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Necrotizing lymphadenitis is arare disease. It is often misdiagnosed because of the lack of typical clinical manifestations. It is worth noting that necrotizing lymphadenitis may be aprecursor lesion of systemic lupus erythematosus or tumours, so regular follow-up is needed to facilitate early diagnosis. Here, we report acase and conduct aliterature analysis summarizing the clinical features of necrotizing lymphadenitis and its treatment and management practices.

A16-year-old young woman presented with fever and lymphadenopathy as the main clinical manifestations, accompanied by arash during fever that disappeared as the fever subsided. After completing imaging and laboratory examinations, we excluded other diseases such as infections, autoimmune diseases, and malignant tumours. Finally, we diagnosed the patient with necrotizing lymphadenitis based on the results of lymph node biopsy. The symptoms of the patient improved after glucocorticoid treatment, and she was followed up for half ayear without recurreducing unnecessary evaluation and treatment.
An effective therapeutic option has not yet been established for hepatocellular carcinoma (HCC) invading the hepatic vein (HV) or inferior vena cava (IVC). This study aimed to determine the therapeutic effect of transarterial chemoembolization (TACE) in HCC patients with HV or IVC invasion, and to build a risk prediction model.

Data from patients who underwent TACE as a first-line treatment for HCC invading the HV or IVC between 1997 and 2019 were retrospectively evaluated.

Data from 296 patients were included (1997-2006 comprised the training cohort, n = 174; 2007-2019 comprised the validation cohort, n = 122). The median post-TACE survival was 7.3 months and an objective tumor response was achieved in 34.1% of patients. Multivariable Cox analysis of the training cohort identified five pretreatment factors (maximal tumor size > 10 cm, infiltrative HCC, combined portal vein invasion, extrahepatic metastasis, and ECOG performance status 1), which were used to create predictive models for overall survieated with TACE, five factors were selected from a multivariate Cox regression model for overall survival. • The combination of these factors helped to identify two prognostic categories low- and high-risk. • The predictive model can help to select candidates who will benefit most from TACE in this patient group.
A convolutional neural network (CNN) was adapted to automatically detect early-stage nasopharyngeal carcinoma (NPC) and discriminate it from benign hyperplasia on a non-contrast-enhanced MRI sequence for potential use in NPC screening programs.

We retrospectively analyzed 412 patients who underwent T2-weighted MRI, 203 of whom had biopsy-proven primary NPC confined to the nasopharynx (stage T1) and 209 had benign hyperplasia without NPC. Thirteen patients were sampled randomly to monitor the training process. We applied the Residual Attention Network architecture, adapted for three-dimensional MR images, and incorporated a slice-attention mechanism, to produce a CNN score of 0-1 for NPC probability. Threefold cross-validation was performed in 399 patients. CNN scores between the NPC and benign hyperplasia groups were compared using Student's t test. Receiver operating characteristic with the area under the curve (AUC) was performed to identify the optimal CNN score threshold.

In each fold, significant d-based algorithm had a sensitivity of 92.4% and specificity of 90.6% for detecting early-stage nasopharyngeal carcinoma.
• The convolutional neural network (CNN)-based algorithm could automatically discriminate between malignant and benign diseases using T2-weighted fat-suppressed MR images. selleckchem • The CNN-based algorithm had an accuracy of 91.5% with an area under the receiver operator characteristic curve of 0.96 for discriminating early-stage T1 nasopharyngeal carcinoma from benign hyperplasia. • The CNN-based algorithm had a sensitivity of 92.4% and specificity of 90.6% for detecting early-stage nasopharyngeal carcinoma.
We aimed to find the best machine learning (ML) model using
F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for evaluating metastatic mediastinal lymph nodes (MedLNs) in non-small cell lung cancer, and compare the diagnostic results with those of nuclear medicine physicians.

A total of 1329 MedLNs were reviewed. Boosted decision tree, logistic regression, support vector machine, neural network, and decision forest models were compared. The diagnostic performance of the best ML model was compared with that of physicians. The ML method was divided into ML with quantitative variables only (MLq) and adding clinical information (MLc). We performed an analysis based on the
F-FDG-avidity of the MedLNs.

The boosted decision tree model obtained higher sensitivity and negative predictive values but lower specificity and positive predictive values than the physicians. There was no significant difference between the accuracy of the physicians and MLq (79.8% vs. 76.8%, p = 0. than nuclear medicine physicians. • Machine learning could improve the diagnostic significance of metastatic mediastinal lymph nodes, especially in mediastinal lymph nodes with low 18F-FDG-avidity.
• Machine learning using two-class boosted decision tree model revealed the highest value of area under curve, and it showed higher sensitivity and negative predictive values but lower specificity and positive predictive values than nuclear medicine physicians. • The diagnostic results from machine learning method after adding clinical information to the quantitative variables improved accuracy significantly than nuclear medicine physicians. • Machine learning could improve the diagnostic significance of metastatic mediastinal lymph nodes, especially in mediastinal lymph nodes with low 18F-FDG-avidity.
• Identify, assure, and measure major sources of variability affecting the MRI-directed biopsy pathway for prostate cancer diagnosis.• Develop strategies to control and minimize variations that impair pathway effectiveness including the performance of main players and team working.• Assure end-to-end quality of the diagnostic chain with robust multidisciplinary team working.
• Identify, assure, and measure major sources of variability affecting the MRI-directed biopsy pathway for prostate cancer diagnosis.• Develop strategies to control and minimize variations that impair pathway effectiveness including the performance of main players and team working.• Assure end-to-end quality of the diagnostic chain with robust multidisciplinary team working.
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