CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma.

Background: To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images. Methods: This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS. Results: To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively. Conclusions: This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: A retrospective cohort study.

OBJECTIVE: We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in patients with locally advanced cervical cancer (LACC) undergoing intensity-modulated radiation therapy and brachytherapy. DESIGN: A retrospective cohort study. SETTING: Xiangya Hospital of Central South University, Changsha, Hunan, China. POPULATION: Two hundred and fifty-seven LACC patients who were treated with intensity-modulated radiotherapy from 2014 to 2017. METHODS: Patients were allocated into the training/validation sets (3:1 ratio) using proportional random sampling, resulting in the same proportion of groups in the two sets. We extracted 254 radiomic features from each of the gross target volume, pelvis and sacral vertebrae. The sequentially backward elimination support vector machine algorithm was used for feature selection and end point prediction. MAIN OUTCOMES AND MEASURES: Clinical end points include tumour complete response (CR), 5-year overall survival (OS), anaemia, and leucopenia. RESULTS: A combination of ten clinicopathological parameters and 34 radiomic features performed best for predicting CR (validation balanced accuracy: 80.8%). The validation balanced accuracy of 54 radiomic features was 85.8% for OS, and their scores can stratify patients into the low-risk and high-risk groups (5-year OS: 95.5% versus 36.4%, p < 0.001). The clinical and radiomic models were also predictive of anaemia and leucopenia (validation balanced accuracies: 71.0% and 69.9%). CONCLUSION: This study demonstrated that combining clinicopathological parameters with CT-based radiomics may have value for predicting clinical end points in LACC. If validated, this model may guide therapeutic strategy to optimise the effectiveness and minimise toxicity or treatment for LACC.

The length of stay and inpatient burden in inpatients with different psoriasis subtypes.

Background: Heavy disease burden of psoriasis has been indicated by previous studies. However, the cost of care and length of stay (LOS) in inpatients with different psoriasis subtypes were rarely addressed. This study aimed to investigate the cost of care and LOS in Chinese patients with different psoriasis types and to clarify the independent factors affecting LOS. Methods: We conducted a cross-sectional study by enrolling patients with psoriasis who were hospitalized between 13 Feb 2017 and 29 Mar 2021. Demographic and clinical characteristics of the patients were collected by reviewing their Electronic Medical Records. Multivariate linear regression was used to estimate the associations with adjustments. Results: A total of 310 adult patients with psoriasis were included (mean cost of care: 13.0±22.3 kCNY; mean LOS: 7.9±4.3 days). Statistically significant differences were found among patients with different psoriasis subtypes in LOS (P<0.001) but not in the cost of care (P=0.530). Relative to psoriasis vulgaris, pustular psoriasis (Adjusted coefficient: 2.37, 95% confidence interval (CI): 0.87-3.87) and erythrodermic psoriasis (Adjusted coefficient: 2.92, 95%CI: 1.38-4.47) were significantly associated with an increased LOS. Meanwhile, respiratory tract infections (Adjusted coefficient: 1.60, 95%CI: 0.11-3.10) also significantly increased the LOS. On the contrary, a decreased LOS was found in psoriatic arthritis patients treated with TNF-alpha inhibitors (Adjusted coefficient: -2.21, 95%CI: -4.37 to -0.05). Conclusions: LOS differed significantly among different psoriasis subtypes while the inpatient burden for a single hospitalization was alike. Infection is an important factor associated with a longer LOS. TNF-alpha inhibitors evidently reduced the total hospital stay period for patients with psoriatic arthritis.

Extraction of entity relations from Chinese medical literature based on multi-scale CRNN.

Background: Entity relation extraction technology can be used to extract entities and relations from medical literature, and automatically establish professional mapping knowledge domains. The classical text classification model, convolutional neural networks for sentence classification (TEXTCNN), has been shown to have good classification performance, but also has a long-distance dependency problem, which is a common problem of convolutional neural networks (CNNs). Recurrent neural networks (RNN) address the long-distance dependency problem but cannot capture text features at a specific scale in the text. Methods: To solve these problems, this study sought to establish a model with a multi-scale convolutional recurrent neural network for Sentence Classification (TEXTCRNN) to address the deficiencies in the 2 neural network structures. In entity relation extraction, the entity pair is generally composed of a subject and an object, but as the subject in the entity pair of medical literature is always omitted, it is difficult to use this coding method to obtain general entity position information. Thus, we proposed a new coding method to obtain entity position information to re-establish the relationship between subject and object and complete the entity relation extraction. Results: By comparing the benchmark neural network model and 2 typical multi-scale TEXTCRNN models, the TEXTCRNN [bidirectional long- and short-term memory (BiLSTM)] and TEXTCRNN [double-layer stacking gated recurrent unit (GRU)], the results showed that the multi-scale CRNN model had the best F1 value performance, and the TEXTCRNN (double-layer stacking GRU) was more capable of entity relation classification when the same entity word did not belong to the same entity relation. Conclusions: The experimental results of the entity relation extraction from Pharmacopoeia of the People's Republic of China-Guidelines for Clinical Drug Use-Volume of Chemical Drugs and Biological Products showed that entity relation extraction could effectively proceed using the new labeling method. Additionally, compared to typical neural network models, including the TEXTCNN, GRU, and BiLSTM, the multi-scale convolutional recurrent neural network structure had advantages across several evaluation indicators.

Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features.

Objectives: To develop and validate the model for distinguishing brain abscess from cystic glioma by combining deep transfer learning (DTL) features and hand-crafted radiomics (HCR) features in conventional T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). Methods: This single-center retrospective analysis involved 188 patients with pathologically proven brain abscess (102) or cystic glioma (86). One thousand DTL and 105 HCR features were extracted from the T1WI and T2WI of the patients. Three feature selection methods and four classifiers, such as k-nearest neighbors (KNN), random forest classifier (RFC), logistic regression (LR), and support vector machine (SVM), for distinguishing brain abscess from cystic glioma were compared. The best feature combination and classifier were chosen according to the quantitative metrics including area under the curve (AUC), Youden Index, and accuracy. Results: In most cases, deep learning-based radiomics (DLR) features, i.e., DTL features combined with HCR features, contributed to a higher accuracy than HCR and DTL features alone for distinguishing brain abscesses from cystic gliomas. The AUC values of the model established, based on the DLR features in T2WI, were 0.86 (95% CI: 0.81, 0.91) in the training cohort and 0.85 (95% CI: 0.75, 0.95) in the test cohort, respectively. Conclusions: The model established with the DLR features can distinguish brain abscess from cystic glioma efficiently, providing a useful, inexpensive, convenient, and non-invasive method for differential diagnosis. This is the first time that conventional MRI radiomics is applied to identify these diseases. Also, the combination of HCR and DTL features can lead to get impressive performance.

Entity relation extraction from electronic medical records based on improved annotation rules and BiLSTM-CRF.

BACKGROUND: Extracting entities and their relationships from electronic medical records (EMRs) is an important research direction in the development of medical informatization. Recently, a method was proposed to transform entity relation extraction into entity recognition by using annotation rules, and then solve the problem of relation extraction by an entity recognition model. However, this method cannot deal with one-to-many entity relationship problems. METHODS: This paper combined the bidirectional long- and short-term memory-conditional random field (BiLSTM-CRF) deep learning model with an improvement of sequence annotation rules, hided relationships between entities in entity labels, then the problem of one-to-many named entity relation extraction in EMRs was transformed into entity recognition based on relation sets, and entity extraction was carried out through the entity recognition model. RESULTS: Entity extraction was achieved through the entity recognition model. The result of entity recognition was transformed into the corresponding entity relationship, thus completing the task of one-to-many entity relation extraction by the improved annotation rules, the accuracy rate of proposed method reaches 83.46%, the recall rate is 81.12%, and the value of comprehensive index F1 is 0.8227. CONCLUSIONS: Through the annotation analysis of EMRs, our experimental results show that the improved annotation rules can effectively complete the task of one-to-many medical entity relation extraction from EMRs.

Prevalence of hepatitis B virus, hepatitis C virus, human immunodeficiency virus and Treponema pallidum infections in hospitalized patients before transfusion in Xiangya hospital Central South University, China from 2011 to 2016.

BACKGROUND: Human immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV) and Treponema pallidum (TP) infections are considered classic transfusion-transmissible infections (TTIs). Few data are available about the prevalence of TTIs in patients before blood transfusion in China. This study aimed to investigate the seroprevalence of four TTIs among patients before blood transfusion in Xiangya Hospital Central South University, China. METHODS: From 2011 to 2016, 442,121 hospitalized patients before possible blood transfusion were tested for hepatitis B surface antigen (HBsAg), anti-HCV, syphilis antibody (anti-TP) and anti-HIV. RESULTS: Of the 442,121 patients, the overall positivity of the four TTI serum markers was 15.35%. The positive rates of HBsAg, anti-HCV, anti-HIV and anti-TP were 10.98, 1.43, 0.16 and 2.78%, respectively. TTI serum markers showed a significant difference by gender, with positive rates of 17.98% for males and 12.79% for females. The prevalence of TTI serum markers varied significantly by age. The overall co-infection rate was 0.63%, and the top three multiple infections were HBV-TP, HBV-HCV, and HCV-TP. The co-infection rates of HBV-TP and HBV-HCV showed a significant decrease from 2011 to 2016, while the rates of other co-infections remained stable. CONCLUSIONS: The prevalence of TTIs in patients before blood transfusion is much higher compared to that in blood donors in the region. The infection rates of HIV and TP increased, and the infection rate of HBsAg decreased in recent years.