderate COVID-19, and are at high risk for progression to severe COVID-19, including hospitalization or death. However, questions have been raised about the value of remdesivir in treating COVID-19, and governing bodies worldwide have been hesitant to approve this medication. Nevertheless, in the context of the public health emergency and the urgent need for effective treatments for patients with COVID-19, remdesivir has been approved by several authorities worldwide. Here, we discuss the characteristics and applications of remdesivir, and various challenging studies with different outcomes about its efficacy are also reviewed.

Background: Radiation necrosis (RN) is a clinically relevant complication of stereotactic radiosurgery (SRS) for intracranial metastasis (ICM) treatments. Radiation necrosis development is variable following SRS. It remains unclear if risk factors for and clinical outcomes following RN may be different for melanoma patients. We reviewed patients with ICM from metastatic melanoma to understand the potential impact of RN in this patient population. Methods: Patients who received SRS for ICM from melanoma at Mayo Clinic Arizona between 2013 and 2018 were retrospectively reviewed. Data collected included demographics, tumor characteristics, radiation parameters, prior surgical and systemic treatments, and patient outcomes. Radiation necrosis was diagnosed by clinical evaluation including brain magnetic resonance imaging (MRI) and, in some cases, tissue evaluation. Results: Radiation necrosis was diagnosed in 7 (27%) of 26 patients at 1.6 to 38 months following initial SRS. Almost 92% of all patients received systemic therapy and 35% had surgical resection prior to SRS. Patients with RN trended toward having larger ICM and a prior history of surgical resection, although statistical significance was not reached. Among patients with resection, those who developed RN had a longer period between surgery and SRS start (mean 44 vs 33 days). Clinical improvement following treatment for RN was noted in 2 (29%) patients. Conclusions: Radiation necrosis is relatively common following SRS for treatment of ICM from metastatic melanoma and clinical outcomes are poor. Further studies aimed at mitigating RN development and identifying novel approaches for treatment are warranted.

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there have been multiple peaks of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus virus 2) infection, mainly due to the emergence of new variants, each with a new set of mutations in the viral genome, which have led to changes in the pathogenicity, transmissibility, and morbidity. The Omicron variant is the most recent variant of concern (VOC) to emerge and was recognized by the World Health Organization (WHO) on 26 November 2021. The Omicron lineage is phylogenetically distinct from earlier variants, including the previously dominant Delta SARS-CoV-2 variant. The reverse transcription–polymerase chain reaction (RT–PCR) test, rapid antigen assays, and chest computed tomography (CT) scans can help diagnose the Omicron variant. Furthermore, many agents are expected to have therapeutic benefits for those infected with the Omicron variant, including TriSb92, molnupiravir, nirmatrelvir, and their combination, corticosteroids, and interleukin-6 (IL-6) receptor blockers. Despite being milder than previous variants, the Omicron variant threatens many lives, particularly among the unvaccinated, due to its higher transmissibility, pathogenicity, and infectivity. Mounting evidence has reported the most common clinical manifestations of the Omicron variant to be fever, runny nose, sore throat, severe headache, and fatigue. This review summarizes the essential features of the Omicron variant, including its history, genome, transmissibility, clinical manifestations, diagnosis, management, and the effectiveness of existing vaccines against this VOC.

AbstractBackgroundThere is a knowledge gap regarding lobar versus sublobar resection for atypical carcinoid (AC) of the lung. As such, the authors sought to understand and analyze the outcomes of sublobar resection versus lobectomy in this patient population.MethodsA retrospective analysis using the National Cancer Database was performed to compare overall survival (OS) between patients treated with lobectomy and patients treated with sublobar resection for AC of the lung between the years 2004 and 2016. Patient characteristics were compared with χ2 tests. The Kaplan–Meier method was used to estimate OS distributions, and the log‐rank test was used to compare distributions by treatment strategy. A multivariable Cox regression model was used to assess associations between the treatment strategy and OS. A propensity score matching method was also implemented to further eliminate treatment selection bias in the study sample.ResultsThe database identified 669 patients with T1–T4 and N0–N3 lung ACs that were surgically resected. Unadjusted Kaplan–Meier survival curves did not demonstrate an OS difference between lobectomy and sublobar resection (p = .094). After propensity score matching, curves demonstrated a numerical improvement in OS with lobectomy; however, it was not statistically significant (p = .5). In a subgroup analysis, lobectomy and node‐negative disease were associated with the best OS, whereas sublobar resection and node‐positive disease were associated with the worst OS (p < .0001). Nodal involvement was associated with worse survival, regardless of surgical treatment (p < .0001).ConclusionsIn patients with T1–T4 and N0–N3 ACs of the lung, lobectomy was not associated with an improvement in OS in comparison with sublobar resection.

Abstract The common side effects of COVID‐19 vaccination were mostly self‐restricted local reactions that quickly resolved. Nevertheless, rare autoimmune hepatitis cases have been reported in some vaccinated with mRNA COVID‐19 vaccines. This article presents a young man who developed fulminant hepatitis a few days after vaccination with the first dose of the AstraZeneca COVID‐19 vaccine. A 35‐year‐old man was admitted to our hospital with generalized weakness, abdominal pain, and jaundice. He received the first dose of the AstraZeneca COVID‐19 vaccine 8 days earlier. He was admitted to the hospital with a chief complaint of abdominal pain. On admission and because of his high D‐dimers, low platelet count, and low Fibrinogen level, vaccine‐induced immune thrombosis thrombocytopenia was suspected, which was ruled out later. Then, after a surge in his liver function tests, decreasing platelet, and abnormal clotting tests, fulminant hepatitis was considered for this patient. Several bacterial, viral, and autoimmune etiologies were then suspected, with all ruled out. Thus, fulminant hepatitis secondary to his AstraZeneca COVID‐19 vaccine was confirmed. Unfortunately, he died 3 days later of disseminated intravascular coagulopathy, after which a liver necropsy was performed, indicating drug/toxin‐induced hepatitis.