Effect of cetuximab plus FOLFOX4 regimen on clinical outcomes in advanced gastric carcinoma patients receiving evidence-based care.

BACKGROUND: Although chemotherapy is effective for treating advanced gastric carcinoma (aGC), it may lead to an adverse prognosis. Establishing a highly effective and low-toxicity chemotherapy regimen is necessary for improving efficacy and outcomes in aGC patients. AIM: To determine the efficacy and safety of cetuximab (CET) combined with the FOLFOX4 regimen (infusional fluorouracil, folinic acid, and oxaliplatin) as first-line therapy for patients with aGC, who received evidence-based care (EBC). METHODS: A total of 117 aGC patients who received EBC from March 2019 to March 2022 were enrolled. Of these, 60 in the research group (RG) received CET + FOLFOX4 as first-line therapy, whereas 57 in the control group (CG) received FOLFOX4. The efficacy [clinical response rate (RR) and disease control rate (DCR)], safety (liver and kidney dysfunction, leukopenia, thrombocytopenia, rash, and diarrhea), serum tumor marker expression [STMs; carbohydrate antigen (CA) 19-9, CA72-4, and carcinoembryonic antigen (CEA)], inflammatory indicators [interleukin (IL)-2 and IL-10], and quality of life (QOL) of the two groups were compared. RESULTS: A markedly higher RR and DCR were observed in the RG compared with the CG, with an equivalent safety profile between the two groups. RG exhibited notably reduced CA19-9, CA72-4, CEA, and IL-2 levels following treatment, which were lower than the pre-treatment levels and those in the CG. Post-treatment IL-10 was statistically increased in RG, higher than the pre-treatment level and the CG. Moreover, a significantly improved QOL was evident in the RG. CONCLUSION: The CET + FOLFOX4 regimen is highly effective as first-line treatment for aGC patients receiving EBC. It facilitates the suppression of STMs, ameliorates the serum inflammatory microenvironment, and enhances QOL, without increased adverse drug effects.

Amide Proton Transfer-Weighted MRI, Associations with Clinical Severity and Prognosis in Ischemic Strokes.

BACKGROUND: The National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin scale (mRS) scores have important shortcomings. Amide proton transfer-weighted (APTw) imaging might offer more valuable information in ischemic strokes assessment. PURPOSE: To utilize APTw, apparent diffusion coefficient (ADC), and computed tomography perfusion (CTP) for the assessment of clinical symptom severity and 90-day prognosis in patients diagnosed with ischemic stroke. STUDY TYPE: Prospective. SUBJECTS: 61 patients (mean age 63.2 ± 9.7 years; 46 males, 15 females) with ischemic strokes were included in the study. FIELD STRENGTH/SEQUENCE: 3T/turbo spin echo (TSE) T1 -weighted imaging, T2 -weighted imaging, T2 -fluid attenuated inversion recovery (T2 -FLAIR), diffusion-weighted imaging (DWI), and single-shot TSE APTw imaging. ASSESSMENT: APTw, ADC, and CTP were used to compare patient subgroups and construct a prognostic nomogram model. STATISTICAL TESTS: Kolmogorov-Smirnov test, t-test, Mann-Whitney U test, chi-square test, Pearson correlation analysis, multivariate logistic regression analysis, decision curve analysis (DCA), receiver operating characteristic curves (ROCs). The significance threshold was set at P < 0.05. RESULTS: Correlation analysis revealed that APTw and NIHSS exhibit the highest correlation (r = -0.634, 95% confidence interval [CI] -0.418 to -0.782), surpassing that of ADC and lesion size. Multivariable analysis revealed APTw (odds ratio [OR] 0.905, 95% CI 0.845-0.970), ADC (OR 0.745, 95% CI 0.609-0.911), and infarct core-cerebral blood volume (IC-CBV) (OR 0.547, 95% CI 0.310-0.964) as potential risk factors associated with a poor prognosis. The nomogram model demonstrated the highest predictive efficacy, with an area under the curve (AUC) of 0.960 (95% CI 0.911-0.988), exceeding that of APTw, ADC, and IC-CBV individually. DATA CONCLUSION: The APTw technique holds potential value in categorizing and managing patients with ischemic stroke, offering guidance for the implementation of clinical treatment strategies. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Therapeutic effect and mechanism of Yougui Wan in rats with intervertebral disk degeneration.

OBJECTIVE: To explore the potential mechanism of Yougui Wan on deformed lumbar intervertebral disk structure in rats. METHODS: Thirty male Sprague-Dawley rats were randomly divided into 3 groups, with 10 rats in each group. The animals in the blank control group were healthy rats without specific treatment, and those in the model group and traditional Chinese medicine (TCM) group were used to establish the intervertebral disk degeneration (IDD) model by puncturing the annulus. Four weeks after modeling, rats in the TCM group were administered Yougui Wan by gavage for 2 consecutive weeks. Serum interleukin-6 (IL-10), macrophage migration inhibitory factor (MIF) and tumor necrosis factor alpha (TNF-α) levels were measured by ELISA, and the protein expression levels of collagen II and Notch1 in intervertebral disk tissues were examined by Western blotting. Apoptosis was detected by the TUNEL method. RESULTS: Compared with those in the blank group, IL-10, MIF and TNF-α levels in the model group and TCM group were increased (P < 0.05), the protein expression levels of collagen II were decreased, and the protein expression levels of Notch1 were increased. Compared with those in the model group, the levels of IL-10 in the TCM group were increased (P < 0.05), the levels of MIF and TNF-α were decreased (P < 0.05), the protein expression levels of collagen II were increased, and the protein expression levels of Notch1 were decreased. CONCLUSION: Yougui Wan can inhibit the inflammatory response in IDD rats, reduce the degradation of extracellular matrix, reduce apoptosis in nucleus pulposus cells, and alleviate intervertebral disk degeneration. The mechanism may be related to the regulation of the Notch signaling pathway.

Intracranial Inflammatory Myofibroblastic Tumor: A Literature Review and a Rare Case Misdiagnosed as Acoustic Neuroma.

Inflammatory myofibroblastic tumor (IMT) stands as a rare neoplasm, initially documented by Bahadori and Liebow in 1973; however, its biological behavior and underlying pathogenesis continue to elude comprehensive understanding. Throughout the years, this tumor has been designated by various alternative names, including pseudosarcomatoid myofibroblastoma, fibromyxoid transformation, and plasma cell granuloma among others. In 2002, the World Health Organization (WHO) officially classified it as a soft tissue tumor and designated it as IMT. While IMT primarily manifests in the lungs, the common clinical symptoms encompass anemia, low-grade fever, limb weakness, and chest pain. The mesentery, omentum, and retroperitoneum are subsequent sites of occurrence with intracranial involvement being exceedingly rare. Due to the absence of specific clinical symptoms and characteristic radiographic features, diagnosing intracranial inflammatory myofibroblastic tumor (IIMT) remains challenging. Successful instances of pharmacological treatment for IIMT indicate that surgery may not be the sole therapeutic recourse, thus underscoring the imperative of an accurate diagnosis and apt treatment selection to improve patient outcomes.

Diagnosis and differential diagnosis of tertiary androgenetic alopecia with severe alopecia areata based on high-resolution MRI.

BACKGROUND AND AIM: No previous study investigated the anatomical changes of the scalp and hair follicles between tertiary androgenetic alopecia and severe alopecia areata using high-resolution magnetic resonance imaging (HR-MRI). This study aimed to explore the value of HR-MRI in assessing alopecia. MATERIALS AND METHODS: Forty-eight people were included in this study. The imaging indicators of the vertex and occipital scalp were recorded and compared. The logistic regression model was developed for the indicators that differed between tertiary androgenetic alopecia and severe alopecia areata. The receiver-operating characteristic (ROC) curve was used to assess the diagnostic efficacy of the model for tertiary androgenetic alopecia and severe alopecia areata. RESULTS: At the vertex, the thickness of the subcutaneous tissue layer, follicle depth, relative follicle depth, total number of follicles within a 2-cm distance, and number of strands reaching the middle and upper third of the subcutaneous fat layer within a 2-cm distance were statistically different between patients with tertiary androgenetic alopecia, those with severe alopecia areata, and healthy volunteers (p < 0.05). The logistic regression model suggested that the subcutaneous tissue layer thickness was important in discriminating tertiary androgenetic alopecia from severe alopecia areata. The ROC curve showed that the area under the curve, sensitivity, specificity, and best cutoff values of the subcutaneous tissue layer were 0.886, 94.4%, 70%, and 4.31 mm, respectively. CONCLUSIONS: HR-MRI can observe the changes in anatomical structures of the scalp and hair follicles in patients with alopecia. HR-MRI can be applied to the differential diagnosis of tertiary androgenetic alopecia and severe alopecia areata.

Machine learning based on gadoxetic acid-enhanced MRI for differentiating atypical intrahepatic mass-forming cholangiocarcinoma from poorly differentiated hepatocellular carcinoma.

PURPOSE: The study was to develop a Gd-EOB-DTPA-enhanced MRI radiomics model for differentiating atypical intrahepatic mass-forming cholangiocarcinoma (aIMCC) from poorly differentiated hepatocellular carcinoma (pHCC). MATERIALS AND METHODS: A total of 134 patients (51 aIMCC and 83 pHCC) who underwent Gadoxetic acid-enhanced MRI between March 2016 and March 2022 were enrolled in this study and then randomly assigned to the training and validation cohorts by 7:3 (93 patients and 41 patients, respectively). The radiomics features were extracted from the hepatobiliary phase of Gadoxetic acid-enhanced MRI. In the training cohort, the SelectKBest and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features. The clinical, radiomics, and clinical-radiomics model were established using four machine learning algorithms. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve. Comparison of the radiomics and clinical-radiomics model was done by the Delong test. The clinical usefulness of the model was evaluated using decision curve analysis (DCA). RESULTS: In 1132 extracted radiomic features, 15 were selected to develop radiomics signature. For identifying aIMCC and pHCC, the radiomics model constructed by random forest algorithm showed the high performance (AUC = 0.90) in the training cohort. The performance of the clinical-radiomics model (AUC = 0.89) was not significantly different (P = 0.88) from that of the radiomics model constructed by random forest algorithm (AUC = 0.86) in the validation cohort. DCA demonstrated that the clinical-radiomics model constructed by random forest algorithm had a high net clinical benefit. CONCLUSION: The clinical-radiomics model is an effective tool to distinguish aIMCC from pHCC and may provide additional value for the development of treatment plans.

Prediction of Renal Function 1 Year After Transplantation Using Machine Learning Methods Based on Ultrasound Radiomics Combined With Clinical and Imaging Features.

Kidney transplantation is the most effective treatment for advanced chronic kidney disease (CKD). If the prognosis of transplantation can be predicted early after transplantation, it might improve the long-term survival of patients with transplanted kidneys. Currently, studies on the assessment and prediction of renal function by radiomics are limited. Therefore, the present study aimed to explore the value of ultrasound (US)-based imaging and radiomics features, combined with clinical features to develop and validate the models for predicting transplanted kidney function after 1 year (TKF-1Y) using different machine learning algorithms. A total of 189 patients were included and classified into the abnormal TKF-1Y group, and the normal TKF-1Y group based on their estimated glomerular filtration rate (eGFR) levels 1 year after transplantation. The radiomics features were derived from the US images of each case. Three machine learning methods were employed to establish different models for predicting TKF-1Y using selected clinical and US imaging as well as radiomics features from the training set. Two US imaging, four clinical, and six radiomics features were selected. Then, the clinical (including clinical and US image features), radiomics, and combined models were developed. The area under the curves (AUCs) of the models was 0.62 to 0.82 within the test set. Combined models showed statistically higher AUCs than the radiomics models (all p-values <.05). The prediction performance of different models was not significantly affected by the different machine learning algorithms (all p-values >.05). In conclusion, US imaging features combined with clinical features could predict TKF-1Y and yield an incremental value over radiomics features. A model integrating all available features may further improve the predictive efficacy. Different machine learning algorithms may not have a significant impact on the predictive performance of the model.

Diagnostic value of radiomics model based on gray-scale and contrast-enhanced ultrasound for inflammatory mass stage periductal mastitis/duct ectasia.

Objective: The present study aimed to investigate the clinical application value of the radiomics model based on gray-scale ultrasound (GSUS) and contrast-enhanced ultrasound (CEUS) images in the differentiation of inflammatory mass stage periductal mastitis/duct ectasia (IMSPDM/DE) and invasive ductal carcinoma (IDC). Methods: In this retrospective study, 254 patients (IMSPDM/DE: 129; IDC:125) were enrolled between January 2018 and December 2020 as a training cohort to develop the classification models. The radiomics features were extracted from the GSUS and CEUS images. The least absolute shrinkage and selection operator (LASSO) regression model was employed to select the corresponding features. Based on these selected features, logistic regression analysis was used to aid the construction of these three radiomics signatures (GSUS, CEUS and GSCEUS radiomics signature). In addition, 80 patients (IMSPDM/DE:40; IDC:40) were recruited between January 2021 and November 2021 and were used as the validation cohort. The best radiomics signature was selected. Based on the clinical parameters and the radiomics signature, a classification model was built. Finally, the classification model was assessed using nomogram and decision curve analyses. Results: Three radiomics signatures were able to differentiate IMSPDM/DE from IDC. The GSCEUS radiomics signature outperformed the other two radiomics signatures and the AUC, sensitivity, specificity, and accuracy were estimated to be 0.876, 0.756, 0.804, and 0.798 in the training cohort and 0.796, 0.675, 0.838 and 0.763 in the validation cohort, respectively. The lower patient age (p<0.001), higher neutrophil count (p<0.001), lack of pausimenia (p=0.023) and GSCEUS radiomics features (p<0.001) were independent risk factors of IMSPDM/DE. The classification model that included the clinical factors and the GSCEUS radiomics signature outperformed the GSCEUS radiomics signature alone (the AUC values of the training and validation cohorts were 0.962 and 0.891, respectively). The nomogram was applied to the validation cohort, reaching optimal discrimination, with an AUC value of 0.891, a sensitivity of 0.888, and a specificity of 0.750. Conclusions: The present study combined the clinical parameters with the GSCEUS radiomics signature and developed a nomogram. This GSCEUS radiomics-based classification model could be used to differentiate IMSPDM/DE from IDC in a non-invasive manner.

Pre-treatment spectral CT combined with CT perfusion can predict hemorrhagic transformation after thrombolysis in patients with acute ischemic stroke.

OBJECTIVE: To investigate the value of pre-treatment spectral CT angiography (CTA) in predicting hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) treatment in acute ischemic stroke (AIS) patients. MATERIALS AND METHODS: AIS patients who underwent IVT with recombinant tissue plasminogen activator and pre-treatment head and neck spectral CTA and head CT perfusion (CTP) from January 2018 to June 2020 were reviewed retrospectively. Finally, 20 patients were included in the HT group and 22 age-matched patients were included in the non-HT group. Spectral and CTP parameters of the region of interest on pre-treatment CTA axial raw images and CTP images, including the infarct core (IC) and ischemic penumbral (IP) regions, were recorded. The differences in clinical variables, CTP, collateral scores and spectral parameters between the two groups were analyzed. Three multivariate logistic regression models were then developed, where model 1 included clinical and spectral parameters, model 2 included clinical and CTP parameters and a combined model included clinical, CTP, and spectral parameters. Receiver operating characteristic analysis was used to evaluate the performance of the multivariate model. RESULTS: Patients with HT had higher Safe Implementation of Treatments in Stroke (SITS) score (p = 0.023), the volume of perfusion lesions (p = 0.005), the volume of IP (p = 0.003), the mean transit time (MIT) in the IC area (p = 0.012), as well as the TTP in IP area (p = 0.015) compared with patients without HT. The HT group showed significantly lower CBF in the IC area (p = 0.019), iodine concentration (p = 0.017) and the effective atomic number (p = 0.024) in the IP area than non-HT group. And the slope of the spectral curve of the HT group in the IP region was larger than that of the non-HT group (p = 0.023). Gender, age, SITS score, the volume of entire perfusion lesion, CBF and MIT in the IC area, TTP in the IP area, as well as iodine concentration in the IP area were included in the final multivariate model for predicting HT. And CBF in the IC area (OR = 0.779, 95 % CI:0.609-0.996, p = 0.046) as well as the iodine concentration of IP area (OR = 0.343, 95 % CI: 0.131-0.901, p = 0.030) were proved to be independent predictors for HT. The combined model including clinical, spectral, and CTP parameters, showed improved accuracy compared to the other two models, while the Delong test did not suggest a statistically significant difference (both p values > 0.05). CONCLUSIONS: The iodine concentration of IP area derived from pre-treatment spectral CTA was an independent predictor of HT after IVT treatment for AIS patients. Moreover, multivariate models combined with clinical, spectral, and CTP parameters may be able to predict HT.

Diagnosis and differential diagnosis of dermatofibrosarcoma protuberans: Utility of high-resolution dynamic contrast-enhanced (DCE) MRI.

BACKGROUND: Dermatofibrosarcoma protuberans (DFSP) is a kind of low-grade malignant spindle cell neoplasm, the diagnosis, and treatment, which have markedly attracted clinicians' attention for its repeated recurrence. High-resolution magnetic resonance imaging (HR-MRI) has shown unique capabilities in diagnosis of various cutaneous tumors. MATERIALS AND METHODS: Data of 29 patients with clinically suspected DFSPs and undergoing dynamic contrast-enhanced (DCE) HR-MRI preoperatively were prospectively collected. The HR-MRI qualitative features were evaluated and compared. The DCE-associated quantitative parameters and the time-signal intensity curve (TIC) types were provided using DCE sequences. RESULTS: A total of 7 DFSPs, nine dermatofibromas (DF, including four cases of cellular variant [CDF]), 12 keloids, and one nodular fasciitis were enrolled. DFSP showed the largest major diameter and the deepest depth. Five DFSPs (71.4%) showed ill-defined margins as well as infiltration of peripheral adipose. All DFSPs showed irregular shape. Most DFSPs presented hyperintensity on T2 WI (71.4%) and iso-intensity on T1 WI (85.7%). Six cases (85.7%) had significant enhancement, and six cases (85.7%) had homogeneous enhancement. There were significant differences of Ktrans , Kep , Ve and iAUC values among DFSPs, DFs, and keloids, and DFSP had the highest values for these parameters. Six DFSPs (85.7%) and four CDFs (100%) showed type-III TICs, while the other lesions showed type-Ⅰor type-Ⅱ TICs. CONCLUSIONS: DCE-HR-MRI could show the growth characteristics of DFSPs, which was of great value for the diagnosis and differential diagnosis of DFSPs and was helpful for the determination of treatment options, thereby to improve the prognosis of patients.

Machine Learning-Based Ultrasound Radiomics for Evaluating the Function of Transplanted Kidneys.

The aim of the study described here was to investigate the value of different machine learning models based on the clinical and radiomic features of 2-D ultrasound images to evaluate post-transplant renal function (pTRF). We included 233 patients who underwent ultrasound examination after renal transplantation and divided them into the normal pTRF group (group 1) and the abnormal pTRF group (group 2) based on their estimated glomerular filtration rates. The patients with abnormal pTRF were further subdivided into the non-severe renal function impairment group (group 2A) and the severe impairment group (group 2B). The radiomic features were extracted from the 2-D ultrasound images of each case. The clinical and ultrasound image features as well as radiomic features from the training set were selected, and then five machine learning algorithms were used to construct models for evaluating pTRF. Receiver operating characteristic curves were used to evaluate the discriminatory ability of each model. A total of 19 radiomic features and one clinical feature (age) were retained for discriminating group 1 from group 2. The area under the receiver operating characteristic curve (AUC) values of the models ranged from 0.788 to 0.839 in the test set, and no significant differences were found between the models (all p values >0.05). A total of 17 radiomic features and 1 ultrasound image feature (thickness) were retained for discriminating group 2A from group 2B. The AUC values of the models ranged from 0.689 to 0.772, and no significant differences were found between the models (all p values >0.05). Machine learning models based on clinical and ultrasound image features, as well as radiomics features, from 2-D ultrasound images can be used to evaluate pTRF.

Reduced-Dose Full-Body CT in Lymphoma Follow-up: A Pilot Study.

BACKGROUND: How to reduce the radiation dose received from full-body CT scans during the follow-up of lymphoma patients is a concern. OBJECTIVE: The aim of the study was to investigate the image quality and radiation dose of reduced-dose full-body computerized tomography (CT) in lymphoma patients during the follow-up. METHODS: 121 patients were included and divided into conventional CT group (group 1, 120-kVp, n = 61) or reduced-dose CT group (group 2, 100-kVp combined dual-energy CT (DECT), n = 60). 140-kVp polychromatic images and 70-keV monochromatic images were reconstructed from DECT. The abdominal virtual non-enhanced (VNE) images were reconstructed from monochromatic images. Two radiologists rated the overall image quality with a five-point scale and graded the depiction of lesions using a four-point scale. The objective image quality was evaluated using image noise, signal-to-noise ratio, and contrast-to-noise ratio. The radiation dose and image quality were compared between the groups. RESULTS: The comparable subjective image quality was observed between 70-keV and 120-kVp images in the neck, while 120-kVp images showed better objective image quality. 70-keV images showed better objective image quality in the chest. While the subjective image quality of abdominal VNE images was inferior to that of true non-enhanced images, the improved objective image quality was observed in VNE images. In the abdominal arterial phase, similar subjective image quality was observed between the groups. Abdominal 70-keV images in the arterial phase showed improved objective image quality. Similar image quality was obtained in the abdominal venous phase between the groups. The effective radiation dose in group 2 showed a significant reduction. CONCLUSION: The application of reduced-dose full-body CT can significantly reduce the radiation dose for lymphoma patients during the follow-up while maintaining or improving the image quality.

Native T1-mapping and diffusion-weighted imaging (DWI) can be used to identify lung cancer pathological types and their correlation with Ki-67 expression.

Background: This study aimed to explore the value of native T1-mapping and diffusion-weighted imaging (DWI) in differentiating the pathological types and degree of tumor differentiation of lung cancer and their correlation with Ki-67 protein expression. Methods: A total of 78 consecutive lung cancer patients who received chest magnetic resonance imaging (MRI) scans between May 2020 and June 2021 were enrolled in this study. Two radiologists independently analyzed the apparent diffusion coefficient (ADC) and T1 values for each lesion. The intraclass correlation coefficient (ICC) and Bland-Altman plots were generated to assess interobserver agreement of the T1 and ADC mean values in lesions. The difference in ADC and T1 values among different pathological types, as well as between high- and low-differentiated lung cancers were analyzed, and diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve analysis. The correlation between ADC value, T1 value, and Ki-67 protein expression index was determined. Results: The ADC and T1 values showed excellent interobserver agreement (ICC 0.820, 0.942, respectively). There was a significant difference in ADC values between small cell carcinoma and squamous carcinoma (P<0.05), and between small cell carcinoma and adenocarcinoma (P<0.05), but not between squamous carcinoma and adenocarcinoma (P>0.05). A significant difference in T1 values was observed between small cell carcinoma (P<0.05) and adenocarcinoma, and between squamous carcinoma (P<0.05) and adenocarcinoma, but not between squamous carcinoma and small cell carcinoma (P>0.05). There were statistically significant differences in ADC and T1 values between the moderately and highly differentiated group and the poorly differentiated group (P<0.05). ROC curve analysis showed that the T1 combined with ADC value had high diagnostic value for the degree of differentiation of the tumor [area under the curve (AUC) =0.912]. Pearson correlation analysis showed a significant positive correlation between T1 value and Ki-67 index (r=0.66, P<0.001) and a significant negative correlation between ADC value and Ki-67 index (r=-0.45, P<0.01). Conclusions: T1 and ADC values can be used to distinguish the pathological type and differentiation degree of lung cancer.

W-band circular TM11 mode converter for gyrotrons.

This work proposes a methodology to convert a rectangular TE10 mode to a circular TM11 mode using an H-plane power divider at W-band. The divider evenly splits the input signal into two parts with the same amplitude and phase. One of the waves then goes through a wider rectangular waveguide with a lower cutoff frequency. After propagating through a specific length, the two waves differ by 180°. The two out-of-phase waves can jointly synthesize the circular TM11 mode with high mode purity. This power divider is structurally simple and capable of high-power operation. The full-wave simulation shows that the metal's conductivity affects the transmission of two-mode converters joined back-to-back. The measured back-to-back transmission agrees with the simulation result except for minor quantitative differences. The measured 3-dB bandwidth is 2.8 GHz with a center frequency of 93.6 GHz, which warrants the success of the TM11 mode gyrotrons.

Inspiratory and Expiratory Chest High-resolution CT: Small-airway Disease Evaluation in Patients with COVID-19.

BACKGROUND: An outbreak of coronavirus disease 2019 (COVID-19) has occurred worldwide. However, the small-airway disease in patients with COVID-19 has not been explored. AIM: This study aimed to explore the small-airway disease in patients with COVID-19 using inspiratory and expiratory chest high-resolution computed tomography (CT). METHODS: This multicenter study included 108 patients with COVID-19. The patients were classified into five stages (0-IV) based on the CT images. The clinical and imaging data were compared among CT images in different stages. Patients were divided into three groups according to the time interval from the initial CT scan, and the clinical and air trapping data were compared among these groups. The correlation between clinical parameters and CT scores was evaluated. RESULTS: The clinical data, including age, frequency of breath shortness and dyspnea, neutrophil percentage, lymphocyte count, PaO2, PaCO2, SaO2, and time interval between the onset of illness and initial CT, showed significant differences among CT images in different stages. A significant difference in the CT score of air trapping was observed between stage I and stage III. A low negative correlation was found between the CT score of air trapping and the time interval between the onset of symptoms and initial CT. No significant difference was noted in the frequency and CT score of air trapping among different groups. CONCLUSION: Some patients with COVID-19 developed small-airway disease. Air trapping was more distinguished in the early stage of the disease and persisted during the 2-month follow-up. Longer-term follow-up studies are needed to confirm the findings.

Clinical value of high-resolution dynamic contrast-enhanced (DCE) MRI in diagnosis of cutaneous squamous cell carcinoma.

BACKGROUND: While uncomplicated cases of skin squamous cell carcinoma (cSCC) can be treated with surgery topical therapy alone, more objective and non-invasive examination methods are needed to guide clinicians to make more detailed biopsy and surgical plans for lesions with atypical or subcutaneous growth. High-resolution magnetic resonance imaging (HR-MRI) is a novel skin imaging method. MATERIALS AND METHODS: Prospective collection of 19 patients with clinically suspected cSCC. All patients underwent high-resolution DCE-MRI using a 70-mm microscopy coil before operation. The imaging features and results of surgical pathology were recorded. Ktrans , Kep , Ve values, and the time-signal curve (TIC) types were determined using DCE images. RESULTS: 16 cases of cSCC, 3 cases of acanthoma. The subcutaneous invasion of all lesions was clearly displayed, of which 8 lesions invaded the subcutaneous fat layer, 5 invaded the muscle layer, 1 invaded the periosteum, 2 invaded the cap fascia, and the layer of all lesions invasion judged by HR-MR imaging was consistent with the postoperative pathology. The main manifestations of cSCC were ill-defined margin, obvious inhomogeneous enhancement, higher perfusion parameters value and type-III TIC, while acanthoma showed well-defined and type-I TIC. Some imaging findings (such as boundary, enhancement) and DCE perfusion parameters of the two groups overlap. CONCLUSION: High-resolution DCE-MRI can fully and directly display the subcutaneous invasion of cSCC, and more work needs to be done to prove its value. Next, we will expand the sample size, and further explore its value in the differential diagnosis and prognosis evaluation of cSCC from acanthoma or other skin tumors.

Clinical characteristics and changes of chest CT features in 307 patients with common COVID-19 pneumonia infected SARS-CoV-2: A multicenter study in Jiangsu, China.

OBJECTIVE: The study was aimed to describe the clinical characteristics and evaluate the dynamic changes of chest CT features in the first three weeks in the common type fo COVID-19 pneumonia patients in Jiangsu Province. METHODS: 307 patients infected SARS-CoV-2 classified as common type were enrolled in the study. 628 chest CT scans were divided into three groups based on the time interval between symptoms and chest CT scan. The clinical characteristics were descriptively analyzed.The chest CT features were quantitatively evaluated. Mann-Whitney U test was used to test the differences in three groups and between men and women. Spearman rank correlation was used to test the association between the arterial blood gas(ABG) analysis results and chest CT scores. RESULTS: Fever (69.1%) and cough (62.8%) were common symptoms. 111(36.2%) patients were anorexia. GGO was the most common manifestation of COVID-19 pneumonia, which could be followed by consolidation and fibrosis. Lower lobe or subpleural region was the most common distribution form of lesion. More lung lobes were involved in the third week. Total chest CT scores in the second week were higher than the first week. Fibrosis Scores increased in the second and third week. Total CT score, GGO score and fibrosis score of male patients were significantly higher than female in the second week. Male patients had higher consolidation score and fibrosis score than female in the third week. Total CT score and GGO score had weak to moderate correlation with arterial blood gas indices. CONCLUSION: Changes in chest CT were difficult to assess quantitatively in the first third weeks. Male patients recovered slower than female in the second week. Although CT score had correlations with arterial blood gas indices, long-term follow-up of pulmonary function test is needed to determine the recovery of lung.

High-resolution Chest CT Features and Clinical Characteristics of Patients Infected with COVID-19 in Jiangsu, China.

BACKGROUND: A pneumonia associated with the coronavirus disease 2019 (COVID-19) recently emerged in China. It was recognized as a global health hazard. METHODS: 234 inpatients with COVID-19 were included. Detailed clinical data, chest HRCT basic performances and certain signs were recorded Ground-glass opacity (GGO), consolidation, fibrosis and air trapping were quantified. Both clinical types and CT stages were evaluated. RESULTS: Most patients (approximately 90%) were classified as common type and with epidemiologic history. Fever and cough were main symptoms. Chest CT showed abnormal attenuation in bilateral multiple lung lobes, distributed in the lower and/or periphery of the lungs (94.98%), with multiple shapes. GGO and vascular enhancement sign were most frequent seen, followed by interlobular septal thickening and air bronchus sign as well as consolidation, fibrosis and air trapping. There were significant differences in most of CT signs between different stage groups. The SpO2 and OI were decreased in stage IV, and the CT score of consolidation, fibrosis and air trapping was significantly lower in stage I (P<0.05). A weak relevance was between the fibrosis score and the value of PaO2 and SpO2 (P<0.05). CONCLUSIONS: Clinical performances of patients with COVID-19, mostly with epidemiologic history and typical symptoms, were critical valuable in the diagnosis of the COVID-19. While chest HRCT provided the distribution, shape, attenuation and extent of lung lesions, as well as some typical CT signs of COVID-19 pneumonia.

A combined DTI and resting state functional MRI study in patients with postherpetic neuralgia.

PURPOSE: To explore the brain microstructural and functional changes in patients with postherpetic neuralgia (PHN). MATERIALS AND METHODS: 12 PHN patients and 12 healthy volunteers were enrolled. Diffusion tensor imaging (DTI) and resting-state functional MRI (rfMRI) sequences were scanned by a 3T MR scanner. Fractional anisotropy (FA) and mean diffusivity (MD) t-maps were obtained following DTI data processing. The amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were obtained following rfMRI data processing. A two sample t-test was performed to compare the FA, MD, ALFF and fALFF differences between the PHN patients and healthy controls. RESULTS: No significant differences were noted with regard to the parameters gender, age and education years between the two groups. FA, MD, ALFF and/or fALFF indicated significant alterations in specific pain or pain-related brain regions, such as brainstem, cerebellum, parietal lobe, precuneus, frontal lobe, temporal lobe, postcentral and precentral gyrus, corpus callosum, cingulate gyrus, putamen and insula. CONCLUSION: Multi-local alterations of spontaneous brain activity could form a network related to chronic pain, sensory discrimination, emotion and cognition, suggesting complicated central mechanisms of PHN. The combined-action of brain microstructure and function may play a critical role in comprehension of neurological mechanisms of PHN-induced pain.

A clinical exploration of neoadjuvant chemotherapy with tegafur, gimeracil, and oteracil potassium capsules combined with oxaliplatin for advanced gastric cancer.

BACKGROUND: Advanced gastric cancer refers to tumor invasion into the gastric muscularis propria or even the layer beyond, and has low early gastric cancer diagnosis rate. PURPOSE: To determine the clinical efficacy and side effects of neoadjuvant chemotherapy with tegafur, gimeracil, and oteracil potassium capsules (TGOP) combined with oxaliplatin (SOX regimen) in patients with advanced gastric cancer. METHODS: We evaluated 25 patients with advanced gastric cancer who were admitted and treated with neoadjuvant chemotherapy with the SOX regimen (intravenous injection of 130 mg/m(2) oxaliplatin on day 1 followed by oral administration of 60 mg TGOP twice daily on days 1-14), every 3 weeks. The clinical efficacy and side effects of the SOX regimen were evaluated after two courses of treatment, before surgery. RESULTS: Of the 25 patients enrolled in this study, 23 completed two courses of neoadjuvant chemotherapy, and of these, 12 achieved downstaging as determined by the clinical TNM stage, resulting in a total response rate of 52.2%. The 23 patients underwent surgery, with 22 receiving radical resection (95.7%). Among these 23 patients, R0 resection was achieved in 16 (69.6%) and pathological complete remission was observed in one. CONCLUSION: Neoadjuvant chemotherapy with TGOP combined with oxaliplatin was effective for advanced gastric cancer and had tolerable side effects.