Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study.

Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different populations were defined; a) healthy (no COVID-19), b) mild COVID-19 (no ventilator required), c) severe COVID-19 (ventilator required), and the presence of lung shape differences between them were explored using baseline chest CT. Significant lung shape differences were observed along mediastinal surfaces of the lungs across all severity of COVID-19 disease. Additionally, differences were seen on basal surfaces of the lung when compared between healthy and severe COVID-19 patients. Finally, an AI model (a 3D residual convolutional network) characterizing these shape differences coupled with lung infiltrates (ground-glass opacities and consolidation regions) was found to be associated with COVID-19 severity.

Nonlinear Elastic Bottlebrush Polymer Hydrogels Modulate Actomyosin Mediated Protrusion Formation in Mesenchymal Stromal Cells.

The nonlinear elasticity of many tissue-specific extracellular matrices is difficult to recapitulate without the use of fibrous architectures, which couple strain-stiffening with stress relaxation. Herein, bottlebrush polymers are synthesized and crosslinked to form poly(ethylene glycol)-based hydrogels and used to study how strain-stiffening behavior affects human mesenchymal stromal cells (hMSCs). By tailoring the bottlebrush polymer length, the critical stress associated with the onset of network stiffening is systematically varied, and a unique protrusion-rich hMSC morphology emerges only at critical stresses within a biologically accessible stress regime. Local cell-matrix interactions are quantified using 3D traction force microscopy and small molecule inhibitors are used to identify cellular machinery that plays a critical role in hMSC mechanosensing of the engineered, strain-stiffening microenvironment. Collectively, this study demonstrates how covalently crosslinked bottlebrush polymer hydrogels can recapitulate strain-stiffening biomechanical cues at biologically relevant stresses and be used to probe how nonlinear elastic matrix properties regulate cellular processes.

Emergency department imaging utilization post-transcatheter aortic valve replacement: single institution 7-year experience.

PURPOSE: This study aims to highlight presentations, acute findings and imaging phenotypes of patients presenting to the emergency department (ED) within 30 days of a transcatheter aortic valve replacement (TAVR). METHODS: A retrospective review of patients diagnosed with aortic valve disease who underwent a TAVR between Jan 2015 and Nov 2021 at a large academic medical center was completed. From an initial 1271 patients, 146 were included based on their presentation to the ED within 30 days post-TAVR procedure. Patient data, including ED presentation details and imaging results, were recorded and de-identified. RESULTS: Of the 146 post-TAVR patients, there were 168 ED visits within 30 days. The median time to ED after TAVR was 12 days. Respiratory symptoms were the most common complaint (27%). Neurological (23%) and cardiovascular symptoms (18%) followed. Cross-sectional imaging was conducted 250 times across visits, with an average of 1.7 scans per patient. CTs were most frequently used, followed by ultrasounds, especially echocardiograms and duplex extremity vasculature ultrasounds. 30.1% of patients had acute findings from imaging. Specific findings included heart failure (5.5%), access site complications (5.5%), pneumonia (5.5%), intracranial pathologies (3.4% for strokes and 0.7% for hematoma), and pleural effusion (3.4%). Echocardiograms and CTA chest were most associated with significant acute findings. CONCLUSION: Our study highlights the vital role of early and accurate imaging in post-TAVR patients within 30 days post-procedure. As transcatheter approaches rise in popularity, emergency radiologists become instrumental in diagnosing common post-procedural presentations. Continued research is essential to devise post-discharge strategies to curtail readmissions and related costs. Proper imaging ensures prompt, effective care, enhancing overall patient outcomes.

3.0-T MR-guided transgluteal in-bore-targeted prostate biopsy under local anesthesia in patients without rectal access: a single-institute experience and review of literature.

PURPOSE: To describe the technique and evaluate the performance of MRI-guided transgluteal in-bore-targeted biopsy of the prostate gland under local anesthesia in patients without rectal access. METHODS: Ten men (mean age, 69 (range 57-86) years) without rectal access underwent 13 MRI-guided transgluteal in-bore-targeted biopsy of the prostate gland under local anesthesia. All patients underwent mp-MRI at our institute prior to biopsy. Three patients had prior US-guided transperineal biopsy which was unsuccessful in one, negative in one, and yielded GG1 (GS6) PCa in one. Procedure time, complications, histopathology result, and subsequent management were recorded. RESULTS: Median interval between rectal surgery and presentation with elevated PSA was 12.5 years (interquartile range (IQR) 25-75, 8-36.5 years). Mean PSA was 11.9 (range, 4.8 -59.0) ng/ml and PSA density was 0.49 (0.05 -3.2) ng/ml/ml. Distribution of PI-RADS v2.0/2.1 scores of the targeted lesions were PI-RADS 5-3; PI-RADS 4-6; and PI-RADS 3-1. Mean lesion size was 1.5 cm (range, 1.0-3.6 cm). Median interval between MRI and biopsy was 5.5 months (IQR 25-75, 1.5-9 months). Mean procedure time was 47.4 min (range, 29-80 min) and the number of cores varied between 3 and 5. Of the 13 biopsies, 4 yielded clinically significant prostate cancer (csPca), with a Gleason score ≥ 7, 1 yielded insignificant prostate cancer (Gleason score = 6), 7 yielded benign prostatic tissue, and one was technically unsuccessful. 3/13 biopsies were repeat biopsies which detected csPCa in 2 out of the 3 patients. None of the patients had biopsy-related complication. Biopsy result changed management to radiation therapy with ADT in 2 patients with the rest on active surveillance. CONCLUSION: MRI-guided transgluteal in-bore-targeted biopsy of the prostate gland under local anesthesia is feasible in patients without rectal access.

Cytoplasmic accumulation and plasma membrane association of anillin and Ect2 promote confined migration and invasion.

Cells migrating in confinement experience mechanical challenges whose consequences on cell migration machinery remain only partially understood. Here, we demonstrate that a pool of the cytokinesis regulatory protein anillin is retained during interphase in the cytoplasm of different cell types. Confinement induces recruitment of cytoplasmic anillin to plasma membrane at the poles of migrating cells, which is further enhanced upon nuclear envelope (NE) rupture(s). Rupture events also enable the cytoplasmic egress of predominantly nuclear RhoGEF Ect2. Anillin and Ect2 redistributions scale with microenvironmental stiffness and confinement, and are observed in confined cells in vitro and in invading tumor cells in vivo. Anillin, which binds actomyosin at the cell poles, and Ect2, which activates RhoA, cooperate additively to promote myosin II contractility, and promote efficient invasion and extravasation. Overall, our work provides a mechanistic understanding of how cytokinesis regulators mediate RhoA/ROCK/myosin II-dependent mechanoadaptation during confined migration and invasive cancer progression.

The Undermined ACGME Subcompetency: A Roadmap for Radiology Residency Programs to Foster Residents-as-Educators.

Radiology Residency programs in the United States use a set of six core competencies as laid out by the Accreditation Council for Graduate Medical Education (ACGME) to evaluate the foundational skills of every resident. Despite the fact that educational skills are included under the heading of Practice-Based Learning and Improvement in the ACGME guidelines for radiology residents, it is often underappreciated and undervalued, when compared with medical knowledge or patient care. In this paper, the authors lay out the important role of residents-as-educators and how it can be inculcated as part of formal training during residency. They enunciate five pillars for academic programs to build and maintain the pedagogical skills of their radiology residents: Training, Practicing, Providing Feedback, Mentoring, and Changing the Culture. The authors believe that implementing this will holistically benefit radiology residents as well as radiology in building future educators. The authors also delineate the challenges that programs currently face in implementation and ways to overcome them.

Analysis of ChatGPT publications in radiology: Literature so far.

OBJECTIVE: To perform a detailed qualitative and quantitative analysis of the published literature on ChatGPT and radiology in the nine months since its public release, detailing the scope of the work in the short timeframe. METHODS: A systematic literature search was carried out of the MEDLINE, EMBASE databases through August 15, 2023 for articles that were focused on ChatGPT and imaging/radiology. Articles were classified into original research and reviews/perspectives. Quantitative analysis was carried out by two experienced radiologists using objective scoring systems for evaluating original and non-original research. RESULTS: 51 articles were published involving ChatGPT and radiology/imaging dating from 26 Jan 2023 to the last article published on 14 Aug 2023. 23 articles were original research while the rest included reviews/perspectives or brief communications. For quantitative analysis scored by two readers, we included 23 original research and 17 non-original research articles (after excluding 11 letters as responses to previous articles). Mean score for original research was 3.20 out of 5 (across five questions), while mean score for non-original research was 1.17 out of 2 (across six questions). Mean score grading performance of ChatGPT in original research was 3.20 out of five (across two questions). DISCUSSION: While it is early days for ChatGPT and its impact in radiology, there has already been a plethora of articles talking about the multifaceted nature of the tool and how it can impact every aspect of radiology from patient education, pre-authorization, protocol selection, generating differentials, to structuring radiology reports. Most articles show impressive performance of ChatGPT which can only improve with more research and improvements in the tool itself. There have also been several articles which have highlighted the limitations of ChatGPT in its current iteration, which will allow radiologists and researchers to improve these areas.

Assessing appropriate responses to ACR urologic imaging scenarios using ChatGPT and Bard.

Artificial intelligence (AI) has recently become a trending tool and topic regarding productivity especially with publicly available free services such as ChatGPT and Bard. In this report, we investigate if two widely available chatbots chatGPT and Bard, are able to show consistent accurate responses for the best imaging modality for urologic clinical situations and if they are in line with American College of Radiology (ACR) Appropriateness Criteria (AC). All clinical scenarios provided by the ACR were inputted into ChatGPT and Bard with result compared to the ACR AC and recorded. Both chatbots had an appropriate imaging modality rate of of 62% and no significant difference in proportion of correct imaging modality was found overall between the two services (p>0.05). The results of our study found that both ChatGPT and Bard are similar in their ability to suggest the most appropriate imaging modality in a variety of urologic scenarios based on ACR AC criteria. Nonetheless, both chatbots lack consistent accuracy and further development is necessary for implementation in clinical settings. For proper use of these AI services in clinical decision making, further developments are needed to improve the workflow of physicians.

Fibrillar adhesion dynamics govern the timescales of nuclear mechano-response via the vimentin cytoskeleton.

The cell nucleus is continuously exposed to external signals, of both chemical and mechanical nature. To ensure proper cellular response, cells need to regulate not only the transmission of these signals, but also their timing and duration. Such timescale regulation is well described for fluctuating chemical signals, but if and how it applies to mechanical signals reaching the nucleus is still unknown. Here we demonstrate that the formation of fibrillar adhesions locks the nucleus in a mechanically deformed conformation, setting the mechanical response timescale to that of fibrillar adhesion remodelling (~1 hour). This process encompasses both mechanical deformation and associated mechanotransduction (such as via YAP), in response to both increased and decreased mechanical stimulation. The underlying mechanism is the anchoring of the vimentin cytoskeleton to fibrillar adhesions and the extracellular matrix through plectin 1f, which maintains nuclear deformation. Our results reveal a mechanism to regulate the timescale of mechanical adaptation, effectively setting a low pass filter to mechanotransduction.

Radiology Reading Room for the Future: Harnessing the Power of Large Language Models Like ChatGPT.

Radiology has usually been the field of medicine that has been at the forefront of technological advances, often being the first to wholeheartedly embrace them. Whether it's from digitization to cloud side architecture, radiology has led the way for adopting the latest advances. With the advent of large language models (LLMs), especially with the unprecedented explosion of freely available ChatGPT, time is ripe for radiology and radiologists to find novel ways to use the technology to improve their workflow. Towards this, we believe these LLMs have a key role in the radiology reading room not only to expedite processes, simplify mundane and archaic tasks, but also to increase the radiologist's and radiologist trainee's knowledge base at a far faster pace. In this article, we discuss some of the ways we believe ChatGPT, and the likes can be harnessed in the reading room.

Cytoskeletal activation of NHE1 regulates cell volume and DNA methylation.

The regulation of mammalian cell volume is crucial for maintaining key cellular processes. Cells can rapidly respond to osmotic and hydrostatic pressure imbalances during environmental challenges, generating fluxes of water and ions that alter volume within minutes. While the role of ion pump and leak in cell volume regulation has been well-established, the role of the actomyosin cytoskeleton and its substantial interplay with ion transporters are still unclear. In this work, we discover a system of cell volume regulation controlled by cytoskeletal activation of ion transporters. Under hypotonic shock, NIH-3T3 and MCF-10A display a 20% secondary volume increase (SVI) following the initial regulatory volume decrease. We show that SVI is initiated by Ca 2+ influx through stretch activated channel Piezo1 and subsequent actomyosin remodeling. Rather than contracting cells, actomyosin triggers cell swelling by activating Na + -H + exchanger 1 (NHE1) through their co-binding partner ezrin. Cytoskeletal activation of NHE1 can be similarly triggered by mechanical stretch and attenuated by soft substrates. This mechanism is absent in certain cancer cell lines such as HT1080 and MDA-MB-231, where volume regulation is dominated by intrinsic response of ion transporters. Moreover, cytoskeletal activation of NHE1 during SVI induces nuclear deformation, leading to DNA demethylation and a significant, immediate transcriptomic response in 3T3 cells, a phenomenon that is absent in HT1080 cells. Overall, our findings reveal the central role of Ca 2+ and actomyosin-mediated mechanosensation in the regulation of ion transport, cell volume, DNA methylation, and transcriptomics.

Clinical Implementation of an Artificial Intelligence Tool in the Detection and Management of Pneumothoraces in Patients With COVID-19.

In this report, we present a series involving critically ill patients with known coronavirus disease (COVID-19) infection where a portable X-ray machine equipped with artificial intelligence (AI) software aided in the urgent radiographic diagnosis of pneumothorax. These cases demonstrate how real-world clinical employment of AI tools capable of analyzing and prioritizing studies in the radiologist's worklist can potentially lead to earlier detection of emergent findings like pneumothorax. The use of AI tools in this manner has the potential to both improve radiology workflow and add significant clinical value in managing critically ill patient populations, such as those with severe COVID-19 infection.

Emergency department imaging utilization of cancer patients treated with bevacizumab: single-institution 8-year experience.

PURPOSE: This study aims to highlight the presentations, imaging, and clinical outcomes of cancer patients presenting to the emergency department (ED) while receiving bevacizumab (Avastin) therapy. METHODS: Our retrospective study was based on data from a single institution to identify cancer patients who presented acutely to the ED between 2014 and 2021 within 3 months of beginning bevacizumab who subsequently received diagnostic imaging with CT, MRI, ultrasound, and/or nuclear medicine ventilation/perfusion (VQ) scans. Data gathered included presenting symptoms grouped by body system, imaging impressions, and clinical outcomes, including hospitalization and discontinuation of bevacizumab after each ED visit. Imaging examinations and patient charts were reviewed by a team of fellowship-trained radiologists, radiology residents, and medical students. RESULTS: A total of 84 patients who presented to the ED were included for analysis. This included 32 (38.1%) males and 52 (61.9%) females, with a mean age of 61.2 years and an age range of 29-91 years. Neurological symptoms were the most common presenting symptoms, followed by abdominal symptoms and respiratory symptoms. Head imaging with CT and MRI was the most common imaging ordered with 55 total examinations, followed by abdominal imaging with 37 CT abdomen/pelvis (A/P) examinations, and then CT chest imaging with 22 examinations. Imaging revealed a serious adverse drug reaction in 21 (25.0%) patients, disease progression in 19 (22.6%), and no acute imaging findings in 44 (52.4%) patients. Imaging diagnoses were significantly associated with treatment planning, with a positive determination of bevacizumab-related serious adverse reaction on imaging leading to discontinuation of bevacizumab (p = 0.001). CONCLUSION: Multimodality imaging was a commonly used assessment tool for cancer patients receiving bevacizumab who presented to the ED. Imaging played a crucial role in diagnosis in these patients, especially of treatment-related serious adverse reactions and disease progression. Positive imaging findings of serious adverse reactions affected patient management including discontinuation of bevacizumab.

Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study.

Introduction: For locally advanced rectal cancers, in vivo radiological evaluation of tumor extent and regression after neoadjuvant therapy involves implicit visual identification of rectal structures on magnetic resonance imaging (MRI). Additionally, newer image-based, computational approaches (e.g., radiomics) require more detailed and precise annotations of regions such as the outer rectal wall, lumen, and perirectal fat. Manual annotations of these regions, however, are highly laborious and time-consuming as well as subject to inter-reader variability due to tissue boundaries being obscured by treatment-related changes (e.g., fibrosis, edema). Methods: This study presents the application of U-Net deep learning models that have been uniquely developed with region-specific context to automatically segment each of the outer rectal wall, lumen, and perirectal fat regions on post-treatment, T2-weighted MRI scans. Results: In multi-institutional evaluation, region-specific U-Nets (wall Dice = 0.920, lumen Dice = 0.895) were found to perform comparably to multiple readers (wall inter-reader Dice = 0.946, lumen inter-reader Dice = 0.873). Additionally, when compared to a multi-class U-Net, region-specific U-Nets yielded an average 20% improvement in Dice scores for segmenting each of the wall, lumen, and fat; even when tested on T2-weighted MRI scans that exhibited poorer image quality, or from a different plane, or were accrued from an external institution. Discussion: Developing deep learning segmentation models with region-specific context may thus enable highly accurate, detailed annotations for multiple rectal structures on post-chemoradiation T2-weighted MRI scans, which is critical for improving evaluation of tumor extent in vivo and building accurate image-based analytic tools for rectal cancers.

Imaging Depiction of Hypoxic Pulmonary Vasoconstriction Using Dual-Energy CT.

In this article, we aim to highlight the utility of dual-energy computed tomography (DECT) in demonstrating imaging changes due to hypoxic pulmonary vasoconstriction (HPV). DECT allows detailed image reconstructions that have been shown to better characterize cardiothoracic pathologies, as compared to conventional CT techniques. DECT simultaneously detects two different X-ray energies, which enables generation of iodine density maps, virtual monoenergetic images, and effective atomic number maps (Zeff), among others. DECT has been shown to have utility in the assessment of benign versus malignant pulmonary nodules, pulmonary embolism, myocardial perfusion defects, and other conditions. Herein, we describe four cases of indeterminate pulmonary pathology when imaged with conventional CT in which subsequent use of DECT-derived image reconstructions demonstrated HPV as the underlying pathophysiological mechanism. The goal of this article is to understand the imaging appearance of HPV on DECT and discuss how HPV may mimic other causes of perfusion defects.

Development and Implementation of Integrated Radiologist-Speech Pathologist Report for Modified Barium Swallow Study: Experience at a Multi-hospital Single Health Care System.

Dysphagia, or a disorder of swallowing, is very common and is reported in 1 out of 25 adults with approximately 1 million new cases per year in the United States alone. This also disproportionately impacts elderly patients, with a prevalence of 17%. Patients with dysphagia may have severe clinical complications such as starvation, dehydration, and airway obstruction- which may further increase mortality. Hence, timely and accurate diagnosis of dysphagia is hence crucial in management considerations. The gold standard for evaluating and diagnosing dysphagia is a modified barium swallow study (MBSS). The study is typically performed as a collaborative effort between a speech language pathologist (SLP) and a radiologist, who bring their individual skill sets to the table. Current MBSS reporting involves separately dictated and interpreted reports from the SLP and radiologist. In this paper, we elucidate our experience in a multi-institutional healthcare system wherein we have devised a single, integrated report for MBSS, which involves collaborative effort between SLP and the radiologist. We weight the advantages and disadvantages of unified reporting, the challenges of implementing it in a large healthcare system, and note how it can help improve efficiency and deliver unified patient care. We hope that this would be a template for other institutions as well as improve standardization of reporting techniques.

Automated analysis of computerized morphological features of cell clusters associated with malignancy on bile duct brushing whole slide images.

BACKGROUND: Bile duct brush specimens are difficult to interpret as they often present inflammatory and reactive backgrounds due to the local effects of stricture, atypical reactive changes, or previously installed stents, and often have low to intermediate cellularity. As a result, diagnosis of biliary adenocarcinomas is challenging and often results in large interobserver variability and low sensitivity OBJECTIVE: In this work, we used computational image analysis to evaluate the role of nuclear morphological and texture features of epithelial cell clusters to predict the presence of pancreatic and biliary tract adenocarcinoma on digitized brush cytology specimens. METHODS: Whole slide images from 124 patients, either diagnosed as benign or malignant based on clinicopathological correlation, were collected and randomly split into training (ST , N = 58) and testing (Sv , N = 66) sets, with the exception of cases diagnosed as atypical on cytology were included in Sv . Nuclear boundaries on cell clusters extracted from each image were segmented via a watershed algorithm. A total of 536 quantitative morphometric features pertaining to nuclear shape, size, and aggregate cluster texture were extracted from within the cell clusters. The most predictive features from patients in ST were selected via rank-sum, t-test, and minimum redundancy maximum relevance (mRMR) schemes. The selected features were then used to train three machine-learning classifiers. RESULTS: Malignant clusters tended to exhibit lower textural homogeneity within the nucleus, greater textural entropy around the nuclear membrane, and longer minor axis lengths. The sensitivity of cytology alone was 74% (without atypicals) and 46% (with atypicals). With machine diagnosis, the sensitivity improved to 68% from 46% when atypicals were included and treated as nonmalignant false negatives. The specificity of our model was 100% within the atypical category. CONCLUSION: We achieved an area under the receiver operating characteristic curve (AUC) of 0.79 on Sv , which included atypical cytological diagnosis.

Integrating Radiomics With Clinicoradiological Scoring Can Predict High-Risk Patients Who Need Surgery in Crohn's Disease: A Pilot Study.

BACKGROUND: Early identification of Crohn's disease (CD) patients at risk for complications could enable targeted surgical referral, but routine magnetic resonance enterography (MRE) has not been definitively correlated with need for surgery. Our objective was to identify computer-extracted image (radiomic) features from MRE associated with risk of surgery in CD and combine them with clinical and radiological assessments to predict time to intervention. METHODS: This was a retrospective single-center pilot study of CD patients who had an MRE within 3 months prior to initiating medical therapy. Radiomic features were extracted from annotated terminal ileum regions on MRE and combined with clinical variables and radiological assessment (via Simplified Magnetic Resonance Index of Activity scoring for wall thickening, edema, fat stranding, ulcers) in a random forest classifier. The primary endpoint was high- and low-risk groups based on need for surgery within 1 year of MRE. The secondary endpoint was time to surgery after treatment. RESULTS: Eight radiomic features capturing localized texture heterogeneity within the terminal ileum were significantly associated with risk of surgery within 1 year of treatment (P < .05); yielding a discovery cohort area under the receiver-operating characteristic curve of 0.67 (n = 50) and validation cohort area under the receiver-operating characteristic curve of 0.74 (n = 23). Kaplan-Meier analysis of radiomic features together with clinical variables and Simplified Magnetic Resonance Index of Activity scores yielded the best hazard ratio of 4.13 (P = (7.6 × 10-6) and concordance index of 0.71 in predicting time to surgery after MRE. CONCLUSIONS: Radiomic features on MRE may be associated with risk of surgery in CD, and in combination with clinicoradiological scoring can yield an accurate prognostic model for time to surgery.

Extracellular fluid viscosity enhances cell migration and cancer dissemination.

Cells respond to physical stimuli, such as stiffness1, fluid shear stress2 and hydraulic pressure3,4. Extracellular fluid viscosity is a key physical cue that varies under physiological and pathological conditions, such as cancer5. However, its influence on cancer biology and the mechanism by which cells sense and respond to changes in viscosity are unknown. Here we demonstrate that elevated viscosity counterintuitively increases the motility of various cell types on two-dimensional surfaces and in confinement, and increases cell dissemination from three-dimensional tumour spheroids. Increased mechanical loading imposed by elevated viscosity induces an actin-related protein 2/3 (ARP2/3)-complex-dependent dense actin network, which enhances Na+/H+ exchanger 1 (NHE1) polarization through its actin-binding partner ezrin. NHE1 promotes cell swelling and increased membrane tension, which, in turn, activates transient receptor potential cation vanilloid 4 (TRPV4) and mediates calcium influx, leading to increased RHOA-dependent cell contractility. The coordinated action of actin remodelling/dynamics, NHE1-mediated swelling and RHOA-based contractility facilitates enhanced motility at elevated viscosities. Breast cancer cells pre-exposed to elevated viscosity acquire TRPV4-dependent mechanical memory through transcriptional control of the Hippo pathway, leading to increased migration in zebrafish, extravasation in chick embryos and lung colonization in mice. Cumulatively, extracellular viscosity is a physical cue that regulates both short- and long-term cellular processes with pathophysiological relevance to cancer biology.

An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study.

Objective: The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models-radiomics (MRM), clinical (MCM), and combined clinical-radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. Methods: We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D 1 T ( N = 473 ) , and 40% test set D 1 V ( N = 314 ) . The patients from institution-2 were used for an independent validation test set D 2 V ( N = 110 ) . A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within D 1 T . Results: The three out of the top five features identified using D 1 T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (MRM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709-0.799) on D 1 T , 0.836 on D 1 V , and 0.748 D 2 V . The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743-0.825) on D 1 T , 0.813 on D 1 V , and 0.688 on D 2 V . Finally, the combined model, MRCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774-0.853) on D 1 T , 0.847 on D 1 V , and 0.771 on D 2 V . The MRCM had an overall improvement in the performance of ~5.85% ( D 1 T : p = 0.0031; D 1 V p = 0.0165; D 2 V : p = 0.0369) over MCM. Conclusion: The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.