Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study

This study aims to automatically analyze and extract abnormalities in the lung field due to Coronavirus Disease 2019 (COVID-19). Types of abnormalities that can be detected are Ground Glass Opacity (GGO) and consolidation. The proposed method can also identify the location of the abnormality in the lung field, that is, the central and peripheral lung area. The location and type of these abnormalities affect the severity and confidence level of a patient suffering from COVID-19. The detection results using the proposed method are compared with the results of manual detection by radiologists. From the experimental results, the proposed system can provide an average error of 0.059 for the severity score and 0.069 for the confidence level. This method has been implemented in a web-based application for general users.•A method to detect the appearance of viral pneumonia imaging features, namely Ground Glass Opacity (GGO) and consolidation on the chest Computed Tomography (CT) scan images.•This method can separate the lung field to the right lung and the left lung, and it also can identify the detected imaging feature's location in the central or peripheral of the lung field.•Severity level and confidence level of the patient's suffering are measured.

Deep learning for risk stratification of thymoma pathological subtypes based on preoperative CT images

OBJECTIVES

This study aims to develop an innovative, deep model for thymoma risk stratification using preoperative CT images. Current algorithms predominantly focus on radiomic features or 2D deep features and require manual tumor segmentation by radiologists, limiting their practical applicability.

METHODS

The deep model was trained and tested on a dataset comprising CT images from 147 patients (82 female; mean age, 54 years ± 10) who underwent surgical resection and received subsequent pathological confirmation. The eligible participants were divided into a training cohort (117 patients) and a testing cohort (30 patients) based on the CT scan time. The model consists of two stages: 3D tumor segmentation and risk stratification. The radiomic model and deep model (2D) were constructed for comparative analysis. Model performance was evaluated through dice coefficient, area under the curve (AUC), and accuracy.

RESULTS

In both the training and testing cohorts, the deep model demonstrated better performance in differentiating thymoma risk, boasting AUCs of 0.998 and 0.893 respectively. This was compared to the radiomic model (AUCs of 0.773 and 0.769) and deep model (2D) (AUCs of 0.981 and 0.760). Notably, the deep model was capable of simultaneously identifying lesions, segmenting the region of interest (ROI), and differentiating the risk of thymoma on arterial phase CT images. Its diagnostic prowess outperformed that of the baseline model.

CONCLUSIONS

The deep model has the potential to serve as an innovative decision-making tool, assisting on clinical prognosis evaluation and the discernment of suitable treatments for different thymoma pathological subtypes.

KEY POINTS

• This study incorporated both tumor segmentation and risk stratification. • The deep model, using clinical and 3D deep features, effectively predicted thymoma risk. • The deep model improved AUCs by 16.1pt and 17.5pt compared to radiomic model and deep model (2D) respectively.

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

Background

Accurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade.

Methods

A total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice.

Results

The intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort.

Conclusion

This study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

Evaluation of the therapeutic action of original antiviral drug in SARS-CoV-2

Purpose of this article is to study the possible direct antiviral effect of "Armenikum" on SARS-CoV-2, conduct an in vitro study on the SARS-CoV-2 encephalomocarditis virus, and an in vivo study on the Syrian hamster model. Human coronavirus SARS-CoV-2 (delta strain) was used as the virus. Two groups of four-specimen hamsters were used to study the therapeutic activity of the drug during 48 h after infecting. One group of hamsters served as positive control and was infected with the virus at a similar dose as experimental one and was used as a control of pathology induced by the viral infection till the end of the experiment. Another group of hamsters (four of them) was injected physiological solution and was used as a control. The Syrian hamsters underwent a clinical blood test and computed tomography. "Armenikum" in the form of an injection has a significant antiviral effect on the human coronavirus SARS-CoV-2, credibly reducing the titers of the virus and the time of its elimination from the Syrian hamsters, significantly mitigating the viral infection. "Armenikum" in the form of an injection drug almost completely removes the pathological effect of the virus in the lungs of the hamsters.

Specific molecular peak analysis by ion mobility spectrometry of volatile organic compounds in urine of COVID-19 patients: A novel diagnostic approach

INTRODUCTION

SARS-CoV-2 is usually diagnosed from naso-/oropharyngeal swabs which are uncomfortable and prone to false results. This study investigated a novel diagnostic approach to Covid-19 measuring volatile organic compounds (VOC) from patients' urine.

METHODS

Between June 2020 and February 2021, 84 patients with positive RT-PCR for SARS-CoV-2 were recruited as well as 54 symptomatic individuals with negative RT-PCR. Midstream urine samples were obtained for VOC analysis using ion mobility spectrometry (IMS) which detects individual molecular components of a gas sample based on their size, configuration, and charge after ionization.

RESULTS

Peak analysis of the 84 Covid and 54 control samples showed good group separation. In total, 37 individual specific peaks were identified, 5 of which (P134, 198, 135, 75, 136) accounted for significant differences between groups, resulting in sensitivities of 89-94% and specificities of 82-94%. A decision tree was generated from the relevant peaks, leading to a combined sensitivity and specificity of 98% each.

DISCUSSION

VOC-based diagnosis can establish a reliable separation between urine samples of Covid-19 patients and negative controls. Molecular peaks which apparently are disease-specific were identified. IMS is an additional non-invasive and cheap device for the diagnosis of this ongoing endemic infection. Further studies are needed to validate sensitivity and specificity.

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days

Objectives

This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT).

Methods

From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results.

Results

Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful.

Conclusion

The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

Interest of Chest CT to Assess the Prognosis of SARS-CoV-2 Pneumonia: An In-Hospital-Based Experience in Sub-Saharan Africa

Methods

We included all patients with respiratory symptoms (dyspnea, fever, and cough) and/or respiratory failure admitted to the SOS Médecins de nuit SARL hospital, DR Congo, during the 2nd and 3rd waves of the COVID-19 pandemic. The diagnosis of COVID-19 was established based on RT-PCR anti-SARS-CoV-2 tests (G1 (RT-PCR positive) vs. G2 (RT-PCR negative)), and all patients had a chest CT on the day of admission. We retrieved the digital files of patients, precisely the clinical, biological, and chest CT parameters of the day of admission as well as the vital outcome (survival or death). Chest CT were read by a very high-definition console using Advantage Windows software and exported to the hospital network using the RadiAnt DICOM viewer. To determine the threshold for the percentage of lung lesions associated with all-cause mortality, we used ROC curves. Factors associated with death, including chest CT parameters, were investigated using logistic regression analysis.

Results

The study included 200 patients (average age 56.2 ± 15.2 years; 19% diabetics and 4.5% obese), and COVID-19 was confirmed among 56% of them (G1). Chest CT showed that ground glass (72.3 vs. 39.8%), crazy paving (69.6 vs. 17.0%), and consolidation (83.9 vs. 22.7%), with bilateral and peripheral locations (68.8 vs. 30.7%), were more frequent in G1 vs. G2 (p < 0.001). No case of pulmonary embolism and fibrosis had been documented. The lung lesions affecting 30% of the parenchyma were informative in predicting death (area under the ROC curve at 0.705, p = 0.017). In multivariate analysis, a percentage of lesions affecting 50% of the lung parenchyma increased the risk of dying by 7.194 (1.656-31.250).

Conclusion

The chest CT demonstrated certain characteristic lesions more frequently in patients in whom the diagnosis of COVID-19 was confirmed. The extent of lesions affecting at least half of the lung parenchyma from the first day of admission to hospital increases the risk of death by a factor of 7.

Severity of COVID-19 infection in patients with COVID-19 combined with diabetes

PURPOSE

This study aimed to analyse the correlation between blood glucose control and the severity of COVID-19 infection in patients with diabetes.

METHODS

Clinical and imaging data of a total of 146 patients with diabetes combined with COVID-19 who visited our hospital between December 2022 and January 2023 were retrospectively collected. The patients were divided into the 'good blood glucose control' group and the 'poor blood glucose control' group based on an assessment of their blood glucose control. The clinical data, computed tomography (CT) appearance and score and the severity of COVID-19 infection of the two groups were compared, with the severity of COVID-19 infection being the dependent variable to analyse other influencing factors.

RESULTS

The group with poor blood glucose control showed a higher lobar involvement degree and total CT severity score (CTSS) than the group with good blood glucose control (13.30 ± 5.25 vs. 10.38 ± 4.84, p < 0.05). The two groups exhibited no statistically significant differences in blood lymphocyte, leukocyte, C-reaction protein, pleural effusion, consolidation, ground glass opacity or crazy-paving signs. Logistic regression analysis showed that the total CTSS significantly influences the clinical severity of patients (odds ratio 1.585, p < 0.05), whereas fasting plasma glucose and blood glucose control are not independent factors influencing clinical severity (both p > 0.05). The area under the curve (AUC) of CTSS prediction of critical COVID-19 was 0.895 with sensitivity of 79.3% and specificity of 88.1% when the threshold value is 12.

CONCLUSION

Blood glucose control is significantly correlated with the CTSS; the higher the blood glucose is, the more severe the lung manifestation. The CTSS can also be used to evaluate and predict the clinical severity of COVID-19.

Use of serum KL-6 and chest radiographic severity grade to predict 28-day mortality in COVID-19 patients with pneumonia: a retrospective cohort study

BACKGROUND

Coronavirus disease 2019 (COVID-19) has had a global social and economic impact. An easy assessment procedure to handily identify the mortality risk of inpatients is urgently needed in clinical practice. Therefore, the aim of this study was to develop a simple nomogram model to categorize patients who might have a poor short-term outcome.

METHODS

A retrospective cohort study of 189 COVID-19 patients was performed at Shanghai Ren Ji Hospital from December 12, 2022 to February 28, 2023. Chest radiography and biomarkers, including KL-6 were assessed. Risk factors of 28-day mortality were selected by a Cox regression model. A nomogram was developed based on selected variables by SMOTE strategy. The predictive performance of the derived nomogram was evaluated by calibration curve.

RESULTS

In total, 173 patients were enrolled in this study. The 28-day mortality event occurred in 41 inpatients (23.7%). Serum KL-6 and radiological severity grade (RSG) were selected as the final risk factors. A nomogram model was developed based on KL-6 and RSG. The calibration curve suggested that the nomogram model might have potential clinical value. The AUCs for serum KL-6, RSG, and the combined score in the development group and validation group were 0.885 (95% CI: 0.804-0.952), 0.818 (95% CI: 0.711-0.899), 0.868 (95% CI: 0.776-0.942) and 0.932 (95% CI: 0.862-0.997), respectively.

CONCLUSIONS

Our results suggested that the nomogram based on KL-6 and RSG might be a potential method for evaluating 28-day mortality in COVID-19 patients. A high combined score might indicate a poor outcome in COVID-19 patients with pneumonia.

Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study

BACKGROUND

Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours.

METHODS

We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model.

RESULTS

The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively.

CONCLUSIONS

The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery

Background

Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling.

Methods

A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created.

Results

Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence (P < .05). The combined model based on these above factors had significantly higher predictive performance (AUC = 0.895, 95% confidence interval [CI] = [0.839-0.937]) than the clinical data model (AUC = 0.735, 95% CI = [0.6 62-0.800]) and radiomics model (AUC = 0.842, 95% CI = [0.779-0.894]). Decision curve analysis also confirmed the higher clinical net benefit of the combined model, and the same results were validated in the test set. The novel nomogram developed based on the combined model helps predict the recurrence of MH-GIST.

Conclusions

The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making.

Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases

OBJECTIVES

To evaluate and analyze radiomics models based on non-contrast-enhanced computed tomography (CT) and different phases of contrast-enhanced CT in predicting Ki-67 proliferation index (PI) among patients with pathologically confirmed gastrointestinal stromal tumors (GISTs).

METHODS

A total of 383 patients with pathologically proven GIST were divided into a training set (n = 218, vendor 1) and 2 validation sets (n = 96, vendor 2; n = 69, vendors 3-5). Radiomics features extracted from the most recent non-contrast-enhanced and three contrast-enhanced CT scan prior to pathological examination. Random forest models were trained for each phase to predict tumors with high Ki-67 proliferation index (Ki-67>10%) and were evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics on the validation sets.

RESULTS

Out of 107 radiomics features extracted from each phase of CT images, four were selected for analysis. The model trained using the non-contrast-enhanced phase achieved an AUC of 0.792 in the training set and 0.822 and 0.711 in the two validation sets, similar to models trained on different contrast-enhanced phases (p > 0.05). Several relevant features, including NGTDM Busyness and tumor size, remained predictive in non-contrast-enhanced and different contrast-enhanced images.

CONCLUSION

The results of this study indicate that a radiomics model based on non-contrast-enhanced CT matches that of models based on different phases of contrast-enhanced CT in predicting the Ki-67 PI of GIST. GIST may exhibit similar radiological patterns irrespective of the use of contrast agent, and such radiomics features may help quantify these patterns to predict Ki-67 PI of GISTs.

CLINICAL RELEVANCE STATEMENT

GIST may exhibit similar radiomics patterns irrespective of contrast agent; thus, radiomics models based on non-contrast-enhanced CT could be an alternative for risk stratification in GIST patients with contraindication to contrast agent.

KEY POINTS

• Performance of radiomics models in predicting Ki-67 proliferation based on different CT phases is evaluated. • Non-contrast-enhanced CT-based radiomics models performed similarly to contrast-enhanced CT in risk stratification in GIST patients. • NGTDM Busyness remains stable to contrast agents in GISTs in radiomics models.

Dural involvement in central nervous system langerhans cells histiocytosis (LCH) on FDG PET/CT: Case report and review of CNS manifestations of LCH on PET/CT

We report a case of multisystem Langerhans cell histiocytosis in a pediatric patient with central nervous system involvement, highlighting F-18(FDG) uptake characteristics of dural sites of disease. We also highlight the advantages of functional data offered by FDG-PET as a useful follow-up tool to assess viability and, therefore, treatment response of previously known central nervous system lesions. The utility of recognizing characteristic patterns of FDG uptake in dural disease is also applicable in cases of diagnostic uncertainty, such as when evaluating isolated dural lesions or when distinguishing between Langerhans cell histiocytosis and similar appearing lesions such as meningiomas.

Transmission electron microscopy reveals the presence of SARS-CoV-2 in human spermatozoa associated with an ETosis-like response

BACKGROUND

Severe acute syndrome coronavirus 2 can invade a variety of tissues, including the testis. Even though this virus is scarcely found in human semen polymerase chain reaction tests, autopsy studies confirm the viral presence in all testicular cell types, including spermatozoa and spermatids.

OBJECTIVE

To investigate whether the severe acute syndrome coronavirus 2 is present inside the spermatozoa of negative polymerase chain reaction-infected men up to 3 months after hospital discharge.

MATERIALS AND METHODS

This cross-sectional study included 13 confirmed moderate-to-severe COVID-19 patients enrolled 30-90 days after the diagnosis. Semen samples were obtained and examined with real-time polymerase chain reaction for RNA detection and by transmission electron microscopy.

RESULTS

In moderate-to-severe clinical scenarios, we identified the severe acute syndrome coronavirus 2 inside spermatozoa in nine of 13 patients up to 90 days after discharge from the hospital. Moreover, some DNA-based extracellular traps were reported in all studied specimens.

DISCUSSION AND CONCLUSION

Although severe acute syndrome coronavirus 2 was not present in the infected men's semen, it was intracellularly present in the spermatozoa till 3 months after hospital discharge. The Electron microscopy (EM) findings also suggest that spermatozoa produce nuclear DNA-based extracellular traps, probably in a cell-free DNA-dependent manner, similar to those previously described in the systemic inflammatory response to COVID-19. In moderate-to-severe cases, the blood-testes barrier grants little defence against different pathogenic viruses, including the severe acute syndrome coronavirus 2. The virus could also use the epididymis as a post-testicular route to bind and fuse to the mature spermatozoon and possibly accomplish the reverse transcription of the single-stranded viral RNA into proviral DNA. These mechanisms can elicit extracellular cell-free DNA formation. The potential implications of our findings for assisted conception must be addressed, and the evolutionary history of DNA-based extracellular traps as preserved ammunition in animals' innate defence might improve our understanding of the severe acute syndrome coronavirus 2 pathophysiology in the testis and spermatozoa.

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

Analysis of Vocal Signatures of COVID-19 in Cough Sounds: A Newer Diagnostic Approach Using Artificial Intelligence

BACKGROUND

Artificial intelligence (AI) based models are explored increasingly in the medical field. The highly contagious pandemic of coronavirus disease 2019 (COVID-19) affected the world and availability of diagnostic tools high resolution computed tomography (HRCT) and/or real-time reverse transcriptase-polymerase chain reaction (RTPCR) was very limited, costly and time consuming. Therefore, the use of AI in COVID-19 for diagnosis using cough sounds can be efficacious and cost effective for screening in clinic or hospital and help in early diagnosis and further management of patients.

OBJECTIVES

To develop an accurate and fast voice-processing AI software to determine voice-based signatures in discriminating COVID-19 and non-COVID-19 cough sounds for screening of COVID-19.

METHODOLOGY

A prospective study involving 117 patients was performed based on online and/or offline voice data collection of cough sounds of COVID-19 patients in isolation ward of a tertiary care teaching hospital and non-COVID-19 participants using a smart phone. A website-based AI software was developed to identify the cough sounds as COVID-19 or non-COVID-19. The data were divided into three segments including training set, validation set and test set. A pre-processing algorithm was utilized and combined with Short Time Fourier Transform feature representation and Logistic regression model. A precise software was used to identify vocal signatures and K-fold cross validation was carried out.

RESULT

A total of 117 audio recordings of cough sounds were collected through the developed website after inclusion-exclusion criteria out of which 52 have been marked belonging to COVID-19 positive, while 65 were marked as COVID-19 negative/unsure /never had COVID-19, which were assumed to be COVID-19 negative based on RT-PCR test results. The mean and standard error values for the accuracies attained at the end of each experiment in training, validation and testing set were found to be 67.34%±0.22, 58.57%±1.11 and 64.60%±1.79 respectively. The weight values were found to be positive which were contributing towards predicting the samples as COVID-19 positive with large spikes around 7.5 kHz, 7.8 kHz, 8.6 kHz and 11 kHz which can be used for classification.

CONCLUSION

The proposed AI based approach can be a helpful screening tool for COVID-19 using vocal sounds of cough. It can help the health system by reducing the cost burden and improving overall diagnosis and management of the disease.

Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis

PURPOSE

We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC).

METHODS

Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed.

RESULTS

Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively.

CONCLUSION

The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.

Evaluating Acute Pulmonary Changes in Coronavirus Disease 2019: A Comparative Analysis of Computed Tomography, Chest Radiography, Lung Ultrasound, Magnetic Resonance Imaging, and Positron Emission Tomography with Fluorodeoxyglucose Modalities

This review explores imaging's crucial role in acute Coronavirus Disease 2019 (COVID-19) assessment. High Resolution Computer Tomography is especially effective in detection of lung abnormalities. Chest radiography has limited utility in the initial stages of COVID-19 infection. Lung Ultrasound has emerged as a valuable, radiation-free tool in critical care, and Magnetic Resonance Imaging shows promise as a Computed Tomography alternative. Typical and atypical findings of COVID-19 by each of these modalities are discussed with emphasis on their prognostic value. Considerations for pediatric and immunocompromised cases are outlined. A comprehensive diagnostic approach is recommended, as radiological diagnosis remains challenging in the acute phase.

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

BACKGROUND

Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers.

METHODS

Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score.

RESULTS

A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers.

CONCLUSIONS

Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas

A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and typing of meningiomas are crucial for developing comprehensive and individualized diagnosis and treatment plans. The advancement of artificial intelligence (AI) in medical imaging, particularly radiomics and deep learning (DL), has contributed to the increasing research on meningioma grading and classification. These techniques are fast and accurate, involve fully automated learning, are non-invasive and objective, enable the efficient and non-invasive prediction of meningioma grades and classifications, and provide valuable assistance in clinical treatment and prognosis. This article provides a summary and analysis of the research progress in radiomics and DL for meningioma grading and classification. It also highlights the existing research findings, limitations, and suggestions for future improvement, aiming to facilitate the future application of AI in the diagnosis and treatment of meningioma.

CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors

To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.

Lobulated tumor contour as a predictor of preoperative tumor invasion of the lung or pericardium in thymoma patients

PURPOSE

Preoperative investigations to detect invasion to neighboring organs are important for deciding on the most appropriate surgical approach for thymoma. We evaluated preoperative computed tomography (CT) findings in thymoma patients to identify the CT features associated with tumor invasion.

METHODS

Clinicopathologic information on 193 patients who underwent surgical resection for thymoma at Chiba University Hospital between 2002 and 2016 was collected retrospectively. The surgical pathology identified invasion of thymoma in 35 patients: in the lung (n = 18), pericardium (n = 11), or both (n = 6). Contact lengths between the tumor contour and lung (CLTL) or pericardium (CLTP) were measured at the maximum section of the tumor on axial CT. Univariate and multivariate analyses were performed to analyze the relationship between pathological invasion of the lung or pericardium and the clinicopathologic features.

RESULTS

The mean CLTL and CLTP were significantly longer in patients with invasion of the neighboring organs than in those without invasion. A lobulated tumor contour was identified in 95.6% of the patients with invasion of the neighboring organs. A multivariate analysis revealed that a lobulated tumor contour was significantly associated with both lung and pericardial invasion.

CONCLUSIONS

A lobulated tumor contour was significantly associated with lung and/or pericardial invasion in thymoma patients.

The Novel Green Learning Artificial Intelligence for Prostate Cancer Imaging: A Balanced Alternative to Deep Learning and Radiomics

The application of artificial intelligence (AI) on prostate magnetic resonance imaging (MRI) has shown promising results. Several AI systems have been developed to automatically analyze prostate MRI for segmentation, cancer detection, and region of interest characterization, thereby assisting clinicians in their decision-making process. Deep learning, the current trend in imaging AI, has limitations including the lack of transparency "black box", large data processing, and excessive energy consumption. In this narrative review, the authors provide an overview of the recent advances in AI for prostate cancer diagnosis and introduce their next-generation AI model, Green Learning, as a promising solution.

Langerhans cell histiocytosis of the thyroid mimicking thyroiditis in a boy: a case report and literature review

BACKGROUND

Langerhans cell histiocytosis affecting the thyroid commonly presents with nonspecific clinical and radiological manifestations. Thyroid Langerhans cell histiocytosis is typically characterized by non-enhancing hypodense lesions with an enlarged thyroid on computed tomography medical images. Thyroid involvement in LCH is uncommon and typically encountered in adults, as is salivary gland involvement. Therefore, we present a unique pediatric case featuring simultaneous salivary and thyroid involvement in LCH.

CASE PRESENTATION

A 3-year-old boy with complaints of an anterior neck mass persisting for 1 to 2 months, accompanied by mild pain, dysphagia, and hoarseness. A physical examination revealed a 2.5 cm firm and tender mass in the left anterior neck. Laboratory examinations revealed normal thyroid function test levels. Ultrasonography revealed multiple heterogeneous hypoechoic nodules with unclear and irregular margins in both lobes of the thyroid. Contrast-enhanced neck computed tomography revealed an enlarged thyroid gland and bilateral submandibular glands with non-enhancing hypointense nodular lesions, and multiple confluent thin-walled small (< 1.5 cm) cysts scattered bilaterally in the lungs. Subsequently, a left thyroid excisional biopsy was performed, leading to a histopathological diagnosis of LCH. Immunohistochemical analysis of the specimen demonstrated diffuse positivity for S-100, CD1a, and Langerin and focal positivity for CD68. The patient received standard therapy with vinblastine and steroid, and showed disease regression during regular follow-up of neck ultrasonography.

CONCLUSIONS

Involvement of the thyroid and submandibular gland as initial diagnosis of Langerhans cell histiocytosis is extremely rare. It is important to investigate the involvement of affected systems. A comprehensive survey and biopsy are required to establish a definitive diagnosis.

"KAIZEN" method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals

Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the "KAIZEN checklist", which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models' AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.

An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio

Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables-gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.

Predicting the risk stratification of gastrointestinal stromal tumors using machine learning-based ultrasound radiomics

PURPOSE

This study aimed to use conventional ultrasound features, ultrasound radiomics, and machine learning algorithms to establish a predictive model to assess the risk of post-surgical recurrence of gastrointestinal stromal tumors (GISTs).

METHODS

This retrospective analysis included 230 patients with pathologically diagnosed GISTs. Radiomic features were extracted from manually annotated images. Radiomic features plus conventional ultrasound features were selected using the SelectKbest analysis of variance and stratified tenfold cross-validation recursive elimination methods. Finally, five different machine learning algorithms (logistic regression [LR], support vector machine [SVM], random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) were established to predict risk stratification of GISTs. The predictive performance of the established model was mainly evaluated based on the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy, whereas the predictive performance of the optimal machine learning algorithm and a radiologist's subjective assessment were compared using McNemar's test.

RESULTS

Seven radiomics features and one conventional ultrasound feature were selected to construct the machine learning models for GIST risk classification. The mentioned five machine learning models were able to predict the malignant potential of GISTs. LR and SVM outperformed other classifiers on the test set, with LR achieving an accuracy of 0.852 (AUC, 0.881; sensitivity, 0.871; specificity, 0.826) and SVM achieving an accuracy of 0.852 (AUC, 0.879; sensitivity, 0.839; specificity, 0.870), and proved significantly better than the radiologist (accuracy, 0.691; sensitivity, 0.645; specificity, 0.813).

CONCLUSION

Machine learning-based ultrasound radiomics features are able to noninvasively predict the biological risk of GISTs.

Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer

The diffusion-weighted imaging (DWI) technique is known for its capability to differentiate the diffusion of water molecules between cancerous and non-cancerous cervix tissues, which enhances the accuracy of detection. Despite the potential of DWI-MRI, its accuracy is limited by technical factors influencing in vivo data acquisition, thus impacting the quantification of radiomics features. This study aimed to measure the radiomics stability of manual and semi-automated segmentation on contrast limited adaptive histogram equalization (CLAHE)-enhanced DWI-MRI cervical images. Eighty diffusion-weighted MRI images were obtained from patients diagnosed with cervical cancer, and an active contour model was used to analyze the data. Radiomics analysis was conducted to extract the first statistical order, shape, and textural features with intraclass correlation coefficient (ICC) measurement. The results of the CLAHE segmentation approach showed a marked improvement when compared to the manual and semi-automated segmentation methods, with an ICC value of 0.990 ± 0.005 (p<0.05), compared to 0.864 ± 0.033 (p<0.05) and 0.554 ± 0.185 (p>0.05), respectively. The CLAHE segmentation displayed a higher level of robustness than the manual groups in terms of the features present in both categories. Thus, CLAHE segmentation is owing to its potential to generate radiomics features that are more durable and consistent.

The influence of perioperative nursing intervention in patients with ureteral calculi treated with URSL and its correlation to adverse event incidence: A retrospective study

To evaluate the effectiveness of perioperative nursing intervention in patients undergoing ureteroscopic lithotripsy (URSL) for ureteral stones and its implications for the incidence of adverse events, a total of 144 patients with ureteral stones admitted to our hospital from January 2021 to December 2022 were selected for retrospective analysis. They were divided into 2 groups based on their different nursing methods, with 72 patients in each group. The control group (CG) received routine nursing intervention, while the study group (SD) received refined perioperative nursing intervention. The surgical situation, effective stone removal rate, postoperative pain, inflammatory factors, stress response, and incidence of adverse events were compared between the 2 groups. In comparison with the CG, the SD demonstrated a significant reduction in gastrointestinal recovery time, urinary catheter removal time, and hospitalization duration, all presenting statistically significant disparities (P < .05). Notably, the SD exhibited a one-time stone removal rate significantly superior to that of the CG (P < .05). Similarly, the postoperative pain index was significantly lower in the SD (P < .05). Pre- and post-surgical serotonin (5-HT) levels in the SD were markedly lower than in the CG (P < .05). Postoperative levels of Interleukin-10 (IL-10), C-reactive protein (CRP), and white blood cells (WBC) were elevated in both groups, and gradually declined as the patients recovered. However, postoperative levels of IL-10, CRP, and WBC were significantly lower in the SD (P < .05). The SD also showed significantly lower levels of malondialdehyde and higher levels of superoxide dismutase (P < .05). Postoperative levels of cortisol, adrenocorticotropic hormone, and norepinephrine were elevated and progressively returned to normal over time, and were significantly lower in the study group (P < .05). Furthermore, the SD experienced a significant reduction in adverse event incidence compared with the CG (P < .05). Implementing refined perioperative nursing interventions for patients undergoing URSL can effectively decrease the incidence of adverse events, diminish the surgical stimulation of inflammation markers and oxidative stress indicators, and foster patient recovery.

Factors associated with gastrointestinal stromal tumor rupture and pathological risk: A single-center retrospective study

BACKGROUND

Gastrointestinal stromal tumor (GIST) is a rare gastrointestinal mesenchymal tumor with potential malignancy. Once the tumor ruptures, regardless of tumor size and mitotic number, it can be identified into a high-risk group. It is of great significance for the diagnosis, treatment, and prognosis of GIST if non-invasive examination can be performed before surgery to accurately assess the risk of tumor.

AIM

To identify the factors associated with GIST rupture and pathological risk.

METHODS

A cohort of 50 patients with GISTs, as confirmed by postoperative pathology, was selected from our hospital. Clinicopathological and computed tomography data of the patients were collected. Logistic regression analysis was used to evaluate factors associated with GIST rupture and pathological risk grade.

RESULTS

Pathological risk grade, tumor diameter, tumor morphology, internal necrosis, gas-liquid interface, and Ki-67 index exhibited significant associations with GIST rupture (P < 0.05). Gender, tumor diameter, tumor rupture, and Ki-67 index were found to be correlated with pathological risk grade of GIST (P < 0.05). Multifactorial logistic regression analysis revealed that male gender and tumor diameter ≥ 10 cm were independent predictors of a high pathological risk grade of GIST [odds ratio (OR) = 11.12, 95% confidence interval (95%CI): 1.81-68.52, P = 0.01; OR = 22.96, 95%CI: 2.19-240.93, P = 0.01]. Tumor diameter ≥ 10 cm, irregular shape, internal necrosis, gas-liquid interface, and Ki-67 index ≥ 10 were identified as independent predictors of a high risk of GIST rupture (OR = 9.67, 95%CI: 2.15-43.56, P = 0.01; OR = 35.44, 95%CI: 4.01-313.38, P < 0.01; OR = 18.75, 95%CI: 3.40-103.34, P < 0.01; OR = 27.00, 95%CI: 3.10-235.02, P < 0.01; OR = 4.43, 95%CI: 1.10-17.92, P = 0.04).

CONCLUSION

Tumor diameter, tumor morphology, internal necrosis, gas-liquid, and Ki-67 index are associated with GIST rupture, while gender and tumor diameter are linked to the pathological risk of GIST. These findings contribute to our understanding of GIST and may inform non-invasive examination strategies and risk assessment for this condition.

The clinical, functional, and radiological effect of long-term used immunosuppressive therapy for post-COVID-19 interstitial lung disease

BACKGROUND AND AIM

Persistent interstitial lung disease (ILD) after COVID-19 infection can lead to severe loss of respiratory function and a decrease in the quality of life. There is no consensus regarding the treatment of post-COVID-19-ILD. This study aims to investigate the effectiveness of immunosuppressive treatment for this group of patients.

METHODS

This retrospective observational study included patients diagnosed with post-COVID-19-ILD from 2021 to 2022. Patients who had pulmonary symptoms, required prolonged oxygen therapy, and/or had restrictive pulmonary function test (PFT) and/or DLCO <80%, with diffuse parenchymal involvement on high-resolution computed tomography (HRCT), were given immunosuppressive treatment with methylprednisolone and/or mycophenolate mofetil (MMF) and followed up for 6 months.

RESULTS

Among the 48 patients, 35 were treated. Two patients were excluded due to discontinued treatment and passed away before the study period ended. Of 33 cases, 21 (66.6%) were treated with methylprednisolone, 11 (33%) with methylprednisolone + MMF, and 1 (0.4%) with MMF alone. Comparing baseline and 6th-month data revealed significant improvement in mMRC score, saturation (SpO2), FVC, FVC%, FEV%, and DLCO% values (p<0.005). While regression was observed in all radiologic findings, regression in ground glass and reticulation was statistically significant (p<0.005). When the 1st and 6th-month data were compared, a significant increase was observed in SpO2 and DLCO% values (p=0.016) and there was a significant regression in reticulation radiologically (p=0.01).

CONCLUSIONS

Long-term immunosuppressive therapy may be preferred in proper cases of post-COVID-19-ILD as an effective and safe treatment option that improves the quality of life, respiratory parameters, and radiologic findings.

Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors

BACKGROUND

This study aimed to identify the diagnostic value of models constructed using computed tomography-based radiomics features for discrimination of benign and early stage malignant ovarian tumors.

METHODS

The imaging and clinicopathological data of 197 cases of benign and early stage malignant ovarian tumors (FIGO stage I/II), were retrospectively analyzed. The patients were randomly assigned into training data set and validation data set. Radiomics features were extracted from images of plain computed tomography scan and contrast-enhanced computed tomography scan, were then screened in the training data set, and a radiomics model was constructed. Multivariate logistic regression analysis was used to construct a radiomic nomogram, containing the traditional diagnostic model and the radiomics model. Moreover, the decision curve analysis was used to assess the clinical application value of the radiomics nomogram.

RESULTS

Six textural features with the greatest diagnostic efficiency were finally screened. The value of the area under the receiver operating characteristic curve showed that the radiomics nomogram was superior to the traditional diagnostic model and the radiomics model (P < 0.05) in the training data set. In the validation data set, the radiomics nomogram was superior to the traditional diagnostic model (P < 0.05), but there was no statistically significant difference compared to the radiomics model (P > 0.05). The calibration curve and the Hosmer-Lemeshow test revealed that the three models all had a great degree of fit (All P > 0.05). The results of decision curve analysis indicated that utilization of the radiomics nomogram to distinguish benign and early stage malignant ovarian tumors had a greater clinical application value when the risk threshold was 0.4-1.0.

CONCLUSIONS

The computed tomography-based radiomics nomogram could be a non-invasive and reliable imaging method to discriminate benign and early stage malignant ovarian tumors.

A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in 18F-PSMA-1007 PET Images of Patients Affected by Prostate Cancer

BACKGROUND

Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness.

AIM

We investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from 18F-PSMA-1007 Positron Emission Tomography (PET) images of 78 patients (43 Low Risk, 35 High Risk). Segmentation was repeated for each patient, thus leading to three datasets of segmentations. Then, feature extraction was performed for each dataset, and 1781 features (107 original, 930 Laplacian of Gaussian (LoG) features, 744 wavelet features) were extracted. Feature robustness and reproducibility were assessed through the intra class correlation coefficient (ICC) to measure agreement between the three segmentation methods. To assess the impact that the three methods had on machine learning models, feature selection was performed through a hybrid descriptive-inferential method, and selected features were given as input to three classifiers, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost and Neural Networks (NN), whose performance in discriminating between low-risk and high-risk patients have been validated through 30 times repeated five-fold cross validation.

CONCLUSIONS

Our study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68-0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34-0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.

Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers

Coronavirus disease (COVID-19) remains a significant global health challenge, prompting a transition from emergency response to comprehensive management strategies. Furthermore, the emergence of new variants of concern, such as BA.2.286, underscores the need for early detection and response to new variants, which continues to be a crucial strategy for mitigating the impact of COVID-19, especially among the vulnerable population. This study aims to anticipate patients requiring intensive care or facing elevated mortality risk throughout their COVID-19 infection while also identifying laboratory predictive markers for early diagnosis of patients. Therefore, haematological, biochemical, and demographic variables were retrospectively evaluated in 8,844 blood samples obtained from 2,935 patients before intensive care unit admission using an interpretable machine learning model. Feature selection techniques were applied using precision-recall measures to address data imbalance and evaluate the suitability of the different variables. The model was trained using stratified cross-validation with k=5 and internally validated, achieving an accuracy of 77.27%, sensitivity of 78.55%, and area under the receiver operating characteristic (AUC) of 0.85; successfully identifying patients at increased risk of severe progression. From a medical perspective, the most important features of the progression or severity of patients with COVID-19 were lactate dehydrogenase, age, red blood cell distribution standard deviation, neutrophils, and platelets, which align with findings from several prior investigations. In light of these insights, diagnostic processes can be significantly expedited through the use of laboratory tests, with a greater focus on key indicators. This strategic approach not only improves diagnostic efficiency but also extends its reach to a broader spectrum of patients. In addition, it allows healthcare professionals to take early preventive measures for those most at risk of adverse outcomes, thereby optimising patient care and prognosis.

Investigation of the diagnostic importance and accuracy of CT in the chest compared to the RT-PCR test for suspected COVID-19 patients in Jordan

This article aims to synthesize the existing literature on the implementation of public policies to incentivize the development of treatments for rare diseases, (diseases with very low prevalence and therefore with low commercial interest) otherwise known as orphan drugs. The implementation of these incentives in the United States (US), Japan, and in the European Union (EU) seems to be related to a substantial increase in treatments for these diseases, and has influenced the way the pharmaceutical research & development (R&D) system operates beyond this policy area. Despite the success of the Orphan Drug model, the academic literature also highlights the negative implications that these public policies have on affordability and access to orphan drugs, as well as on the prioritization of certain disease rare areas over others. The synthesis focuses mostly on the United States' Orphan Drug Act (ODA) as a model for subsequent policies in other regions and countries. It starts with a historical overview of the creation of the term "rare diseases", continues with a summary of the evidence available on the US ODA's positive and negative impacts, and provides a summary of the different proposals to reform these incentives in light of the negative outcomes described. Finally, it describes some key aspects of the Japanese and European policies, as well as some of the challenges captured in the literature related to their impact in Low- and Middle-Income Countries (LMICs).

COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests

Artificial intelligence (AI) systems for detection of COVID-19 using chest X-Ray (CXR) imaging and point-of-care blood tests were applied to data from four low resource African settings. The performance of these systems to detect COVID-19 using various input data was analysed and compared with antigen-based rapid diagnostic tests. Participants were tested using the gold standard of RT-PCR test (nasopharyngeal swab) to determine whether they were infected with SARS-CoV-2. A total of 3737 (260 RT-PCR positive) participants were included. In our cohort, AI for CXR images was a poor predictor of COVID-19 (AUC = 0.60), since the majority of positive cases had mild symptoms and no visible pneumonia in the lungs. AI systems using differential white blood cell counts (WBC), or a combination of WBC and C-Reactive Protein (CRP) both achieved an AUC of 0.74 with a suggested optimal cut-off point at 83% sensitivity and 63% specificity. The antigen-RDT tests in this trial obtained 65% sensitivity at 98% specificity. This study is the first to validate AI tools for COVID-19 detection in an African setting. It demonstrates that screening for COVID-19 using AI with point-of-care blood tests is feasible and can operate at a higher sensitivity level than antigen testing.

Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma

PURPOSE

To explore the efficiency of a contrast-enhanced CT-based radiomics nomogram integrated with radiomics signature and clinically independent predictors to distinguish mass-like thymic hyperplasia (ml-TH) from low-risk thymoma (LRT) preoperatively.

METHODS

135 Patients with histopathology confirmed ml-TH (n = 65) and LRT (n = 70) were randomly divided into training set (n = 94) and validation set (n = 41) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to obtain the optimal features. Based on the selected features, four machine learning models, support vector machine (SVM), logistic regression (LR), extreme gradient boosting (XGBOOST), and random forest (RF) were constructed. Multivariate logistic regression was used to establish a radiomics nomogram containing clinically independent predictors and radiomics signature. Receiver operating characteristic (ROC), DeLong test, and calibration curves were used to detect the performance of the radiomics nomogram in training set and validation set.

RESULTS

In the validation set, the area under the curve (AUC) value of LR (0.857; 95% CI: 0.741, 0.973) was the highest of the four machine learning models. Radiomics nomogram containing radiomics signature and clinically independent predictors (including age, shape, and net enhancement degree) had better calibration and identification in the training set (AUC: 0.959; 95% CI: 0.922, 0.996) and validation set (AUC: 0.895; 95% CI: 0.795, 0.996).

CONCLUSION

We constructed a contrast-enhanced CT-based radiomics nomogram containing clinically independent predictors and radiomics signature as a noninvasive preoperative prediction method to distinguish ml-TH from LRT. The radiomics nomogram we constructed has potential for preoperative clinical decision making.

A radiomics-clinical combined nomogram-based on non-enhanced CT for discriminating the risk stratification in GISTs

PURPOSE

To discriminate the risk stratification in gastrointestinal stromal tumors (GISTs) by preoperatively constructing a model of nonenhanced computed tomography (NECT).

METHODS

A total of 111 GISTs patients (77 in the training group and 34 in the validation Group) from two hospitals between 2015 and 2022 were collected retrospectively. One thousand and thirty-seven radiomics features were extracted from non-contract CT images, and the optimal radiomics signature was determined by univariate analysis and LASSO regression. The radiomics model was developed and validated from the ten optimal radiomics features by three methods. Covariates (clinical features, CT findings, and immunohistochemical characteristics) were collected to establish the clinical model, and both the radiomics features and the covariates were used to build the combined model. The effectiveness of the three models was evaluated by the Delong test.

RESULTS

The experimental results showed that the clinical models (75.3%, 70.6%), the radiomics models (79.2%, 79.4%) and the combined models (81.8%, 82.4%) all had high accuracy in predicting the pathological risk of GIST in both training and validation groups. The AUC values of the combined models were significantly higher in both the training groups (0.921 vs 0.822, p= 0.032) and the validation groups (0.913 vs 0.792, p= 0.019) than that of the clinical models. According to the calibration curve, the combined model nomogram is clinically useful.

CONCLUSIONS

The clinical-radiomics combined model and based on NECT performed well in discriminating the risk stratification in GISTs. As a quantitative technique, radiomics is capable of predicting the malignant potential and guiding treatment preoperatively.

Demystifying PCR tests, challenges, alternatives, and future: A quick review focusing on COVID and fungal infections

The polymerase chain reaction (PCR) technique is one of the most potent tools in molecular biology. It is extensively used for various applications ranging from medical diagnostics to forensic science and food quality testing. This technique has facilitated to survive COVID-19 pandemic by identifying the virus-infected individuals effortlessly and effectively. This review explores the principles, recent advancements, challenges, and alternatives of PCR technique in the context of COVID-19 and fungal infections. The introduction of PCR technique for anyone new to this field is the primary aim of this review and thereby equips them to understand the science of COVID-19 and related fungal infections in a simplistic manner.

Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability

Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test-retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test-retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and manual tumor segmentations for radiomic feature extraction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to filter high repeatable features. We evaluated model reliability in both internal generalizability and robustness, which were quantified by training and testing AUC and prediction ICC. Higher testing performance was found at higher feature ICC thresholds, but it dropped significantly at ICC = 0.95 for the test-retest model. Similar optimal reliability can be achieved with testing AUC = 0.7-0.8 and prediction ICC > 0.9 at the ICC threshold of 0.9. It is recommended to include feature repeatability analysis using image perturbation in any radiomic study when test-retest is not feasible, but care should be taken when deciding the optimal feature repeatability criteria.

Radiomics in the evaluation of ovarian masses - a systematic review

OBJECTIVES

The study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions.

METHODS

MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS) was utilised to assess radiomic methodology.

RESULTS

After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications, 10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias. The highest RQS achieved was 61.1%, and the lowest was - 16.7%.

CONCLUSION

Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical translation.

CLINICAL RELEVANCE STATEMENT

Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation.

KEY POINTS

• Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses. • Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. • Modelling with larger cohorts and real-world evaluation is required before clinical translation.

Activation of Inflammasome complex in nasopharyngeal epithelial cells from patients with Coronavirus disease 2019 contributes to inflammatory state and worse disease outcomes

Pathogenesis of Coronavirus disease 2019 (COVID-19) has been associated with dysregulation of both adaptive and innate immune systems. Hence, we determined the contribution of inflammasome in the nasopharyngeal epithelial cells isolated from COVID-19 subjects to disease pathogenesis and outcomes. Epithelial cells from 150 COVID-19 patients and 150 healthy controls were yielded through nasopharyngeal swab sampling. Patients were categorized into three groups of those with clinical presentations/need hospitalization, with clinical presentations/no need hospitalization and cases without clinical symptoms/no need hospitalization. Finally, the transcriptional amount of inflammasome related genes were assessed in the nasopharyngeal epithelial cells using qPCR. There was a significant upregulation of nod-like receptor (NLR) family pyrin domain containing 1 (NLRP1), nod-like receptor (NLR) family pyrin domain containing 3 (NLRP3), Apoptosis-associated speck-like protein containing a CARD (ASC) and Caspase-1 mRNA expressions in patients compared to controls. NLRP1, NLRP3, ASC and Caspase-1 were upregulated in epithelial cells of patients with clinical symptoms/need hospitalization and cases with clinical symptoms/no need hospitalization when compared to controls. There was a correlation between expression of inflammasome-related genes and clinicopathological features. Abnormal expression of inflammasome-related genes in the nasopharyngeal epithelial cells obtained from COVID-19 patients may be of prognostic value to determine the intensity of the disease's outcomes and requirement for alternative supports in hospitals.

Development and Validation of Multi-Omics Thymoma Risk Classification Model Based on Transfer Learning

The paper aims to develop prediction model that integrates clinical, radiomics, and deep features using transfer learning to stratifying between high and low risk of thymoma. Our study enrolled 150 patients with thymoma (76 low-risk and 74 high-risk) who underwent surgical resection and pathologically confirmed in Shengjing Hospital of China Medical University from January 2018 to December 2020. The training cohort consisted of 120 patients (80%) and the test cohort consisted of 30 patients (20%). The 2590 radiomics and 192 deep features from non-enhanced, arterial, and venous phase CT images were extracted and ANOVA, Pearson correlation coefficient, PCA, and LASSO were used to select the most significant features. A fusion model that integrated clinical, radiomics, and deep features was developed with SVM classifiers to predict the risk level of thymoma, and accuracy, sensitivity, specificity, ROC curves, and AUC were applied to evaluate the classification model. In both the training and test cohorts, the fusion model demonstrated better performance in stratifying high and low risk of thymoma. It had AUCs of 0.99 and 0.95, and an accuracy of 0.93 and 0.83, respectively. This was compared to the clinical model (AUCs of 0.70 and 0.51, accuracy of 0.68 and 0.47), the radiomics model (AUCs of 0.97 and 0.82, accuracy of 0.93 and 0.80), and the deep model (AUCs of 0.94 and 0.85, accuracy of 0.88 and 0.80). The fusion model integrating clinical, radiomics and deep features based on transfer learning was efficient for noninvasively stratifying high risk and low risk of thymoma. The models could help to determine surgery strategy for thymoma cancer.

Chest computed tomography findings of patients infected with Covid-19 and their association with disease evolution stages

INTRODUCTION

To describe CT findings in patients with confirmed Covid-19 infection and correlate them with the disease evolution stages.

METHODS

This is a historical cohort observational analytical study carried out with outpatients, inpatients, and emergency patients from a private hospital in Maceió/AL, Brazil. The final sample consisted of 390 patients with positive RT-PCR for Covid-19 with available laboratory tests and chest CT results.

RESULTS

The most frequent initial symptoms were cough, fever, dyspnea and headache. The most commonly found comorbidities were hypertension, diabetes mellitus and obesity. A total of 22% of the CT scans showed no alterations; ground-glass opacity was the most frequently found one. There was a significant association between age, comorbidities, pulmonary involvement, ground-glass opacity, mosaic attenuation and percentage of pulmonary involvement with death. The analysis of the disease stages showed a significant association with laboratory data (CRP and platelet levels), ground-glass opacity and mosaic attenuation with the disease evolution stages in relation to the days since symptom onset.

CONCLUSION

The disease evolution of Covid-19 occurs in stages, and this study describes tomographic findings in patients with confirmed Covid-19 infection and shows they vary depending on the disease evolution stages.

IMPLICATIONS FOR PRACTICE

This paper provides important addition to the various records that have been accumulated through the Covid-19 pandemic.

Radiomics in gastrointestinal stromal tumours: an up-to-date review

Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours.

Lung ultrasound in the COVID-19 era: a lesson to be learned for the future

Lung Ultrasound (LUS) is a reliable, radiation free and bedside imaging technique to assess several pulmonary diseases. Although the diagnosis of COVID-19 is made with the nasopharyngeal swab, detection of pulmonary involvement is key for a safe patient management. LUS is a valid alternative to explore, in paucisymptomatic self-presenting patients, the presence and extension of pneumonia compared to High Resolution Computed Tomography (HRCT) that represent the gold standard. This is a single-centre prospective study with 131 patients enrolled. Twelve lung areas were explored reporting a semiquantitative assessment to obtain the LUS score. Each patient performed reverse-transcription polymerase chain reaction test (rRT-PCR), hemogasanalysis and HRCT. We observed an inverse correlation between LUSs and pO2, P/F, SpO2, AaDO2 (p value < 0.01), a direct correlation with LUSs and AaDO2 (p value < 0.01). Compared with HRCT, LUS showed sensitivity and specificity of 81.8% and 55.4%, respectively, and VPN 75%, VPP 65%. Therefore, LUS can represent an effective alternative tool to detect pulmonary involvement in COVID-19 compared to HRCT.

Visual Perception and Convolutional Neural Network-Based Robotic Autonomous Lung Ultrasound Scanning Localization System

Under the situation of severe COVID-19 epidemic, lung ultrasound (LUS) has been proved to be an effective and convenient method to diagnose and evaluate the extent of respiratory disease. However, the traditional clinical ultrasound (US) scanning requires doctors not only to be in close contact with patients but also to have rich experience. In order to alleviate the shortage of medical resources and reduce the work stress and risk of infection for doctors, we propose a visual perception and convolutional neural network (CNN)-based robotic autonomous LUS scanning localization system to realize scanned target recognition, probe pose solution and movement, and the acquisition of US images. The LUS scanned targets are identified through the target segmentation and localization algorithm based on the improved CNN, which is using the depth camera to collect the image information; furthermore, the method based on multiscale compensation normal vector is used to solve the attitude of the probe; finally, a position control strategy based on force feedback is designed to optimize the position and attitude of the probe, which can not only obtain high-quality US images but also ensure the safety of patients and the system. The results of human LUS scanning experiment verify the accuracy and feasibility of the system. The positioning accuracy of the scanned targets is 15.63 ± 0.18 mm, and the distance accuracy and rotation angle accuracy of the probe position calculation are 6.38 ± 0.25 mm and 8.60° ±2.29° , respectively. More importantly, the obtained high-quality US images can clearly capture the main pathological features of the lung. The system is expected to be applied in clinical practice.

Frequency of Developing COVID-19 Pneumonia in Patients Who Were Vaccinated Double-Dose CoronaVac: Data of the Pandemic Authorized Hospital in Northern Cyprus

Background

RT-PCR is the leading method used in the diagnosis of COVID-19, caused by 2019-nCoV. CT applications also provide a fast and easy diagnosis for detecting pneumonia caused by the SARS-CoV-2 virus. The current study, aimed to compare the lung involvement of vaccinated (two-dose CoronaVac) and unvaccinated patients in the early stage of COVID-19 disease.

Methods

In the current retrospective study, which included patients diagnosed with RT-PCR COVID-19 positivity (n=651) between 01 July 2021-15 September 2021, patient information was obtained from the authorized hospital of the pandemic. Data included patients' chest CT scans and whether patients had been vaccinated (two-dose CoronaVac) information.

Results

The ratio of vaccination with double-dose CoronaVac in positive patients was 74.3%. The ratio of patients with normal lung appearance was 61.8%. It was determined that the ratio of involvement in both lungs of patients who were vaccinated with a double dose was significantly lower than the ratio of involvement in patients who were never vaccinated (p <0.001).

Conclusion

In this study, it was determined that pneumonia cases were less common in individuals vaccinated with double-dose CoronaVac. In this study, it was also determined that the protection of the vaccine was higher in females than in males and that the protection of the double-dose CoronaVac vaccine was higher in the 50-60 age group compared to 60 older patients.

The baseline metabolism parameters of 18F‑FDG PET/CT as promising prognostic biomarkers in pediatric Langerhans cell histiocytosis

Background

Langerhans cell histiocytosis (LCH) is a rare myeloid precursor cell inflammatory neoplasia, which agonizes, maims, and even kills patients. Although clinical outcomes have steadily improved over the past decades, the progression/relapse rate of LCH remains high. The purpose of this study was to evaluate the prognostic value of the pre-treatment metabolism parameters of baseline 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F‑FDG PET/CT) in children with LCH.

Methods

This cross-sectional study retrospectively and consecutively included 37 children (24 males and 13 females; median age, 5.1 years; range, 2.4-7.8 years) with pre-treatment 18F-FDG PET/CT from September 2020 to September 2022 in Nuclear Medicine Department, Beijing friendship hospital, Capital Medical University, Beijing, China. These patients were then all admitted to the hospital and diagnosed with LCH by biopsy, in Hematology Center, Beijing Children's Hospital, Capital Medical University, Beijing, China. Five metabolism parameters of 18F-FDG PET/CT were analyzed, including maximum standardized uptake, tumor-to-normal liver standard uptake value ratio, tumor-to-normal bone marrow standard uptake value ratio, sum of metabolic tumor volume (sMTV), and sum of total lesion glycolysis (sTLG) of all lesions. Patients were followed up for at least 1 year or until disease progression/relapse. Univariate and multivariate analyses of progression-free survival was performed.

Results

During follow-up, 11 (29.7%) patients had disease progression/relapse. Univariate analysis revealed that the risk organ involvement, the treatment response at the 5th or 11th week, pre-treatment sMTV, and sTLG were significantly associated with progression-free survival (P=0.024, 0.018, 0.006, 0.006, and 0.042, respectively). Multivariate COX analysis revealed that non-response at the 11th week, pre-treatment sMTV >32.55 g/cm3, and sTLG >98.86 g (P=0.002, 0.020, 0.026, respectively) were risk factors for progression-free survival.

Conclusions

The baseline metabolism parameters of 18F-FDG PET/CT could be promising imaging biomarkers for predicting prognosis in children with LCH.

COVID-19 and interstitial lung diseases: A multifaceted look at the relationship between the two diseases

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although it has been a fatal disease for many patients, the development of treatment strategies and vaccines have progressed over the past 3 years, and our society has become able to accept COVID-19 as a manageable common disease. However, as COVID-19 sometimes causes pneumonia, post-COVID pulmonary fibrosis (PCPF), and worsening of preexisting interstitial lung diseases (ILDs), it is still a concern for pulmonary physicians. In this review, we have selected several topics regarding the relationships between ILDs and COVID-19. The pathogenesis of COVID-19-induced ILD is currently assumed based mainly on the evidence of other ILDs and has not been well elucidated specifically in the context of COVID-19. We have summarized what has been clarified to date and constructed a coherent story about the establishment and progress of the disease. We have also reviewed clinical information regarding ILDs newly induced or worsened by COVID-19 or anti-SARS-CoV-2 vaccines. Inflammatory and profibrotic responses induced by COVID-19 or vaccines have been thought to be a risk for de novo induction or worsening of ILDs, and this has been supported by the evidence obtained through clinical experience over the past 3 years. Although COVID-19 has become a mild disease in most cases, it is still worth looking back on the above-reviewed information to broaden our perspectives regarding the relationship between viral infection and ILD. As a representative etiology for severe viral pneumonia, further studies in this area are expected.