Alterations of the amplitude of low-frequency fluctuation induced by repetitive transcranial magnetic stimulation combined with antidepressants treatment for major depressive disorder.

We investigated the neuroimaging changes and clinical efficacy of repetitive transcranial magnetic stimulation (rTMS) combined with antidepressants in major depressive disorder (MDD) patients. We scanned 35 patients with MDD and 27 healthy controls (HC) with resting-state functional magnetic resonance imaging (fMRI) before and after treatment. We analyzed amplitude of low-frequency fluctuation (ALFF) and the correlation with clinical variables. The rate of significant efficacy after treatment was higher in the combination treatment group than in the antidepressant group, although not statistically significant. At baseline, ALFF increased in the left middle temporal, brain stem, and left cerebellum and decreased in the right anterior cingulate (ACC), right orbital frontal cortex (OFC), and right caudate. ALFF increased in the left fusiform and decreased in the right lingual gyrus, left middle occipital gyrus, and left superior occipital gyrus after antidepressants. ALFF increased in the right ACC, right OFC, and right rectus after combination treatment. ALFF changes in the right ACC/OFC were negatively correlated with HAMD changes. After treatment, abnormal activity in some brain regions normalized, but these regions differed between the two treatment groups. rTMS combined with antidepressants therapy may improve MDD symptoms by improving neuronal activity levels in the right ACC and right OFC.

Impaired Sensitivity to Thyroid Hormone and Risk of Carotid Plaque Development in a Chinese Health Check-Up Population: A Large Sample Cross-Sectional Study.

Objective: Previous research on the correlation between thyroid function and carotid plaque has revealed conflicting results, possibly attributable to the sensitivity of thyroid hormone indices. In this study, we aimed to analyze the association between thyroid hormone sensitivity indices and the risk of carotid plaque development in a Chinese health check-up population. Methods: A total of 19,388 health check-up subjects were included in this study (mean age: 50.78±10.17 years). Central sensitivity to thyroid hormone was evaluated using the thyroid feedback quantile-based index (TFQI), the Chinese-referenced parametric TFQI (PTFQI), the TSH index (TSHI), and the thyrotropin thyroxine resistance index (TT4RI), while peripheral sensitivity to thyroid hormone was assessed by free triiodothyronine/free thyroxine (FT3/FT4) ratio. Multivariable logistic regression analyses were performed to detect the association between thyroid hormone sensitivity indices and carotid plaque risk, and subgroup analysis was also conducted to explore this association stratified by sex, age, obesity, and the status of smoking, drinking, diabetes, hypertension and dyslipidemia. Results: Among the 19,388 participants, 3753 (19.4%) had carotid plaque. In multivariable adjustment models, the risk of carotid plaque was positively associated with TSHI (odds ratio [OR]: 1.23; 95% confidence interval [CI]: 1.18~1.28), TT4RI (OR: 1.28; 95% CI: 1.23~1.33), TFQI (OR: 1.06; 95% CI: 1.02~1.10), and PTFQI (OR: 1.11; 95% CI: 1.07~1.16), respectively. Conversely, the risk of carotid plaque was negatively correlated with FT3/FT4 (OR: 0.94; 95% CI: 0.90~0.98). In stratified analyses, all thyroid hormone sensitivity indices significantly increased the risk of carotid plaque especially in females, subjects<65 years, non-obese individuals, and those without current smoking, drinking, diabetes, hypertension and dyslipidemia. Conclusion: In Chinese health check-up populations, a considerable connection between reduced sensitivity to thyroid hormones and carotid plaque has been observed, especially in females, those younger than 65 years, non-obese individuals, and those without any current smoking, drinking, diabetes, hypertension, or dyslipidemia.

Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study.

PURPOSE: To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment. MATERIAL AND METHODS: A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis. RESULTS: The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p < 0.0001). CONCLUSIONS: This radiomics model resulted as a powerful, non-invasive auxiliary tool for accurately predicting prostate cancer aggressiveness. The radiomics model could reduce unnecessary biopsies and help improve the diagnostic performance of radiologists' PI-RADS. Yet, prospective studies are still needed to validate the radiomics models further. CRITICAL RELEVANCE STATEMENT: The radiomics model with MRI may help to accurately screen out clinically significant prostate cancer, thereby assisting physicians in making individualized treatment plans. KEY POINTS: • The diagnostic performance of the radiomics model using the Random Forest algorithm is comparable to the Prostate Imaging Reporting and Data System (PI-RADS) obtained by radiologists. • The performance of the adjusted Prostate Imaging Reporting and Data System (PI-RADS) was improved, which implied that the radiomics model could be a potential radiological assessment tool. • The radiomics model lowered the percentage of equivocal cases. Moreover, the Rad-scores can be used to characterize prostate cancer aggressiveness.

Low Dose Iodinated Contrast Material and Radiation for Virtual Monochromatic Imaging in Craniocervical Dual-Layer Spectral Detector Computed Tomography Angiography: A Prospective and Randomized Study.

RATIONALE AND OBJECTIVES: To investigate the feasibility of virtual monochromatic imaging (VMI) of dual-layer spectral detector computed tomography (SDCT) to reduce iodinated contrast material (CM) and radiation dose in craniocervical computed tomography angiography (CTA). MATERIALS AND METHODS: A total of 280 consecutively selected patients performed craniocervical CTA with SDCT were prospectively selected and randomly divided into four groups (A, DoseRight index (DRI) 31, iopromide 370mgI/mL, volume 0.8 mL/kg; B, DRI 26, iopromide 370mgI/mL, volume 0.4 mL/kg; C, DRI 26, ioversol 320mgI/mL, volume 0.4 mL/kg; D, DRI 26, iohexol 300mgI/mL, volume 0.4 mL/kg). 50-70 kiloelectron volts (keV) VMIs in group B were reconstructed and compared to group A to select the optimal keV. Then, the optimal keV in groups B, C and D was reconstructed and compared. Objective image quality, including vascular attenuation, image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), was evaluated. Subjective image quality was assessed using a 5-point Likert scale. In addition, the effective dose (ED), iodine load and iodine delivery rate (IDR) were compared between groups A and D. RESULTS: 55 keV VMI was the optimal VMI in group B. The objective and subjective image quality of 55 keV VMI in group B were equal to or better than those of the CI in group A. The SNR, CNR and subjective image quality in group D were similar to those in group B (P > 0.05). The ED, iodine load and IDR of group D were reduced by 44%, 59% and 19%, respectively, when compared to those of group A. CONCLUSION: Low dose iodinated CM and radiation for 55 keV VMI in craniocervical CTA using SDCT could still provide equivalent or better image quality than the conventional scanning protocol.

Persistence of post-traumatic stress disorder in Chinese Shidu parents is associated with combined gray and white matter abnormalities.

Post-traumatic stress disorder (PTSD) is one of the most common mental health disorders among Shidu parents. Identification of gray and white matter differences between persistence of PTSD (P-PTSD) and remission of PTSD (R-PTSD) is crucial to determine their prognosis. A total of 37 Shidu parents with PTSD were followed for five years. Surface-based morphometry and diffusion tensor imaging were carried out to analyze the differences in gray and white matter between P-PTSD and R-PTSD. Finally, 30 patients with PTSD were enrolled, including 12 with P-PTSD and 18 with R-PTSD. Compared with patients with R-PTSD, patients with P-PTSD exhibited lower fractional anisotropy (FA) in Cluster 1 (including body of the corpus callosum, superior longitudinal fasciculus, corticospinal tract) and Cluster 2 (including inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, splenium of the corpus callosum) in the left cerebral hemisphere and higher cortical thickness in the right lateral occipital cortex (LOC). In patients with P-PTSD, FA values of Cluster 2 were negatively correlated with cortical thickness of the right LOC. These results suggest that among Shidu parents, differences were observed in gray and white matter between P-PTSD and R-PTSD. Moreover, some certain gray and white matter abnormalities were often present simultaneously in P-PTSD.

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.

Native T1 mapping for non-invasive quantitative evaluation of renal function and renal fibrosis in patients with chronic kidney disease.

Background: To investigate the role of native T1 mapping in the non-invasive quantitative assessment of renal function and renal fibrosis (RF) in chronic kidney disease (CKD) patients. Methods: A prospective analysis of 71 consecutive patients [no RF (0%): 9 cases; mild RF (<25%): 36 cases; moderate RF (25-50%): 17 cases; severe RF (>50%): 9 cases] who were clinically diagnosed with CKD that was pathologically confirmed and who underwent magnetic resonance imaging (MRI) examination between October 2021 and September 2022 was performed. T1-C (mean cortical T1 value), T1-M (mean medullary T1 value), ΔT1 (mean corticomedullary difference) and T1% (mean corticomedullary ratio) values were compared. Correlations between T1 parameters and clinical and histopathological values were analyzed. Regression analysis was performed to determine independent predictors of RF. The areas under the receiver operating characteristic curve (AUC) were calculated to assess the diagnostic value of RF. Results: The T1-C, ΔT1 and T1% values (P<0.05) were significantly different in the CKD group, but T1-M was not (P>0.05). The ΔT1 and T1% values showed significant differences in pairwise comparisons among CKD subgroups (P<0.05) except for CKD 2 and 3. ΔT1 and T1% were moderately correlated with the estimated glomerular filtration rate (ΔT1: rs=-0.561; T1%: r=-0.602), serum creatinine (ΔT1: rs=0.591; T1%: rs=0.563), blood urea nitrogen (ΔT1: rs=0.433; T1%: rs=0.435) and histopathological score (ΔT1: rs=0.630; T1%: rs=0.658). ΔT1 and T1%, but not T1-C, were independent predictors of RF (P<0.05). ΔT1 and T1% were set as -410.07 ms and 0.8222 with great specificity [ΔT1: 91.7% (77.5-98.2%); T1%: 97.2% (85.5-99.9%)] to identify mild RF and moderate-severe RF. The optimal cutoff values for differentiating severe RF from mild-moderate RF were -343.81 ms (ΔT1) and 0.8359 (T1%) with high sensitivity [both 100% (66.4-100%)] and specificity [ΔT1: 90.6% (79.3-96.9%); T1%: 94.3% (84.3-98.8%)]. Conclusions: ΔT1 and T1% overwhelm T1-C for assessment of renal function and RF in CKD patients. ΔT1 and T1% identify patients with <25% and >50% fibrosis, which can guide clinical decision-making and help to avoid biopsy-related bleeding.

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.

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.

A nomogram based on LI-RADS features, clinical indicators and quantitative contrast-enhanced MRI parameters for predicting glypican-3 expression in hepatocellular carcinoma.

Purpose: Noninvasively assessing the tumor biology and microenvironment before treatment is greatly important, and glypican-3 (GPC-3) is a new-generation immunotherapy target for hepatocellular carcinoma (HCC). This study investigated the application value of a nomogram based on LI-RADS features, quantitative contrast-enhanced MRI parameters and clinical indicators in the noninvasive preoperative prediction of GPC-3 expression in HCC. Methods and materials: We retrospectively reviewed 127 patients with pathologically confirmed solitary HCC who underwent Gd-EOB-DTPA MRI examinations and related laboratory tests. Quantitative contrast-enhanced MRI parameters and clinical indicators were collected by an abdominal radiologist, and LI-RADS features were independently assessed and recorded by three trained intermediate- and senior-level radiologists. The pathological and immunohistochemical results of HCC were determined by two senior pathologists. All patients were divided into a training cohort (88 cases) and validation cohort (39 cases). Univariate analysis and multivariate logistic regression were performed to identify independent predictors of GPC-3 expression in HCC, and a nomogram model was established in the training cohort. The performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC) and the calibration curve in the training cohort and validation cohort, respectively. Results: Blood products in mass, nodule-in-nodule architecture, mosaic architecture, contrast enhancement ratio (CER), transition phase lesion-liver parenchyma signal ratio (TP-LNR), and serum ferritin (Fer) were independent predictors of GPC-3 expression, with odds ratios (ORs) of 5.437, 10.682, 5.477, 11.788, 0.028, and 1.005, respectively. Nomogram based on LI-RADS features (blood products in mass, nodule-in-nodule architecture and mosaic architecture), quantitative contrast-enhanced MRI parameters (CER and TP-LNR) and clinical indicators (Fer) for predicting GPC-3 expression in HCC was established successfully. The nomogram showed good discrimination (AUC of 0.925 in the training cohort and 0.908 in the validation cohort) and favorable calibration. The diagnostic sensitivity and specificity were 76.9% and 92.3% in the training cohort, 76.8% and 93.8% in the validation cohort respectively. Conclusion: The nomogram constructed from LI-RADS features, quantitative contrast-enhanced MRI parameters and clinical indicators has high application value, can accurately predict GPC-3 expression in HCC and may help noninvasively identify potential patients for GPC-3 immunotherapy.

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.

Holographically Activatable Nanoprobe via Glutathione/Albumin-Mediated Exponential Signal Amplification for High-Contrast Tumor Imaging.

Glutathione (GSH)-activatable probes hold great promise for in vivo cancer imaging, but are restricted by their dependence on non-selective intracellular GSH enrichment and uncontrollable background noise. Here, a holographically activatable nanoprobe caging manganese tetraoxide is shown for tumor-selective contrast enhancement in magnetic resonance imaging (MRI) through cooperative GSH/albumin-mediated cascade signal amplification in tumors and rapid elimination in normal tissues. Once targeting tumors, the endocytosed nanoprobe effectively senses the lysosomal microenvironment to undergo instantaneous decomposition into Mn2+ with threshold GSH concentration of ≈ 0.12 mm for brightening MRI signals, thus achieving high contrast tumor imaging and flexible monitoring of GSH-relevant cisplatin resistance during chemotherapy. Upon efficient up-regulation of extracellular GSH in tumor via exogenous injection, the relaxivity-silent interstitial nanoprobe remarkably evolves into Mn2+ that are further captured/retained and re-activated into ultrahigh-relaxivity-capable complex by stromal albumin in the tumor, and simultaneously allows the renal clearance of off-targeted nanoprobe in the form of Mn2+ via lymphatic vessels for suppressing background noise to distinguish tiny liver metastasis. These findings demonstrate the concept of holographic tumor activation via both tumor GSH/albumin-mediated cascade signal amplification and simultaneous background suppression for precise tumor malignancy detection, surveillance, and surgical guidance.

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.

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.

Self-activated arsenic manganite nanohybrids for visible and synergistic thermo/immuno-arsenotherapy.

Arsenotherapy has been clinically exploited to treat a few types of solid tumors despite of acute promyelocytic leukemia using arsenic trioxide (ATO), however, its efficacy is hampered by inadequate delivery of ATO into solid tumors owing to the absence of efficient and biodegradable vehicles. Precise spatiotemporal control of subcellular ATO delivery for potent arsenotherapy thus remains challengeable. Herein, we report the self-activated arsenic manganite nanohybrids for high-contrast magnetic resonance imaging (MRI) and arsenotherapeutic synergy on triple-negative breast cancer (TNBC). The nanohybrids, composed of arsenic­manganese-co-biomineralized nanoparticles inside albumin nanocages (As/Mn-NHs), switch signal-silent background to high proton relaxivity, and simultaneously afford remarkable subcellular ATO level in acidic and glutathione environments, together with reduced ATO resistance against tumor cells. Then, the nanohybrids enable in vivo high-contrast T1-weighted MRI signals in various tumor models for delineating tumor boundary, and simultaneously yield efficient arsenotherapeutic efficacy through multiple apoptotic pathways for potently suppressing subcutaneous and orthotopic breast models. As/Mn-NHs exhibited the maximum tumor-to-normal tissue (T/N) contrast ratio of 205% and tumor growth inhibition rate of 88% at subcutaneous 4T1 tumors. These nanohybrids further yield preferable synergistic antitumor efficacy against both primary and metastatic breast tumors upon combination with concurrent thermotherapy. More importantly, As/Mn-NHs considerably induce immunogenic cell death (ICD) effect to activate the immunogenically "cold" tumor microenvironment into "hot" one, thus synergizing with immune checkpoint blockade to yield the strongest tumor inhibition and negligible metastatic foci in the lung. Our study offers the insight into clinically potential arsenotherapeutic nanomedicine for potent therapy against solid tumors.

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.

Calcium phosphate-based nanomedicine mediated CRISPR/Cas9 delivery for prostate cancer therapy.

Introduction: Erythropoietin producing hepatocyte receptor A2 (EphA2) is widely presented in the tumor cells, closely related to tumor cell migration, not cell apoptosis and proliferation. Based on its high expression in castration-resistant prostate cancer (CRPC), we herein develop a CRISPR-Cas9-based genome-editing nanomedicine to target erythropoietin producing hepatocyte receptor A2 for the treatment of castration-resistant prostate cancer. Methods: To this end, TAT was designed to stabilize the distribution of calcium, and then bound to ribonucleoprotein (RNP) to form nanoparticles RNP@CaP-TAT. Results: This nanoparticle has a simple synthesis process with good biocompatible, to achieve the knockout of tumor cells (PC-3) targeting erythropoietin producing hepatocyte receptor A2 gene and to effectively suppress the migration of tumor cells. Discussion: This delivery genome editing system provides a promising gene therapy strategy for the treatment of castration-resistant prostate cancer, showing good potential against castration-resistant prostate cancer tumor metastasis. In addition, it can be extended to other types of cancer with highly heterogeneous gene expression.

Comparing the diagnostic value of 18F-FDG PET/CT scan and bone marrow biopsy in newly diagnosed pediatric neuroblastoma and ganglioneuroblastoma.

Objective: This study aims to compare the diagnostic value of 18F-fluorodeoxyglucose (18-FDG) positron emission tomography (PET)/computed tomography (CT) (18F-FDG PET/CT) scan and bone marrow biopsy (BMB) for evaluating bone marrow infiltration (BMI) in newly diagnosed pediatric neuroblastoma (NB) and ganglioneuroblastoma (GNB). Methods: We retrospectively reviewed 51 patients with newly diagnosed NB and GNB between June 1, 2019 and May 31, 2022. Each patient had undergone 18F-FDG PET/CT and BMB within 1 week and received no treatment. Clinical data were collected and statistically analyzed, including age, sex, pathologic type, and laboratory parameters. 18F-FDG PET/CT and BMB revealed the result of bone lesions. Results: A concordance analysis showed that, in this study population, 18F-FDG PET/CT and BMB were in moderate agreement (Cohen's Kappa = 0.444; p = 0.001), with an absolute agreement consistency of 72.5% (37 of 51). The analysis of the receiver operating characteristic (ROC) curve determined that the areas under the ROC curve (AUCs) of SUVBM and SUV/HE-SUVmax were 0.971 (95% CI: 0.911-1.000; p < 0.001) and 0.917 (95% CI: 0.715-1.000; p < 0.001) to predict bone-bone marrow involvement (BMI), respectively. Conclusion: 18F-FDG PET/CT detects BMI with good diagnostic accuracy and can reduce unnecessary invasive inspections in newly diagnosed pediatric NB and GNB, especially patterns C and D. The analysis of the semi-quantitative uptake of 18F-FDG, including SUVBM and SUVBM/HE-SUVmax, enables an effective differentiation between patterns A and B.

The Role of PSA Density among PI-RADS v2.1 Categories to Avoid an Unnecessary Transition Zone Biopsy in Patients with PSA 4-20 ng/mL.

OBJECTIVE: To evaluate the role of prostate-specific antigen density (PSAD) in different Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) categories to avoid an unnecessary biopsy in transition zone (TZ) patients with PSA ranging from 4 to 20 ng/mL. MATERIALS AND METHODS: In this retrospective and single-center study, 333 biopsy-naïve patients with TZ lesions who underwent biparametric magnetic resonance imaging (bp-MRI) were analyzed from January 2016 to March 2020. Multivariate logistic regression analyses were performed to determine independent predictors of clinically significant prostate cancer (cs-PCa). The receiver operating characteristic (ROC) curve was used to compare diagnostic performance. RESULTS: PI-RADS v2.1 and PSAD were the independent predictors for TZ cs-PCa in patients with PSA 4-20 ng/mL. 0.9% (2/213), 10.0% (7/70), and 48.0% (24/50) of PI-RADS v2.1 score 1-2, 3, and 4-5 had TZ cs-PCa. However, for patients with PI-RADS v2.1 score 1-2, there were no obvious changes in the detection of TZ cs-PCa (0.8% (1/129), 1.3% (1/75), and 0.0% (0/9)) combining with different PSAD stratification (PSAD < 0.15, 0.15-0.29, and ≥0.30 ng/mL/mL). For patients with PI-RADS v2.1 score ≥ 3, the TZ cs-PCa detection rate significantly varied according to different PSAD stratification. A PI-RADS v2.1 score 3 and PSAD < 0.15 and 0.15-0.29 ng/mL/mL had 8.6% (3/35) and 3.7% (1/27) of TZ cs-PCa, while a PI-RADS v2.1 score 3 and PSAD ≥ 0.30 ng/mL/mL had a higher TZ cs-PCa detection rate (37.5% (3/8)). A PI-RADS v2.1 score 4-5 and PSAD <0.15 ng/mL/mL had no cs-PCa (0.0% (0/9)). In contrast, a PI-RADS v2.1 score 4-5 and PSAD 0.15-0.29 and ≥0.30 ng/mL/mL had the highest cs-PCa detection rate (50.0% (10/20), 66.7% (14/21)). It showed the highest AUC in the combination of PI-RADS v2.1 and PSAD (0.910), which was significantly higher than PI-RADS v2.1 (0.889, P = 0.039) or PSAD (0.803, P < 0.001). CONCLUSIONS: For TZ patients with PSA 4-20 ng/mL, PI-RADS v2.1 score ≤ 2 can avoid an unnecessary biopsy regardless of PSAD. PI-RADS v2.1 score ≥ 3 may avoid an unnecessary biopsy after combining with PSAD. PI-RADS v2.1 combined with PSAD could significantly improve diagnostic performance.

A nomogram based on PI-RADS v2.1 and clinical indicators for predicting clinically significant prostate cancer in the transition zone.

BACKGROUND: This study attempted to develop a nomogram for predicting clinically significant prostate cancer (cs-PCa) in the transition zone (TZ) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators. METHODS: We retrospectively reviewed 383 patients with suspicious prostate lesions in the TZ as a training cohort and 128 patients as the validation cohort from January 2015 to March 2020. Multivariable logistic regression analysis was performed to determine independent predictors for building a nomogram, and the performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve and decision curve. RESULTS: The PI-RADS v2.1 score and prostate-specific antigen density (PSAD) were independent predictors of TZ cs-PCa. The prediction model had a significantly higher AUC (0.936) than the individual predictors (0.914 for PI-RADS v2.1 score, P=0.045, 0.842 for PSAD, P<0.001). The nomogram showed good discrimination (AUC of 0.936 in the training cohort and 0.963 in the validation cohort) and favorable calibration. When the PI-RADS v2.1 score was combined with PSAD, the diagnostic sensitivity and specificity were 80.7% and 93.8%, respectively, which were better than those of the PI-RADS v2.1 score (sensitivity, 74.2%; specificity, 92.5%) and PSAD (sensitivity, 66.1%; specificity, 88.2%). CONCLUSIONS: The newly constructed nomogram exhibits satisfactory predictive accuracy and consistency for TZ cs-PCa. PI-RADS v2.1 based on bp-MRI is a strong predictor in the detection of TZ cs-PCa. Adding PSAD to PI-RADS v2.1 could improve its diagnostic performance, thereby avoiding unnecessary biopsies.