Exploring the Role of Neutrophil-Related Genes in Osteosarcoma via an Integrative Analysis of Single-Cell and Bulk Transcriptome.

BACKGROUND: The involvement of neutrophil-related genes (NRGs) in patients with osteosarcoma (OS) has not been adequately explored. In this study, we aimed to examine the association between NRGs and the prognosis as well as the tumor microenvironment of OS. METHODS: The OS data were obtained from the TARGET-OS and GEO database. Initially, we extracted NRGs by intersecting 538 NRGs from single-cell RNA sequencing (scRNA-seq) data between aneuploid and diploid groups, as well as 161 up-regulated differentially expressed genes (DEGs) from the TARGET-OS datasets. Subsequently, we conducted Least Absolute Shrinkage and Selection Operator (Lasso) analyses to identify the hub genes for constructing the NRG-score and NRG-signature. To assess the prognostic value of the NRG signatures in OS, we performed Kaplan-Meier analysis and generated time-dependent receiver operating characteristic (ROC) curves. Gene enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to ascertain the presence of tumor immune microenvironments (TIMEs) and immunomodulators (IMs). Additionally, the KEGG neutrophil signaling pathway was evaluated using ssGSEA. Subsequently, PCR and IHC were conducted to validate the expression of hub genes and transcription factors (TFs) in K7M2-induced OS mice. RESULTS: FCER1G and C3AR1 have been identified as prognostic biomarkers for overall survival. The findings indicate a significantly improved prognosis for OS patients. The effectiveness and precision of the NRG signature in prognosticating OS patients were validated through survival ROC curves and an external validation dataset. The results clearly demonstrate that patients with elevated NRG scores exhibit decreased levels of immunomodulators, stromal score, immune score, ESTIMATE score, and infiltrating immune cell populations. Furthermore, our findings substantiate the potential role of SPI1 as a transcription factor in the regulation of the two central genes involved in osteosarcoma development. Moreover, our analysis unveiled a significant correlation and activation of the KEGG neutrophil signaling pathway with FCER1G and C3AR1. Notably, PCR and IHC demonstrated a significantly higher expression of C3AR1, FCER1G, and SPI1 in Balb/c mice induced with K7M2. CONCLUSIONS: Our research emphasizes the significant contribution of neutrophils within the TIME of osteosarcoma. The newly developed NRG signature could serve as a good instrument for evaluating the prognosis and therapeutic approach for OS.

Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study.

OBJECTIVES: Exploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model. METHODS: A total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM. RESULTS: The AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively. CONCLUSION: The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics.

Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

Development and Validation of a Biparametric MRI Deep Learning Radiomics Model with Clinical Characteristics for Predicting Perineural Invasion in Patients with Prostate Cancer.

RATIONALE AND OBJECTIVES: Perineural invasion (PNI) is an important prognostic biomarker for prostate cancer (PCa). This study aimed to develop and validate a predictive model integrating biparametric MRI-based deep learning radiomics and clinical characteristics for the non-invasive prediction of PNI in patients with PCa. MATERIALS AND METHODS: In this prospective study, 557 PCa patients who underwent preoperative MRI and radical prostatectomy were recruited and randomly divided into the training and the validation cohorts at a ratio of 7:3. Clinical model for predicting PNI was constructed by univariate and multivariate regression analyses on various clinical indicators, followed by logistic regression. Radiomics and deep learning methods were used to develop different MRI-based radiomics and deep learning models. Subsequently, the clinical, radiomics, and deep learning signatures were combined to develop the integrated deep learning-radiomics-clinical model (DLRC). The performance of the models was assessed by plotting the receiver operating characteristic (ROC) curves and precision-recall (PR) curves, as well as calculating the area under the ROC and PR curves (ROC-AUC and PR-AUC). The calibration curve and decision curve were used to evaluate the model's goodness of fit and clinical benefit. RESULTS: The DLRC model demonstrated the highest performance in both the training and the validation cohorts, with ROC-AUCs of 0.914 and 0.848, respectively, and PR-AUCs of 0.948 and 0.926, respectively. The DLRC model showed good calibration and clinical benefit in both cohorts. CONCLUSION: The DLRC model, which integrated clinical, radiomics, and deep learning signatures, can serve as a robust tool for predicting PNI in patients with PCa, thus aiding in developing effective treatment strategies.

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.

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.

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.