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Abstract To scientifically and effectively manage exhaust pollution in underground confined spaces and reduce hazards from personnel exhaust exposure, a systematic study of the distribution characteristics of diesel locomotive exhaust emissions in mines is needed. Aiming at large underground coal mining operations, an experimental platform for the physical simulation of vehicle exhaust transport in restricted spaces is designed and created. The Hongliulin Coal Mine in Shaanxi Province, China, is used as a prototype, and the platform consists of a coal mining face system, ventilation system, exhaust emission system, monitoring and surveillance system, and control platform. The experimental platform with carbon dioxide simulation of diesel exhaust pollution, in the role of different wind speed, the concentration of carbon dioxide is a downward trend, the wind speed is in the low wind speed on the 0.2 m of the concentration difference of 25.75%; the experimental platform exhaust transport process presents a clear “three-zone” change rule; experimental platform carbon dioxide concentration and the field nitrogen dioxide concentration change trend is consistent. The platform functions and features can provide guidance for mine exhaust management and exhaust exposure hazard prevention and control.

Abstract Background To compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC). Methods Two hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors’ 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA). Results There were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model. Conclusions Both 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC.

ObjectiveTo develop diagnostic models for differentiating gastric neuroendocrine carcinoma (g-NEC) and gastric mixed adeno-neuroendocrine carcinoma (g-MANEC) from gastric adenocarcinoma (g-ADC) based on traditional contrast enhanced CT imaging features and radiomics features.MethodsWe retrospectively analyzed 90 g-(MA)NEC (g-MANEC and g-NEC) patients matched 1:1 by T-stage with 90 g-ADC patients. Traditional CT features were analyzed using univariable and multivariable logistic regression. Tumor segmentation and radiomics features extraction were performed with Slicer and PyRadiomics. Feature selection was conducted through univariable analysis, correlation analysis, LASSO, and multivariable stepwise logistic. The combined model incorporated clinical and radiomics predictors. Diagnostic performance was assessed with ROC curves and DeLong’s test. The models’ diagnostic efficacy was further validated in subgroup of g-NEC vs. g-ADC and g-MANEC vs. g-ADC cases.ResultsTumor necrosis and lymph node metastasis were independent predictors for differentiating g-(MA)NEC from g-ADC (P < 0.05). The clinical model’s AUC was 0.700 (training) and 0.667(validation). Five radiomics features were retained, with the radiomics model showing AUC of 0.809 (training) and 0.802 (validation). The combined model’s AUCs were 0.853 (training) and 0.812 (validation), significantly outperforming the clinical model (P < 0.05). Subgroup analysis revealed that the combined model exhibited acceptable performance in differentiating g-NEC from g-ADC and g-MANEC from g-ADC, with AUC of 0.887 and 0.823 in the training cohort and 0.852 and 0.762 in the validation cohort.ConclusionA combined model based on traditional CT imaging and radiomic features provides a non-invasive and effective preoperative diagnostic method for differentiating g-(MA)NEC from g-ADC.

ObjectiveTo identify the most sensitive imaging examination method to evaluate the prognosis of esophageal squamous cell carcinoma (ESCC).Materials and methodsThirty patients with esophageal squamous cell carcinoma (ESCC) participated in the study and underwent chemoradiotherapy (CRT). They were divided into two groups based on their survival status: the survival group and non-survival group. The diagnostic tests were utilized to determine the most effective imaging examination method for assessing the prognosis.Results1. There were no significant differences in tumor length shown on esophagography or computed tomography (CT) or the maximal esophageal wall thickness shown on CT at the specified time points between the two groups. 2. The tumor length on diffusion-weighted imaging (DWI) in the survival group was significantly lower than in the non-survival group at the end of the sixth week of treatment (P=0.001). The area under the ROC curve was 0.840 (P=0.002), and the diagnostic efficiency was moderately accurate. 3. The apparent diffusion coefficient (ADC) values of the survival group were significantly higher than those in the non-survival group at the end of the fourth week and sixth week of treatment (both P

Abstract To investigate clinical data and computed tomographic (CT) imaging features in differentiating gastric schwannomas (GSs) from gastric stromal tumours (GISTs) in matched patients, 31 patients with GSs were matched with 62 patients with GISTs (1:2) in sex, age, and tumour site. The clinical and imaging data were analysed. A significant (P

ObjectiveTo explore the value of the features of lymph nodes (LNs) with a short-axis diameter ≥6 mm in predicting lymph node metastasis (LNM) in advanced gastric adenocarcinoma (GAC) based on dual-energy CT (DECT) radiomics.Materials and methodsData of patients with GAC who underwent radical gastrectomy and LN dissection were retrospectively analyzed. To ensure the correspondence between imaging and pathology, metastatic LNs were only selected from patients with pN3, nonmetastatic LNs were selected from patients with pN0, and the short-axis diameters of the enrolled LNs were all ≥6 mm. The traditional features of LNs were recorded, including short-axis diameter, long-axis diameter, long-to-short-axis ratio, position, shape, density, edge, and the degree of enhancement; univariate and multivariate logistic regression analyses were used to establish a clinical model. Radiomics features at the maximum level of LNs were extracted in venous phase equivalent 120 kV linear fusion images and iodine maps. Intraclass correlation coefficients and the Boruta algorithm were used to screen significant features, and random forest was used to build a radiomics model. To construct a combined model, we included the traditional features with statistical significance in univariate analysis and radiomics scores (Rad-score) in multivariate logistic regression analysis. Receiver operating curve (ROC) curves and the DeLong test were used to evaluate and compare the diagnostic performance of the models. Decision curve analysis (DCA) was used to evaluate the clinical benefits of the models.ResultsThis study included 114 metastatic LNs from 36 pN3 cases and 65 nonmetastatic LNs from 28 pN0 cases. The samples were divided into a training set (n=125) and a validation set (n=54) at a ratio of 7:3. Long-axis diameter and LN shape were independent predictors of LNM and were used to establish the clinical model; 27 screened radiomics features were used to build the radiomics model. LN shape and Rad-score were independent predictors of LNM and were used to construct the combined model. Both the radiomics model (area under the curve [AUC] of 0.986 and 0.984) and the combined model (AUC of 0.970 and 0.977) outperformed the clinical model (AUC of 0.772 and 0.820) in predicting LNM in both the training and validation sets. DCA showed superior clinical benefits from radiomics and combined models.ConclusionThe models based on DECT LN radiomics features or combined traditional features have high diagnostic performance in determining the nature of each LN with a short-axis diameter of ≥6 mm in advanced GAC.

Objective MicroRNA (miR)-22-3p is expressed in atherosclerosis (AS), but its function and regulatory mechanisms remain unclear. Therefore, the effects of miR-22-3p in AS were assessed in this study. Methods MiR-22-3p expression was assessed in AS, and miR-22-3p target genes were predicted using sequencing transcriptomics. The effect of miR-22-3p agomir on atherosclerotic lesions in an AS mouse model were determined by Oil red O, Masson’s, and sirius red staining, and by anti-smooth muscle actin and macrophage antigen-3 immunostaining. Gene expression in AS was evaluated by western blot and immunofluorescence. Results MiR-22-3p was expressed in AS and control samples (32.5% and 33.9% levels, respectively, relative to total miRNA among six highly expressed miRNAs). In the mouse model of AS, miR-22-3p agomir significantly reduced lipid deposition, proliferation of aortic collagen fibres, and macrophage content. Additionally, inducible nitric oxide synthase, interleukin-6, and tumour necrosis factor-α levels were significantly reduced, and levels of arginase 1 and CD206 were significantly enhanced. MiR-22-3p was found to target janus kinase 1( JAK1 ), and significantly inhibited the activation of NLR family pyrin domain containing 3 (NLRP3) and JAK1 in mice. Conclusions MiR-22-3p appears to reduce the inflammatory response in AS, which might be achieved by inducing the M2 macrophage phenotype and suppressing NLRP3 activation via JAK1.

Abstract Objective To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for preoperative differentiation of pulmonary nodular mucinous adenocarcinoma (PNMA) from pulmonary tuberculoma (PTB). Method A total of 124 and 53 patients with PNMA and PTB, respectively, were retrospectively analyzed from January 2017 to November 2022 in The Fourth Affiliated Hospital of Hebei Medical University (Ligang et al., A machine learning model based on CT and clinical features to distinguish pulmonary nodular mucinous adenocarcinoma from tuberculoma, 2023). A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into a training group and a test group at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (LR), support vector machine (SVM) and random forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. The clinical model was developed using logistic regression. Finally, a combined model was established based on clinical factors and radiomics features. We externally validated the three models in a group of 68 patients (46 and 22 patients with PNMA and PTB, respectively) from Xing Tai People’s Hospital (30 and 14 patients with PNMA and PTB, respectively) and The First Hospital of Xing Tai (16 and 8 patients with PNMA and PTB, respectively). The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the predictive value of the developed models. Results The combined model established by the logistic regression method had the best performance. The ROC-AUC (also a decision curve analysis) of the combined model was 0.940, 0.990 and 0.960 in the training group, test group and external validation group, respectively, and the combined model showed good predictive performance for the differentiation of PNMA from PTB. The Brier scores of the combined model were 0.132 and 0.068 in the training group and test group, respectively. Conclusion The combined model incorporating radiomics features and clinical parameters may have potential value for the preoperative differentiation of PNMA from PTB.

Abstract In this paper, the organic pigments indigo and isatin were detected by high performance liquid chromatography (HPLC) in The Mural of Four Buddhas which is in Cave 3 from the ancient Chinese Tiantishan grottoes (Ming Dynasty, East Slope). By analysing the preservation conditions of the mural and the environmental conditions of the place where the Tiantishan grottoes are located, we speculated that the isatin detected in this mural was mainly produced by the oxidative decomposition of indigo by ozone (O3), rather than by photodegradation of indigo. We have used theoretical calculation software Gaussian09 (G09) and Amsterdam Density Functional (ADF) module in the Amsterdam Modeling Suite (AMS) software to simulate the reaction mechanism of O3 oxidation of indigo, and the end products of the oxidation of the natural plant dye indigo by O3 were identified as isatin, C16H10N2O3 and C8H6NO3 using HPLC, fluorescence spectroscopy and high performance liquid chromatography-mass spectrometry(HPLC–MS). This finding confirmed the accuracy of the mechanism of indigo fading by O3 oxidation. These findings provided a theoretical basis for subsequent research into the derivation of natural organic dyes in the face of increasing O3 pollution and for better protection of valuable historical and cultural heritage such as ancient Chinese grottoes murals.

Key points Radiomics have potentials to differentiate the invasiveness of pGGNs lung adenocarcinoma. Clinical-radiographic feature adds discriminative value to radiomics in pGGNs pathological subtype. Combined clinical-radiographic-radiomic model can successfully stratify patients into noninvasive pGGNs and invasive pGGNs in patients with resectable lung adenocarcinoma.

BackgroundPulmonary sclerosing pneumocytoma (PSP) is a rare lung tumor that is mostly isolated and commonly reported among middle-aged East Asian women. Recently, Immunohistochemistry (IHC) analysis has suggested that PSP is of primitive epithelial origin, most likely derived from type II alveolar air cells. Patients with PSP are generally asymptomatic and usually detected for other unrelated reasons during routine imaging. Several studies have already investigated the computed tomography (CT) features of PSP and their correlation with pathology. Magnetic resonance imaging (MRI) is a radiation-free imaging technique with important diagnostic value for specific pulmonary nodules. However, very few case reports or studies focus on the MRI findings of PSP.Case reportWe reported a case of an asymptomatic 56-year-old female with a solitary, well-defined soft-tissue mass in the lower lobe of the left lung. The mass showed iso-to-high signal intensity (SI) than muscle on T1-weighted image (T1WI) and T2-weighted image (T2WI) and a much higher SI on fat-suppressed T2WI, diffusion-weighted image, and apparent diffusion coefficient image. Contrast-enhanced fat-suppressed T1WI revealed noticeable inhomogeneous progressive enhancement throughout the mass. The mass revealed early enhancement without a significant peak, followed by a plateau pattern on dynamic contrast-enhanced MRI images. The patient underwent left basal segmentectomy via thoracoscopic surgery. Histopathology and IHC results of the surgical specimen confirmed that it was a PSP. We concluded that the MRI findings of PSP might adequately reflect the different components within the tumor and aid clinicians in preoperative diagnosis and assessment. To the best of our knowledge, this is the most comprehensive case report on the MRI findings of PSP.ConclusionThe MRI findings of PSP correspond to its histopathological features. Here, we present a case of PSP with the most comprehensive MRI findings, emphasizing the importance of multiple-sequence MRI in diagnosing PSP.

Objective: This study aimed to develop and validate predictive models using clinical parameters, radiomic features, and a combination of both for invasive mucinous adenocarcinoma (IMA) of the lung in patients with lung adenocarcinoma. Method: A total of 173 and 391 patients with IMA and non-IMA, respectively, were retrospectively analyzed from January 2017 to September 2022 in our hospital. Propensity Score Matching was used to match the 2 groups of patients. A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into training and test groups at a ratio of 7:3. The least absolute shrinkage and selection operator algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (logistic), support vector machine (SVM), and decision tree. The best-performing model was adopted, and the radiomics score (Radscore) was then computed. A clinical model was developed using logistic regression. Finally, a combined model was established based on a clinical model and a radiomics model. The area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis were used to evaluate the predictive value of the developed models. Results: Both clinical and radiomics models established using the logistic method showed the best performance. The Delong test revealed that the combined model was superior to the clinical and radiomics models ( P = .018 and .020, respectively). The ROC-AUC (also decision curve analysis) of the combined model was 0.840 and 0.850 in the training and testing groups, respectively, which showed good predictive performance for IMA. The Brier scores for the combined model were 0.161 and 0.154 in the training and testing groups, respectively. Conclusion: The combined model incorporating radiomic CT features and clinical predictors may have the potential to predict IMA in patients with lung cancer.

Objective To differentiate gastric leiomyomas (GLs) and gastric stromal tumors (GSTs) based on preoperative enhanced computed tomography characteristics. Methods Twenty-six pathologically confirmed GLs were propensity score-matched to 26 GSTs in a 1:1 ratio based on sex, age, tumor site, and tumor size. Tumor shape and contour, mucosal ulceration, growth pattern, enhancement pattern and degree, longest diameter, and longest diameter/vertical diameter ratio were compared between the groups. Hemorrhage, calcification, peripheral invasion, and distant metastasis were also included in the regression analysis for differentiation of the two tumors. Results Mucosal ulceration was significantly more frequent in GSTs than GLs. The enhancement degree of GSTs was significantly higher than that of GLs in the arterial and portal venous phases. Using enhancement degrees of 18 HU and 23 HU in the arterial phase and venous phase as cutoff values, respectively, we found that an enhancement degree of

PURPOSEWe aimed to evaluate and compare the growth patterns among pathological types of inde- terminate subsolid nodules in patients without a history of cancer as observed on computed tomography (CT).METHODSThis retrospective study included 77 consecutive patients with 80 indeterminate subsolid nod- ules on unenhanced thin-section CT. Subsolid nodules were classified into 2 growth pattern groups based on volume: growth (n = 35) and non-growth (n = 42). According to the pathologi- cal diagnosis, subsolid nodules were further subdivided into 3 groups: adenocarcinoma in situ (growth, n = 8 vs. non-growth, n = 22), minimally invasive adenocarcinoma (n = 14 vs. n = 15), and invasive adenocarcinoma (n=13 vs. n=5). Kaplan–Meier and Cox proportional hazards regres- sion analyses were performed to identify the risk factors for subsolid nodules growth. The CT findings of the 35 subsolid nodules in the growth group were compared among the 3 pathologi- cal groups.RESULTSIn the growth group, the overall mean volume doubling time and mass doubling time (MDT) were 811.5 days and 616.5 days, respectively. Patient’s age (odds ratio=1.041, P=.045) and CT subtype of non-solid nodule and part-solid nodule (odds ratio=3.430, P=.002) could predict subsolid nodule growth. The baseline volume, mass, and mean CT value were larger in the inva- sive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .01). The shortest volume doubling time was observed in the invasive adenocarcinoma group, followed by the minimally invasive adenocarcinoma group and the adenocarcinoma in situ group. A shorter mass doubling time was observed in the minimally invasive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .02).CONCLUSIONAs age increases, the risk of pulmonary subsolid nodule growth increases by 4% each year, and part-solid nodules have a 3 times higher risk of growth compared to non-solid nodules in patients with no history of cancer. Subsolid nodules with more aggressive pathological charac- teristics grow at a faster rate.

Abstract Background To investigate the value of contrast-enhanced CT (CECT)-derived imaging features in predicting lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC) patients. Methods One hundred and ninety-seven patients with postoperative pathologically confirmed esophageal squamous cell carcinoma treated in our hospital between January 2017 and January 2019 were enrolled in our study, including fifty-nine patients with LVI and one hundred and thirty-eight patients without LVI. The CECT-derived imaging features of all patients were analyzed. The CECT-derived imaging features were divided into quantitative features and qualitative features. The quantitative features consisted of the CT attenuation value of the tumor (CTVTumor), the CT attenuation value of the normal esophageal wall (CTVNormal), the CT attenuation value ratio of the tumor-to-normal esophageal wall (TNR), the CT attenuation value difference between the tumor and normal esophageal wall (ΔTN), the maximum thickness of the tumor measured by CECT (Thickness), the maximum length of the tumor measured by CECT (Length), and the gross tumor volume measured by CECT (GTV). The qualitative features consisted of an enhancement pattern, tumor margin, enlarged blood supply or drainage vessels to the tumor (EVFDT), and tumor necrosis. For the clinicopathological characteristics and CECT-derived imaging feature analysis, the chi-squared test was used for categorical variables, the Mann–Whitney U test was used for continuous variables with a nonnormal distribution, and the independent sample t-test was used for the continuous variables with a normal distribution. The trend test was used for ordinal variables. The association between LVI status and CECT-derived imaging features was analyzed by univariable logistic analysis, followed by multivariable logistic regression and receiver operating characteristic (ROC) curve analysis. Results The CTVTumor, TNR, ΔTN, Thickness, Length, and GTV in the group with LVI were higher than those in the group without LVI (P

ObjectiveThe aim of this study was to evaluate the sensitivity and accuracy of multi-sequence 3T magnetic resonance imaging (MRI) in the detection of different types of pulmonary nodules.MethodsA total of 68 patients with pulmonary nodules identified using computed tomography (CT) subsequently underwent MRI. Using CT images with a slice thickness of 1 mm as the gold standard, the sensitivity of three MRI sequences in detecting different types of pulmonary nodules was calculated, and the image quality was also evaluated. Nodule types included solid nodules, ground glass nodules (GGN), and part-solid nodules (PSN). Statistical analyses of data were conducted using the software SPSS 21.0. The intra-class correlation coefficient was calculated in order to compare the consistency of nodule size in both MRI and CT.ResultsCT detected 188 pulmonary nodules in 68 patients, including 87 solid nodules and 101 sub-solid nodules, the latter comprising 46 PSNs and 55 GGNs. The average nodule diameter was approximately 7.7 mm. The sensitivity of MRI in detecting nodules ≥ 6 mm in diameter and those of > 8 mm in diameter was 92% and 100%, respectively, and the sequence with the highest detection rate was T2-BLADE. In relation to solid nodules, the sequence with the highest detection rate was T1 Star-VIBE, while the T2-BLADE sequence demonstrated the highest detection rate of sub-solid nodules. The image quality of the T1 Star-VIBE sequence was better than that of both the T2-HASTE and the T2-BLADE sequences. The consistency of CT and MRI sequences for nodule size was high with a consistency coefficient of 0.94–0.98.ConclusionThe detection rate of MRI for nodules with a diameter of > 8 mm was 100%. The T2-BLADE sequence had the highest detection sensitivity. The sequence with the best image quality was the T1 Star-VIBE.

ObjectivesTo build a radiomics model and combined model based on dual-energy CT (DECT) for diagnosing serosal invasion in gastric adenocarcinoma.Materials and methods231 gastric adenocarcinoma patients were enrolled and randomly divided into a training (n = 132), testing (n = 58), and independent validation (n = 41) cohort. Radiomics features were extracted from the rectangular ROI of the 120-kV equivalent mixed images and iodine map (IM) images in the venous phase of DECT, which was manually delineated perpendicularly to the gastric wall in the deepest location of tumor infiltration, including the peritumoral adipose tissue within 5 mm outside the serosa. The random forest algorithm was used for radiomics model construction. Traditional features were collected by two radiologists. Univariate and multivariate logistic regression was used to construct the clinical model and combined model. The diagnostic efficacy of the models was evaluated using ROC curve analysis and compared using the Delong’s test. The calibration curves were used to evaluate the calibration performance of the combined model.ResultsBoth the radiomics model and combined model showed high efficacy in diagnosing serosal invasion in the training, testing and independent validation cohort, with AUC of 0.90, 0.90, and 0.85 for radiomics model; 0.93, 0.93, and 0.89 for combined model. The combined model outperformed the clinical model (AUC: 0.76, 0.76 and 0.81).ConclusionThe radiomics model and combined model constructed based on tumoral and peritumoral radiomics features derived from DECT showed high diagnostic efficacy for serosal invasion in gastric adenocarcinoma.

PurposeThe purpose of this research was to evaluate the feasibility and efficacy of 125I seed brachytherapy as salvage treatment for recurrence from non-anaplastic thyroid cancer refractory to other modalities.MethodsBetween June 2006 and September 2019, fifteen patients with recurrent non-anaplastic thyroid cancer were treated with 125I seed brachytherapy. 125I seeds were implanted into the tumor under the guidance of CT and/or ultrasound images with the median prescription dose of 120 Gy (range, 100-140 Gy). The median seed number was 80 (range 10-214). Clinical efficacy was evaluated with Response Evaluation Criteria in Solid Tumors.FindingsFifteen patients were selected, eleven of whom had papillary carcinoma, two suffered from follicular carcinoma, and two were diagnosed with medullary carcinoma. These patients had twenty-four nodes in total. After they received salvage surgery and/or radioactive iodine (RAI) therapy, local recurrence was detected in all of them. No less than one node was observed in everyone’s cervical or supraclavicular areas, and four patients had lung metastatic. The median follow-up period lasted 48 months (range, 5-93 months). All patients did not develop locoregional recurrence after experiencing 125I seed brachytherapy. Only three of them formed new metastases in nontarget regional nodes after brachytherapy, and additional brachytherapy can solve all regional failure problems. No significant adverse events were observed in any patient.ImplicationsFor the chosen patients, 125I seed brachytherapy is feasible for treating refractory local recurrence from non-anaplastic thyroid cancer. Further studies are required to determine the role of 125I seed brachytherapy in the treatment of thyroid cancer.

Background and PurposeChemoradiotherapy is the standard treatment for moderate and advanced oesophageal cancer. The aim of this study was to establish a predictive model based on enhanced computed tomography examination, and to evaluate its clinical value for detecting locoregional recurrence-free survival (LRFS) in cases of oesophageal squamous cell carcinoma after radiotherapy.Materials and MethodsIn total, 218 patients with pathologically diagnosed oesophageal squamous cell carcinoma who received radical chemoradiotherapy from July 2016 to December 2017 were collected in this study. Patients were randomly divided into either a training group (n=153) or a validation group (n=65) in a 7:3 ratio. Clinical patient information was then recorded. The enhanced computed tomography scan images of the patients were imported into 3D-slicer software (version 4.8.1), and the radiomic features were extracted by the Python programme package. In the training group, the dimensionality reduction of the radiomic features was implemented by Lasso regression, and then a radiological label, the model of predicting LRFS, was established and evaluated. To achieve a better prediction performance, the radiological label was combined with clinical risk factor information to construct a radiomics nomogram. A receiver operating characteristic curve was used to evaluate the efficacy of different models. Calibration curves were used to assess the consistency between the predicted and observed recurrence risk, and the Hosmer-Lemeshow method was used to test model fitness. The C-index evaluated the discriminating ability of the prediction model. Decision curve analysis was used to determine the clinical value of the constructed prediction model.ResultsOf the 218 patients followed up in this study, 44 patients (28.8%) in the training group and 21 patients (32.3%) in the validation group experienced recurrence. There was no difference in LRFS between the two groups (χ2 =0.525, P=0.405). Lasso regression was used in the training group to select six significant radiomic features. The radiological label established using these six features had a satisfactory prediction performance. The C-index was 0.716 (95% CI: 0.645–0.787) in the training group and 0.718 (95% CI: 0.612–0.825) in the validation group. The radiomics nomogram, which included the radiological label and clinical risk factors, achieved a better prediction than the radiological label alone. The C-index was 0.742 (95% CI: 0.674–0.810) in the training group and 0.715 (95% CI: 0.609–0.820) in the validation group. The results of the calibration curve and decision curve analyses indicated that the radiomics nomogram was superior in predicting LRFS of oesophageal carcinoma after radiotherapy.ConclusionsA radiological label was successfully established to predict the LRFS of oesophageal squamous cell carcinoma after radiotherapy. The radiomics nomogram was complementary to the clinical prognostic features and could improve the prediction of the LRFS after radiotherapy for oesophageal cancer.

PurposeThe goal of current study was to introduce noninvasive and reproducible MRI methods for in vivo functional assessment of lung adenocarcinoma (LUAD).MethodsForty-four patients with pathologically confirmed LUAD were included in this study. All the lesions were classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). The IA lesions were further divided into five subtype patterns, including acinar, lepidic, papillary, micropapillary and solid. Tumors were grouped depending on predominant subtype: low grade (AIS, MIA or lepidic predominant), intermediate grade (papillary or acinar predominant) and high grade (micropapillary, or solid predominant). Spirometry was performed according to American Thoracic Society guidelines. For each patient, Intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) analysis and oxygen-enhanced MRI (OE-MRI) analysis were performed. Spearman’s test was used to assess the relationship between a) whole lung mean percent signal enhancement (PSE) and pulmonary function tests (PFTs) parameters; b) IVIM-derived parameters and PFTs parameters; c) tumor mean PSE and IVIM-derived parameters. Kruskal -Wallis tests were applied to test the difference of tumor mean PSE and IVIM-derived parameters between different histological tumor grades. Receiver operating characteristics (ROC) analysis was used to evaluate the diagnostic performance.ResultsWhole lung mean PSE was significantly positively correlated with PFTs parameters (r = 0.40 ~ 0.44, P < 0.05). f value derived from IVIM-DWI was significantly negatively correlated with PFTs parameters (r = -0.38 ~ -0.47, P < 0.05). Both tumor mean PSE (P = 0.030 < 0.05) and f (P = 0.022 < 0.05) could differentiate different histological grades. f was negatively correlated with tumor mean PSE (r = -0.61, P < 0.001). For the diagnostic performance, the combination of tumor mean PSE and f outperformed than using tumor mean PSE or f alone in both sensitivity and area under the ROC curve.ConclusionsThe combined measurement of OE-MRI and IVIM-DWI may serve as a promising method for the noninvasive and non-radiation evaluation of pulmonary function. Quantitative analyses achieved by OE-MRI and IVIM-DWI offer an approach of the classification of LUAD subtypes.

ObjectiveThis study aimed to evaluate the visibility of different subgroups of lung nodules of 6 mm, the sensitivity of the PETRA sequence reached 97%, with a higher rate for nodules located in the upper lung fields than those in the middle and lower lung fields. Strong agreement was found between the CT and PETRA results (correlation coefficients = 0.97).ConclusionThe PETRA technique had high sensitivity for different type of nodule detection and enabled accurate assessment of their diameter and morphologic characteristics. It may be an effective alternative to CT as a tool for screening and follow up pulmonary nodules.

ObjectivesTo develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians.Patients and MethodsThis retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC.ResultsIn the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P

Atmospheric reactive oxygen species (ROS) play a key role in the process of air pollution and oxidative damage to organisms. The analysis of ROS was carried out by the capture-derivative method. Therefore, it is necessary to prepare an effective molecular membrane to trap and detect ROS. Electrospinning membranes were prepared by combining the electrospinning technique with chrysin, baicalein, scutellarin, genistein, quercetin, and baicalin. By comparing the structures of the membranes before and after the reaction, the fluorescence enhancement characteristics of the reactive molecular membranes and the atmospheric radicals were studied. The ability of the active molecular membranes to trap atmospheric radicals was also studied. It was found that the genistein active molecular membrane had good trapping ability in four environments. The fluorescence enhancement rates in ROS, OH radical and O3 simulated environments were 39.32%, 7.99% and 11.92%, respectively. The fluorescence enhancement rate in atmospheric environment was 16.16%. Indeed, the sites where the atmospheric radicals react with the active molecular membranes are discussed. It is found that it is mainly related to the 5,7 phenolic hydroxyl of ring A, catechol structure and the coexistence structure of 4′ phenolic hydroxyl of ring B and 7 phenolic hydroxyl of ring A. Therefore, the genistein molecular membrane has shown great potential in its trapping ability and it is also environmentally friendly.

We load the natural active molecules onto the spin film in an array using electrospinning techniques. The electrospun active molecular membranes we obtain in optimal parameters exhibit excellent capacity for scavenging radical. The reaction capacity of three different membranes for free radicals are shown as follow, glycyrrhizin acid membrane > quercetin membrane > α-mangostin membrane. The prepared active molecular electrospun membranes with a large specific surface area and high porosity could increase the interaction area between active molecules and free radicals. Additionally, it also has improved anti-airflow impact strength, anti-contaminant air molecular interference ability, and the ability to capture free radicals.

This study investigated the utility of quantifying iodine concentration (IC) in perigastric adipose tissue, using dual-energy computed tomography (DECT), for the detection of T4a-stage gastric cancer. Fifty-four patients with gastric cancer were enrolled at the Fourth Hospital of Hebei Medical University between January and June 2013. Patients were imaged preoperatively with conventional computed tomography (CT) scans and DECT, and the IC in perigastric fat adjacent to the tumor calculated from arterial phase (AP) and portal venous phase (PVP) images. The patients subsequently received surgical treatment (gastrectomy), and histologic analysis of resected specimens was used as a 'gold standard' reference for cancer staging. Receiver operating characteristic (ROC) curve analysis was employed to assess the utility of DECT for identifying T4a-stage gastric cancer, with optimal IC thresholds determined from the area under the ROC curve (AUC). Postoperative histology revealed that 32 patients had serosal invasion (group A), and 22 did not (group B). The accuracy of conventional CT for distinguishing stage T4 from non-T4 stages was 68.5% (37/54). IC was significantly higher in group A than in group B (AP: 0.60±0.34 vs. 0.09±0.19 mg/mL, p

Words offer a unique opportunity to separate the processing mechanisms of object subcomponents from those of the whole object, because the phonological or semantic information provided by the word subcomponents (i.e., sublexical information) can conflict with that provided by the whole word (i.e., lexical information). Previous studies have revealed some of the specific brain regions and temporal information involved in sublexical information processing. However, a comprehensive spatiotemporal neural network for sublexical processing remains to be fully elucidated due to the low temporal or spatial resolutions of previous neuroimaging studies. In this study, we recorded stereoelectroencephalography signals with high spatial and temporal resolutions from a large sample of 39 epilepsy patients (both sexes) during a Chinese character oral reading task. We explored the activated brain regions and their connectivity related to three sublexical effects: phonological regularity (whether the whole character's pronunciation aligns with its phonetic radical), phonological consistency (whether characters with the same phonetic radical share the same pronunciation), and semantic transparency (whether the whole character's meaning aligns with its semantic radical). The results revealed that sublexical effects existed in the inferior frontal gyrus, precentral and postcentral gyri, temporal lobe, and middle occipital gyrus. Additionally, connectivity from the middle occipital gyrus to the postcentral gyrus and from postcentral gyrus to the fusiform gyrus was associated with the sublexical effects. These findings provide valuable insights into the spatiotemporal dynamics of sublexical processing and object recognition in the brain. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study was to compare the prognostic prediction performances of the American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) 8th staging system and the Japan Esophageal Society (JES) 11th staging system for patients with esophageal squamous cell carcinoma who underwent radical (chemo) radiotherapy. Methods: In total, 574 patients were enrolled and categorized according to the tumor, node metastasis (TNM) AJCC/UICC 8th and JES 11th editions. Survival rates and disease-free survival were computed using the Kaplan–Meier technique. The log-rank test was used for survival difference analysis. Results: (1) The 8th AJCC/UICC N staging exhibited significant stratification for overall survival (OS) and progression-free survival (PFS). JES 11th showed significant OS stratification, but PFS was not well-stratified for N2–N4. (2) Both staging systems demonstrated significant stratification for OS and PFS. (3) AJCC/UICC 8th TNM staging yielded significantly well-stratified OS and PFS in the differing staging group. JES 11th failed to stratify OS and PFS for stages III and IVA. (4) AJCC/UICC 8th TNM stratified OS and PFS significantly well for lower and middle region tumors, whereas JES 11th inadequately stratified stages III and IVA. (5) Significant multivariable analysis results indicated that AJCC/UICC 8th independently predicted poor OS and PFS. Conclusions: In Chinese patients with esophageal squamous cell carcinoma who underwent radical (chemo) radiotherapy, the AJCC/ UICC 8th edition exhibited superior prognostic prediction capabilities compared with the JES 11th edition. [ABSTRACT FROM AUTHOR]