CT and clinical assessment in asymptomatic and pre-symptomatic patients with early SARS-CoV-2 in outbreak settings.

OBJECTIVES: The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2. METHODS: Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression. RESULTS: Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms. CONCLUSIONS: COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure. KEY POINTS: • Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1-5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

Iterative prototyping based on lessons learned from the falloposcope in vivo pilot study experience.

Significance: High grade serous ovarian cancer is the most deadly gynecological cancer, and it is now believed that most cases originate in the fallopian tubes (FTs). Early detection of ovarian cancer could double the 5-year survival rate compared with late-stage diagnosis. Autofluorescence imaging can detect serous-origin precancerous and cancerous lesions in ex vivo FT and ovaries with good sensitivity and specificity. Multispectral fluorescence imaging (MFI) can differentiate healthy, benign, and malignant ovarian and FT tissues. Optical coherence tomography (OCT) reveals subsurface microstructural information and can distinguish normal and cancerous structure in ovaries and FTs. Aim: We developed an FT endoscope, the falloposcope, as a method for detecting ovarian cancer with MFI and OCT. The falloposcope clinical prototype was tested in a pilot study with 12 volunteers to date to evaluate the safety and feasibility of FT imaging prior to standard of care salpingectomy in normal-risk volunteers. In this manuscript, we describe the multiple modifications made to the falloposcope to enhance robustness, usability, and image quality based on lessons learned in the clinical setting. Approach: The ∼0.8 mm diameter falloposcope was introduced via a minimally invasive approach through a commercially available hysteroscope and introducing a catheter. A navigation video, MFI, and OCT of human FTs were obtained. Feedback from stakeholders on image quality and procedural difficulty was obtained. Results: The falloposcope successfully obtained images in vivo. Considerable feedback was obtained, motivating iterative improvements, including accommodating the operating room environment, modifying the hysteroscope accessories, decreasing endoscope fragility and fiber breaks, optimizing software, improving fiber bundle images, decreasing gradient-index lens stray light, optimizing the proximal imaging system, and improving the illumination. Conclusions: The initial clinical prototype falloposcope was able to image the FTs, and iterative prototyping has increased its robustness, functionality, and ease of use for future trials.

Cone-Beam Computed Tomography-Based Spatial Prediction of Drug Dose After Transarterial Chemoembolization Using Radiopaque Drug-Eluting Beads in Woodchuck Hepatocellular Carcinoma.

OBJECTIVES: The aims of this study were to develop a model to estimate drug dose delivered to tumors after transarterial chemoembolization (TACE) with radiopaque drug-eluting beads (DEBs) based on DEB density on cone-beam computed tomography (CT) and to evaluate drug penetration into tissue in a woodchuck hepatoma model. MATERIALS AND METHODS: Transarterial chemoembolization was performed in woodchucks with hepatocellular carcinoma (N = 5) using DEBs (70-150 µm, LC Bead LUMI) loaded with doxorubicin. Livers were resected 45 minutes after embolization, immediately frozen, and cut using liver-specific, 3D-printed sectioning molds. Doxorubicin levels in tumor specimens were measured by high-performance liquid chromatography and correlated with DEB iodine content that was measured using prototype cone-beam CT-based embolization treatment planning software. Doxorubicin penetration into tissue surrounding DEBs was assessed by fluorescence microscopy of tumor sections. Fluorescence intensity was converted into doxorubicin concentration using calibration standards. Intensity-thresholded color heatmaps were generated representing extravascular drug penetration. RESULTS: Consistent segmentation of DEBs on cone-beam CT was achieved using a semiautomated intensity thresholding method. A positive linear correlation (0.96) was found between DEB iodine content measured on cone-beam CT and the amount of doxorubicin measured in tumor specimens. Prediction of doxorubicin levels in tumor sections that were not included in model development was accurate, with a root-mean-square error of 0.08 mg of doxorubicin. Tumor penetration of eluted doxorubicin resulted in concentration gradients where drug content decreased with increasing distance from blood vessels containing DEBs. Drug penetration was greater for blood vessels containing DEB clusters compared with single DEB, with higher doxorubicin concentrations extending further away from the vessels. CONCLUSIONS: Estimation of drug dose delivered during transarterial chemoembolization in a woodchuck hepatocellular carcinoma model was possible using DEB radiopacity on cone-beam CT as a surrogate marker. Doxorubicin penetration was greatest adjacent to vessels containing DEB clusters compared with single DEB. Intraprocedural estimation of the spatial distribution of drug dose within the tumor could enable real-time adjustments to DEB delivery, to maximize treatment coverage or identify regions of tumor at risk for undertreatment.

Comparison of Smartphone Augmented Reality, Smartglasses Augmented Reality, and 3D CBCT-guided Fluoroscopy Navigation for Percutaneous Needle Insertion: A Phantom Study.

PURPOSE: To compare needle placement performance using an augmented reality (AR) navigation platform implemented on smartphone or smartglasses devices to that of CBCT-guided fluoroscopy in a phantom. MATERIALS AND METHODS: An AR application was developed to display a planned percutaneous needle trajectory on the smartphone (iPhone7) and smartglasses (HoloLens1) devices in real time. Two AR-guided needle placement systems and CBCT-guided fluoroscopy with navigation software (XperGuide, Philips) were compared using an anthropomorphic phantom (CIRS, Norfolk, VA). Six interventional radiologists each performed 18 independent needle placements using smartphone (n = 6), smartglasses (n = 6), and XperGuide (n = 6) guidance. Placement error was defined as the distance from the needle tip to the target center. Placement time was recorded. For XperGuide, dose-area product (DAP, mGy*cm2) and fluoroscopy time (sec) were recorded. Statistical comparisons were made using a two-way repeated measures ANOVA. RESULTS: The placement error using the smartphone, smartglasses, or XperGuide was similar (3.98 ± 1.68 mm, 5.18 ± 3.84 mm, 4.13 ± 2.38 mm, respectively, p = 0.11). Compared to CBCT-guided fluoroscopy, the smartphone and smartglasses reduced placement time by 38% (p = 0.02) and 55% (p = 0.001), respectively. The DAP for insertion using XperGuide was 3086 ± 2920 mGy*cm2, and no intra-procedural radiation was required for augmented reality. CONCLUSIONS: Smartphone- and smartglasses-based augmented reality reduced needle placement time and radiation exposure while maintaining placement accuracy compared to a clinically validated needle navigation platform.

Smartphone- versus smartglasses-based augmented reality (AR) for percutaneous needle interventions: system accuracy and feasibility study.

PURPOSE: To compare the system accuracy and needle placement performance of smartphone- and smartglasses-based augmented reality (AR) for percutaneous needle interventions. METHODS: An AR platform was developed to enable the superimposition of annotated anatomy and a planned needle trajectory onto a patient in real time. The system accuracy of the AR display on smartphone (iPhone7) and smartglasses (HoloLens1) devices was evaluated on a 3D-printed phantom. The target overlay error was measured as the distance between actual and virtual targets (n = 336) on the AR display, derived from preprocedural CT. The needle overlay angle was measured as the angular difference between actual and virtual needles (n = 12) on the AR display. Three operators each used the iPhone (n = 8), HoloLens (n = 8) and CT-guided freehand (n = 8) to guide needles into targets in a phantom. Needle placement error was measured with post-placement CT. Needle placement time was recorded from needle puncture to navigation completion. RESULTS: The target overlay error of the iPhone was comparable to the HoloLens (1.75 ± 0.59 mm, 1.74 ± 0.86 mm, respectively, p = 0.9). The needle overlay angle of the iPhone and HoloLens was similar (0.28 ± 0.32°, 0.41 ± 0.23°, respectively, p = 0.26). The iPhone-guided needle placements showed reduced error compared to the HoloLens (2.58 ± 1.04 mm, 3.61 ± 2.25 mm, respectively, p = 0.05) and increased time (87 ± 17 s, 71 ± 27 s, respectively, p = 0.02). Both AR devices reduced placement error compared to CT-guided freehand (15.92 ± 8.06 mm, both p < 0.001). CONCLUSION: An augmented reality platform employed on smartphone and smartglasses devices may provide accurate display and navigation guidance for percutaneous needle-based interventions.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.