Anormal pulmonary function and residual CT abnormalities in rehabilitating COVID-19 patients after discharge To the editor , We read with great interest an article recently published in the Journal of Infection, in which, the authors followed up the pulmonary function and chest CT changes in two critically ill patients with COVID-19. 1 The two patients' distinct outcomes that seems be related with age, the young case recovered without abnormalities on chest CT and lung function tests, while the older case had residual radiological changes and impaired lung function during the follow-up period. Recent evidences have suggested that lung is the most affected organ by COVID-19. 2 Persistent impairment of pulmonary function ranging from months to even years after discharge has been reported in other coronavirus infections, such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). 3-6 However, post-discharge imaging or lung functional data are scarce for COVID-19 survivors. Hence, we aimed to report the pulmonary function and chest CT changes in these patients with different severities. Laboratory confirmed COVID-19 patients were enrolled from March 26 to May 1, 2020 from a designated hospital of Hubei province. According to the newest COVID-19 guidelines released by the National Health Commission of China, the disease severities were classified as mild, moderate, severe and critical illness. 7 The criteria for discharge were as follows: 7 (1) normal temperature lasting longer than three days, (2) resolved respiratory symptoms, (3) substantially improved acute exudative lesions on chest CT images, and (4) a series of two repetitive negative reverse transcription-polymerase chain reaction test results, separated by at least one day. The pulmonary function test (XEEK portable PFT X1, Xiamen, China) was performed following the American Thoracic Society/European Respiratory Society (ATS-ERS) guidelines on COVID-19 patients after discharge. The chest CT closest to the data of the pulmonary function test was reviewed independently by two cardiothoracic radiologists, who blinded to the clinical information. Final decisions were reached by consensus. This study was approved by the ethics committee of The First Affiliated Hospital of Jinan University, and written informed consent was obtained from all patients. We performed lung function test on 18 COVID-19 patients after discharge, which included 12 cases of moderate illness, five cases of severe illness and one case of critical illness. The mean age of the patients was 50.7 ± 12.1 (range, 28-67 years) and 10 were male. Only four patients (22.2%) had one or more underlying disease, such as hypertension, diabetes, and hypothyroidism. No patients had chronic lung diseases. There were no significant differences between the non-severe and severe groups in regard

ObjectivePreoperative identification of lymphovascular invasion (LVI) in patients with invasive breast cancer is challenging due to absence of reliable biomarkers or tools in clinical settings. We aimed to establish and validate multiparametric magnetic resonance imaging (MRI)-based radiomic models to predict the risk of lymphovascular invasion (LVI) in patients with invasive breast cancer.MethodsThis retrospective study included a total of 175 patients with confirmed invasive breast cancer who had known LVI status and preoperative MRI from two tertiary centers. The patients from center 1 was randomly divided into a training set (n=99) and a validation set (n = 26), while the patients from center 2 was used as a test set (n=50). A total of 1409 radiomic features were extracted from the T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), respectively. A three-step feature selection including SelectKBest, interclass correlation coefficients (ICC), and least absolute shrinkage and selection operator (LASSO) was performed to identify the features most associated with LVI. Subsequently, a Support Vector Machine (SVM) classifier was trained to develop single-layer radiomic models and fusion radiomic models. Model performance was evaluated and compared by the area under the curve (AUC), sensitivity, and specificity.ResultsBased on one feature of wavelet-HLH_gldm_GrayLevelVariance, the ADC radiomic model achieved an AUC of 0.87 (95% confidence interval [CI]: 0.80–0.94) in the training set, 0.87 (0.70-1.00) in the validation set, and 0.77 (95%CI: 0.64-0.86) in the test set. However, the combination of radiomic features derived from other MR sequences failed to yield incremental value.ConclusionsADC-based radiomic model demonstrated a favorable performance in predicting LVI prior to surgery in patients with invasive breast cancer. Such model holds the potential for improving clinical decision-making regarding treatment for breast cancer.

As a major infectious disease, tuberculosis (TB) still poses a threat to people’s health in China. As a triage test for TB, reading chest radiography with traditional approach ends up with high inter-radiologist and intra-radiologist variability, moderate specificity and a waste of time and medical resources. Thus, this study established a deep convolutional neural network (DCNN) based artificial intelligence (AI) algorithm, aiming at diagnosing TB on posteroanterior chest X-ray photographs in an effective and accurate way. Altogether, 5,000 patients with TB and 4,628 patients without TB were included in the study, totaling to 9,628 chest X-ray photographs analyzed. Splitting the radiographs into a training set (80.4%) and a testing set (19.6%), three different DCNN algorithms, including ResNet, VGG, and AlexNet, were trained to classify the chest radiographs as images of pulmonary TB or without TB. Both the diagnostic accuracy and the area under the receiver operating characteristic curve were used to evaluate the performance of the three AI diagnosis models. Reaching an accuracy of 96.73% and marking the precise TB regions on the radiographs, ResNet algorithm-based AI outperformed the rest models and showed excellent diagnostic ability in different clinical subgroups in the stratification analysis. In summary, the ResNet algorithm-based AI diagnosis system provided accurate TB diagnosis, which could have broad prospects in clinical application for TB diagnosis, especially in poor regions with high TB incidence.

Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have been conducted targeting at TB to reduce the diagnostic burden. However, most researches are conducted in the developed urban areas. The feasibility of applying AI in low-resource settings remains unexplored. In this study, we apply an automated detection (AI) system to screen a large population in an underdeveloped area and evaluate feasibility and contribution of apply AI to help local radiologists detect and diagnose TB using chest X-ray (CXR) images. First, we divide image data into one training dataset including 2627 TB-positive cases and 7375 TB-negative cases and one testing dataset containing 276 TB-positive cases and 619 TB-negative cases, respectively. Next, in building AI system, the experiment includes image labeling and preprocessing, model training and testing. A segmentation model named TB-UNet is also built to detect diseased regions, which uses ResNeXt as the encoder of U-Net. We use AI-generated confidence score to predict the likelihood of each testing case being TB-positive. Then, we conduct two experiments to compare results between the AI system and radiologists with and without AI assistance. Study results show that AI system yields TB detection accuracy of 85%, which is much higher than detection accuracy of radiologists (62%) without AI assistance. In addition, with AI assistance, the TB diagnostic sensitivity of local radiologists is improved by 11.8%. Therefore, this study demonstrates that AI has great potential to help detection, prevention, and control of TB in low-resource settings, particularly in areas with scant doctors and higher rates of the infected population.

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