Quality assessment of clinical practice guidelines for adult obstructive sleep apnea: A systematic review.

OBJECTIVE: Clinical Practice Guidelines (CPGs) are crucial in standardizing the management of obstructive sleep apnea (OSA) in adults. However, there has been insufficient evaluation of the overall quality of CPGs for adult OSA. This review aimed to comprehensively assess the overall quality of CPGs in the field of adult OSA. METHODS: A systematic search was conducted on various literature databases, guideline-related databases, and academic websites from January 2013 to December 2023 to select CPGs relevant to adult OSA. The methodological and reporting quality of the eligible CPGs were thoroughly appraised by three reviewers using the AGREE II instrument and RIGHT checklist, respectively. RESULTS: This review included 44 CPGs, consisting of 42 CPGs in English and 2 CPGs in Chinese. The assessment of methodological quality revealed that four domains attained an average standardized score above 60%. Among the domains, "clarity of presentation" received the highest standardized score of 85.10%, while the lowest standardized score was observed in the "rigor of development" domain with the value of 56.77%. The evaluation of reporting quality indicated an overall reporting rate of 51.30% for the eligible CPGs, with only three domains achieving an average reporting rate higher than 50%. The domain with the highest reporting rate was "basic information" at 60.61%, while the domain with the lowest reporting rate was "review and quality assurance" at 15.91%. Furthermore, a significantly positive correlation was found between the AGREE II standardized scores and the RIGHT reporting rates (r = 0.808, P < 0.001). CONCLUSIONS: The overall quality of the currently available guidelines for adult OSA demonstrated considerable variability. Researchers should prioritize the utilization of evidence-based methods and adhere to the items listed in the RIGHT checklist when developing CPGs to enhance efficient clinical decision-making and promote the translation of evidence into practice.

Multiparametric MRI-based VI-RADS: can it predict 1- to 5-year recurrence of bladder cancer?

OBJECTIVES: To evaluate whether Vesical Imaging-Reporting And Data System (VI-RADS) scores based on multiparametric MRI (mp-MRI) can predict bladder cancer (BCa) recurrence. METHODS: In this retrospective study, 284 patients with pathologically confirmed bladder neoplasms from November 2011 to October 2020 were included. Two radiologists blindly and independently scored mp-MRI scans according to VI-RADS. Scoring inconsistency was resolved in consensus. The latest follow-up was completed in December 2022. Pearson's correlation analyses, independent-sample t-tests, and receiver operating characteristic analyses were performed to assess the efficacy of VI-RADS score for the 1- to 5-year recurrence prognostication. RESULTS: Based on the latest follow-up, 37 (of 284, 13.0%), 69 (of 284, 24.3%), 70 (of 234, 29.9%), 72 (of 190, 37.9%), and 63 (of 135, 46.7%) patients had cancer recurrence at 1- to 5-year follow-up, respectively. VI-RADS scores showed significantly intergroup differences between recurrent and nonrecurrent cases during 1- to 4-year surveillance (p < 0.05). The recurrence-free survival was significantly higher in patients with VI-RADS scores of 1 or 2, compared to those with scores of 3, 4, or 5 (p < 0.05). Areas under the receiver operating characteristic curves for 1- to 5-year recurrence prediction were 0.744, 0.686, 0.656, 0.595, and 0.536, respectively. VI-RADS score of 3 or more was the threshold for 1-year recurrence assessment, and VI-RADS more than 3 was the cutoff for 2-year recurrence prediction. CONCLUSION: VI-RADS score has potential in preoperative prognostication of BCa recurrence, but its predictive power decreases over time. CLINICAL RELEVANCE STATEMENT: VI-RADS has potential in bladder cancer recurrence assessment, but its prognostic value decreases over time. Patients with VI-RADS ≥ 3 may be more likely to recur in 1 or 2 years postoperatively, thus should be performed with intensive surveillances. KEY POINTS: • VI-RADS scores had significant differences in 1- to 4-year recurrent and nonrecurrent patient groups. • Patients with VI-RADS scores of ≤ 2 showed more favorable recurrence-free survival outcomes. • The prognostic value of VI-RADS score decreased over time for bladder cancer recurrence prediction.

Radiomics-based T-staging of hollow organ cancers.

Cancer growing in hollow organs has become a serious threat to human health. The accurate T-staging of hollow organ cancers is a major concern in the clinic. With the rapid development of medical imaging technologies, radiomics has become a reliable tool of T-staging. Due to similar growth characteristics of hollow organ cancers, radiomics studies of these cancers can be used as a common reference. In radiomics, feature-based and deep learning-based methods are two critical research focuses. Therefore, we review feature-based and deep learning-based T-staging methods in this paper. In conclusion, existing radiomics studies may underestimate the hollow organ wall during segmentation and the depth of invasion in staging. It is expected that this survey could provide promising directions for following research in this realm.

[Research Progress in Preoperative Evaluation of Lymph Node Metastasis of Bladder Cancer].

Bladder cancer is a common malignant tumor of the urinary system.The prognosis of patients with positive lymph nodes is worse than that of patients with negative lymph nodes.An accurate assessment of preoperative lymph node statushelps to make treatmentdecisions,such as the extent of pelvic lymphadenectomy and the use of neoadjuvant chemotherapy.Imaging examination and pathological examination are the primary methods used to assess the lymph node status of bladder cancer patients before surgery.However,these methods have low sensitivity and may lead to inaccuate staging of patients.We reviewed the research progress and made an outlook on the application of clinical diagnosis,imaging techniques,radiomics,and genomics in the preoperative evaluation of lymph node metastasis in bladder cancer patients at different stages.

Peer pressure and adolescent mobile social media addiction: Moderation analysis of self-esteem and self-concept clarity.

Background: Social media addiction has increasingly been a critical social problem. We explored the association between peer pressure on mobile phone use and adolescent mobile social media addiction and tested whether self-esteem and self-concept clarity could buffer the effect of peer pressure. Methods: 830 adolescents (M age = 14.480, SDage = 1.789) participated in our anonymous cross-sectional questionnaire study. Results: The results showed that peer pressure significantly predicted adolescent mobile social media addiction. Self-esteem moderated the effect of peer pressure on mobile social media addiction in that peer pressure had a weaker effect for adolescents with higher self-esteem. Self-concept clarity moderated the effect of peer pressure on mobile social media addiction in that peer pressure had a weaker effect for adolescents with higher self-esteem. The two moderators also interact in that the moderation of self-esteem was stronger for adolescents with higher self-concept clarity and the moderation of self-concept clarity for adolescents with higher self-esteem. Conclusion: The results highlight the critical role of self-esteem and self-concept clarity in buffering the impact of peer pressure on mobile social media addiction. The findings promote a better understanding of how to buffer the undesirable effect of peer pressure and reduce the risk of mobile social media addiction among adolescents.

The Additional Value of Tri-parametric MRI in Identifying Muscle-invasive Status in Bladder Cancer.

RATIONALE AND OBJECTIVES: Identification of muscle-invasive status (MIS) of bladder cancer (BCa) is critical for treatment decisions. The Vesical Imaging-Reporting and Data System (VI-RADS) has been widely used in preoperatively predicting MIS using tri-parametric MR imaging including T2-weighted (T2W), diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences. While the diagnostic values of radiomics features from bi-parametric MRI such as T2W + DW to identification of MIS have been reported, whether the tri-parametric MRI could provide additional diagnostic value to the radiomics prediction task, and how to integrate DCE features into the radiomics model, which is the objectives of this study, remain unknown. MATERIALS AND METHODS: Patients with postoperatively confirmed BCa lesions (150 in non-muscle-invasive BCa and 56 in muscle-invasive BCa groups) were retrospectively included. Their T2W, DW with apparent diffusion coefficient (ADC) maps, and DCE sequences were acquired using a 3.0T MR system. Regions of interest were manually depicted and VI-RADS scores were assessed by three radiologists. Three predictive models were developed by the radiomics features extracted from sequence combinations of T2W + DW (Model one), T2W + DCE (Model two), and T2W + DW + DCE (Model three), respectively, using the least absolute shrinkage and selection operator. The performance of each model was quantitatively assessed on both the training (n = 165) and testing (n = 41) cohorts. Then a 10 times five-fold cross validation was conducted to assess the overall performance. RESULTS: Three models achieved area under the curve of 0.888, 0.869, and 0.901 in the cross validation, respectively. The tri-parametric model performed significantly superior than the two bi-parametric models and VI-RADS. The analysis of feature coefficients derived from least absolute shrinkage and selection operator algorithm showed features from the tri-parametric MRI are effective in MIS discrimination. CONCLUSION: The tri-parametric MRI provides additional value to the radiomics-based identification of MIS.

Unique and interactive effects of parental neglect, school connectedness, and trait self-control on mobile short-form video dependence among Chinese left-behind adolescents.

BACKGROUND: Mobile short-form video is becoming increasingly popular among Chinese adolescents. Mobile short-form video dependence has become a pressing issue in Chinese adolescents, especially in left-behind adolescents. Previous studies, however, have focused on general mobile phone dependence and neglected specific types of mobile phone dependence. Few studies have explored the environmental and individual predictors of mobile short-form video dependence. OBJECTIVE: Based on theoretical and empirical evidence, the present study examined the unique and interactive effects of parental neglect, school connectedness, and trait self-control on mobile short-form video dependence among Chinese left-behind adolescents. METHODS: A total of 618 left-behind adolescents between 11 and 15 years of age completed the anonymous self-report survey. The PROCESS macro for SPSS was applied for data analysis. RESULTS: Parental neglect was positively associated with mobile short-form video dependence, whereas school connectedness and trait self-control were negatively associated with mobile short-form video dependence in left-behind adolescents. Examination of the two-way interactions indicated that school connectedness and trait self-control could buffer the association between parental neglect and left-behind adolescents' mobile short-form video dependence. However, self-control could not moderate the association between school connectedness and mobile short-form video dependence. In addition, the three-way interaction of parental neglect, school connectedness, and trait self-control showed a significant effect on mobile short-form video dependence. The moderating role of school connectedness was stronger for left-behind adolescents with low trait self-control than for those with high trait self-control, and the moderating role of trait self-control was stronger for left-behind adolescents with low school connectedness than for those with high school connectedness. CONCLUSIONS: The findings contribute significantly to revealing the complex mechanisms of mobile short-form video dependence and providing comprehensive and specific practical suggestions for the prevention and intervention of mobile short-form video dependence among left-behind adolescents.

The Mediating Role of Loneliness and the Moderating Role of Gender between Peer Phubbing and Adolescent Mobile Social Media Addiction.

Mobile social media addiction has been a pressing issue in adolescents. The present study examined the mediation of loneliness between peer phubbing and mobile social media addiction among Chinese adolescents and tested whether gender could moderate the direct and indirect effects of peer phubbing. A total of 830 adolescents between 11 and 18 years of age (Mage = 14.480, SDage = 1.789) completed an anonymous self-report survey. The results showed that peer phubbing was positively associated with mobile social media addiction. Loneliness partially mediated peer phubbing and adolescent mobile social media addiction. There were significant gender differences in the direct and indirect effects of peer phubbing on mobile social media addiction. The direct effect of peer phubbing and the indirect effect through loneliness were relatively higher in girls than in boys. The results highlight the critical role of loneliness in linking peer phubbing to adolescent mobile social media addiction and the vital role of gender in moderating the direct and indirect impacts of peer phubbing. The findings promote a better understanding of how peer phubbing is associated with adolescent mobile phone addiction and for whom the effect of peer phubbing is potent.

Content and shape attention network for bladder wall and cancer segmentation in MRIs.

Accurate segmentation of the bladder wall and cancer is the key to preoperatively predicting patients' muscle-invasive status. However, the segmentation of bladder wall and cancer have many challenges, including complex background distribution, a variety of bladder shapes, and weak boundary. For these issues, we propose a deep network that consists of a content attention module and a shape attention module. In the content attention module, we employ the attention U-Net to emphasize salient image features that are useful for the segmentation task. The shape attention module uses a spatial transform network to introduce a shape prior, which ensures a closed bladder wall in segmentation results. Experimental results show that the proposed model has a competitive performance compared to the existing methods. The mean DSCs of the 5-fold cross-validation was 0.80 and 0.84 for bladder wall and cancer respectively. From the visualization, our approach can mitigate the issue of complex background and weak boundary in bladder wall and cancer segmentation effectively.

Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer.

PURPOSE: To evaluate a new radiomics strategy that incorporates intratumoral and peritumoral features extracted from lung CT images with ensemble learning for pretreatment prediction of lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). METHODS: A total of 105 patients (47 LUSC and 58 LUAD) with pretherapy CT scans were involved in this retrospective study, and were divided into training (n = 73) and testing (n = 32) cohorts. Seven categories of radiomics features involving 3078 metrics in total were extracted from the intra- and peritumoral regions of each patient's CT data. Student's t tests in combination with three feature selection methods were adopted for optimal features selection. An ensemble classifier was developed using five common machine learning classifiers with these optimal features. The performance was assessed using both training and testing cohorts, and further compared with that of Visual Geometry Group-16 (VGG-16) deep network for this predictive task. RESULTS: The classification models developed using optimal feature subsets determined from intratumoral region and peritumoral region with the ensemble classifier achieved mean area under the curve (AUC) of 0.87, 0.83 in the training cohort and 0.66, 0.60 in the testing cohort, respectively. The model developed by using the optimal feature subset selected from both intra- and peritumoral regions with the ensemble classifier achieved great performance improvement, with AUC of 0.87 and 0.78 in both cohorts, respectively, which are also superior to that of VGG-16 (AUC of 0.68 in the testing cohort). CONCLUSIONS: The proposed new radiomics strategy that extracts image features from the intra- and peritumoral regions with ensemble learning could greatly improve the diagnostic performance for the histological subtype stratification in patients with NSCLC.

PIxel-Level Segmentation of Bladder Tumors on MR Images Using a Random Forest Classifier.

Objectives: Regional bladder wall thickening on noninvasive magnetic resonance (MR) images is an important sign of developing urinary bladder cancer (BCa), and precise segmentation of the tumor mass is an essential step toward noninvasive identification of the pathological stage and grade, which is of critical importance for the clinical management of patients with BCa. Methods: In this paper, we proposed a new method based on the high-throughput pixel-level features and a random forest (RF) classifier for the BCa segmentation. First, regions of interest (ROIs) including tumor and wall ROIs were used in the training set for feature extraction and segmentation model development. Then, candidate regions containing both bladder tumor and its neighboring wall tissue in the testing set were segmented. Results: Experimental results were evaluated on a retrospective database containing 56 patients postoperatively confirmed with BCa from the affiliated hospital. The Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) of the tumor regions were adopted to quantitatively assess the overall performance of this approach. The results showed that the mean DSC was 0.906 (95% confidential interval [CI]: 0.852-0.959), and the mean ASSD was 1.190 mm (95% CI: 1.727-2.449), which were higher than those of the state-of-the-art methods for tumor region separation. Conclusion: The proposed Pixel-level BCa segmentation method can achieve good performance for the accurate segmentation of BCa lesion on MR images.

Distinguishing Different Types of Mobile Phone Addiction: Development and Validation of the Mobile Phone Addiction Type Scale (MPATS) in Adolescents and Young Adults.

Researchers have developed various versions of scales to measure mobile phone addiction. Existing scales, however, focus primarily on the overall level of mobile phone addiction but do not distinguish the potential differences between different types of mobile phone addiction. There is a lack of established scales that can measure different types of mobile phone addiction. The present study aimed to uncover the specific types of mobile phone addiction and develop a Mobile Phone Addiction Type Scale (MPATS) for adolescents and young adults. Adolescents and young adults from two high schools and two universities in Central and South China participated in our study. A total of 108 mobile phone addicts (Mage = 17.60 years, SD = 3.568 years; 60.185% males) were interviewed to uncover the specific types of mobile phone addiction. Data from 876 adolescents and young adults (Mage = 16.750 years, SD = 3.159 years; 49.087% males) were tested for item discrimination and exploratory factor analysis. Data from 854 adolescents and young adults (Mage = 16.750 years, SD = 3.098 years; 50.820% males) were analyzed for construct validity, convergent validity, criterion-related validity, and internal consistency reliability. The 26-item Mobile Phone Addiction Type Scale (MPATS) was developed with four factors named mobile social networking addiction, mobile game addiction, mobile information acquisition addiction, and mobile short-form video addiction. The four-factor, 26-item MPATS revealed good construct validity, convergent validity, criterion-related validity, and internal consistency reliability. The new scale is suitable for measuring different types of mobile phone addiction in adolescents and young adults. Limitations and implications are discussed.

Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19.

Objective: A considerable part of COVID-19 patients were found to be re-positive in the SARS-CoV-2 RT-PCR test after discharge. Early prediction of re-positive COVID-19 cases is of critical importance in determining the isolation period and developing clinical protocols. Materials and Methods: Ninety-one patients discharged from Wanzhou Three Gorges Central Hospital, Chongqing, China, from February 10, 2020 to March 3, 2020 were administered nasopharyngeal swab SARS-CoV-2 tests within 12-14 days, and 50 eligible patients (32 male and 18 female) with completed data were enrolled. Average age was 48 ± 11.5 years. All patients underwent non-enhanced chest CT on admission. A total of 568 radiomics features were extracted from the CT images, and 17 clinical factors were collected based on the medical record. Student's t-test and support vector machine-based recursive feature elimination (SVM-RFE) method were used to determine an optimal subset of features for the discriminative model development. Results: After Student's t-test, 62 radiomics features showed significant inter-group differences (p < 0.05) between the re-positive and negative cases, and none of the clinical features showed significant differences. These significant features were further selected by SVM-RFE algorithm, and a more compact feature subset containing only two radiomics features was finally determined, achieving the best predictive performance with the accuracy and area under the curve of 72.6% and 0.773 for the identification of the re-positive case. Conclusion: The proposed radiomics method has preliminarily shown potential in identifying the re-positive cases among the recovered COVID-19 patients after discharge. More strategies are to be integrated into the current pipeline to improve its precision, and a larger database with multi-clinical enrollment is required to extensively verify its performance.

Study Progress of Noninvasive Imaging and Radiomics for Decoding the Phenotypes and Recurrence Risk of Bladder Cancer.

Urinary bladder cancer (BCa) is a highly prevalent disease among aged males. Precise diagnosis of tumor phenotypes and recurrence risk is of vital importance in the clinical management of BCa. Although imaging modalities such as CT and multiparametric MRI have played an essential role in the noninvasive diagnosis and prognosis of BCa, radiomics has also shown great potential in the precise diagnosis of BCa and preoperative prediction of the recurrence risk. Radiomics-empowered image interpretation can amplify the differences in tumor heterogeneity between different phenotypes, i.e., high-grade vs. low-grade, early-stage vs. advanced-stage, and nonmuscle-invasive vs. muscle-invasive. With a multimodal radiomics strategy, the recurrence risk of BCa can be preoperatively predicted, providing critical information for the clinical decision making. We thus reviewed the rapid progress in the field of medical imaging empowered by the radiomics for decoding the phenotype and recurrence risk of BCa during the past 20 years, summarizing the entire pipeline of the radiomics strategy for the definition of BCa phenotype and recurrence risk including region of interest definition, radiomics feature extraction, tumor phenotype prediction and recurrence risk stratification. We particularly focus on current pitfalls, challenges and opportunities to promote massive clinical applications of radiomics pipeline in the near future.

The invasion depth measurement of bladder cancer using T2-weighted magnetic resonance imaging.

BACKGROUND: Invasion depth is an important index for staging and clinical treatment strategy of bladder cancer (BCa). The aim of this study was to investigate the feasibility of segmenting the BCa region from bladder wall region on MRI, and quantitatively measuring the invasion depth of the tumor mass in bladder lumen for further clinical decision-making. This retrospective study involved 20 eligible patients with postoperatively pathologically confirmed BCa. It was conducted in the following steps: (1) a total of 1159 features were extracted from each voxel of both the certain cancerous and wall tissues with the T2-weighted (T2W) MRI data; (2) the support vector machine (SVM)-based recursive feature elimination (RFE) method was implemented to first select an optimal feature subset, and then develop the classification model for the precise separation of the cancerous regions; (3) after excluding the cancerous region from the bladder wall, the three-dimensional bladder wall thickness (BWT) was calculated using Laplacian method, and the invasion depth of BCa was eventually defined by the subtraction of the mean BWT excluding the cancerous region and the minimum BWT of the cancerous region. RESULTS: The segmented results showed a promising accuracy, with the mean Dice similarity coefficient of 0.921. The "soft boundary" defined by the voxels with the probabilities between 0.1 and 0.9 could demonstrate the overlapped region of cancerous and wall tissues. The invasion depth calculated from proposed segmentation method was compared with that from manual segmentation, with a mean difference of 0.277 mm. CONCLUSION: The proposed strategy could accurately segment the BCa region, and, as the first attempt, realize the quantitative measurement of BCa invasion depth.

Survival-relevant high-risk subregion identification for glioblastoma patients: the MRI-based multiple instance learning approach.

OBJECTIVES: Given the glioblastoma (GBM) heterogeneity, survival-relevant high-risk subregions may exist and facilitate prognosis. The study aimed to identify the high-risk subregions on MRI, and to evaluate their survival stratification performance. METHODS: The gross tumor regions (GTRs) were delineated on the normalized MRI of 104 GBM patients. The signal intensity of voxels from 104 GTRs was pooled as global intensity vector, and K-means clustering was performed on it to find the optimal global clusters. Subregions were generated by assigning back voxels that belonged to each global cluster. Finally, a multiple instance learning (MIL) model was built and validated using radiomics features from each subregion. In this process, subregions predicted as positive would be treated as high-risk subregions, and patients with high-risk subregions inside the GTR would be predicted as having short-term survival. RESULTS: After K-means clustering, three global clusters were fixed and 294 subregions of 104 patients were generated. Then, the subregion-level MIL model was trained and tested by 200 (71 patients) and 94 subregions (33 patients). The accuracy, sensitivity, and specificity for survival stratification were 87.88%, 85.71%, and 89.47%. Furthermore, 41 high-risk subregions were correctly predicted from patients with short-term survival, in which the median overlap rate of non-enhancing component was 60%. CONCLUSION: The stratification performance of high-risk subregions identified by the MIL model was higher than the GTR. The non-enhancing area on MRI was the most important component in high-risk subregions. The MIL approach provides a new perspective on the clinical challenges of glioma with coarse-grained labeling. KEY POINTS: • The performance of high-risk subregions was more promising than the GTR for OS stratification. • The non-enhancing component was the most important in the high-risk subregions.

Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study.

OBJECTIVES: To develop a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer (BCa). METHODS: This retrospective study involved 106 eligible patients from two independent clinical centers. All patients underwent a preoperative 3.0 T MRI scan with T2-weighted image (T2WI) and multi-b-value diffusion-weighted image (DWI) sequences. In total, 1404 radiomics features were extracted from the largest region of the reported tumor locations on the T2WI, DWI, and corresponding apparent diffusion coefficient map (ADC) of each patient. A radiomics signature, namely the Radscore, was then generated using the recursive feature elimination approach and a logistic regression algorithm in a training cohort (n = 64). Its performance was then validated in an independent validation cohort (n = 42). The primary imaging and clinical factors in conjunction with the Radscore were used to determine whether the performance could be further improved. RESULTS: The Radscore, generated by 36 selected radiomics features, demonstrated a favorable ability to predict muscle-invasive BCa status in both the training (AUC 0.880) and validation (AUC 0.813) cohorts. Subsequently, integrating the two independent predictors (including the Radscore and MRI-determined tumor stalk) into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.924 and 0.877 in both cohorts, respectively. CONCLUSIONS: The proposed multisequence MRI-based radiomics signature alone could be an effective tool for quantitative prediction of muscle-invasive status of BCa. Integrating the Radscore with MRI-determined tumor stalk could further improve the discriminatory power, realizing more accurate prediction of nonmuscle-invasive and muscle-invasive BCa. KEY POINTS: • DWI is superior to T2WI sequence in reflecting the heterogeneous differences between NMIBC and MIBC, and multisequence MRI helps in the preoperative prediction of muscle-invasive status of BCa. • Co-occurrence (CM), run-length matrix (RLM), and gray-level size zone matrix (GLSZM) features were the favorable feature categories for the prediction of muscle-invasive status of BCa. • The Radscore (proposed multisequence MRI-based radiomics signature) helps predict preoperatively muscle invasion. Combination with the MRI-determined tumor stalk further improves prediction.

Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach.

INTRODUCTION: Developing a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD. METHODS: MRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A non-linear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated. RESULTS: The area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9913, while this value is only 0.8685 for the algorithm using SFC measures. Spatially, the most discriminative 28 connections distributed in the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN), etc. Notably, a large portion of these connections were associated with the FPN, DMN, and VN. Temporally, the most discriminative connections transited from the cortex to deeper regions. CONCLUSION: The results clearly suggested that DFC is superior to SFC and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD as well as improve accurate diagnosis and early intervention of this disorder.

Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer.

BACKGROUND: Non-invasive discrimination between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes of non-small-cell lung cancer (NSCLC) could be very beneficial to the patients unfit for the invasive diagnostic procedures. The aim of this study was to investigate the feasibility of utilizing the multimodal magnetic resonance imaging (MRI) radiomics and clinical features in classifying NSCLC. This retrospective study involved 148 eligible patients with postoperative pathologically confirmed NSCLC. The study was conducted in three steps: (1) feature extraction was performed using the online freely available package with the multimodal MRI data; (2) feature selection was performed using the Student's t test and support vector machine (SVM)-based recursive feature elimination method with the training cohort (n = 100), and the performance of these selected features was evaluated using both the training and the validation cohorts (n = 48) with a non-linear SVM classifier; (3) a Radscore model was then generated using logistic regression algorithm; (4) Integrating the Radscore with the semantic clinical features, a radiomics-clinical nomogram was developed, and its overall performance was evaluated with both cohorts. RESULTS: Thirteen optimal features achieved favorable discrimination performance with both cohorts, with area under the curve (AUC) of 0.819 and 0.824, respectively. The radiomics-clinical nomogram integrating the Radscore with the independent clinical predictors exhibited more favorable discriminative power, with AUC improved to 0.901 and 0.872 in both cohorts, respectively. The Hosmer-Lemeshow test and decision curve analysis results furtherly showed good predictive precision and clinical usefulness of the nomogram. CONCLUSION: Non-invasive histological subtype stratification of NSCLC can be done favorably using multimodal MRI radiomics features. Integrating the radiomics features with the clinical features could further improve the performance of the histological subtype stratification in patients with NSCLC.