To predict symptomatic radiation pneumonitis (RP) in locally advanced non-small cell lung cancer (LA-NSCLC) patients by handcrafted radiomics (HCR) and deep learning (DL) features of three different regions of interest (ROIs) in normal lung tissue. Ninety LA-NSCLC patients from Center A were collected as the development cohort, and an additional 57 patients from Center B were collected as the external validation set. Clinical data and lung dose-volume parameters were collected from the patients, and univariate analysis was conducted. Three ROIs in lung were delineated in pre-radiotherapy CT images: Lung-PTV, Lung-GTV, and PTV-GTV. For each ROI, 1046 HCR features and 512 DL features were respectively extracted, and then correlated. For each ROI, three models (HCR, DLR, and a hybrid model) were developed with feature selection via Spearman correlation, mRMR, the LASSO method, and SVM-based modeling. And finally, their performance was evaluated and compared using AUC values from ROC curves. The optimal model was selected by comparing the best AUC through internal validation results, and then external validation was conducted. Calibration and decision curve analyses were used to evaluate the model. In the development cohort, univariate analysis revealed no statistically significant associations with RP. The correlation analysis revealed a weak correlation between HCR and DL features. In internal validation, for Lung-PTV, Lung-GTV and PTV-GTV, HCR models achieved AUCs of 0.658 (95

Purpose: To construct a nomogram combining Kaiser score (KS), synthetic MRI (syMRI) parameters, apparent diffusion coefficient (ADC), and clinical features to distinguish benign and malignant breast lesions better. Methods: From December 2022 to February 2024, a retrospective cohort of 168 patients with breast lesions diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4 by ultrasound and/or mammography was included. The research population was divided into the training set (n = 117) and the validation set (n = 51) by random sampling with a ratio of 7:3. Breast lesions' KS, ADC, relaxation time of syMRI, and clinical and imaging features were statistically analyzed and compared between malignant and benign groups. Two experienced radiologists independently assigned KS, and measured quantitative values of ADC and parameters of syMRI, and the intraclass correlation coefficient (ICC) was calculated. Independent predictors were identified by univariable and multivariable logistic regression analysis. Then, a nomogram was established, and its performance was evaluated by the area under the curve (AUC), calibration curve, and decision curve. Results: There were 168 lesions (118 malignant and 50 benign) in 168 female patients confirmed by histopathology. The interobserver agreement for each quantitative parameter was excellent. Older patient (OR = 1.091, 95 % confidence interval [CI]: 1.017-1.170, P = 0.014), higher lesions' KS (OR = 288.431, 95 % CI: 34.930-2381.654, P < 0.001), lower ADC (OR = 0.077, 95 % CI: 0.011-0.558, P = 0.011), and lower T2 relaxation time (OR = 0.918, 95 % CI: 0.868-0.972, P = 0.003) were independent predictors of breast malignancies and utilized to establish the nomogram. The accuracy of KS, ADC, T2, and patient age in predicting malignant breast lesions was 88.89 %, 79.48 %, 82.05 %, and 58.97 %, respectively. No significant differences in AUCs of KS, ADC and T2 were observed in distinguishing benign from malignant breast lesions. The nomogram yielded higher AUCs of 0.968 (0.934-0.996) and 0.959 (0.863-0.995) in training and validation sets than KS, ADC, T2, and patient age (p < 0.05). Conclusion: Although there were no significant differences among the AUCs of KS, ADC, and T2, the constructed nomogram incorporating these parameters significantly improves diagnostic performance for distinguishing benign and malignant BI-RADS 4 breast lesions. Future external validation is needed in practical applications.

To evaluate the added value of intratumoral and peritumoral radiomic scores (RS) of iodine density (IoD) map in the preoperative prediction of lymphovascular invasion (LVI) in gastric adenocarcinoma. A total of 226 patients, divided into groups with and without LVI, were randomly assigned to a training set (n = 137), an internal validation set (n = 59), and an external validation set (n = 30) that was recruited from two centers. Clinical characteristics and tumor spectral computed tomography (CT) parameters were collected and measured. Radiomic features were extracted from the IoD map within the intratumoral and peritumoral 1 mm, 3 mm, and 5 mm regions. Logistic regression was used to construct RS and to identify independent risk factors for LVI. The performance of each model was evaluated through receiver operating characteristic (ROC) curve analysis. A clinical-spectral model incorporating tumor CT-T stage, Borrmann classification, cell differentiation grade, and effective atomic number (Z-Eff) as risk factors achieved an area under the curve (AUC) of 0.841 in the training set. After the addition of intratumoral and peritumoral 5 mm RS as a risk factor to the clinical-spectral model, a nomogram was constructed, yielding a greater AUC of 0.925. The nomogram model achieved AUC of 0.872 and 0.947 in the internal and external validation sets, respectively, demonstrating superior performance compared to the clinical-spectral model. Intratumoral and peritumoral 5 mm RS based on iodine density map can provide additional value in enhancing the preoperative prediction of LVI in patients with gastric adenocarcinoma.

Background: The noninvasive prediction of sentinel lymph node (SLN) metastasis using quantitative magnetic resonance imaging (MRI), particularly with synthetic MRI (syMRI), is an emerging field. This study aimed to explore the potential added benefits of syMRI over conventional MRI and diffusion-weighted imaging (DWI) in predicting metastases in SLNs. Methods: This retrospective study consecutively enrolled 101 patients who were diagnosed with breast cancer (BC) and underwent SLN biopsy from December 2022 to October 2023 at the Affiliated Hospital of Jiangnan University. These patients underwent preoperative MRI including conventional MRI, DWI, and syMRI and were categorized into two groups according to the postoperative pathological results: those with and without metastatic SLNs. MRI morphological features, DWI, and syMRI-derived quantitative parameters of breast tumors were statistically compared between these two groups. Binary logistic regression was used to separately develop predictive models for determining the presence of SLN involvement, with variables that exhibited significant differences being incorporated. The performance of each model was evaluated through receiver operating characteristic (ROC) curve analysis, including the area under the curve (AUC), sensitivity, and specificity. Results: Compared to the group of 54 patients with BC but no metastatic SLNs, the group of 47 patients with BC and metastatic SLNs had a significantly larger maximum axis diameter [metastatic SLNs: median 2.40 cm, interquartile range (IQR) 1.50-3.00 cm; no metastatic SLNs: median 1.80 cm, IQR 1.37-2.50 cm; P=0.03], a higher proton density (PD) (78.44 +/- 11.92 vs. 69.20 +/- 10.63 pu; P<0.001), and a lower apparent diffusion coefficient (ADC) (metastatic SLNs: median 0.91x10(-3) mm(2)/s, IQR 0.79-1.01 mm(2)/s; no metastatic SLNs: median 1.02x10(-3) mm(2)/s, IQR 0.92-1.12 mm(2)/s; P=0.001). Moreover, the prediction model with maximum axis diameter and ADC yielded an AUC of 0.71 [95% confidence interval (CI): 0.618-0.802], with a sensitivity of 78.72% and a specificity of 51.85%; After addition of syMRI-derived PD to the prediction model, the AUC increased significantly to 0.86 (AUC: 0.86 vs. 0.71; 95% CI: 0.778-0.922; P=0.002), with a sensitivity of 80.85% and a specificity of 81.50%. Conclusions: Combined with conventional MRI and DWI, syMRI can offer additional value in enhancing the predictive performance of determining SLN status before surgery in patients with BC.

The present study aimed to clarify the hypothesis that auger emitter 125I particles in combination with PARP inhibitor Olaparib could inhibit pancreatic cancer progression by promoting antitumor immune response. Pancreatic cancer cell line (Panc02) and mice subcutaneously inoculated with Panc02 cells were employed for the in vitro and in vivo experiments, respectively, followed by 125I and Olaparib administrations. The apoptosis and CRT exposure of Panc02 cells were detected using flow cytometry assay. QRT-PCR, immunofluorescence, immunohistochemical analysis, and western blot were employed to examine mRNA and protein expression. Experimental results showed that 125I combined with Olaparib induced immunogenic cell death and affected antigen presentation in pancreatic cancer. 125I in combination with Olaparib influenced T cells and dendritic cells by up-regulating CD4, CD8, CD69, Caspase3, CD86, granzyme B, CD80, and type I interferon (IFN)-γ and down-regulating Ki67 in vivo. The combination also activated the cyclic GMP-AMP synthase stimulator of IFN genes (Sting) pathway in Panc02 cells. Moreover, Sting knockdown alleviated the effect of the combination of 125I and Olaparib on pancreatic cancer progression. In summary, 125I in combination with Olaparib inhibited pancreatic cancer progression through promoting antitumor immune responses, which may provide a potential treatment for pancreatic cancer.

PURPOSE: To investigate the feasibility of predicting the early response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on CT radiomics nomogram before treatment. MATERIALS AND METHODS: The clinicopathological data and pre-treatment portal venous phase CT images of 180 consecutive AGC patients who received 3 cycles of NAC are retrospectively analyzed. They are randomly divided into training set (n = 120) and validation set (n = 60) and are categorized into effective group (n = 83) and ineffective group (n = 97) according to RECIST 1.1. Clinicopathological features are compared between two groups using Chi-Squared test. CT radiomic features of region of interest (ROI) for gastric tumors are extracted, filtered and minimized to select optimal features and develop radiomics model to predict the response to NAC using Pyradiomics software. Furthermore, a nomogram model is constructed with the radiomic and clinicopathological features via logistic regression analysis. The receiver operating characteristic (ROC) curve analysis is used to evaluate model performance. Additionally, the calibration curve is used to test the agreement between prediction probability of the nomogram and actual clinical findings, and the decision curve analysis (DCA) is performed to assess the clinical usage of the nomogram model. RESULTS: Four optimal radiomic features are selected to construct the radiomics model with the areas under ROC curve (AUC) of 0.754 and 0.743, sensitivity of 0.732 and 0.750, specificity of 0.729 and 0.708 in the training set and validation set, respectively. The nomogram model combining the radiomic feature with 2 clinicopathological features (Lauren type and clinical stage) results in AUCs of 0.841 and 0.838, sensitivity of 0.847 and 0.804, specificity of 0.771 and 0.794 in the training set and validation set, respectively. The calibration curve generates a concordance index of 0.912 indicating good agreement of the prediction results between the nomogram model and the actual clinical observation results. DCA shows that patients can receive higher net benefits within the threshold probability range from 0 to 1.0 in the nomogram model than in the radiomics model. CONCLUSION: CT radiomics nomogram is a potential useful tool to assist predicting the early response to NAC for AGC patients before treatment.

PURPOSE:To construct a nomogram combining tumor spectral CT parameters and visceral fat area (VFA) to predict postoperative complications (POCs) in patients with gastric cancer (GC). METHOD:This retrospective study included 101 GC patients who underwent preoperative abdominal spectral CT scan and were divided into two groups (37 with POCs and 64 without POCs) according to the Clavien-Dindo classification standard. Logistic regression was used to establish spectral, VFA, and combined models for predicting POCs. The combined prediction model was presented as a nomogram, and the diagnostic performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS:The AUCs of the VFA and spectral model were 0.71 (95% CI: 0.62-0.80) and 0.81 (95% CI: 0.72-0.88), respectively. VFA, the slope of spectral curve (λ) in venous phase (λ-VP) and tumor Hounsfield units on monoenergetic images 40 keV in VP (MonoE40keV-VP) were independent predictors of POCs in GC. The nomogram yielded an AUC of 0.89 (95% CI: 0.81-0.94). The combined model was superior to the VFA or spectral models by comparing their AUCs (P = 0.000 and 0.022). CONCLUSIONS:The nomogram based on two tumor spectral parameters (λ-VP, MonoE40keV-VP) and VFA could serve as a convenient tool for predicting the POCs of GC patients.

Aim: To investigate the computed tomography (CT) and clinical characteristics of immunotherapy-induced pneumonitis (IIP) in patients with advanced solid tumors. Patients & methods: CT and clinical data of 254 patients with advanced solid tumors treated with immune checkpoint inhibitors in our hospital were collected retrospectively. Results: The incidences of IIP in patients with non-small-cell lung cancer, lymphoma and gastrointestinal tumors were 19% (19/100), 9.8% (6/61) and 6.2% (4/65), respectively. The median onset time for all 31 IIP patients was 44 days (interquartile range: 24-65). Most IIP patients (21/31) had grade 1-2 disease. Multifocal ground-glass opacities (seen in 21/31 patients) were the main CT findings of IIP. Conclusion: Patients should be alerted to the risk of IIP, an adverse reaction that has a relatively low incidence but which is sometimes life-threatening.

Abstract Background Colon cancer is a particularly prevalent malignancy that produces postoperative complications (POCs). However, limited imaging modality exists on the accurate diagnosis of POCs. The purpose of this study was therefore to construct a model combining tumor spectral CT parameters and clinical features to predict POCs before surgery in colon cancer. Methods This retrospective study included 85 patients who had preoperative abdominal spectral CT scans and underwent radical colon cancer resection at our institution. The patients were divided into two groups based on the absence (no complication/grade I) or presence (grades II–V) of POCs according to the Clavien-Dindo grading system. The visceral fat areas (VFA) of patients were semi-automatically outlined and calculated on L3-level CT images using ImageJ software. Clinical features and tumor spectral CT parameters were statistically compared between the two groups. A combined model of spectral CT parameters and clinical features was established by stepwise regression to predict POCs in colon cancer. The diagnostic performance of the model was evaluated using the receiver operating characteristic (ROC) curve, including area under the curve (AUC), sensitivity, and specificity. Results Twenty-seven patients with POCs and 58 patients without POCs were included in this study. MonoE40keV-VP and VFA were independent predictors of POCs. The combined model based on predictors yielded an AUC of 0.84 (95% CI: 0.74–0.91), with a sensitivity of 77.8% and specificity of 87.9%. Conclusions The model combining MonoE40keV-VP and VFA can predict POCs before surgery in colon cancer and provide a basis for individualized management plans. Critical relevance statement The model combining MonoE40keV-VP and visceral fat area can predict postoperative complications before surgery in colon cancer and provide a basis for individualized management plans. Key points • Visceral fat area and MonoE40keV-VP were independent predictors of postoperative complications in colon cancer. • The combined model yielded a high AUC, sensitivity, and specificity in predicting postoperative complications. • The combined model was superior to the single visceral fat area or MonoE40keV-VP in predicting postoperative complications. Graphical Abstract

Objective:To investigate the clinical value of CT in the diagnosis of cerebral cysticercosis.Methods:The clinical and CT data of 66 patients with cerebral cysticercosis examined in our hospital from June 2012 to April 2022 were analyzed retrospectively, and the CT manifestations of different types of cerebral cysticercosis were recorded.Results:Among the 66 cases of cerebral cysticercosis, 44 cases were classified as cerebral parenchyma type, 7 cases as ventricular type, 4 cases as meningeal type, and 11 cases as mixed type according to the location. Among them, the brain parenchyma type was divided into 5 cases of acute encephalitis according to the course of the disease. The CT manifestations were brain tissue swelling, and the density of white matter under the cortex of both cerebral hemispheres was widely reduced; In 29 cases of multiple calcification type, CT showed multiple nodular calcification density shadows of different sizes in brain parenchyma; There were 8 cases with calcification and multiple small cystic foci coexisting, and CT showed calcification of nodules in brain parenchyma accompanied by multiple small cystic low density shadows; Calcification with large surrounding cysts in 2 cases.Conclusion:CT examination can determine the location, number and classification of cerebral cysticercosis lesions, and provide a reference for clinical formulation of a reasonable treatment plan.

Objective This study aimed to assess the computed tomography (CT) and magnetic resonance imaging (MRI) features of pancreatic mixed neuroendocrine–non-neuroendocrine neoplasm (MiNEN) and compare them with those of pancreatic ductal adenocarcinoma (PDAC) and neuroendocrine tumor (NET). Methods Twelve patients with pancreatic MiNEN, 24 patients with PDAC, and 24 patients with NET, who underwent both contrast-enhanced CT and MRI, were included. Clinical data and the key imaging features were retrospectively evaluated by two independent readers and compared between MiNEN and PDAC or NET. Univariate and multivariable logistic regression analyses were performed to obtain predictors for pancreatic MiNEN. Results Patients with pancreatic MiNEN more frequently presented with large size and heterogeneous and cystic components compared with PDAC ( p < 0.031) and ill-defined irregular margins, progressive enhancement, and adjacent organ involvement compared with NET ( p < 0.036). However, vascular invasion was less commonly seen in MiNEN than PDAC ( p = 0.010). Moderate enhancement was observed more frequently in MiNEN than in PDAC or NET ( p < 0.001). Multivariate logistic analyses demonstrated that moderate enhancement and ill-defined irregular margin were the most valuable features for the prediction of pancreatic MiNEN ( p ≤ 0.044). The combination of the two features resulted in a specificity of 93.8%, sensitivity of 83.3%, and accuracy of 91.7%. Conclusions We have mainly described the radiological findings of pancreatic MiNEN with ill-defined irregular margin and moderate enhancement compared with PDAC and NET. The combination of imaging features could improve diagnostic efficiency and help in the selection of the correct treatment method.

Background To study the effects of combination TACE and anti-PD-L1 liposome drug in treating HCC in mice models. Methods We constructed the liposome drug with lecithin and cholesterol and mannitol, etc. Besides, the HCC mice model was established through abdominal subcutaneous injection HepG2 cancer cells in SD mice, then the PE-10 polyethylene catheter was used for TACE therapy. The SD mice were separately received TACE treatment, avelumab liposome drug therapy, and TACE combined with avelumab liposome drug therapy. Results The liposomes drug was successfully constructed with a diameter of 125.5 nm. After the mice received TACE and (or) immunotherapy, the combined liposome drug therapy significantly reduced the volume of hepatic carcinoma tissues, besides, the apoptotic rates of hepatic carcinoma cells in the combined liposome drug treatment group was increased obiviously compared with other groups. Moreover, the protein TGFβR2 located in the cellular membrane was obiviously down-regulated in the combined liposome drug therapy, whilst, the expression of SMAD7 and PTPN14 was up-regulated in the treatment groups compared with the mice without treatment, besides, the protein PTPN14 was mainly located in the nucleus. Additionally, the mRNA expression of genes SNAI1 and Vimentin was significantly down-regulated in the combined liposome drug therapy. Conclusion Combination of transcatheter arterial chemoembolization and anti-PD-L1 liposome drug therapy significantly suppressed hepatocellular carcinoma proliferation and metastasis in mice models.

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.

Objective:To explore the value of nomogram based on arterial spin labeling (ASL) MRI perfusion parameters and clinicopathological features in predicting the response to chemoradiotherapy (CRT) in advanced nasopharyngeal carcinoma (ANPC, stage Ⅲ and Ⅳ).Methods:From June 2018 to January 2021, 70 patients with ANPC confirmed by pathology were prospectively enrolled in Affiliated Hospital of Jiangnan University. Nasopharyngeal MRI plain scan, ASL and contrast-enhanced scan were performed before CRT, and routine MRI re-examination was performed within 1 week after the end of CRT. The pre-CRT perfusion parameter tumor blood flow (TBF) from ASL and clinicopathological features were recorded, and the maximum diameter (MD) of the tumor on T 1WI images was measured. The patients were divided into CRT effective group (48 cases) and ineffective group (22 cases) according to the response evaluation criteria in solid tumors. The independent sample t test was used to compare the differences of TBF, age and MD between effective group and ineffective group. The χ 2 test was used to compare the differences of gender, clinical stage and pathological type between the 2 groups. Using binary logistic regression analysis, clinicopathological model and TBF combined clinicopathological model were constructed, and the nomogram of combined model was constructed. The diagnostic efficacy of the models was obtained by receiver operating characteristic (ROC) curve analysis, and the area under the ROC curves (AUC) of the 3 models were compared by DeLong method. The calibration curve for the nomogram was generated, and the concordance index (C index) was acquired. Results:The TBF of the effective group and the ineffective group were (113±9) and (97±14) ml·100 g -1·min -1, with a statistical difference ( t=5.17, P<0.001). The MD value of the effective group was smaller than that of the ineffective group, with a statistical difference ( t=-2.24, P=0.028). There were statistical differences in clinical stage and pathological type between the 2 groups (χ 2 values were 12.21 and 12.95, respectively, both P<0.001). Three independent predictors, including TBF (OR=7.749), clinical stage (OR=0.129) and pathological type (OR=5.228), were included in logistic regression analysis. The AUC, sensitivity and specificity of TBF model in predicting the response to CRT were 0.843, 87.5% and 72.7%, of clinicopathological model were 0.822, 80.2% and 59.1%, of the nomogram model were 0.893, 81.2% and 90.9%. There was no statistical difference of AUC between the nomogram model and TBF model ( Z=1.23, P=0.215). However, the AUC of the nomogram model was greater than that of the clinicopathological model ( Z=2.47, P=0.031). The calibration curve showed that there was a good concordance index (C index=0.892) between the predicted value of nomogram and the actual clinical observation value. Conclusion:TBF, clinical stage and pathological type are independent predictors of the response to CRT in ANPC patients, and the nomogram based on these three factors has a good ability in predicting the response to CRT.

Objective:To build a predictive model for symptomatic radiation pneumonitis(RP) using the pretreatment CT radiomics features, clinical and dosimetric data of lung cancer patients by using machine learning method.Methods:A retrospective analysis of 103 lung cancer patients who underwent radiotherapy in the Affiliated Hospital of Jiangnan University from November 2018 to April 2020 was performed. Total normal lung tissues were segmented as an interested volume in pretreatment CT images, and then 250 radiomics features were extracted. The correlations of RP and clinical or dosimetric features were firstly investigated with univariate analysis. Then all clinical data, dosimetric data and CT radiomics features were collected and considered as predictors for modeling of RP grade ≥ 2. Features were selected through LASSO machine learning method, and the predictive model was built. Finally, nomogram for risk of RP were obtained according to the selected features.Results:The result of univariate analysis showed that symptomatic RP was significantly correlated with lung dosimetric parameters including mean lung dose (MLD), V20 Gy and V30 Gy( t=2.20, 2.34 and 2.93, P<0.05). Four features, including lung dose volume percentage V30 Gyand three radiomics features, entropy feature of GLCM, mean and median feature of wavelet histogram were selected among all clinical, dosimetric features and radiomics features. AUC of the predicted model obtained from selected features reached 0.757. For convenient clinical use, the nomogram were obtained, and then personalized RP risk prediction and early intervention could be performed according to this nomogram. Conclusions:Pretreatment CT radiomics and dosimetric features can be used in predicting symptomatic RP, which will be useful for advanced intervention treatment.

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.

INTRODUCTION MRI has demonstrated its potential in the diagnosis of placenta percreta. Texture analysis is a novel technique to quantify tissue heterogeneity. The study aimed to evaluate the feasibility of using texture analysis based on myometrium-derived T2WI to differentiate placenta accreta from increta. METHODS Participants with MRI and clinical or histopathological diagnosis of placenta increta were retrospectively enrolled. Texture analysis of T2WI was implemented on normal myometrium and placenta increta by MaZda software. With the Fisher discriminant method, parameter selection and reduction were done automatically. Multivariate analysis was used for the comparison of response variables between two groups. The contours of multivariable average vectors were compared using profile analysis. Two-step clustering was performed to assess the importance of parameters. RESULTS There were a total of 23 participants (median age 29 years, range 22-43 years). The pixel intensity distribution was narrow and wide in two first-order histograms taken from normal myometrium and placenta increta, respectively. Multivariate analysis showed nine second-order parameters derived from the histogram were statistically significant (P < 0.05). The results of two-step clustering indicated that three second-order parameters (Mean, Percentile 90%, and Percentile 99%) were important (predictor importance > 0.8). Multivariate analysis of three second-order parameters further showed they were different between normal myometrium and placenta increta. DISCUSSION Texture analysis based on myometrium-derived T2WI may be a useful add-on to MRI in diagnosing placenta increta. TRIAL REGISTRATION Registration number: ChiCTR2000038604 and name of registry: Evaluation of diagnostic accuracy of MRI multi-parameter imaging combined with texture analysis for placenta accreta spectrum disorders (PAD).

Purpose:To explore the separate diagnostic value of preoperative ultrasound (US), magnetic resonance imaging (MRI), and the combination of US and MRI in extrathyroidal extension (ETE) of papillary thyroid carcinoma (PTC).Materials and Methods:This retrospective study was approved by the Affiliated People's Hospital of Jiangsu University review board. A total of 158 PTC patients with ETE received preoperative US and MRI examination and underwent surgery between May 2014 and December 2018 in Affiliated People's Hospital of Jiangsu University. For each case, the US and MRI features of ETE were retrospectively and independently investigated by two radiologists. The clinical assessment for each case was implemented, respectively, using US imaging only, MRI only, and a combination of both modalities at three different time points with one-month intervals.Results:The diagnostic accuracies of US, MRI, and the combined set for T3 (minimal ETE) were 91.7% (88/96), 74.0% (71/96), and 97.9% (94/96), respectively, indicating a significantly different performance (P < 0.001). The diagnostic accuracies for T4 (extensive ETE) were 62.9% (39/62), 87.1% (54/62), and 93.5% (58/62), respectively. The difference between the three methods for T4 was statistically significant (P = 0.000). The diagnostic accuracies for overall ETE were 80.4% (127/158), 79.1% (125/158), and 96.2% (152/158), respectively. The difference between the three methods for ETE was statistically significant (P = 0.001).Conclusion:This study suggests that ETE can be predicted most accurately by the combination of preoperative US and MRI.

Background: To evaluate whether texture analysis of dark intraplacental bands on T2WI can provide a novel methodological viewpoint valuable in assessing the classification of placenta accreta spectrum disorders (PAS disorders).Methods: 174 participants with suspected PAS disorders were consecutively included in the study. Texture analysis was implemented on dark intraplacental bands on T2WI by MaZda software. The two steps of parameter selection and reduction led to a decrease of the parameter space dimensionality. The logistics regression models were constructed with texture parameters to evaluate the classification of PAS disorders.Results: Both run length nonuniformity (RLN) and grey level nonuniformity (Gle) of four directions showed significant differences between participants with placenta accreta, increta and percreta (P﹤0.05). The AUC and cut-off for logistic regression model of accreta vs increta were 0.75 (95% CI: 0.54, 0.90) and 6.72, respectively; corresponding values for logistic regression model of increta vs percreta were 0.81 (95% CI: 0.61, 0.93) and 10.92. The sensitivity and specificity for cut-off of 6.72 were 88.46% and 84.62%, respectively; corresponding values for cut-off of 10.92 were 92.59% and 85.71%.Conclusion: Texture analysis offered promise for more quantitative and objective assessment of PAS disorders than other image modalities. It may be a useful add-on to MRI in evaluating the classification of PAS disorders. Trial registration: Registration number: ChiCTR2000038604 and name of registry: Evaluation of diagnostic accuracy of MRI multi-parameter imaging combined with texture analysis for placenta accreta spectrum disorders (PAD).