Auger emitter in combination with Olaparib suppresses tumor growth via promoting antitumor immune responses in pancreatic cancer.

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.

Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study.

China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73-38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70-76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89-2.92) million hospital admissions and 0.16 (95% CI, 0.13-0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.

Synthetic phase-sensitive inversion-recovery vessel for assessing extramural venous invasion in patients with rectal cancer: imaging quality and added value to T2-wighted imaging.

OBJECTIVES: To evaluate the imaging quality of a synthetic phase-sensitive inversion recovery (SyPSIR) vessel and to add value to T2-weighted imaging (T2WI) for extramural venous invasion (EMVI) detection in patients with rectal cancer. METHODS: Participants in this retrospective study underwent preoperative synthetic MRI between October 2020 and April 2022. SyPSIR image reconstruction was performed with a single inversion time of 10 ms. A junior and a senior radiologist evaluated the imaging quality, including overall imaging quality scores, motion artifact scores, and relative image signal intensity contrast between the tumor and peritumoral vessels (SItumor-vessel), of both T2WI and SyPSIR vessels. Differences in imaging quality between the two methods were assessed using the Wilcoxon signed-rank test and two-sample t-test. EMVI scores were recorded for T2WI and T2WI+SyPSIR vessel. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance. RESULTS: A total of 106 patients (35 EMVI+ and 71 EMVI-) were evaluated. There were no statistically significant differences in the overall image quality scores, motion artifacts, or SItumor-vessel (p = 0.08-0.93) between the T2WI and SyPSIR vessels. On combining T2WI and SyPSIR vessels, the AUC for pathological EMVI+ diagnoses increased from 0.65 to 0.88 for the junior radiologist and from 0.86 to 0.96 for the senior radiologist. Furthermore, the sensitivity of the analyses by junior and senior radiologists increased from 0.40 to 0.77 and 0.49 to 0.86, respectively. CONCLUSION: A SyPSIR vessel can provide additional information to improve the diagnostic efficiency of pathological EMVI in rectal cancer, which may be beneficial for individualized clinical treatment. KEY POINTS: • SyPSIR vessel and T2WI had similar imaging quality. • EMVI evaluation in SyPSIR vessel has a high inter-observer agreement. • The SyPSIR vessel has the potential to improve the diagnostic efficiency of EMVI detection in rectal cancer.

Synthetic MRI in differentiating benign from metastatic retropharyngeal lymph node: combination with diffusion-weighted imaging.

OBJECTIVES: This study aimed to evaluate the synthetic MRI (syMRI), its combination with diffusion-weighted imaging (DWI), and morphological features for discriminating benign from metastatic retropharyngeal lymph nodes (RLNs). METHODS: Fifty-eight patients with a total of 63 RLNs (21 benign and 42 metastatic) were enrolled. The mean and standard deviation of syMRI-derived relaxometry parameters (T1, T2, PD; T1SD, T2SD, PDSD) were obtained from two different regions of interest (namely, partial-lesion and full-lesion ROI). The parameters derived from benign and metastatic RLNs were compared using Student's t or chi-square tests. Logistic regression analysis was used to construct a multi-parameter model of syMRI, syMRI + DWI, and syMRI + DWI + morphological features. Areas under the curve (AUC) were compared using the DeLong test to determine the best diagnostic approach. RESULTS: Benign RLNs had significantly higher T1, T2, PD, and T1SD values compared with metastatic RLNs in both partial-lesion and full-lesion ROI (all p < 0.05). The T1SD obtained from full-lesion ROI showed the best diagnostic performance among all syMRI-derived single parameters. The AUC of combined syMRI multiple parameters (T1, T2, PD, T1SD) were higher than those of any single parameter from syMRI. The combination of synthetic MRI and DWI can improve the AUC regardless of ROI delineation. Furthermore, the combination of synthetic MRI, DWI-derived quantitative parameters, and morphological features can significantly improve the overall diagnostic performance. CONCLUSIONS: The value of syMRI has been validated in differential diagnosis of benign and metastatic RLNs, and syMRI + DWI + morphological features can further improve the diagnostic efficiency for discriminating these two entities. KEY POINTS: • Synthetic MRI was useful in differential diagnosis of benign and metastatic RLNs. • The combination of syMRI, DWI, and morphological features can significantly improve the diagnostic efficiency.

Deep learning-extracted CT imaging phenotypes predict response to total resection in colorectal cancer.

BACKGROUND: Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features. PURPOSE: To evaluate the prognostic value of preoperative computed tomography (CT) image texture features and deep learning self-learning high-throughput features (SHF) on postoperative overall survival in the treatment of patients with colorectal cancer (CRC). MATERIAL AND METHODS: The dataset consisted of 810 enrolled patients with CRC confirmed from 10 November 2011 to 10 February 2018. In contrast, SHF extracted by deep learning with multi-task training mechanism and texture features were extracted from the CT with tumor volume region of interest, respectively, and combined with the Cox proportional hazard (CoxPH) model for initial validation to obtain a RAD score to classify patients into high- and low-risk groups. The SHF stability was further validated in combination with Neural Multi-Task Logistic Regression (N-MTLR) model. The overall recognition ability and accuracy of CoxPH and N-MTLR model were evaluated by C-index and Integrated Brier Score (IBS). RESULTS: SHF had a more significant degree of differentiation than texture features. The result is (SHF vs. texture features: C-index: 0.884 vs. 0.611; IBS: 0.025 vs. 0.073) in the CoxPH model, and (SHF vs. texture features: C-index: 0.861 vs. 0.630; IBS: 0.024 vs. 0.065) in N-MTLR. CONCLUSION: SHF is superior to texture features and has potential application for the preoperative prediction of the individualized treatment of CRC.

External auditory canal and middle ear tumors: characterization by morphology and diffusion features on CT and MRI.

PURPOSE: To explore the value of morphology and diffusion features on CT and MRI in the characterization of external auditory canal and middle ear tumors (EAMETs). METHODS: Forty-seven patients with histologically proved EAMETs (23 benign and 24 malignant) who underwent CT and MRI were retrospectively analyzed in this study. CT and MRI characteristics (including size, shape, signal intensity, border, enhancement degree, and bone changes) and apparent diffusion coefficient (ADC) value were analyzed and compared between benign and malignant EAMETs. Logistic regression, receiver operating characteristic (ROC) curve, and Delong test were performed to assess the diagnostic performance. RESULTS: Compared with benign tumors, the malignant EAMETs are characterized by irregular shape, ill-defined border, invasive bone destruction, and intense enhancement (all p < 0.05). There were no significant differences on the size and signal intensity between benign and malignant tumors. The ADC value of malignant tumors were (879.96 ± 201.15) × 10-6 mm2/s, which was significantly lower than benign ones (p < 0.05). Logistic regression demonstrates the presence of ill-defined margin, invasive bone destruction, and low ADC value (≤ 920.33 × 10-6 mm2/s) have significant relationship with malignant EAMETs. The combination of characterization by morphology and diffusion features on CT and MRI can further improve the diagnostic efficiency when compared with morphology and diffusion features alone (both p < 0.05). CONCLUSION: Some CT and MRI characteristics are helpful in identifying malignant EAMETs from benign ones (especially ill-defined margin, invasive bone destruction, and low ADC value), and the combination of morphology and diffusion features on CT and MRI has best diagnostic efficiency for discriminating these two entities.

Prediction of response to neoadjuvant chemotherapy in advanced gastric cancer: A radiomics nomogram analysis based on CT images and clinicopathological features.

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.

Can Arterial Spin Labeling Perfusion Imaging be Used to Differentiate Nasopharyngeal Carcinoma From Nasopharyngeal Lymphoma?

BACKGROUND: Differentiating nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoma (NPL) is useful for deciding the appropriate treatment. However, the diagnostic accuracy of current imaging methods is low. PURPOSE: To explore the feasibility of arterial spin labeling (ASL) perfusion imaging in the qualitative and quantitative differentiation between NPC and NPL to improve the diagnosis of malignancies in the nasopharynx. STUDY TYPE: Retrospective. POPULATION: Ninety seven patients: NPC (65 cases) and NPL (32 cases), histologically confirmed. FIELD STRENGTH/SEQUENCE: 3T/3D fast spin echo pseudo-continuous ASL imaging with spiral readout scheme, 3D inverse recovery- fast spoiled gradient recalled echo brain volume (BRAVO) imaging. ASSESSMENT: Cerebral blood flow (CBF) images from ASL perfusion imaging were assessed by three radiologists. Each tumor was visually scored based on CBF images. Intratumoral CBF and intramuscular CBF values were obtained from intratumoral and lateral pterygoid muscle areas, respectively. Through dividing intratumoral CBF by intramuscular CBF, normalized CBF (nCBF) was further calculated. STATISTICAL TESTS: Fleiss's kappa and intraclass correlation coefficients (ICCs) were used to assess interobserver agreement among the three readers. The Mann-Whitney U-test was used to compare visual scoring, and an unpaired t-test was performed to compare CBF value between the NPC and NPL groups. The area under the curve (AUC) value was used to quantify the diagnostic ability of each parameter. RESULTS: Good interobserver agreements were validated by high Fleiss's kappa and ICC values (all >0.80). NPCs showed significantly higher visual scores than NPLs (P < 0.05). Both intratumoral CBF and nCBF in NPC were significantly higher than those in NPL (both P < 0.05). Intratumoral CBF showed the highest AUC of 0.861 (P < 0.05) in differentiating NPC (n = 65) from NPL (n = 32), while the AUCs of nCBF and visual scoring were 0.847 and 0.753, respectively. DATA CONCLUSION: For the diagnosis of distinguishing NPC from NPL, ASL perfusion imaging demonstrated high diagnostic efficiency. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Feasibility and reproducibility of T2 mapping and DWI for identifying malignant lymph nodes in rectal cancer.

OBJECTIVES: To evaluate the diagnostic value and reproducibility of T2 mapping versus apparent diffusion coefficients (ADC) for identifying malignant lymph nodes in patients with non-mucinous rectal adenocarcinoma. METHODS: High-resolution magnetic resonance imaging, diffusion-weighted imaging, and T2 mapping were performed on patients with suspected metastatic lymph nodes in the mesorectum or around the superior rectal artery with a short-axis diameter of 4-10 mm. The T2 and ADC values of pathology-confirmed metastatic versus non-metastatic lymph nodes were compared using the independent-samples t test and receiver operating characteristic curves. Intra- and inter-observer reproducibility were tested. The cutoff value for T2 relaxation time was determined. RESULTS: In total, 67 lymph nodes underwent histological analysis, with 24 in the non-metastatic and 43 in the metastatic groups. Intra- and inter-observer agreements for T2 values were 0.999 and 0.998, respectively, which were higher than the ADC values of 0.924 and 0.844, respectively. The mean T2 and ADC values for metastatic lymph nodes (65 ± 7.8 ms and 1.17 ± 0.16 × 10-3 mm2/s, respectively) were significantly lower than for benign lymph nodes(83 ± 5.7 ms and 1.29 ± 0.15 × 10-3 mm2/s, respectively). T2 values had a higher AUC value of 0.990 than the AUC value for ADC of 0.729. With a cutoff value of 77 ms, sensitivity and specificity for T2 values were 95% and 96%, respectively. CONCLUSIONS: T2 mapping had higher diagnostic efficacy and reproducibility than ADC and may be useful in differentiating metastatic from non-metastatic lymph nodes in rectal cancer. KEY POINTS: • Mean T2 values were significantly shorter for malignant versus benign LNs in patients with non-mucinous rectal adenocarcinoma. • The diagnostic efficacy and reproducibility of T2 values were excellent and superior to ADC values.

A pilot study of radiomics signature based on biparametric MRI for preoperative prediction of extrathyroidal extension in papillary thyroid carcinoma.

OBJECTIVE: To investigate efficiency of radiomics signature to preoperatively predict histological features of aggressive extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) with biparametric magnetic resonance imaging findings. MATERIALS AND METHODS: Sixty PTC patients with preoperative MR including T2WI and T2WI-fat-suppression (T2WI-FS) were retrospectively analyzed. Among them, 35 had ETE and 25 did not. Pre-contrast T2WI and T2WI-FS images depicting the largest section of tumor were selected. Tumor regions were manually segmented using ITK-SNAP software and 107 radiomics features were computed from the segmented regions using the open Pyradiomics package. Then, a random forest model was built to do classification in which the datasets were partitioned randomly 10 times to do training and testing with ratio of 1:1. Furthermore, forward greedy feature selection based on feature importance was adopted to reduce model overfitting. Classification accuracy was estimated on the test set using area under ROC curve (AUC). RESULTS: The model using T2WI-FS image features yields much higher performance than the model using T2WI features (AUC = 0.906 vs. 0.760 using 107 features). Among the top 10 important features of T2WI and T2WI-FS, there are 5 common features. After feature selection, the models trained using top 2 features of T2WI and the top 6 features of T2WI-FS achieve AUC 0.845 and 0.928, respectively. Combining features computed from T2WI and T2WI-FS, model performance decreases slightly (AUC = 0.882 based on all features and AUC = 0.913 based on top features after feature selection). Adjusting hyper parameters of the random forest model have negligible influence on the model performance with mean AUC = 0.907 for T2WI-FS images. CONCLUSIONS: Radiomics features based on pre-contrast T2WI and T2WI-FS is helpful to predict aggressive ETE in PTC. Particularly, the model trained using the optimally selected T2WI-FS image features yields the best classification performance. The most important features relate to lesion size and the texture heterogeneity of the tumor region.

Prognostic value of CT radiomics in evaluating lymphovascular invasion in rectal cancer: Diagnostic performance based on different volumes of interest.

OBJECTIVES: This study aims to evaluate diagnostic performance of radiomic analysis using computed tomography (CT) to identify lymphovascular invasion (LVI) in patients diagnosed with rectal cancer and assess diagnostic performance of different lesion segmentations. METHODS: The study is applied to 169 pre-treatment CT images and the clinical features of patients with rectal cancer. Radiomic features are extracted from two different volumes of interest (VOIs) namely, gross tumor volume and peri-tumor tissue volume. The maximum relevance and the minimum redundancy, and the least absolute shrinkage selection operator based logistic regression analyses are performed to select the optimal feature subset on the training cohort. Then, Rad and Rad-clinical combined models for LVI prediction are built and compared. Finally, the models are externally validated. RESULTS: Eighty-three patients had positive LVI on pathology, while 86 had negative LVI. An optimal multi-mode radiology nomogram for LVI estimation is established. The area under the receiver operating characteristic curves of the Rad and Rad-clinical combined model in the peri-tumor VOI group are significantly higher than those in the tumor VOI group (Rad: peri-tumor vs. tumor: 0.85 vs. 0.68; Rad-clinical: peri-tumor vs. tumor: 0.90 vs 0.82) in the validation cohort. Decision curve analysis shows that the peri-tumor-based Rad-clinical combined model has the best performance in identifying LVI than other models. CONCLUSIONS: CT radiomics model based on peri-tumor volumes improves prediction performance of LVI in rectal cancer compared with the model based on tumor volumes.

Preoperative assessment of extrathyroidal extension of papillary thyroid carcinomas by ultrasound and magnetic resonance imaging: a comparative study.

PURPOSE: The purpose of this study was to assess and compare the diagnostic performances of preoperative ultrasonography (US) and magnetic resonance imaging (MRI) in predicting extrathyroidal extension (ETE) in patients with papillary thyroid carcinoma (PTC). MATERIALS AND METHODS: This retrospective study was approved by our institutional review board. Preoperative US and MRI were performed on 225 patients who underwent surgery for PTC between May 2014 and December 2018. The US and MRI features of ETE of each case were retrospectively and independently investigated by two radiologists. The diagnostic performances of US and MRI, including their sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) for ETE, and their accuracy in predicting ETE were analyzed. RESULTS: Higher sensitivity and NPV in predicting minimal ETE were observed in US (87.5% and 76.2%, respectively) compared with MRI (71.3% and 61.7%, respectively) (p = 0.006 and p = 0.046, respectively). Meanwhile, MRI (85.4%) showed higher sensitivity than US (66.7%) in assessing extensive ETE (p = 0.005). MRI also showed significantly higher specificity and PPV than US in assessing overall ETE (p = 0.025 and p = 0.025, respectively). CONCLUSION: Preoperative US should be used as the first line in predicting minimal ETE, and MRI should be added in extensive ETE assessment. Compared with US, MRI had higher specificity and PPV in detecting the overall ETE of PTC.

Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on CT images and clinicopathologic features.

PURPOSE: To predict programmed death-ligand 1 (PD-L1) expression of tumor cells in non-small cell lung cancer (NSCLC) patients by using a radiomics study based on CT images and clinicopathologic features. MATERIALS AND METHODS: A total of 390 confirmed NSCLC patients who performed chest CT scan and immunohistochemistry (IHC) examination of PD-L1 of lung tumors with clinic data were collected in this retrospective study, which were divided into two cohorts namely, training (n = 260) and validation (n = 130) cohort. Clinicopathologic features were compared between two cohorts. Lung tumors were segmented by using ITK-snap kit on CT images. Total 200 radiomic features in the segmented images were calculated using in-house texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features based on its relevance of PD-L1 expression status in IHC results and develop radiomics signature. Radiomics signature and clinicopathologic risk factors were incorporated to develop prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curves were generated and the areas under the curves (AUC) were reckoned to predict PD-L1 expression in both training and validation cohorts. RESULTS: In 200 extracted radiomic features, 9 were selected to develop radiomics signature. In univariate analysis, PD-L1 expression of lung tumors was significantly correlated with radiomics signature, histologic type, and histologic grade (p < 0.05, respectively). However, PD-L1 expression was not correlated with gender, age, tumor location, CEA level, TNM stage, and smoking (p > 0.05). For prediction of PD-L1 expression, the prediction model that combines radiomics signature and clinicopathologic features resulted in AUCs of 0.829 and 0.848 in the training and validation cohort, respectively. CONCLUSION: The prediction model that incorporates the radiomics signature and clinical risk factors has potential to facilitate the individualized prediction of PD-L1 expression in NSCLC patients and identify patients who can benefit from anti-PD-L1 immunotherapy.

Radiomics analysis of multicenter CT images for discriminating mucinous adenocarcinoma from nomucinous adenocarcinoma in rectal cancer and comparison with conventional CT values.

OBJECTIVE: To investigate the value of CT-based radiomics signature for preoperatively discriminating mucinous adenocarcinoma (MA) from nomucinous adenocarcinoma (NMA) in rectal cancer and compare with conventional CT values. METHOD: A total of 225 patients with histologically confirmed MA or NMA of rectal cancer were retrospectively enrolled. Radiomics features were computed from the entire tumor volume segmented from the post-contrast phase CT images. The maximum relevance and minimum redundancy (mRMR) and LASSO regression model were performed to select the best preforming features and build the radiomics models using a training cohort of 155 cases. Then, predictive performance of the models was validated using a validation cohort of 70 cases and receiver operating characteristics (ROC) analysis method. Meanwhile, CT values in post- and pre-contrast phase, as well as their difference (D-values) of tumors in two cohorts were measured by two radiologists. ROC curves were also calculated to assess diagnostic efficacies. RESULTS: One hundred and sixty-three patients were confirmed by pathology as NMA and 62 cases were MA. The radiomics signature comprised 19 selected features and showed good discrimination performance in both the training and validation cohorts. The areas under ROC curves (AUC) are 0.93 (95% confidence interval [CI]: 0.89-0.98) in training cohort and 0.93 (95% CI: 0.87-0.99) in validation cohort, respectively. Three sets of CT values of MA in pre- and post-contrast phase, and their difference (D-value) (31±7.0, 51±12.6 and 20±9.3, respectively) were lower than those of NMA (37±5.6, 69±13.3 and 32±11.7, respectively). Comparing to the radiomics signature, using three sets of conventional CT values yielded relatively low diagnostic performance with AUC of 0.84 (95% CI: 0.78-0.88), 0.75 (95% CI: 0.69-0.81) and 0.78 (95% CI: 0.72-0.83), respectively. CONCLUSION: This study demonstrated that CT radiomics features could be utilized as a noninvasive biomarker to identify MA patients from NMA of rectal cancer preoperatively, which is more accurate than using the conventional CT values.

Magnetic resonance imaging features of fibromas and giant cell tumors of the tendon sheath: differential diagnosis.

PURPOSE: The clinical and imaging characteristics of fibromas of the tendon sheath (FTS) closely resemble those of giant cell tumors of the tendon sheath (GCTTS). We aimed to study MRI features of FTS and GCTTS to distinguish the two entities and improve their differential diagnosis. MATERIALS AND METHODS: We retrospectively analyzed data from 18 patients (9 men, 9 women; age, 17-62 years) and 24 patients (13 men, 11 women; age, 15-67 years) treated between May 2011 and May 2016, with histologically confirmed FTS and GCTTS, respectively. Specific MRI features of the two groups were compared using the independent sample t tests and chi-square tests. RESULTS: FTS exhibited round or oval shapes. Proton-weighted images (PDWI) showed heterogeneous hypointensity that appeared striped or disordered and was located in the lesion center. Enhanced scans demonstrated asymmetrical signal in the foci. GCTTS mostly exhibited a lobulated or casting mold pattern, with a hypointense ring on PDWI. The hypointense components appeared granular/flaky or separated, sometimes behaving as a uniform signal on PDWI. Significant differences in the following features were observed between the two groups: lesion morphology (p < 0.001); imaging features on PDWI, including whether the signal is homogeneous (p < 0.001); the presence of a hypointense ring (p = 0.006); the location and morphology of hypointensity (p < 0.001); bone absorption (p = 0.008); enhancing pattern (p = 0.008); and whether the tumor crossed the joint (p = 0.026). CONCLUSIONS: FTS and GCTTS demonstrate distinctive MRI features, which can be used for differential diagnosis with sensitivities, specificities, and diagnostic accuracies of 83-100%, 29-79%, and 60-89%, respectively. KEY POINTS: • Fibromas and giant cell tumors of the tendon sheath have distinct features on MRI, including differences in lesion morphology and intensity patterns, which can be used for differential diagnosis. • Among other signs, GCTTS are more uniform than FTS, and FTS have a striped or disordered pattern. • Tumors were classified with 90% accuracy into either FTS or GCTTS based on a combination of two features: homogenous signal and hypointensity shape on PDWI.

An application study of low-dose computed tomography perfusion imaging for evaluation of the efficacy of neoadjuvant chemotherapy for advanced gastric adenocarcinoma.

BACKGROUND: This study used low-dose computed tomography (CT) perfusion imaging technology to evaluate the efficacy of neoadjuvant chemotherapy in patients with advanced gastric adenocarcinoma and to determine whether any of the perfusion parameters could predict tumor response to chemotherapy. METHODS: Forty patients with gastric adenocarcinoma (T3-4NxM0) received three cycles of neoadjuvant chemotherapy and low-dose spiral CT perfusion imaging prior to and after the first and third series of chemotherapy. We calculated tissue blood flow (BF) and blood volume (BV) using commercial software. One-way analysis of variance (ANOVA) was used to detect any significant variation of the tested parameters between different times of scanning. Spearman's test was used to evaluate the correlation among perfusion parameters, tumor size and pathological efficacy grade, and clinical response after chemotherapy, respectively. A receiver-operating characteristic analysis was used to determine the optimal diagnostic cutoff value for changes in perfusion parameters and tumor size. RESULTS: One-way ANOVA showed significant differences in BF and BV values between those before and after chemotherapy (p < 0.01). The BF, BV and size reduction rate after three series of chemotherapy were significantly correlated with pathological efficacy grade. BF and BV values after the first and third series of chemotherapy were also significantly correlated with clinical response (p < 0.01, respectively). The diagnostic sensitivity and specificity of the BV reduction rate were higher than those of size reduction rate. CONCLUSIONS: Low-dose CT perfusion imaging is a valuable tool that permits microcirculation evaluation and therefore can evaluate the efficacy of neoadjuvant chemotherapy in patients with advanced gastric adenocarcinoma.

Can low-dose CT perfusion imaging accurately assess response of advanced gastric cancer with neoadjuvant chemotherapy?

PURPOSE: To explore the value of low-dose CT perfusion imaging (LDCTPI) technology and its perfusion parameters in assessing response of neoadjuvant chemotherapy (NAC) in patients with advanced gastric cancer (AGC). METHODS: Thirty patients with AGC were studied prospectively by LDCTPI to measure two parameters including blood flow (BF) and blood volume (BV) of tumor area before and after chemotherapy, respectively. All of the patients received two courses of NAC and surgical resection of gastric tumor within one week after chemotherapy, and then obtained the result of postoperative pathology response for chemotherapy. The comparisons of BF and BV values of AGC before and after chemotherapy were analyzed by paired-samples t-test, respectively; and the correlations between BF as well as BV decrease rates after NAC and the pathology response grade were analyzed by Spearman statistical test. Thirty patients were divided into effective and ineffective groups according to different pathology response grade. Comparisons of BF as well as BV decrease rates between effective and ineffective groups were analyzed by independent-samples t-test, respectively. Receiver operating characteristic (ROC) curves were used to determine the cutoff values of BF and BV decrease rates as evaluation indicators of AGC after NAC and calculate area under the curve (AUC). RESULTS: There were significant differences in BF and BV values of AGC between before and after NAC (p < 0.001), respectively, and there were obvious correlations between BF as well as BV decrease rates and pathology response grade (r = 0.660, p < 0.001; r = 0.706, p < 0.001), respectively. There were also significant differences in BF and BV decrease rates of AGC between effective and ineffective groups (P = 0.001), respectively. If BF decrease rate of 12.1% (AUC was 0.816, P = 0.005) was used as the cutoff value for chemotherapy effectiveness of AGC, the sensitivity of 82% and specificity of 84% were achieved, and if BV decrease rate of 32.8% (AUC was 0.844, P = 0.002) was used as the cutoff value for chemotherapy effectiveness of AGC, the sensitivity of 82% and specificity of 89% were achieved. CONCLUSIONS: BF and BV decrease rates have potential to be used as effective indicators to assess chemotherapy efficacy of AGC from the hemodynamics.

The feasibility of low-dose CT perfusion imaging in gastric cancer.

PURPOSE: To investigate feasibility of applying low-dose CT perfusion imaging (CTPI) to diagnose gastric cancer. MATERIALS AND METHODS: Twenty patients with gastric cancer confirmed by endoscopic biopsy were undergone routine dose (120 kV, 100 mA) and low-dose (120 kV, 50 mA) CTPI examination, respectively. The original data were processed by body perfusion software, and the perfusion parameters values including blood flow (BF), blood volume (BV) and permeability surface (PS) of gastric cancer were measured. Statistical data analyses including paired-samples t test, Pearson correlation analysis and Bland-Altman consistency test were used to compare the perfusion parameters values between the routine dose and low-dose CTPI examinations. Radiation dosage, which the patients received during two CTPI examinations, was also calculated and compared. RESULTS: There were no statistical differences in the BF, BV and PS values between routine dose group and low-dose group (P > 0.05), and there were significant correlation in the BF, BV and PS values between two groups (P <  0.01). The consistency of BF and BV values between the two groups was preferable to that of PS value. The radiation dosage of the low-dose group was much less than that of routine dose group, and the CTDIvol and DLP values of low-dose CTPI were decreased by 50%, respectively. CONCLUSION: The parameters BF and BV values may play a valuable role in the diagnosis and assessment of gastric cancer in low-dose CTPI examination.

Isolation and identification of Acanthamoeba strains from soil and tap water in Yanji, China.

BACKGROUND: Members of the genus Acanthamoeba are widely distributed throughout the world, and some of them are considered pathogenic, as they are capable of causing corneal and central nervous system diseases. In this study, we isolated Acanthamoeba strains from soil and tap water in Yanji, China. METHODS: We identified four strains of Acanthamoeba (CJY/S1, CJY/S2, CJY/S3, and CJY/W1) using mitochondrial DNA restriction fragment length polymorphism (mtDNA RFLP) analysis. Nuclear 18S rDNA sequences were used for phylogenetic analysis and species identification. RESULTS: Genotypic characterization of the isolates showed that they belonged to genotypes T4 (CJY/S1 and CJY/S2), T5 (CJY/S3), and T16 (CJY/W1). Sequence differences between CJY/S1 and Acanthamoeba castellanii Neff, CJY/S2 and Acanthamoeba KA/E7, and CJY/S3 and Acanthamoeba lenticulata 68-2 were 0.31, 0.2, and 0.26%, respectively. 18S ribosomal deoxyribonucleic acid (rDNA) of CJY/W1 had 99% sequence identity to that of Acanthamoeba sp. U/H-C1. Strains CJY/S1 and CJY/S2, isolated from soil, had similar mtDNA RFLP patterns, whereas strain CJY/W1, isolated from tap water, displayed a different pattern. CONCLUSIONS: To the best of our knowledge, this is the first report on the identification of genotypes T4, T5, and T16 from environmental sources in Yanji, China.

Role of CT perfusion imaging in patients with variously differentiated gastric adenocarcinoma.

PURPOSE: To explore the characteristics of variously differentiated gastric cancers on computed tomography (CT) perfusion imaging, including specific perfusion parameter values, and potential clinical applications in the prognosis assessment of gastric cancer. MATERIALS AND METHODS: Fifty patients with gastric cancer confirmed by gastroscope pathology were studied prospectively using CT perfusion imaging examinations on a 64-slice spiral CT scanner. The acquired volume data were used for calculations, mapping, and analysis by using an abdominal tumor perfusion protocol (deconvolution method) in the CT perfusion software package to measure 4 parameters: blood flow (BF), blood volume (BV), mean transit time (MTT), and the permeability surface (PS) area product. The different differentiated Gastric cancers with CT perfusion values were divided into 3 groups: well-differentiated, moderately differentiated and poorly differentiated gastric adenocarcinoma, and compared statistically with one another by statistical software. RESULTS: The mean perfusion values of 10 patients with well-differentiated gastric adenocarcinoma were as follows: BF, 75.28 ± 6.81 mL/100 g/min; BV, 9.01 ± 0.94 mL/100 g; MTT, 9.89 ± 1.65 s; and PS, 10.05 ± 0.71 mL/100 g/min. The mean perfusion values of 24 patients with moderately differentiated gastric adenocarcinoma were as follows: BF, 110.01 ± 31.90 mL/100 g/min; BV, 18.18 ± 5.62 mL/100 g; MTT, 9.81 ± 3.69 s; and PS, 40.08 ± 15.82 mL/100 g/min. The mean perfusion values of 16 patients with poorly differentiated gastric adenocarcinoma were as follows: BF, 138.59 ± 38.09 mL/100 g/min; BV, 21.08 ± 4.11 mL/100 g; MTT, 9.47 ± 1.80 s; and PS, 57.50 ± 13.28 mL/100 g/min. Comparing the 3 groups, differences between the well-differentiated group and the moderate differentiation group were all statistically significant for BF, BV, and PS (p < 0.05, respectively), differences between the well-differentiated group and the poor differentiation group were all statistically significant for BF, BV, and PS (p < 0.05,respectively) as well; While MTT value showed no statistical difference among the 3 groups (p > 0.05). CONCLUSION: Stomach CT perfusion imaging is a functional imaging technology from the perspective of hemodynamics with potential clinical applications. The BF, BV and PS values could serve as indicators of the degree of malignancy and aid in prognostic assessment of gastric cancer.