CT and MRI features of sarcomatoid urothelial carcinoma of the bladder and its differential diagnosis with conventional urothelial carcinoma.

BACKGROUND: Sarcomatoid urothelial carcinoma (SUC) is a rare and highly malignant form of bladder cancer with a poor prognosis. Currently, there is limited information on the imaging features of bladder SUC and reliable indicators for distinguishing it from conventional urothelial carcinoma (CUC). The objective of our study was to identify the unique imaging characteristics of bladder SUC and determine factors that aid in its differential diagnosis. MATERIALS AND METHODS: This retrospective study enrolled 22 participants with bladder SUC and 61 participants with CUC. The clinical, pathologic, and CT/MRI data from both groups were recorded, and a comparison was conducted using univariate analysis and multinomial logistic regression for distinguishing SUC from CUC. RESULTS: The majority of SUCs were located in the trigone of the bladder and exhibited large tumor size, irregular shape, low ADC values, Vesical Imaging-Reporting and Data System (VI-RADS) score ≥ 4, the presence of necrosis, and an invasive nature. Univariate analysis revealed significant differences in terms of tumor location, shape, the maximum long-axis diameter (LAD), the short-axis diameter (SAD), ADC-value, VI-RADS scores, necrosis, extravesical extension (EVE), pelvic peritoneal spread (PPS), and hydronephrosis/ureteral effusion (p < .001 ~ p = .037) between SUCs and CUCs. Multinomial logistic regression found that only SAD (p = .014) and necrosis (p = .003) emerged as independent predictors for differentiating between SUC and CUC. The model based on these two factors achieved an area under curve (AUC) of 0.849 in ROC curve analysis. CONCLUSION: Bladder SUC demonstrates several distinct imaging features, including a high incidence of trigone involvement, large tumor size, and obvious invasiveness accompanied by necrosis. A bladder tumor with a large SAD and evidence of necrosis is more likely to be SUC rather than CUC.

Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study.

BACKGROUND: Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation. METHODS: A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at 5 centers. Five radiology residents and 5 attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) and contouring times per lesion were compared. RESULTS: The BMSS alone yielded a median DSC of 0.91 (95% confidence interval, 0.90-0.92) in the multi-center set and showed comparable performance between the internal and external sets (P = .67). With BMSS assistance, the readers increased the median DSC from 0.87 (0.87-0.88) to 0.92 (0.92-0.92) (P < .001) with a median time saving of 42% (40-45%) per lesion. Resident readers showed a greater improvement than attending readers in contouring accuracy (improved median DSC, 0.05 [0.05-0.05] vs 0.03 [0.03-0.03]; P < .001), but a similar time reduction (reduced median time, 44% [40-47%] vs 40% [37-44%]; P = .92) with BMSS assistance. CONCLUSIONS: The BMSS can be optimally applied to improve the efficiency of brain metastasis delineation in clinical practice.

Whole-tumor histogram analysis of multiple non-Gaussian diffusion models at high b values for assessing cervical cancer.

PURPOSE: To assess the diagnostic potential of whole-tumor histogram analysis of multiple non-Gaussian diffusion models for differentiating cervical cancer (CC) aggressive status regarding of pathological types, differentiation degree, stage, and p16 expression. METHODS: Patients were enrolled in this prospective single-center study from March 2022 to July 2023. Diffusion-weighted images (DWI) were obtained including 15 b-values (0 ~ 4000 s/mm2). Diffusion parameters derived from four non-Gaussian diffusion models including continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM) were calculated, and their histogram features were analyzed. To select the most significant features and establish predictive models, univariate analysis and multivariate logistic regression were performed. Finally, we evaluated the diagnostic performance of our models by using receiver operating characteristic (ROC) analyses. RESULTS: 89 women (mean age, 55 ± 11 years) with CC were enrolled in our study. The combined model, which incorporated the CTRW, DKI, FROC, and IVIM diffusion models, offered a significantly higher AUC than that from any individual models (0.836 vs. 0.664, 0.642, 0.651, 0.649, respectively; p < 0.05) in distinguishing cervical squamous cell cancer from cervical adenocarcinoma. To distinguish tumor differentiation degree, except the combined model showed a better predictive performance compared to the DKI model (AUC, 0.839 vs. 0.697, respectively; p < 0.05), no significant differences in AUCs were found among other individual models and combined model. To predict the International Federation of Gynecology and Obstetrics (FIGO) stage, only DKI and FROC model were established and there was no significant difference in predictive performance among different models. In terms of predicting p16 expression, the predictive ability of DKI model is significantly lower than that of FROC and combined model (AUC, 0.693 vs. 0.850, 0.859, respectively; p < 0.05). CONCLUSION: Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.

Feasible CT features to distinguish incidental rib enhancement from sclerotic metastasis in patients with malignancies.

OBJECTIVE: To investigate the CT features of incidental rib enhancement (RE) and to summarize the CT characteristics for distinguishing the RE from sclerotic metastasis (SM) in patients with malignancies. MATERIAL AND METHODS: This retrospective observational study enrolled 79 patients with RE (involved 133 ribs) during October 2014 and December 2021. Another 53 patients with SM (160 SM) in the same period were selected randomly for comparison. The location, enhancement patterns of RE were reviewed. The CT values of RE regions and SM were measured and statistically analyzed. RESULTS: Most REs (70 patients, 88.6%) were in the 1st to 6th ribs. 50 patients had solitary RE and 29 with multiple REs in a regional distribution. All the REs were closely connected to the intercostal venous plexus (ICVP) ipsilateral to the injection site. No visible abnormalities on unenhanced scans were detected in all REs. One hundred and twenty REs (90.2%) had nodular/patchy enhancement. The CT value of RE regions in the venous phase was lower than that in the arterial phase (589.8 ± 344.2 HU versus 1188.5 ± 325.3 HU, p < 0.001). During the venous phase, most REs (125, 94.0%) shrank or disappeared. SM appeared similar on both contrast-enhanced and unenhanced scans in terms of shape and CT values. CONCLUSION: The RE demonstrated characteristic CT features. The manifestations of nodular/patchy enhancement in the arterial phase, decreased density and shrinkage or disappearance during the venous phase, and no abnormality on unenhanced scans, as well as a close connection with the ICVP, may help differentiate RE from SM.

Evaluation of Quantitative Dual-Energy Computed Tomography Parameters for Differentiation of Parotid Gland Tumors.

RATIONALE AND OBJECTIVES: To assess the diagnostic performance of quantitative parameters from dual-energy CT (DECT) in differentiating parotid gland tumors (PGTs). MATERIALS AND METHODS: 101 patients with 108 pathologically proved PGTs were enrolled and classified into four groups: pleomorphic adenomas (PAs), warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). Conventional CT attenuation and DECT quantitative parameters, including iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number (Zeff), electron density (Rho), double energy index (DEI), and the slope of the spectral Hounsfield unit curve (λHU), were obtained and compared between benign tumors (BTs) and MTs, and further compared among the four subgroups. Logistic regression analysis was used to assess the independent parameters and the receiver operating characteristic (ROC) curves were used to analyze the diagnostic performance. RESULTS: Attenuation, Zeff, DEI, IC, NIC, and λHU in the arterial phase (AP) and venous phase (VP) were higher in MTs than in BTs (p < 0.001-0.047). λHU in VP and Zeff in AP were independent predictors with an area under the curve (AUC) of 0.84 after the combination. Furthermore, attenuation, Zeff, DEI, IC, NIC, and λHU in the AP and VP of MTs were higher than those of PAs (p < 0.001-0.047). Zeff and NIC in AP and λHU in VP were independent predictors with an AUC of 0.93 after the combination. Attenuation and Rho in the precontrast phase; attenuation, Rho, Zeff, DEI, IC, NIC, and λHU in AP; and the Rho in the VP of PAs were lower than those of WTs (p < 0.001-0.03). Rho in the precontrast phase and attenuation in AP were independent predictors with an AUC of 0.89 after the combination. MTs demonstrated higher Zeff, DEI, IC, NIC, and λHU in VP and lower Rho in the precontrast phase compared with WTs (p < 0.001-0.04); but no independent predictors were found. CONCLUSION: DECT quantitative parameters can help to differentiate PGTs.

Response evaluation of hepatocellular carcinoma treated with stereotactic body radiation therapy: magnetic resonance imaging findings.

PURPOSE: To summarize the magnetic resonance imaging manifestations of hepatocellular carcinoma (HCC) with and without progression after stereotactic body radiation therapy (SBRT) and evaluate the treatment effect using the modified Liver Reporting and Data System (LI-RADS). METHODS: Between January 2015 and December 2020, 102 patients with SBRT-treated HCC were included. Tumor size, signal intensity, and enhancement patterns at each follow-up period were analyzed. Three different patterns of enhancement: APHE and wash-out, non-enhancement, and delayed enhancement. For modified LI-RADS, delayed enhancement with no size increase were considered to be a "treatment-specific expected enhancement pattern" for LR-TR non-viable. RESULTS: Patients were divided into two groups: without (n = 96) and with local progression (n = 6). Among patients without local progression, APHE and wash-out pattern demonstrated conversion to the delayed enhancement (71.9%) and non-enhancement (20.8%) patterns, with decreased signal intensity on T1WI(92.9%) and DWI(99%), increased signal intensity on T1WI (99%), and decreased size. The signal intensity and enhancement patterns stabilized after 6-9 months. Six cases with progression exhibited tumor growth, APHE and wash-out, and increased signal intensity on T2WI/DWI. Based on the modified LI-RADS criteria, 74% and 95% showed LR-TR-nonviable in 3 and 12 months post-SBRT, respectively. CONCLUSIONS: After SBRT, the signal intensity and enhancement patterns of HCCs showed a temporal evolution. Tumor growth, APHE and wash-out, and increased signal intensity on T2WI/DWI indicates tumor progression. Modified LI-RADS criteria showed good performance in evaluating nonviable lesions after SBRT.

False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study.

BACKGROUND: Errors have seldom been evaluated in computer-aided detection on brain metastases. This study aimed to analyze false negatives (FNs) and false positives (FPs) generated by a brain metastasis detection system (BMDS) and by readers. METHODS: A deep learning-based BMDS was developed and prospectively validated in a multicenter, multireader study. Ad hoc secondary analysis was restricted to the prospective participants (148 with 1,066 brain metastases and 152 normal controls). Three trainees and 3 experienced radiologists read the MRI images without and with the BMDS. The number of FNs and FPs per patient, jackknife alternative free-response receiver operating characteristic figure of merit (FOM), and lesion features associated with FNs were analyzed for the BMDS and readers using binary logistic regression. RESULTS: The FNs, FPs, and the FOM of the stand-alone BMDS were 0.49, 0.38, and 0.97, respectively. Compared with independent reading, BMDS-assisted reading generated 79% fewer FNs (1.98 vs 0.42, P < .001); 41% more FPs (0.17 vs 0.24, P < .001) but 125% more FPs for trainees (P < .001); and higher FOM (0.87 vs 0.98, P < .001). Lesions with small size, greater number, irregular shape, lower signal intensity, and located on nonbrain surface were associated with FNs for readers. Small, irregular, and necrotic lesions were more frequently found in FNs for BMDS. The FPs mainly resulted from small blood vessels for the BMDS and the readers. CONCLUSIONS: Despite the improvement in detection performance, attention should be paid to FPs and small lesions with lower enhancement for radiologists, especially for less-experienced radiologists.

The value of CT features and demographic data in the differential diagnosis of type 2 papillary renal cell carcinoma from fat-poor angiomyolipoma and oncocytoma.

PURPOSES: To determine the CT features and demographic data predictive of type 2 papillary renal cell carcinoma (PRCC) that can help distinguish this neoplasm from fat-poor angiomyolipoma (fpAML) and oncocytoma. METHODS: Fifty-four patients with type 2 PRCC, 48 with fpAML, and 47 with oncocytoma in the kidney from multiple centers were retrospectively reviewed. The demographic data and CT features of type 2 PRCC were analyzed and compared with those of fpAML and oncocytoma by univariate analysis and multiple logistic regression analysis to determine the predictive factors for differential diagnosis. Then, receiver operating characteristic (ROC) curve analysis was performed to further assess the logistic regression model and set the threshold level values of the numerical parameters. RESULTS: Older age (≥ 46.5 years), unenhanced lesion-to-renal cortex attenuation (RLRCA) < 1.21, corticomedullary ratio of lesion to renal cortex net enhancement (RLRCNE) < 0.32, and size ≥ 30.1 mm were independent predictors for distinguishing type 2 PRCC from fpAML (OR 14.155, 8.332, and 57.745, respectively, P < 0.05 for all). The area under the curve (AUC) of the multiple logistic regression model in the ROC curve analysis was 0.970. In the combined evaluation, the four independent predictors had a sensitivity and specificity of 0.896 and 0.889, respectively. A corticomedullary RLRCNE < 0.61, irregular shape, and male sex were independent predictors for the differential diagnosis of type 2 PRCC from oncocytoma (OR 15.714, 12.158, and 6.175, respectively, P < 0.05 for all). In the combined evaluation, the three independent predictors had a sensitivity and specificity of 0.889 and 0.979, respectively. The AUC of the multiple logistic regression model in the ROC curve analysis was 0.964. CONCLUSION: The combined application of CT features and demographic data had good ability in distinguishing type 2 PRCC from fpAML and oncocytoma, respectively.

Morphologic CT and MRI features of primary parotid squamous cell carcinoma and its predictive factors for differential diagnosis with mucoepidermoid carcinoma.

BACKGROUND: Primary parotid squamous cell carcinoma (SCC) is a rare entity with a poor prognosis. Pathologically, the diagnosis of it requires the exclusion of parotid mucoepidermoid carcinoma (MEC). Currently, the imaging features of primary parotid SCC and the predictive indicators for differential diagnosis of the two entities have not been well reported. Our purpose was to identify the imaging characteristics of primary parotid SCC and to determine the predictive factors for its' differential diagnosis. RESULTS: Thirty-one participants with primary parotid SCC and 59 with primary parotid MEC were enrolled. Clinical, CT and MRI features were reviewed and compared by univariate analysis. Then, multinomial logistic regression was used to determine the predictors to distinguish parotid SCC from MEC. Most primary parotid SCCs exhibited irregular shape, ill-defined margin, incomplete or no capsule, heterogeneous and marked or moderate enhancement, necrosis, local tumor invasiveness (LTI). Age, maximal dimension, shape, degree of enhancement, gradual enhancement, necrosis, and LTI were different between the primary parotid SCCs and MECs in univariate analysis (p < 0.05). While in multinomial logistic regression analysis, only age and necrosis were the independent predictors for distinguishing parotid SCC from MEC, and this model exhibited an area under curve of 0.914 in ROC curve analysis. CONCLUSIONS: Primary parotid SCC has some distinct imaging features including the large tumor size, irregular shape, ill-defined margin, and particularly the marked central necrosis. Patients with age ≥ 51.5 years and necrosis on the image of the primary tumor in the parotid gland could be more likely to be SCCs than MECs.

Phase II trial of efficacy, safety and biomarker analysis of sintilimab plus anlotinib for patients with recurrent or advanced endometrial cancer.

BACKGROUND: Although co-inhibition of the angiogenesis and programmed death 1 (PD-1) pathways is proposed as an effective anticancer strategy, studies in Chinese patients with endometrial cancer are sufficient. Anlotinib is an oral multi-targeted tyrosine kinase inhibitor affecting tumor angiogenesis and proliferation; sintilimab is an anti-PD-1 monoclonal antibody. METHODS: This was a phase II trial using Simon's two-stage design. This study enrolled patients with endometrial cancer who had progressed after platinum-based chemotherapy. Sintilimab 200 mg was administered intravenously on day 1 every 3 weeks, and anlotinib 12 mg was administered on days 1-14 in a 21-day cycle. The primary endpoint was the objective response rate (ORR) using the immune-related Response Evaluation Criteria in Solid Tumors criteria. Immunohistochemistry and whole-exome sequencing were used as correlative investigations. RESULTS: Between November 2019 and September 2020, 23 eligible patients were enrolled. The ORR and disease control rates were 73.9% (95% CI, 51.6 to 89.8) and 91.3% (95% CI, 72.0 to 98.9), respectively, with 4 complete and 12 partial responses. With a median follow-up of 15.4 months (95% CI, 12.6 to 18.3), the median progression-free survival was not reached, and the probability of PFS >12 months was 57.1% (95% CI, 33.6 to 75.0). Exploratory analysis revealed that mutations in the homologous repair pathway showed a trend for higher ORR (100% vs 0%, p=0.07). Treatment-related grade 3/4 adverse events were observed in 50.0% of the patients. CONCLUSIONS: Sintilimab plus anlotinib demonstrated robust therapeutic benefits with tolerable toxicity in endometrial cancer. TRIAL REGISTRATION NUMBER: NCT04157491.

Role of computed tomography features in the differential diagnosis of chromophobe renal cell carcinoma from oncocytoma and angiomyolipoma without visible fat.

Background: Chromophobe renal cell carcinoma (chRCC) is often confused with oncocytoma and angiomyolipoma without visible fat (AML.wovf). The aim of this study was to determine computed tomography (CT) features predictive of chRCC to distinguish it from oncocytoma and AML.wovf. Methods: This multicenter study enrolled 38 patients with chRCC, 32 with oncocytoma, and 43 with AML.wovf of the kidney. The clinical and imaging features of all cases were reviewed retrospectively, and associations between the features and histopathology were analyzed using univariate analysis, followed by multinomial logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used to evaluate logistic regression models and determine optimal cut-off values for numeric data. Results: Univariate analysis revealed significant differences between chRCC and oncocytoma in tumor ratios of lesion to renal cortex net enhancement (RLRCNE) on both corticomedullary and nephrographic phase images (P<0.001 for both) and calcification (P=0.035). On multinomial logistic regression analysis, only corticomedullary RLRCNE remained an independent predictor for the differential diagnosis of chRCC from oncocytoma (P<0.001), with an optimal cut-off value of 0.53. Comparing chRCC and AML.wovf, univariate analysis revealed significant differences in age (P=0.003), segmental enhancement inversion (SEI) (P=0.006), corticomedullary RLRCNE (P<0.001), unenhanced ratio of lesion to renal cortex attenuation (RLRCA; P<0.001), size (P<0.001), enhancement pattern over time (P=0.017), angle (P=0.014), and central scar (P<0.001). Only unenhanced RLRCA (P<0.001), size (P=0.003), and enhancement pattern over time (P=0.002) remained as independent predictors on multinomial logistic regression analysis, with optimal cut-off values of 1.13 and 30.9 mm for RLRCA and size, respectively. On ROC curve analysis of the logistic regression models, the areas under curve (AUC) were 0.888 and 0.963 for chRCC versus oncocytoma and AML.wovf, respectively. Conclusions: Corticomedullary RLRCNE on CT images was an independent predictor for the differential diagnosis of chRCC from oncocytoma. Unenhanced RLRCA, size, and enhancement pattern over time on CT had predictive value for discriminating chRCC from AML.wovf.

Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas.

BACKGROUND: To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). MATERIALS AND METHODS: CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. RESULTS: CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704-0.906). CONCLUSION: The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

The CT and MRI Features of Primary Intrahepatic Lymphoepithelioma-Like Cholangiocarcinoma.

OBJECTIVE. The purpose of our study was to retrospectively characterize the CT and MRI features of primary intrahepatic lymphoepithelioma-like cholangiocarcinoma (LELCC). MATERIALS AND METHODS. Eleven patients (10 women and one man; age range, 30-63 years) with 11 pathologically proven LELCCs were enrolled retrospectively from April 2016 to December 2018. Triphasic enhanced images were obtained of all patients: MR images of five patients, CT images of five patients, and both CT and MR images of one patient. The clinical data and CT and MRI findings were reviewed. RESULTS. All LELCC cases were associated with Epstein-Barr virus (EBV) infection. Eight of the 11 patients had hepatitis B virus (HBV) infection. The tumor diameter ranged from 1.1 to 8.7 cm. All tumors were well defined with a smooth or lobulated margin. A cystic area was noted in two of the 11 tumors. After the administration of contrast material, the tumors showed homogeneous (n = 7) or heterogeneous (n = 4) hypervascular arterial enhancement and gradual washout, delayed central scar or irregular enhancement (n = 9), delayed circular thin or incomplete pseudocapsule enhancement (n = 7), and homogeneous hypointensity in the hepatobiliary phase (n = 2). No cirrhosis, focal dilatation of intrahepatic ducts, or satellite nodules were detected. Lymphadenopathy were detected in four patients, appearing as hypervascular enhancement and no necrosis (even in multiple nodes > 3 cm) or as moderate peripheral enhancement and necrosis. CONCLUSION. A liver mass in a middle-aged woman with EBV and HBV infection that appears on CT and MRI to have a well-defined boundary and a combination of hypervascularity, washout, delayed intratumoral enhancement, or pseudocapsule enhancement may suggest an imaging diagnosis of primary LELCC. More cases are needed to better understand this disease.

Theranostic Nanomedicine for Synergistic Chemodynamic Therapy and Chemotherapy of Orthotopic Glioma.

Glioma is a common primary brain malignancy with a poor prognosis. Chemotherapy is the first-line treatment for brain tumors but low efficiency of drugs in crossing the blood-brain barrier (BBB) and drug resistance related to tumor hypoxia thwart its efficacy. Herein, a theranostic nanodrug (iRPPA@TMZ/MnO) is developed by incorporating oleic acid-modified manganese oxide (MnO) and temozolomide (TMZ) into a polyethylene glycol-poly(2-(diisopropylamino)ethyl methacrylate-based polymeric micelle containing internalizing arginine-glycine-aspartic acid (iRGD). The presence of iRGD provides the nanodrug with a high capacity of crossing the BBB and penetrating the tumor tissue. After accumulation in glioma, the nanodrug responds to the tumor microenvironment to simultaneously release TMZ, Mn2+, and O2. The released TMZ induces tumor cell apoptosis and the released Mn2+ causes intracellular oxidative stress that kill tumor cells via a Fenton-like reaction. The O2 produced in situ alleviates tumor hypoxia and enhances the chemotherapy/chemodynamic therapeutic effects against glioma. The Mn2+ can also serve as a magnetic resonance imaging (MRI) contrast agent for tumor imaging during therapy. The study demonstrates the great potential of this multifunctional nanodrug for MRI-visible therapy of brain glioma.

Targeted Magnetic Resonance Imaging and Modulation of Hypoxia with Multifunctional Hyaluronic Acid-MnO2 Nanoparticles in Glioma.

Manganese dioxide (MnO2 )-based nanoparticles are a promising tumor microenvironment-responsive nanotheranostic carrier for targeted magnetic resonance imaging (MRI) and for alleviating tumor hypoxia. However, the complexity and potential toxicity of the present common synthesis methods limit their clinical application. Herein, multifunctional hyaluronic acid-MnO2 nanoparticles (HA-MnO2 NPs) are synthesized in a simple way by directly mixing sodium permanganate with HA aqueous solutions, which serve as both a reducing agent and a surface-coating material. The obtained HA-MnO2 NPs show an improved water-dispersibility, fine colloidal stability, low toxicity, and responsiveness to the tumor microenvironment (high H2 O2 and high glutathione, low pH). After intravenous injection, HA-MnO2 NPs exhibit a high imaging sensitivity for detecting rat intracranial glioma with MRI for a prolonged period of up to 3 d. These nanoparticles also effectively alleviate the tumor hypoxia in a rat model of intracranial glioma. The downregulation of VEGF and HIF-1α expression in intracranial glioma validates the sustained attenuation effect of HA-MnO2 NPs on tumor hypoxia. These results show that HA-MnO2 NPs can be used for sensitive, targeted MRI detection of gliomas and simultaneous attenuation of tumor hypoxia.

Prediction of pulmonary metastasis in pulmonary nodules (≤10 mm) detected in patients with primary extrapulmonary malignancy at thin-section staging CT.

PURPOSE: To investigate the predictive clinical and imaging factors associated with pulmonary metastasis in pulmonary nodules (PNs) ≤10 mm in patients with primary extrapulmonary malignancy (PEPM) on initial CT as well as the inter-nodular imaging features in the non-solitary PNs patients, to make a more reliable diagnosis and appropriate management of the PNs at an earlier stage after detection. MATERIALS AND METHODS: 161 patients with PNs ≤10 mm were reviewed from April 2013 to December 2013. The nature of PNs were determined on the interval change in imaging features on serial CT images (158 patients) and histologically proven (three patients). Independent predictors of changed PNs on initial CT were examined by multivariate regression analysis. RESULTS: 36.6% of patients developed interval change in nodules size. The average interval of the first change was 65.0 days (29-144 days). Tumor staging of III (P = 0.011) and IV (P < 0.001), the nodules number of 2-4 (P = 0.016), 5-9 (P < 0.001) and 10-20 (P < 0.001), the nodules margin of being smooth (P = 0.001) and slight lobulated (P < 0.001), and nodules with no near short strips (P = 0.001) were significant predictors of changed PNs. For patients with non-solitary PNs, 40.2% had PNs with identical imaging features, the incidence rate of change of which (74.3%) was significantly higher compared with that of varied features (32.7%), P < 0.001; and 94.3% of patients had all nodules per patient showing consistent prognosis. CONCLUSIONS: For PNs ≤10 mm in patients with PEPM on baseline CT, the morphological characteristics and primary malignancies stage could differentiate the majority of the PNs. The interval for further CT evaluation of uncertain PNs should be early at 1-3 months after detection, and increased alert is needed for the possibility of pulmonary metastasis when an early interval change was detected.

MR imaging features and staging of neuroendocrine carcinomas of the uterine cervix with pathological correlations.

OBJECTIVE: To determine MR imaging features and staging accuracy of neuroendocrine carcinomas (NECs) of the uterine cervix with pathological correlations. METHODS: Twenty-six patients with histologically proven NECs, 60 patients with squamous cell carcinomas (SCCs), and 30 patients with adenocarcinomas of the uterine cervix were included. The clinical data, pathological findings, and MRI findings were reviewed retrospectively. MRI features of cervical NECs, SCCs, and adenocarcinomas were compared, and MRI staging of cervical NECs was compared with the pathological staging. RESULTS: Cervical NECs showed a higher tendency toward a homogeneous signal intensity on T2-weighted imaging and a homogeneous enhancement pattern, as well as a lower ADC value of tumour and a higher incidence of lymphadenopathy, compared with SCCs and adenocarcinomas (P < 0.05). An ADC value cutoff of 0.90 × 10-3 mm2/s was robust for differentiation between cervical NECs and other cervical cancers, with a sensitivity of 63.3 % and a specificity of 95 %. In 21 patients who underwent radical hysterectomy and lymphadenectomy, the overall accuracy of tumour staging by MR imaging was 85.7 % with reference to pathology staging. CONCLUSION: Homogeneous lesion texture and low ADC value are likely suggestive features of cervical NECs and MR imaging is reliable for the staging of cervical NECs. KEY POINTS: • Cervical NECs show a tendency of lesion homogeneity and lymphadenopathy • Low ADC values are found in cervical NECs • MRI is an accurate imaging modality for the cervical NEC staging.

Spectral CT with monochromatic imaging and metal artifacts reduction software for artifacts reduction of ¹²5I radioactive seeds in liver brachytherapy.

PURPOSE: To investigate the optimal monochromatic energy for artifacts reduction from (125)I seeds as well as image improvement in the vicinity of seeds on monochromatic images with and without metal artifacts reduction software (MARS) and to compare this with traditional 120-kVp images, so as to evaluate the application value of gemstone spectral imaging for reducing artifacts from (125)I seeds in liver brachytherapy. MATERIALS AND METHODS: A total of 45 tumors from 25 patients treated with (125)I seed brachytherapy in the liver were enrolled in this study. Multiphasic spectral computed tomography (CT) scanning was performed for each patient. After a delay time of 15 s of portal vein phase, a traditional 120-kVp scan was performed, focusing on several planes of (125)I seeds only. The artifact index (AI) in the vicinity of seeds and the standard deviation (SD) of the CT density of region of interest in the outside liver parenchyma were calculated. Artifact appearance was evaluated and classified on reconstructed monochromatic S and 120-kVp images. Image quality in the vicinity of seeds of three data sets were evaluated using a 1-5 scale scoring method. The Friedman rank-sum test was used to estimate the scoring results of image quality. RESULTS: The greatest noise in monochromatic images was found at 40 keV (SD = 27.38, AI = 206.40). The optimal monochromatic energy was found at 75 keV, which provided almost the least image noise (SD = 10.01) and good performance in artifact reduction (AI = 102.73). Image noise and AI reduction at 75 keV was decreased by 63.44 and 50.23%, compared with at 40 keV. Near-field thick artifacts were obvious in all 45 lesions, in 120-kVp images, and 75-keV images, but basically reduced in 75 keV MARS images and artifacts completely invisible in 7 lesions. The number of diagnosable images (score ≥3) was significantly more in the 75-keV MARS group (28/45), and the 75-keV group (22/45) than in the 120-kVp group (11/45) (p < 0.0167 for both). Compared with 120-kVp images alone, 75-keV images plus 75-keV MARS images can increase tumor visibility around seeds and increase the proportion of diagnostic images to 84.4% (38/45). CONCLUSION: Spectral CT producing 75-keV MARS images could substantially reduce near-field thick artifacts caused by (125)I seeds and improve image quality, even to a state of being completely free from artifacts. Spectral CT imaging (with and without MARS) can provide more accurate CT images for estimating efficacy after (125)I seed brachytherapy in the liver.

Primary nasopharyngeal non-Hodgkin's lymphoma: imaging patterns on MR imaging.

OBJECTIVES: To summarize the distinct imaging features of different subtypes of primary nasopharyngeal non-Hodgkin's lymphomas (NHLs). MATERIALS AND METHODS: Clinical data and magnetic resonance imaging findings of 71 patients with histologically proven primary nasopharyngeal NHLs were retrospectively reviewed. The tumor distribution, signal intensity, lesion texture, contrast enhancement properties, extra-chamber involvement, regional structure invasion, and cervical lymphadenopathy were evaluated and compared between different subtypes of NHLs. RESULTS: Of the patients, 70.4% had B-cell lymphomas; 64.8% had symmetrical and diffuse involvement of nasopharynx walls; and 19.7% had superficial ulcerations. Extra-chamber involvement and regional structure invasion occurred in most patients. The frequency of neck node involvement was up to 83.10%; 62.7% of them were bilateral involvement. Patients with T-cell or nature killer/T-cell NHLs had a higher incidence of superficial ulcerations, nasal cavity, and paranasal sinus invasion than B-cell NHLs (P<.05). Patients with B-cell NHLs had a higher incidence of cervical lymphadenopathy specifically in Level VA and parotid region than T-cell or nature killer/T-cell (NK/T-cell) NHLs (P<.05). CONCLUSION: Primary nasopharyngeal NHLs had some characteristic imaging features and different subtypes of nasopharyngeal NHLs had some distinct imaging features.