Towards Consistency in Pediatric Brain Tumor Measurements: Challenges, Solutions, and the Role of AI-Based Segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumor from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images. MATERIALS AND METHODS: Multiparametric preoperative MR imaging scans of 339 pediatric patients (n = 293 internal and n = 46 external cohorts) with a variety of tumor subtypes were preprocessed and manually segmented into 4 tumor subregions, ie, enhancing tumor, nonenhancing tumor, cystic components, and peritumoral edema. After training, performances of the 2 models on internal and external test sets were evaluated with reference to ground truth manual segmentations. Additionally, concordance was assessed by comparing the volume of the subregions as a percentage of the whole tumor between model predictions and ground truth segmentations using the Pearson or Spearman correlation coefficients and the Bland-Altman method. RESULTS: The mean Dice score for nnU-Net internal test set was 0.9 (SD, 0.07) (median, 0.94) for whole tumor; 0.77 (SD, 0.29) for enhancing tumor; 0.66 (SD, 0.32) for nonenhancing tumor; 0.71 (SD, 0.33) for cystic components, and 0.71 (SD, 0.40) for peritumoral edema, respectively. For DeepMedic, the mean Dice scores were 0.82 (SD, 0.16) for whole tumor; 0.66 (SD, 0.32) for enhancing tumor; 0.48 (SD, 0.27) for nonenhancing tumor; 0.48 (SD, 0.36) for cystic components, and 0.19 (SD, 0.33) for peritumoral edema, respectively. Dice scores were significantly higher for nnU-Net (P ≤ .01). Correlation coefficients for tumor subregion percentage volumes were higher (0.98 versus 0.91 for enhancing tumor, 0.97 versus 0.75 for nonenhancing tumor, 0.98 versus 0.80 for cystic components, 0.95 versus 0.33 for peritumoral edema in the internal test set). Bland-Altman plots were better for nnU-Net compared with DeepMedic. External validation of the trained nnU-Net model on the multi-institutional Brain Tumor Segmentation Challenge in Pediatrics (BraTS-PEDs) 2023 data set revealed high generalization capability in the segmentation of whole tumor, tumor core (a combination of enhancing tumor, nonenhancing tumor, and cystic components), and enhancing tumor with mean Dice scores of 0.87 (SD, 0.13) (median, 0.91), 0.83 (SD, 0.18) (median, 0.89), and 0.48 (SD, 0.38) (median, 0.58), respectively. CONCLUSIONS: The pediatric-specific data-trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

Visa Opportunities for International Medical Graduates Applying for U.S. Academic Radiology Department Faculty Positions: A National Survey.

BACKGROUND. International medical graduates (IMGs) are a source of physicians who could help alleviate radiologist workforce shortages in the United States. However, IMGs may face barriers in obtaining appropriate visas (e.g., H-1B or O-1 visas) to allow faculty employment. OBJECTIVE. The purpose of this study was to assess the policies and experiences of U.S. academic radiology departments in offering visas to IMGs applying for faculty positions. METHODS. A web-based survey on policies and experiences in offering visas to IMG faculty candidates was distributed to chairs of U.S. radiology departments with a diagnostic radiology training program recognized by the National Resident Matching Program. Individual survey questions were optional. The initial survey and subsequent reminders were sent from October 7, 2022, through November 7, 2022. RESULTS. The survey response rate was 81% (143/177). A total of 24% (28/115), 38% (44/115), 17% (20/115), and 20% (23/115) of departments offered H-1B visas to IMG faculty frequently, sometimes, rarely, and never, respectively; 3% (3/113), 27% (31/113), 22% (25/113), and 48% (54/113) of departments offered O-1 visas frequently, sometimes, rarely, and never, respectively. However, 41% (46/113) and 5% (6/113) of departments had default policies of offering H-1B and O-1 visas for IMG faculty candidates, respectively. The most common reasons given for why departments did not offer visas included, for both H-1B and O-1 visas, the time-consuming process, lack of reliability of candidates' starting time, and the expense of the visa application; for O-1 visas, the reasons given also included lack of expertise. A total of 15% (16/108) of departments set their own visa policies, 75% (81/108) followed institutional policies, and 10% (11/108) followed policies set by other entities (e.g., state government). CONCLUSION. Although to at least some extent most U.S. academic radiology departments offer H-1B and O-1 visas for IMGs seeking faculty positions, use of such visas typically is not the departments' default policy. A variety of barriers contributed to visas not being offered. The departments' visa policies were primarily determined at the institutional level. CLINICAL IMPACT. The identified barriers faced by U.S. academic radiology departments in offering visas to IMG faculty candidates impact the role of IMGs in helping to address radiologist workforce shortages.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network.

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

Adding Value to Liquid Biopsy for Brain Tumors: The Role of Imaging.

Clinical management in neuro-oncology has changed to an integrative approach that incorporates molecular profiles alongside histopathology and imaging findings. While the World Health Organization (WHO) guideline recommends the genotyping of informative alterations as a routine clinical practice for central nervous system (CNS) tumors, the acquisition of tumor tissue in the CNS is invasive and not always possible. Liquid biopsy is a non-invasive approach that provides the opportunity to capture the complex molecular heterogeneity of the whole tumor through the detection of circulating tumor biomarkers in body fluids, such as blood or cerebrospinal fluid (CSF). Despite all of the advantages, the low abundance of tumor-derived biomarkers, particularly in CNS tumors, as well as their short half-life has limited the application of liquid biopsy in clinical practice. Thus, it is crucial to identify the factors associated with the presence of these biomarkers and explore possible strategies that can increase the shedding of these tumoral components into biological fluids. In this review, we first describe the clinical applications of liquid biopsy in CNS tumors, including its roles in the early detection of recurrence and monitoring of treatment response. We then discuss the utilization of imaging in identifying the factors that affect the detection of circulating biomarkers as well as how image-guided interventions such as focused ultrasound can help enhance the presence of tumor biomarkers through blood-brain barrier (BBB) disruption.

Statistical plots in oncologic imaging, a primer for neuroradiologists.

The simplest approach to convey the results of scientific analysis, which can include complex comparisons, is typically through the use of visual items, including figures and plots. These statistical plots play a critical role in scientific studies, making data more accessible, engaging, and informative. A growing number of visual representations have been utilized recently to graphically display the results of oncologic imaging, including radiomic and radiogenomic studies. Here, we review the applications, distinct properties, benefits, and drawbacks of various statistical plots. Furthermore, we provide neuroradiologists with a comprehensive understanding of how to use these plots to effectively communicate analytical results based on imaging data.

Fibroblastic and Myofibroblastic Soft-Tissue Tumors: Imaging Spectrum and Radiologic-Pathologic Correlation.

Fibroblastic and myofibroblastic tumors are a variable group of neoplasms ranging from benign to malignant. These lesions may affect patients of any age group but are more frequently encountered in the pediatric population. Patient clinical presentation depends on the location, growth pattern, adjacent soft-tissue involvement, and pathologic behavior of these neoplasms. In the 2020 update to the World Health Organization (WHO) classification system, these tumors are classified on the basis of their distinct biologic behavior, histomorphologic characteristics, and molecular profiles into four tumor categories: (a) benign (eg, fibrous hamartoma of infancy, nodular fasciitis, proliferative fasciitis, fibroma of the tendon sheath, calcifying aponeurotic fibroma); (b) intermediate, locally aggressive (eg, desmoid fibromatosis); (c) intermediate, rarely metastasizing (eg, dermatofibrosarcoma protuberans, myxoinflammatory fibroblastic sarcoma, low-grade myofibroblastic sarcoma, infantile fibrosarcoma); and (d) malignant (eg, sclerosing epithelioid fibrosarcomas; low-grade fibromyxoid sarcoma; myxofibrosarcoma; fibrosarcoma, not otherwise specified). Detection of various components of solid tumors at imaging can help in prediction of the presence of corresponding histopathologic variations, thus influencing diagnosis, prognosis, and treatment planning. For example, lesions with a greater myxoid matrix or necrotic components tend to show higher signal intensity on T2-weighted MR images, whereas lesions with hypercellularity and dense internal collagen content display low signal intensity. In addition, understanding the radiologic-pathologic correlation of soft-tissue tumors can help to increase the accuracy of percutaneous biopsy and allow unnecessary interventions to be avoided. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Radiomics for characterization of the glioma immune microenvironment.

Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Hydatid Disease: A Pictorial Review of Uncommon Locations.

Hydatid disease is a zoonotic infection caused primarily by the tapeworm parasite, Echinococcus granulosus. It is considered an endemic disease in the Mediterranean region.  In about 90% of cases, hydatid cysts are found in the liver and lungs; however, any other organ in the body may be affected, particularly in endemic areas. When encountering cystic lesions in these areas, the physician should always keep hydatid disease as a possible diagnosis in mind. To avoid life-threatening conditions such as anaphylactic shock or pressure effect on vital organs, timely diagnosis, and proper management are critical. When a rare site is involved, hydatid disease should be diagnosed using a combination of serologic assays and imaging modalities such as ultrasonography, computed tomography (CT), and magnetic resonance imaging (MRI). These imaging modalities can also be used to determine the extent of the disease and assess possible complications. Here, we present a pictorial review of typical imaging manifestations of hydatid cysts in unusual sites. Being aware of these imaging features will assist physicians in making an accurate, timely diagnosis and subsequently, providing optimal management.

Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n=215 internal and n=29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n=151), validation (n=43), and withheld internal test (n=21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median±SD) was 0.91±0.10/0.88±0.16 for the whole tumor, 0.73±0.27/0.84±0.29 for ET, 0.79±19/0.74±0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98±0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements. Key Points: We proposed automated tumor segmentation and brain extraction on pediatric MRI.The volumetric measurements using our models agree with ground truth segmentations. Importance of the Study: The current response assessment in pediatric brain tumors (PBTs) is currently based on bidirectional or 2D measurements, which underestimate the size of non-spherical and complex PBTs in children compared to volumetric or 3D methods. There is a need for development of automated methods to reduce manual burden and intra- and inter-rater variability to segment tumor subregions and assess volumetric changes. Most currently available automated segmentation tools are developed on adult brain tumors, and therefore, do not generalize well to PBTs that have different radiological appearances. To address this, we propose a deep learning (DL) auto-segmentation method that shows promising results in PBTs, collected from a publicly available large-scale imaging dataset (Children's Brain Tumor Network; CBTN) that comprises multi-parametric MRI scans of multiple PBT types acquired across multiple institutions on different scanners and protocols. As a complementary to tumor segmentation, we propose an automated DL model for brain tissue extraction.

Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers.

INTRODUCTION: Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes. METHODS: Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes. RESULTS: K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p < 0.05) as well as the gene expression of BRAF (p<0.05). It was also found that age (p < 0.05), tumor location (p < 0.01), and tumor histology (p < 0.0001) differed significantly between the imaging subtypes. CONCLUSION: In this exploratory work, it was found that clustering of pLGGs based on radiomic features identifies distinct, imaging-based subtypes that correlate with important molecular markers and demographic details. This finding supports the notion that incorporation of radiomic data could augment our ability to better characterize pLGGs.

Patterns of Antiepileptic Drug Reactions in Children: A Multicenter Study.

Objectives: Antiepileptic drugs are among the most common triggers of cutaneous adverse reactions. About 5-17% of epileptic patients develop idiosyncratic skin reactions at some point during their treatment course, most of which occur within the first two months of drug initiation. This study aimed to investigate the pattern of cutaneous drug reactions associated with anticonvulsant use among the pediatric population in Iran to identify high-risk individuals. Materials & Methods: In this retrospective descriptive study, medical records of children aged two months to 14 years, who were diagnosed with drug reactions due to anticonvulsant drugs between April 2007 and March 2018, were reviewed, and relevant information were extracted. This multicenter study was conducted in several provinces of Iran. Results: A total of 186 cases with a final diagnosis of the antiepileptic drug-induced eruption were evaluated. The median age of participants was 36 months (range: 2-168), and 56% were male. In approximately 70% of the children, the phenobarbital was the culprit. The median time interval between initiation of the causative drug and development of rash and fever was 10 and 7 days, respectively. The most common rash type was maculopapular rashes (69%). Overall, 33% of the patients only received antihistamines after discontinuation of the causative drug. Conclusion: Similar to previously published studies in Iran, phenobarbital was the main cause of cutaneous drug reactions to antiepileptic drugs, indicating the necessity of paying more attention when prescribing phenobarbital for Iranian pediatrics.

Tumor-infiltrating lymphocyte therapy for lung cancer and its future paradigms.

INTRODUCTION: Lung cancer is the leading cause of cancer death, with an estimated 1.8 million deaths contributing to this cancer in 2020. Despite advances in treatment options and various approaches being attempted, the survival rate remains low. AREAS COVERED: In this review, we aim to provide an overview of the efficacy of tumor-infiltrating lymphocyte (TIL) therapy for lung cancer based on existing clinical trials. We also discuss the current challenges and future landscape of this treatment modality. EXPERT OPINION: Lung cancer can be a suitable candidate for TIL therapy due to its high mutational burden. Specifically, it has shown promising results for non-small cell lung cancer resistant to immune checkpoint inhibitors. Still, there are many restrictions associated with the ex vivo expansion and delivery of TILs, limiting their availability. For this reason, applying TIL for the treatment of lung cancer has not been extensively investigated yet and only a few clinical trials have shown favorable results of TIL therapy in patients with lung cancer. Thus, facilitating this costly, labor-intensive and time-consuming process is of utmost importance to increase the number of performed studies and to detect eligible patients who could benefit most from this treatment modality.

The interplay between aryl hydrocarbon receptor, H. pylori, tryptophan, and arginine in the pathogenesis of gastric cancer.

Several risk factors are known to be involved in the initiation and development of gastric cancer. Among them, H. pylori is one of the most prominent with multiple virulence factors contributing to its pathogenicity. In this study, we have discussed an interesting immunological cycle exploring the interplay between H. pylori, aryl hydrocarbon receptor (AHR), tryptophan, arginine, and the metabolites of these two amino acids in the development of gastric cancer. AHR is a ligand-activated transcription factor which acts as a regulator for a diverse set of genes and has various types of exogenous and endogenous ligands. The tryptophan metabolite, kynurenine, is one of these ligands that can interact with AHR, leading to immune suppression and subsequently, susceptibility to gastric cancer. On the other hand, H. pylori downregulates the expression of AHR and AHR repressor (AHRR), leading to increased inflammatory cytokine production. A metabolite of the kynurenine pathway, xanthurenic acid, is a potent inhibitor of a terminal enzyme in the synthetic pathway of tetrahydrobiopterin (BH4). BH4, itself, is a cofactor in the process of nitric oxide (NO) production from arginine that has been shown to have immune-enhancing properties. Arginine has also been evidenced to have anti-tumoral function through inducing apoptosis in gastric cell lines; however, controversy exists regarding the anti-tumor role of arginine and BH4, since they are also associated with increased NO production, subsequently promoting tumor angiogenesis. Hence, although several synergistic connections result in immunity improvement, these correlations can also act as a double-edged sword, promoting tumor development. This emphasizes on the need for further investigations to better understand this complex interplay.

Primary Pleuropulmonary Synovial Sarcoma: Report of Two Cases and a Comprehensive Review of the Literature.

Primary sarcomas of the lung represent less than 0.5% of all primary lung tumors and comprise a heterogeneous group of malignancies including synovial sarcoma (SS). Primary pleuropulmonary SS has non-specific presentations, such as chest pain, shortness of breath and cough, and its associated imaging features resemble those of other intrathoracic malignancies. The diagnosis of these tumors needs to be confirmed by cytogenetic and molecular studies. Here, we describe two rare cases of primary pleuropulmonary SS who were admitted to our hospital. We also provide a concise review of clinical, radiological, and histopathological characteristics of pleuropulmonary SS after exploring 168 studies (415 corresponding patients) that were identified through a literature search.

Organizing pneumonia-like pattern in COVID-19.

INTRODUCTION: Organizing pneumonia (OP) is a radio-histologic pattern that forms in response to lung damage in patients with focal or diffuse lung injury. OP is frequently observed subsequent to viral-induced lung damage and is associated with a diverse range of clinical outcomes. MATERIAL AND METHODS: We included 210 patients (mean age: 55.8 ± 16.5 years old; 61% male) with mild Coronavirus disease 2019 (COVID-19) who underwent chest computed tomography (CT) from 25 February to 22 April, 2020. The patients were divided into two groups based on the presence (n = 103) or absence of typical OP-like pattern (n =107) on initial chest CT. The extent of lung involvement and final outcome was compared across the two groups. Serial changes in imaging were also evaluated in 36 patients in the OP-group with a second CT scan. RESULTS: Duration from symptom onset to presentation was significantly higher in the OP group (7.07 ± 3.71 versus 6.13 ± 4.96 days, p = 0.008). A higher COVID-19-related mortality rate was observed among patients with OP-like pattern (17.5% vs 3.7%, p = 0.001).There was no significant difference in the overall involvement of the lungs (p = 0.358), but lower lobes were significantly more affected in the OP group (p < 0.001). Of the 36 patients with follow-up imaging (mean duration of follow-up = 8.3 ± 2.1 days), progression of infiltration was seen in more than 61% of patients while lesions had resolved in only 22.2% of cases. CONCLUSIONS: Our observation indicates that physicians should carefully monitor for the presence of OP-like pattern on initial CT as it is associated with a poor outcome. Furthermore, we recommend interval CT to evaluate the progression of infiltrations in these patients.

Balloon-occluded antegrade transvenous obliteration of rectal varices: A case report.

Patients with liver cirrhosis frequently experience rectal variceal bleeding subsequent to portal hypertension. Unlike gastroesophageal variceal bleeding, a well-established guideline does not exist in terms of management of bleeding rectal varices. A 75-year-old male with non-alcoholic-steatohepatitis induced cirrhosis presented with a 3-day history of severe rectorrhagia. Considering patient's clinical history, TIPS was not performed and thus, a novel endovascular technique termed balloon-occluded antegrade transvenous obliteration was considered. Under conscious sedation, an occlusion was made through balloon catheter by sclerotic agents including air/sodium tetradecyl sulfate/Lipiodol. After the procedure, and in the 6 months follow up period the patient's hemodynamic status was stable and he recovered without any serious complications. Balloon-occluded antegrade transvenous obliteration is a feasible and safe modality for treating rectal varices bleeding and could be used as an alternative approach in patients with contraindications to traditional treatments.

CRISPR/Cas9 revitalizes adoptive T-cell therapy for cancer immunotherapy.

Cancer immunotherapy has gained attention as the supreme therapeutic modality for the treatment of various malignancies. Adoptive T-cell therapy (ACT) is one of the most distinctive modalities of this therapeutic approach, which seeks to harness the potential of combating cancer cells by using autologous or allogenic tumor-specific T-cells. However, a plethora of circumstances must be optimized to produce functional, durable, and efficient T-cells. Recently, the potential of ACT has been further realized by the introduction of novel gene-editing platforms such as the CRISPR/Cas9 system; this technique has been utilized to create T-cells furnished with recombinant T-cell receptor (TCR) or chimeric antigen receptor (CAR) that have precise tumor antigen recognition, minimal side effects and treatment-related toxicities, robust proliferation and cytotoxicity, and nominal exhaustion. Here, we aim to review and categorize the recent breakthroughs of genetically modified TCR/CAR T-cells through CRISPR/Cas9 technology and address the pearls and pitfalls of each method. In addition, we investigate the latest ongoing clinical trials that are applying CRISPR-associated TCR/CAR T-cells for the treatment of cancers.