B-cell acute lymphoblastic leukemia associated with hypereosinophilia: a case report and brief literature review.

Few cases of B-cell acute lymphoblastic leukemia (B-ALL)-eosinophilia (ALL-eo) association have been reported. The lack or absence of blasts in the peripheral blood smear (PBS) along with urticarial rash, fever, arthralgia, myalgia, sweating, and dyspnea are common features of this condition. Herein, we report a 16-year-old male patient admitted to the emergency department with urticaria and generalized itching. PBS was examined, and eosinophils (90%) were seen in different fields. However, blast cells were not seen in PBS. In a bone marrow examination, terminal deoxynucleotidyl transferase-positive and CD20-positive lymphoid blasts were reported along with eosinophilia. Eventually, the B-ALL diagnosis was confirmed for the patient, and he was started on the Berlin-Frankfurt-Münster chemotherapy regimen. The association of B-ALL with hypereosinophilia is a rare condition. We hope this case report and literature review can help clinicians to manage this rare condition properly.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy.

BACKGROUND: Accurate 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. PURPOSE: To 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. METHODS: PBSPT 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. RESULTS: Compared 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. CONCLUSIONS: An 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.

Evaluating large language models on a highly-specialized topic, radiation oncology physics.

Purpose: We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have large test-taker populations and ample test preparation resources in circulation, they may not allow for accurately assessing the true potential of LLMs. This paper proposes evaluating LLMs on a highly-specialized topic, radiation oncology physics, which may be more pertinent to scientific and medical communities in addition to being a valuable benchmark of LLMs. Methods: We developed an exam consisting of 100 radiation oncology physics questions based on our expertise. Four LLMs, ChatGPT (GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against medical physicists and non-experts. The performance of ChatGPT (GPT-4) was further explored by being asked to explain first, then answer. The deductive reasoning capability of ChatGPT (GPT-4) was evaluated using a novel approach (substituting the correct answer with "None of the above choices is the correct answer."). A majority vote analysis was used to approximate how well each group could score when working together. Results: ChatGPT GPT-4 outperformed all other LLMs and medical physicists, on average, with improved accuracy when prompted to explain before answering. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices across a number of trials, whether correct or incorrect, a characteristic that was not observed in the human test groups or Bard (LaMDA). In evaluating deductive reasoning ability, ChatGPT (GPT-4) demonstrated surprising accuracy, suggesting the potential presence of an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall, its intrinsic properties did not allow for further improvement when scoring based on a majority vote across trials. In contrast, a team of medical physicists were able to greatly outperform ChatGPT (GPT-4) using a majority vote. Conclusion: This study suggests a great potential for LLMs to work alongside radiation oncology experts as highly knowledgeable assistants.

Five-year follow-up after stereotactic body radiotherapy for medically inoperable early-stage non-small cell lung cancer: a multicenter study.

Background: Stereotactic body radiotherapy (SBRT) has proven to provide high rates of tumor control for patients with early-stage non-small cell lung cancer (NSCLC). We are reporting a multicenter experience of long-term clinical outcomes and adverse effect profiles of patients with medically inoperable early-stage NSCLC treated with SBRT. Methods: A total of 145 early-stage NSCLC patients underwent SBRT at the Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Shandong Cancer Hospital and Institute, and Shanghai Pulmonary Hospital between October 2012 and March 2019. Four-dimensional computed tomography (4D-CT) simulation was used for all patients. All received a biologically effective dose (BED; α/ß=10) of 96-120 Gy with the prescribed isodose line covering >95% of the planning target volume (PTV). Survival was analyzed by the Kaplan-Meier method. Survival was estimated using the Kaplan-Meier method. Results: The median tumor diameter was 2.2 (range, 0.5-5.2) cm. The median follow-up was of 65.6 months. Thirty-five patients (24.1%) developed disease recurrence. The rates of local, regional, and distant disease recurrence were, respectively, 5.1%, 7.4%, and 13.2% at 3 years; and 9.6%, 9.8%, and 15.8% at 5 years. Progression-free survival (PFS) rates at 3 and 5 years were 69.2% and 60.5% respectively; the overall survival (OS) rates were 78.1% and 70.1%, respectively. Five patients (3.4%) experienced grade 3 treatment-related adverse events (AEs). No patient experienced grade 4 or 5 toxicity. Conclusions: From our retrospective analysis with long-term follow-up in Chinese population, SBRT achieved high rate of local control (LC) and low toxicity in patients with early-stage NSCLC. This study offered robust long-term outcome data of SBRT in the Chinese population, which was very rarely reported in China before.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy.

PURPOSE: To 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. METHODS: PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) 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, 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. RESULTS: Compared 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 the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s.

Anticoagulant therapy in COVID-19: A narrative review.

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.

Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer.

BACKGROUND: Deformable 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. PURPOSE: A 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. METHODS: A 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. RESULTS: The 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. CONCLUSION: A 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.

The Use of Remdesivir in Patients with COVID-19.

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.

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer.

PURPOSE: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. METHODS: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). RESULTS: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. CONCLUSION: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.

Clinical Outcomes of Stereotactic Radiosurgery-Related Radiation Necrosis in Patients with Intracranial Metastasis from Melanoma.

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.

SARS-CoV-2 Omicron (B.1.1.529) Variant: A Challenge with COVID-19.

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.

The role of cytokines and their antagonists in the treatment of COVID-19 patients.

The coronavirus disease 2019 (COVID-19) has various presentations, of which immune dysregulation or the so-called cytokine storm syndrome (COVID-CSS) is prominent. Even though cytokines are vital regulators of body immunoinflammatory responses, their exaggerated release can be harmful. This hyperinflammatory response is more commonly observed during severe COVID-19 infections, caused by the excessive release of pro-inflammatory cytokines, such as interleukin-1 (IL-1), IL-6, IL-8, tumour necrosis factor, granulocyte-macrophage colony-stimulating factor, and interferon-gamma, making their blockers and antagonists of great interest as therapeutic options in this condition. Thus, the pathophysiology of excessive cytokine secretion is outlined, and their most important blockers and antagonists are discussed, mainly focussing on tocilizumab, an interleukin-6 receptor blocker approved to treat severe COVID-19 infections.

Lobar versus sublobar resection for atypical lung carcinoid: An analysis from the National Cancer Database.

BACKGROUND: There 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. METHODS: A 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. RESULTS: The 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). CONCLUSIONS: In 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.

Quality and Safety Considerations in Image Guided Radiation Therapy: An ASTRO Safety White Paper Update.

PURPOSE: This updated report on image guided radiation therapy (IGRT) is part of a series of consensus-based white papers previously published by the American Society for Radiation Oncology addressing patient safety. Since the first white papers were published, IGRT technology and procedures have progressed significantly such that these procedures are now more commonly used. The use of IGRT has now extended beyond high-precision treatments, such as stereotactic radiosurgery and stereotactic body radiation therapy, and into routine clinical practice for many treatment techniques and anatomic sites. Therefore, quality and patient safety considerations for these techniques remain an important area of focus. METHODS AND MATERIALS: The American Society for Radiation Oncology convened an interdisciplinary task force to assess the original IGRT white paper and update content where appropriate. Recommendations were created using a consensus-building methodology, and task force members indicated their level of agreement based on a 5-point Likert scale from "strongly agree" to "strongly disagree." A prespecified threshold of ≥75% of raters who selected "strongly agree" or "agree" indicated consensus. SUMMARY: This IGRT white paper builds on the previous version and uses other guidance documents to primarily focus on processes related to quality and safety. IGRT requires an interdisciplinary team-based approach, staffed by appropriately trained specialists, as well as significant personnel resources, specialized technology, and implementation time. A thorough feasibility analysis of resources is required to achieve the clinical and technical goals and should be discussed with all personnel before undertaking new imaging techniques. A comprehensive quality-assurance program must be developed, using established guidance, to ensure IGRT is performed in a safe and effective manner. As IGRT technologies continue to improve or emerge, existing practice guidelines should be reviewed or updated regularly according to the latest American Association of Physicists in Medicine Task Group reports or guidelines. Patient safety in the application of IGRT is everyone's responsibility, and professional organizations, regulators, vendors, and end-users must demonstrate a clear commitment to working together to ensure the highest levels of safety.

A review of the potential neurological adverse events of COVID-19 vaccines.

Despite the advantages of getting access to the coronavirus disease 2019 (COVID-19) vaccines, their potential ability to induce severe adverse events (AEs) has been a significant concern. Neurological complications are significant among the various adverse events following immunization (AEFI) due to their likely durability and debilitating sequelae. Neurological AEs following COVID-19 vaccination can either exacerbate or induce new-onset neuro-immunologic diseases, such as myasthenia gravis (MG) and Guillain-Barre syndrome (GBS). The more severe spectrum of AEs post-COVID19 vaccines has included seizures, reactivation of the varicella-zoster virus, strokes, GBS, Bell's palsy, transverse myelitis (TM), and acute disseminated encephalomyelitis (ADEM). Here, we discuss each of these neurological adverse effects separately.

International consensus on radiotherapy in metastatic non-small cell lung cancer.

Background: Lung cancer is the leading cause of cancer-related death worldwide, with non-small cell lung cancer (NSCLC) accounting for most cases. While radiotherapy has historically served as a palliative modality in metastatic NSCLC, considerable advances in its technology and the continuous development of cutting-edge therapeutic agents, such as targeted therapy and immune checkpoint inhibitors (ICIs), are increasing its role in the multi-disciplinary management of the disease. Methods: International radiotherapy experts were convened to consider and reach consensuses on the clinical utilities of radiotherapy in metastatic NSCLC, with the aim to provide patient-focused, up to date, evidence-based, recommendations to assist cancer specialists in the management of patients with metastatic NSCLC worldwide. Results: Timely radiotherapy can offer rapid symptom alleviation and allow subsequent aggressive treatment approaches in patients with heavy tumor burden and/or oncologic emergencies. In addition, appropriate incorporation of radiotherapy as concurrent, consolidation, or salvage therapy makes it possible to achieve long-term survival, or even cure, for patients with oligo-metastatic disease. Cranial radiotherapy plays an important role in the management of brain metastasis, potentially augmenting the response and prolonging survival associated with targeted agents and ICIs. However, key questions remain, such as the appropriate choice of radiation techniques, optimal sequence of systemic therapies and radiotherapy, and optimal patient selection for such combination strategies. Although a strong rationale for combining radiotherapy and ICIs exists, its optimal parameters in this setting remain to be established. Conclusions: In the modern era, radiotherapy serves not only as a palliative tool in metastatic NSCLC, but also plays active roles in patients with oligo-focal disease, CNS metastasis and receiving ICIs.

Do we miss rare adverse events induced by COVID-19 vaccination?

Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has caused many complications, the invention of coronavirus disease 2019 (COVID-19) vaccines has also brought about several adverse events, from common side effects to unexpected and rare ones. Common vaccine-related adverse reactions manifest locally or systematically following any vaccine, including COVID-19 vaccines. Specific side effects, known as adverse events of particular interest (AESI), are unusual and need more evaluation. Here, we discuss some of the most critical rare adverse events of COVID-19 vaccines.

Cardiopulmonary Toxicity Following Intensity-Modulated Proton Therapy (IMPT) Versus Intensity-Modulated Radiation Therapy (IMRT) for Stage III Non-Small Cell Lung Cancer.

INTRODUCTION: Intensity-modulated proton therapy (IMPT) has the potential to reduce radiation dose to normal organs when compared to intensity-modulated radiation therapy (IMRT). We hypothesized that IMPT is associated with a reduced rate of cardiopulmonary toxicities in patients with Stage III NSCLC when compared with IMRT. METHODS: We analyzed 163 consecutively treated patients with biopsy-proven, stage III NSCLC who received IMPT (n = 35, 21%) or IMRT (n = 128, 79%). Patient, tumor, and treatment characteristics were analyzed. Overall survival (OS), freedom-from distant metastasis (FFDM), freedom-from locoregional relapse (FFLR), and cardiopulmonary toxicities (CTCAE v5.0) were calculated using the Kaplan-Meier estimate. Univariate cox regressions were conducted for the final model. RESULTS: Median follow-up of surviving patients was 25.5 (range, 4.6-58.1) months. Median RT dose was 60 (range, 45-72) Gy [RBE]. OS, FFDM, and FFLR were not different based on RT modality. IMPT provided significant dosimetric pulmonary and cardiac sparing when compared to IMRT. IMPT was associated with a reduced rate of grade more than or equal to 3 pneumonitis (HR 0.25, P = .04) and grade more than or equal to 3 cardiac events (HR 0.33, P = .08). Pre-treatment predicted diffusing capacity for carbon monoxide less than equal to 57% (HR 2.8, P = .04) and forced expiratory volume in the first second less than equal to 61% (HR 3.1, P = .03) were associated with an increased rate of grade more than or equal to 3 pneumonitis. CONCLUSIONS: IMPT is associated with a reduced risk of clinically significant pneumonitis and cardiac events when compared with IMRT without compromising tumor control in stage III NSCLC. IMPT may provide a safer treatment option, particularly for high-risk patients with poor pretreatment pulmonary function.