Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study.

PURPOSE: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI). METHODS: We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals). PI-RADS assessment was performed by expert radiologists. We developed DL models for the classification between benign and malignant lesions (DL-BM) and that between csPCa and non-csPCa (DL-CS). An integrated model combining PI-RADS and the DL-CS model, abbreviated as PIDL-CS, was developed. The performances of the DL models and PIDL-CS were compared with that of PI-RADS. RESULTS: In each external validation cohort, the area under the receiver operating characteristic curve (AUC) values of the DL-BM and DL-CS models were not significantly different from that of PI-RADS (P > 0.05), whereas the AUC of PIDL-CS was superior to that of PI-RADS (P < 0.05), except for one external validation cohort (P > 0.05). The specificity of PIDL-CS for the detection of csPCa was much higher than that of PI-RADS (P < 0.05). CONCLUSION: Our proposed DL models can be a potential non-invasive auxiliary tool for predicting csPCa. Furthermore, PIDL-CS greatly increased the specificity of csPCa detection compared with PI-RADS assessment by expert radiologists, greatly reducing unnecessary biopsies and helping radiologists achieve a precise diagnosis of csPCa.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study.

BACKGROUND: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. PURPOSE: To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). STUDY TYPE: Retrospective. POPULATION: Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). FIELD STRENGTH/SEQUENCES: T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T. ASSESSMENT: The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter. STATISTICAL TESTS: Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. RESULTS: The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]). DATA CONCLUSION: TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study.

PURPOSE: To develop machine learning-based radiomics models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models. METHODS: The pre-biopsy MRI datas of 463 patients classified as PI-RADS 3 lesions were collected from 4 medical institutions retrospectively. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC images. The ANOVA feature ranking method and support vector machine classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The AUC was used to compared the predictive performance of PSAD with each model. Hosmer-lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model. RESULTS: The difference of PSAD between PCa and benign lesions was statistically significant (P = 0.006), with the mean AUC of 0.701 for predicting clinically significant prostate cancer (internal test AUC = 0.709 vs. external validation AUC = 0.692, P = 0.013) and 0.630 for predicting all cancer (internal test AUC = 0.637 vs. external validation AUC = 0.623, P = 0.036). T2WI-model with the mean AUC of 0.717 for predicting csPCa (internal test AUC = 0.738 vs. external validation AUC = 0.695, P = 0.264) and 0.634 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P = 0.019) and 0.778 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P = 0.047). CONCLUSIONS: The radiomics model based on machine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions, and has relatively high generalization ability between different date set.

Diagnostic Value of Artificial Intelligence-Assistant Diagnostic System Combined With Contrast-Enhanced Ultrasound in Thyroid TI-RADS 4 Nodules.

OBJECTIVES: This study evaluated the diagnostic value of artificial intelligence-assistant diagnostic system combined with contrast-enhanced ultrasound in The American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) 4 category thyroid nodules. METHODS: Thyroid nodules that were evaluated as ACR TI-RADS 4 by conventional ultrasound were selected, all of which had pathological or fine needle aspiration (FNA) results. All nodules were examined by contrast-enhanced ultrasound (CEUS) and artificial intelligence (AI) analysis. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of AI, CEUS and their combined diagnosis were compared; Analyzed and compared the diagnostic efficiency of AI, CEUS and their combined diagnosis. RESULTS: A total of 148 thyroid nodules were included in 140 patients, including 58 malignant nodules and 89 benign nodules. The sensitivity of combined diagnosis was significantly higher than that of AI or CEUS alone (P < .05). The NPV of AI, CEUS and combined diagnosis were statistically significant (P < .05). There was no significant difference in the diagnostic efficacy between AI and CEUS (P > .05), but there was a significant difference in NPV between AI and combined diagnosis (P < .05). The AUC of the combined diagnosis was 0.859, which was higher than that of AI, CEUS alone. CONCLUSIONS: AI has a high diagnostic efficiency, which was helpful for radiologists to make rapid assessment. AI combined CEUS can significantly improve the diagnostic sensitivity and NPV, which was beneficial for the early detection of malignant nodules.

Dissociated response to PD-1 inhibitors combined with radiotherapy in patients with advanced metastatic solid tumors: a single-center experience.

BACKGROUND: Anti-programmed death 1/anti-programmed death ligand 1 (PD-1/PD-L1) combined with radiotherapy (RT) has a synergistic effect on systemic tumor control. A dissociated response (DR), characterized by some lesions shrinking and others growing, has been recognized with immune checkpoint inhibitor (ICI) monotherapy or combination therapy. The objective of this study was to assess the frequency and clinical benefit of DR in patients with advanced metastatic solid tumors receiving PD-1 inhibitors in combination with RT. METHODS: We conducted a single-center retrospective analysis of patients with advanced metastatic solid tumors receiving PD-1 inhibitor combined with RT at the Department of Radiotherapy & Oncology, The Second People's Hospital Affiliated with Soochow University. Treatment response was assessed for each measurable lesion according to the Response Evaluation Criteria in Solid Tumours ( RECIST) v 1.1 guidelines. Patterns of response are divided into four groups: (1) DR, (2) uniform response, (3) uniform progression, and (4) only stable lesions. The overall survival (OS) of different groups was compared using Kaplan-Meier methods and log-rank tests. RESULTS: Between March 2019 and July 2022, 93 patients were included. The median follow-up was 10.5 months (95% CI 8.8-12.1). The most common tumor types were lung cancer (19.8%), colorectal adenocarcinoma (17.2%), and esophageal cancer (10.8%). DR was observed in 22 (23.7%) patients. The uniform progression and DR are two different patterns of progression. After confirming progression, the overall survival of patients with DR was significantly longer than that of patients with uniform progression (9.9 months (95%CI 5.7-14.1) vs. 4.2 months (95%CI 1.9-6.5), P = 0.028). Compared with DR patients who did not continue PD-1 inhibitor combined with RT or PD-1 inhibitor monotherapy (n = 12), DR patients who continued treatment (n = 10) had significantly longer OS (15.7 (95%CI 3.5-27.9) vs 8.2 (95%CI 5.6-10.8) months, P = 0.035). CONCLUSIONS: DR is not uncommon (23.7%) in patients with advanced metastatic solid tumors treated with PD-1 inhibitors combined with RT and shows a relatively favorable prognosis. Some patients with DR may benefit from continued PD-1 inhibitor therapy in combination with RT or PD-1 inhibitor monotherapy and may have longer OS.

Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer.

BACKGROUND: Preoperative prediction of microsatellite instability (MSI) status in colorectal cancer (CRC) patients is of great significance for clinicians to perform further treatment strategies and prognostic evaluation. Our aims were to develop and validate a non-invasive, cost-effective reproducible and individualized clinic-radiomics nomogram method for preoperative MSI status prediction based on contrast-enhanced CT (CECT)images. METHODS: A total of 76 MSI CRC patients and 200 microsatellite stability (MSS) CRC patients with pathologically confirmed (194 in the training set and 82 in the validation set) were identified and enrolled in our retrospective study. We included six significant clinical risk factors and four qualitative imaging data extracted from CECT images to build the clinics model. We applied the intra-and inter-class correlation coefficient (ICC), minimal-redundancy-maximal-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) for feature reduction and selection. The selected independent prediction clinical risk factors, qualitative imaging data and radiomics features were performed to develop a predictive nomogram model for MSI status on the basis of multivariable logistic regression by tenfold cross-validation. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots and Hosmer-Lemeshow test were performed to assess the nomogram model. Finally, decision curve analysis (DCA) was performed to determine the clinical utility of the nomogram model by quantifying the net benefits of threshold probabilities. RESULTS: Twelve top-ranked radiomics features, three clinical risk factors (location, WBC and histological grade) and CT-reported IFS were finally selected to construct the radiomics, clinics and combined clinic-radiomics nomogram model. The clinic-radiomics nomogram model with the highest AUC value of 0.87 (95% CI, 0.81-0.93) and 0.90 (95% CI, 0.83-0.96), as well as good calibration and clinical utility observed using the calibration plots and DCA in the training and validation sets respectively, was regarded as the candidate model for identification of MSI status in CRC patients. CONCLUSION: The proposed clinic-radiomics nomogram model with a combination of clinical risk factors, qualitative imaging data and radiomics features can potentially be effective in the individualized preoperative prediction of MSI status in CRC patients and may help performing further treatment strategies.

Preliminary study on brain resting-state networks and cognitive impairments of patients with obstructive sleep apnea-hypopnea syndrome.

BACKGROUND: To investigate functional changes in brain resting-state networks (RSNs) in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) and their correlations with sleep breathing disorders and neurocognitive performance. METHODS: In this study, 18 OSAHS patients and 18 matched healthy controls underwent neurocognitive assessment and magnetic resonance imaging (MRI). Group-level independent component analysis (ICA) and statistical analyses were used to explore between-group differences in RSNs and the relationship between functional changes in RSNs, sleep breathing disorders and neurocognitive performance. RESULTS: The OSAHS patients performed worse on neuropsychological tests than the healthy controls. Eight RSNs were identified, and between-group analyses showed that OSAHS patients displayed significantly decreased functional connectivity in the bilateral posterior cingulate gyri (PCC) within the default mode network (DMN), the right middle frontal gyrus (MFG) within the dorsal attention network (DAN), and the left superior temporal gyrus (STG) within the ventral attention network (VAN), and increased functional connectivity in the right superior frontal gyrus (SFG) within the salience network (SN). Further correlation analyses revealed that the average ICA z-scores in the bilateral PCC were correlated with sleep breathing disorders. CONCLUSIONS: Our findings demonstrate that the DMN, SN, DAN, and VAN are impaired during the resting state and are associated with decreased functionally distinct aspects of cognition in patients with OSAHS. Moreover, the intermittent hypoxia and sleep fragmentation caused by OSAHS are likely to be the main influencing factors.

Inhibition of KIF20A by transcription factor IRF6 affects the progression of renal clear cell carcinoma.

BACKGROUND: Renal clear cell carcinoma (ccRCC) is one of the most common malignant tumors, whose incidence is increasing year by year. IRF6 plays an important role in the occurrence of tumors, although there is yet no report on its expression in ccRCC. METHODS: The expression of IRF6 and KIF20A in ccRCC was predicted by GEPIA and HAP databases. In addition, GEPIA database predicted the relationship between IRF6 and KIF20A expressions and the pathological staging, overall survival, and disease-free survival of ccRCC. The possible binding sites of IRF6 and KIF20A promoters were predicted by JASPAR database and verified by luciferase and ChIP assays. The specific effects of IRF6 on ccRCC cell proliferation, invasion and apoptosis were subsequently examined at both cellular level and animal level. RESULTS: The database predicted down-regulated IRF6 expression in renal carcinoma tissues and its correlation with poor prognosis. IRF6 overexpression inhibited cRCC cell proliferation, invasion and migration. In addition, up-regulated KIF20A expression in renal carcinoma tissues and its association with prognosis were also predicted. Interference with KIF20A inhibited the proliferation, invasion, and migration of ccRCC cells. Finally, we confirmed that KIF20A is a functional target of IRF6 and can partially reverse the effects of IRF6 on the proliferation, invasion and migration of ccRCC cells. CONCLUSION: Inhibition of KIF20A by transcription factor IRF6 affects cell proliferation, invasion and migration in renal clear cell carcinoma.

Diagnostic Accuracy and Interobserver Agreement of PI-RADS Version 2 and Version 2.1 for the Detection of Transition Zone Prostate Cancers.

BACKGROUND. PI-RADS version 2.1 (v2.1) introduced a number of key changes to the assessment of transition zone (TZ) lesions. OBJECTIVE. The purpose of this study was to evaluate interobserver agreement and diagnostic accuracy for detecting TZ prostate cancer (PCa) and clinically significant PCa (csPCa) by use of PI-RADS v2 and PI-RADS v2.1 among radiologists with different levels of experience. METHODS. This retrospective study included 355 biopsy-naïve patients who from January 2017 to March 2020 underwent prostate MRI that showed a TZ lesion and underwent subsequent biopsy. PCa was diagnosed in 93 patients (International Society of Urological Pathology [ISUP] grade group 1, n = 34; ISUP grade group ≥ 2, n = 59) and non-cancerous lesions in 262 patients. Five radiologists with varying experience in prostate MRI scored lesions using PI-RADS v2 and PI-RADS v2.1 in sessions separated by at least 4 weeks. Interobserver agreement was evaluated with kappa and Kendall W statistics. ROC curve analysis was used to evaluate performance in detection of TZ PCa and csPCa. RESULTS. Interobserver agreement among all readers was higher for PI-RADS v2.1 than for PI-RADS v2 (mean weighted κ = 0.700 vs 0.622; Kendall W = 0.805 vs 0.728; p = .03). The pooled AUC values for detecting TZ PCa and csPCa were higher among all readers using PI-RADS v2.1 (0.866 vs 0.827 for TZ PCa; 0.929 vs 0.899 for TZ csPCa; p < .001). For detecting TZ PCa, the pooled sensitivity, specificity, and accuracy were 86.9%, 79.4%, and 75.4% among all readers for PI-RADS v2.1 compared with 79.4%, 71.8%, and 73.8% for PI-RADS v2. For detecting TZ csPCa, the pooled sensitivity, specificity, and accuracy were 84.8%, 90.9%, and 89.9% among all readers for PI-RADS v2.1 compared with 81.4%, 89.9%, and 88.5% for PI-RADS v2. Reader 1, who had the least experience, had the lowest sensitivity, specificity, and accuracy (78.0%, 89.2%, and 87.3%). Reader 5, who had the most experience, had the highest sensitivity, specificity, and accuracy (88.1%, 92.9%, and 92.1%) in detecting csPCa. CONCLUSION. PI-RADS v2.1 had better interobserver agreement and diagnostic accuracy than PI-RADS v2 for evaluating TZ lesions. Reader experience continues to affect the performance of prostate MRI interpretation with PI-RADS v2.1. CLINICAL IMPACT. PI-RADS v2.1 is more accurate and reproducible than PI-RADS v2 for the diagnosis of TZ PCa.

COVID-19 with acute cholecystitis: a case report.

BACKGROUND: The 2019 novel coronavirus (COVID-19) presents a major threat to public health and has rapidly spread worldwide since the outbreak in Wuhan, Hubei Province, China in 2019. To date, there have been few reports of the varying degrees of illness caused by the COVID-19. CASE PRESENTATION: A case of 68-year-old female with COVID-19 pneumonia who had constant pain in the right upper quadrant of her abdomen during her hospitalization that was finally diagnosed as acute cholecystitis. Ultrasound-guided percutaneous transhepatic gallbladder drainage (PTGD) was performed, and the real-time fluorescence polymerase chain reaction (RT-PCR) COVID-19 nucleic acid assay of the bile was found to be negative. PTGD, antibacterial and anti-virus combined with interferon inhalation treatment were successful. CONCLUSION: The time course of chest CT findings is typical for COVID-19 pneumonia. PTGD is useful for acute cholecystitis in COVID-19 patients. Acute cholecystitis is likely to be caused by COVID-19 .

Minor components of micropapillary and solid subtypes in lung invasive adenocarcinoma (≤ 3 cm): PET/CT findings and correlations with lymph node metastasis.

OBJECTIVE: To investigate the PET/CT findings in lung invasive adenocarcinoma with minor components of micropapillary or solid contents and its association with lymph node metastasis. MATERIALS AND METHODS: A total of 506 lung invasive adenocarcinoma (≤ 3 cm) patients who underwent a PET/CT examination and resection surgery were included. According to the proportion of solid/micropapillary components, the patients were classified into three groups: solid/micropapillary-negative (SMPN) (n = 258), solid/micropapillary-minor (SMPM; > 5% not predominant) (n = 158) and solid/micropapillary-predominant (SMPP; > 5% most dominant) (n = 90). The patients' PET/CT findings, including SUVmax, MTV, TLG and CT characteristics, and other clinical factors were compared by one-way ANOVA test. Logistic regression analysis was done to identify the most predictive findings for lymph node metastasis. RESULTS: The value of SUVmax, MTV, TLG and tumor size was highest in SMPP group, followed by SMPM and SMPN group (P < 0.001).The areas under the curve for SUVmax, MTV and TLG for node metastasis were 0.822, 0.843 and 0.835, respectively. Univariate analysis found that the SMPP and SMPM group had more lymph node metastasis than the SMPN group (P < 0.001). Furthermore, the lymph node metastasis group had higher CEA, SUVmax, MTV, TLG, tumor size and more pleural invasion (P < 0.001). Logistic regression analysis found that SMPP pathological type, SMPM pathological type, higher CEA and male patients were risk factors for lymph node metastasis (P < 0.01). CONCLUSIONS: Lung invasive adenocarcinoma with micropapillary or solid contents had higher SUVmax, MTV, TLG and tumor size and was associated with lymph node metastasis, even if they were not predominant.

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.

BACKGROUND: Multiparametric MRI (mp-MRI) combined with machine-aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics-based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI-RADS v2) scores. PURPOSE: To develop and validate a radiomics-based model for differentiating PCa and assessing its aggressiveness compared with PI-RADS v2 scores. STUDY TYPE: Retrospective. POPULATION: In all, 182 patients with biopsy-proven PCa and 199 patients with a biopsy-proven absence of cancer were enrolled in our study. FIELD STRENGTH/SEQUENCE: Conventional and diffusion-weighted MR images (b values = 0, 1000 sec/mm2 ) were acquired on a 3.0T MR scanner. ASSESSMENT: A total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T2 WI, respectively. A predictive model was constructed for differentiating PCa from non-PCa and high-grade from low-grade PCa. The diagnostic performance of each radiomics-based model was compared with that of the PI-RADS v2 scores. STATISTICAL TESTS: A radiomics-based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups. RESULTS: For PCa versus non-PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T2 WI, ADC, and T2 WI&ADC features, respectively. For low-grade versus high-grade PCa, the validation model had an AUC of 0.865, 0.888, and 0.93 with T2 WI, ADC, and T2 WI&ADC features, respectively. PI-RADS v2 had an AUC of 0.867 in differentiating PCa from non-PCa and an AUC of 0.763 in differentiating high-grade from low-grade PCa. DATA CONCLUSION: Both the T2 WI- and ADC-based radiomics models showed high diagnostic efficacy and outperformed the PI-RADS v2 scores in distinguishing cancerous vs. noncancerous prostate tissue and high-grade vs. low-grade PCa. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:875-884.

Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Histogram Analysis of Apparent Diffusion Coefficient Maps With Standard and Ultrahigh b-value Diffusion-weighted MR Imaging.

OBJECTIVE: To compare the diagnostic performance of standard and ultrahigh b-value Diffusion-weighted Imaging (DWI) using volumetric histogram analysis in differentiating transition zone (TZ) cancer from benign prostatic hyperplasia (BPH). METHODS: 57 TZ cancer and 61 BPH patients received standard (1000 s/mm) and ultrahigh b-value (2000 s/mm) DWI. The diagnostic ability of ADC histogram parameters derived from two DWI for differentiating TZ cancer from BPH was determined by receiver operating characteristic curve. RESULTS: Median, minimum, the 10th, 25th percentile ADC in both ADC1000 and ADC2000 and skewness in ADC2000 had significant differences between TZ cancer and BPH (for all, P < 0.05).The 10th percentile ADC showed highest area under the ROC curve (AUC) in both ADC1000 and ADC2000.The 10th percentile ADC of ADC2000 showed significantly higher AUC than did ADC1000 (P = 0.0385). CONCLUSIONS: The 10th percentile ADC obtained from ultrahigh b-value DWI performed better for differentiating TZ cancer from BPH.

Salivary gland function in nasopharyngeal carcinoma before and late after intensity-modulated radiotherapy evaluated by dynamic diffusion-weighted MR imaging with gustatory stimulation.

BACKGROUND: Xerostomia caused by radiation-induced salivary glands injury has a considerable impact on patients' quality of life. Nowadays, the existed different methods of evaluating xerostomia in clinical practice there are still some disadvantages and limitations. This study used diffusion-weighted magnetic resonance imaging (DW-MRI) with gustatory stimulation to assess salivary glands function after intensity-modulated radiotherapy (IMRT) in patients with nasopharyngeal carcinoma (NPC). METHODS: DW-MRI was performed in 30 NPC patients and swab method was used to calculate rest and stimulated salivary flow rates (SFR). DW sequence at rest and then repeated ten times during stimulation were obtained. Apparent diffusion coefficients (ADCs) maps of three glands were calculated. Patients before and after RT were recorded as xerostomia and non-xerostomia groups separately. Rest and stimulated ADCs, ADCs increase rates (IRs), time to maximum ADCs (Tmax), ADCs change rates (CRs), rest and stimulated SFR, SFR increase rates (IRs) and SFR change rates (CRs) before and after RT were assessed. RESULTS: The rest and stimulated ADCs of three glands after RT were higher than those before RT (p < 0.001). The rest and stimulated SFR of all salivary glands after RT were lower than those before RT (p < 0.001). A correlation existed between rest ADCs of submandibular glands and rest SFR of submandibular mixed with sublingual glands and full three glands before RT (p = 0.019, p = 0.009), stimulated ADCs and stimulated SFR in parotid glands before RT (p = 0.047). The rest ADCs of parotid glands after RT correlated to XQ scores (p = 0.037). CONCLUSIONS: The salivary glands' ADCs increased after RT both in rest and stimulated state due to the radiation injury and the ADCs correlated with SFR and XQ scores of evaluating the xerostomia in clinical practice.

Rationally Designed Monodisperse Gd2 O3 /Bi2 S3 Hybrid Nanodots for Efficient Cancer Theranostics.

Multifunctional nanotheranostic agents are of particular importance in the field of precise nanomedicine. However, a critical challenge remains in the rational fabrication of monodisperse multicomponent nanoparticles with enhanced multifunctional characteristics for efficient cancer theranostics. Here, a rational and facile synthesis of monodisperse Gd2 O3 /Bi2 S3 hybrid nanodots (Gd/Bi-NDs) is demonstrated as a multifunctional nanotheranostic agent using a albumin nanoreactor for computed tomography (CT)/photoacoustics (PA)/magnetic resonance (MR) imaging and simultaneous photothermal tumor ablation. Two nanoprecipitation reactions in one albumin nanoreactor are simultaneously conducted to generate ultrasmall Gd/Bi-NDs with both orthorhombic Bi2 S3 and cubic Gd2 O3 nanostructures. Their hybrid nanostructure generates distinctly enhanced longitudinal relaxivity in the spatially confined albumin nanocage as compared to monocomponent Gd2 O3 nanodots. Moreover, such hybrid nanodots possess multiple desirable characteristics including superior photobleaching resistance, efficient cellular uptake, preferable tumor accumulation, good in vivo clearance, and negligible acute toxicity, thereby leading to complementary PA/CT/MR imaging with spatial and anatomic characteristics, as well as effective photothermal tumor ablation without regrowth. These results represent a promising approach to fabricate monodisperse multicomponent nanotheranostic agents for efficient cancer theranostics.

Primary Hepatic Ectopic Pregnancy: A Case Report.

BACKGROUND: Hepatic pregnancy is a very rare form of ectopic pregnancy, potentially life-threatening for the mother. CASE: A 33-year-old woman presented with intermittent pain in the upper abdomen of 5 days' duration. An abdominopelvic ultrasound scan was performed, which demonstrated hepatic pregnancy. Later, abdominal computed tomography (CT) and magnetic resonance imaging (MRI) in coronal and axial confirmed the findings and delineated exactly the regional anatomy before surgery. Laparotomy was successfully performed according to the preoperative diagnosis. CONCLUSION: The application of imaging techniques (ultrasound, CT, and MRI scan) is very useful in hepatic pregnancy for defining the regional anatomy in greater detail and is critical in minimizing surgical injury.

Small-worldness and gender differences of large scale brain metabolic covariance networks in young adults: a FDG PET study of 400 subjects.

BACKGROUND: Many studies have demonstrated the small-worldness of the human brain, and have revealed a sexual dimorphism in brain network properties. However, little is known about the gender effects on the topological organization of the brain metabolic covariance networks. PURPOSE: To investigate the small-worldness and the gender differences in the topological architectures of human brain metabolic networks. MATERIAL AND METHODS: FDG-PET data of 400 healthy right-handed subjects (200 women and 200 age-matched men) were involved in the present study. Metabolic networks of each gender were constructed by calculating the covariance of regional cerebral glucose metabolism (rCMglc) across subjects on the basis of AAL parcellation. Gender differences of network and nodal properties were investigated by using the graph theoretical approaches. Moreover, the gender-related difference of rCMglc in each brain region was tested for investigating the relationships between the hub regions and the brain regions showing significant gender-related differences in rCMglc. RESULTS: We found prominent small-world properties in the domain of metabolic networks in each gender. No significant gender difference in the global characteristics was found. Gender differences of nodal characteristic were observed in a few brain regions. We also found bilateral and lateralized distributions of network hubs in the females and males. Furthermore, we first reported that some hubs of a gender located in the brain regions showing weaker rCMglc in this gender than the other gender. CONCLUSION: The present study demonstrated that small-worldness was existed in metabolic networks, and revealed gender differences of organizational patterns in metabolic network. These results maybe provided insights into the understanding of the metabolic substrates underlying individual differences in cognition and behaviors.

Sirt3-MnSOD axis represses nicotine-induced mitochondrial oxidative stress and mtDNA damage in osteoblasts.

Increasing evidence has suggested an important role played by reactive oxygen species in the pathogenesis of osteoporosis. Tobacco smoking is an important risk factor for the development of osteoporosis, and nicotine is one of the major components in tobacco. However, the mechanism by which nicotine promotes osteoporosis is not fully understood. Here, in this study, we found that nicotine-induced mitochondrial oxidative stress and mitochondrial DNA (mtDNA) damage in osteoblasts differentiated from mouse mesenchymal stem cell. The activity of MnSOD, one of the mitochondrial anti-oxidative enzymes, was significantly reduced by nicotine due to the reduced level of Sirt3. Moreover, it was also found that Sirt3 could promote MnSOD activity by deacetylating MnSOD. Finally, Mn(III)tetrakis (4-benzoic acid) porphyrin (MnTBAP, a MnSOD mimetic) was found to markedly reduce the effect of nicotine on osteoblasts. In summary, Sirt3-MnSOD axis was identified as a negative component in nicotine-induced mitochondrial oxidative stress and mtDNA damage, and MnTBAP may serve as a potential therapeutic drug for osteoporosis.

Effect of zoledronic acid on vertebral marrow adiposity in postmenopausal osteoporosis assessed by MR spectroscopy.

OBJECTIVE: Zoledronic acid (ZOL) has a suppressive effect on marrow adiposity in ovariectomized rats. Currently, however, data on the effect of ZOL on marrow fat in humans are unavailable. The purpose of this work was to determine the in vivo effect of ZOL on bone remodeling and marrow adipogenesis in postmenopausal osteoporosis. MATERIALS AND METHODS: In a 12-month, randomized, double-blind, placebo-controlled trial, we studied 100 postmenopausal women with osteoporosis who were randomly given either a single dose of intravenous infusion of ZOL (5 mg) or placebo. All subjects received adequate dietary calcium and vitamin D3. Main outcome measures included bone mineral density by dual-energy X-ray absorptiometry, vertebral marrow fat content by proton MR spectroscopy, serum markers of bone turnover by biochemical analysis. RESULTS: Ninety percent of the participants completed the 12-month follow-up. With respect to baselines, marrow fat content reduced by 8.1% in the ZOL-treated women and increased by 3.0% in the controls (all p < 0.05). In addition, there were significant increases of bone mineral density by 2.8, 2.0, and 1.7% in the lumbar spine, femoral neck, and total hip, respectively, in the ZOL group compared with the placebo group. Serum levels of bone resorption marker CTX and bone formation marker BALP decreased by 33 and 18% in postmenopausal women receiving ZOL. CONCLUSIONS: In postmenopausal women with osteoporosis, a single dose of ZOL therapy significantly reduced marrow adiposity. MR spectroscopy of vertebral marrow fat may therefore serve as a novel tool for BMD-independent efficacy assessment.

[Quantitative analysis of age-related changes of human major salivary glands using magnetic resonance imaging].

OBJECTIVE: To analyze the age-related characteristics of volumes, signal intensities (SIs) of T1-weighted images (T1WI), T2-weighted images (T2WI) and apparent diffusion coefficients (ADCs) of three major salivary glands. METHODS: A total of 300 subjects with normal salivary glands were divided into 4 different age groups and examined with a 1.5 T magnetic resonance (MR) unit. T1WI, T2WI and diffusion-weighted MR images (DW MRI) were obtained and bilateral parotid, submandibular and sublingual glands segmented manually. The volumes, T1WI, T2WI SIs and ADCs of three major salivary glands were measured. And the relative SIs (RSIs) were assessed by comparing with cerebrospinal fluid. RESULTS: The volumes, T1WI and T2WI RSIs of parotid glands were significantly correlated with age (P < 0.01) and showed similar significant tendencies of growing from minority to middle age and decreasing slightly after old age. Significant differences existed between minority and middle age groups in all above-mentioned parameters (P < 0.01). The ADCs of submandibular glands also showed that there was a significant difference between the minority and middle age groups (P < 0.01). CONCLUSION: MRI may be applied for studying the morphological and functional changes of normal major salivary glands with aging. Thus clinical rationales can be provided for assessing the subjects of any age during salivary gland imaging and aging-related researches.