Highlights Holistic information in COVID-19 patients with imaging and non-imaging data can help predict patient outcome in terms of the need for ICU admission. Validation of model over multiple sites is important to establish its generalizablity. Both volume and radiomic features of pulmonary opacities are key to quantifying the extent of lung involvement.

Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-organ segmentation. In this paper, we propose a unified training strategy that enables a novel multi-scale deep neural network to be trained on multiple partially labeled datasets for multi-organ segmentation. In addition, a new network architecture for multi-scale feature abstraction is proposed to integrate pyramid input and feature analysis into a U-shape pyramid structure. To bridge the semantic gap caused by directly merging features from different scales, an equal convolutional depth mechanism is introduced. Furthermore, we employ a deep supervision mechanism to refine the outputs in different scales. To fully leverage the segmentation features from all the scales, we design an adaptive weighting layer to fuse the outputs in an automatic fashion. All these mechanisms together are integrated into a Pyramid Input Pyramid Output Feature Abstraction Network (PIPO-FAN). Our proposed method was evaluated on four publicly available datasets, including BTCV, LiTS, KiTS and Spleen, where very promising performance has been achieved. The source code of this work is publicly shared at https://github.com/DIAL-RPI/PIPO-FAN to facilitate others to reproduce the work and build their own models using the introduced mechanisms.

Purpose: The ability to obtain patient-specific organ doses for CT will open the door to new applications such as personalized selection of scan factors and individualized risk assessment, leading to the ultimate goal of achieving lowdose and optimized CT imaging. One technical barrier to advancing CT dosimetry has been the lack of computational tools for automatic patient-specific multi-organ segmentation of CT images, coupled with rapid organ dose quantification.This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of radiosensitive organs from CT images and GPU-based Monte Carlo rapid organ dose calculation. Methods: A deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate radiosensitive organs from CT images. Two databases are used: the Lung CT Segmentation Challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients each with 5 segmented organs, and the Pancreas-CT (PCT) dataset that contains 43 abdominal CT scan patients each with 8 segmented organs. A five-fold cross-validation of the new method is performed on both sets of data. Dice Similarity Coefficient (DSC) is used to evaluate the segmentation performance against the ground truth. A GPU-based Monte Carlo dose code, ARCHER, is used to calculate patient-specific CT organ doses. The proposed method is tested in terms of Relative Dose Error (RDE). To demonstrate the potential improvement of the new methods, organ dose results are compared against those obtained for population-average phantoms used in an off-line dose reporting software, VirtualDose, at Massachusetts General Hospital. Results: For the group of 60 patients from LCTSC dataset, the median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.93 (heart), 0.88 (spinal cord) and 0.78 (esophagus). For the group of 43 patients from PCT dataset, the median DSCs are found to be 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.89 (stomach), 0.87 (gall bladder), 0.79(pancreas), 0.74 (esophagus), and 0.64 (duodenum). Comparing with the organ dose results from population-averaged phantoms, the new patient-specific method achieved the smaller RDE range on all organs: -4.3%~1.5% (vs -31.5%~33.9%) for the lung, -7.0%~2.3% (vs -15.2%~125.1%) for the heart, -18.8%~40.2% (vs -10.3%~124.1%) for the esophagus, -5.6%~1.6% (vs -20.3%~57.4%) for the spleen, -4.5%~4.6% (vs -19.5%~61.0%) for the pancreas, -2.3%~4.4% (vs -37.8%~75.8%) for the left kidney, -14.9%~5.4% (vs -39.9% ~14.6%) for the gallbladder, -0.9%~1.6%(vs -30.1%~72.5%) for the liver, and -23.0%~11.1% (vs -52.5%~-1.3%) for the stomach. The trained automatic segmentation tool takes less than 5 seconds in a patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel with the segmentation using the GPU-accelerated ARCHER code takes less than 4 seconds in a patient to achieve <0.5% statistical uncertainty in all organ doses for all 103 patients in the...

Purpose Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. Methods We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). Results AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, p < 0.001). Using AI-based scores produced significantly higher ( p < 0.05) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. Conclusions Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.

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Background: Epithelial ovarian cancer (EOC) is the majority ovarian cancer (OC) type with a poor prognosis. This present study aimed to investigate potential prognostic factors including albumin-to-fibrinogen ratio (AFR) for advanced EOC patients with neoadjuvant chemotherapy (NAC) followed by debulking surgery. Methods: A total of 313 advanced EOC patients with NAC followed by debulking surgery from 2010 to 2017 were enrolled. The predictive value of AFR for the overall survival (OS) was evaluated by receiver operating characteristic (ROC) curve analysis. The univariate and multivariate Cox proportional hazards regression analyses were applied to investigate prognostic factors for advanced EOC patients. The association between preoperative AFR and progression free survival (PFS) or OS was determined via the Kaplan-Meier method using log-rank test. Results: The ROC curve analysis showed that the cutoff value of preoperative AFR in predicting OS was determined to be 7.78 with an area under the curve (AUC) of 0.773 (P < 0.001). Chemotherapy resistance, preoperative CA125 and AFR were independent risk factors for PFS in advanced EOC patients. Furthermore, chemotherapy resistance, residual tumor and AFR were significant risk factors for OS by multivariate Cox analysis. A low preoperative AFR (≤7.78) was significantly associated with a worse PFS and OS via the Kaplan-Meier method by log-rank test (P < 0.001). Conclusions: A low preoperative AFR was an independent risk factor for PFS and OS in advanced EOC patients with NAC followed by debulking surgery.

Background COVID‐19 is a new pneumonia. It has been hypothesized that tobacco smoking history may increase severity of this disease in the patients once infected by the underlying coronavirus SARS‐CoV‐2 because smoking and COVID‐19 both cause lung damage. However, this hypothesis has not been tested. Objective Current study was designed to focus on smoking history in patients with COVID‐19 and test this hypothesis that tobacco smoking history increases risk for severe COVID‐19 by damaging the lungs. Methods and results This was a single‐site, retrospective case series study of clinical associations, between epidemiological findings and clinical manifestations, radiographical or laboratory results. In our well‐characterized cohort of 954 patients including 56 with tobacco smoking history, smoking history increased the risk for severe COVID‐19 with an odds ratio (OR) of 5.5 (95% CI: 3.1–9.9; P = 7.3 × 10 −8 ). Meta‐analysis of ten cohorts for 2891 patients together obtained an OR of 2.5 (95% CI: 1.9–3.3; P < 0.00001). Semi‐quantitative analysis of lung images for each of five lobes revealed a significant difference in neither lung damage at first examination nor dynamics of the lung damage at different time‐points of examinations between the smoking and nonsmoking groups. No significant differences were found either in laboratory results including D‐dimer and C‐reactive protein levels except different covariances for density of the immune cells lymphocyte ( P = 3.8 × 10 −64 ) and neutrophil ( P = 3.9 × 10 −46 ). Conclusion Tobacco smoking history increases the risk for great severity of COVID‐19 but this risk is achieved unlikely by affecting the lungs.

: The pandemic novel coronavirus disease (COVID-19) has become a global concern in which respiratory system is not the only one involved. Previous researches have presented the common clinical manifestations including respiratory symptoms (i.e., fever and cough), fatigue and myalgia. However, there is limited evidence for neurological and psychological influences by SARS-CoV-2. In this review, we discuss the common neurological manifestations of COVID-19 including acute cerebrovascular disease (i.e., cerebral hemorrhage) and muscle ache. Possible viral transmission to the nervous system may occur via circulation, an upper nasal transcribrial route and/or conjunctival route. Also, we cannot ignore the psychological influence on the public, medical staff and confirmed patients. Dealing with public psychological barriers and performing psychological crisis intervention are an important part of public health interventions.