Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ.

Background: Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features. Methods: The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models. Results: In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723. Conclusions: Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.

Development of a polarity-sensitive ratiometric fluorescent probe based on the intramolecular reaction of spiro-oxazolidine and its applications for in situ visualizing the fluctuations of polarity during ER stress.

Polarity is a vital element in endoplasmic reticulum (ER) microenvironment, and its variation is closely related to many physiological and pathological activities of ER, so it is necessary to trace fluctuations of polarity in ER. However, most of fluorescent probes for detecting polarity dependent on the changes of single emission, which could be affected by many factors and cause false signals. Ratiometric fluorescent probe with "built-in calibration" can effectively avoid detection errors. Here, we have designed a ratiometric fluorescent probe HM for monitoring the ER polarity based on the intramolecular reaction of spiro-oxazolidine. It forms ring open/closed isomers driven by polarity to afford ratiometric sensing. Probe HM have manifested its ratiometric responses to polarity in spectroscopic results, which could offer much more precise information for the changes of polarity in living cells with the internal built-in correction. It also showed large emission shift ( 133 nm), high selectivity and photo-stability. In biological imaging, HM could selectively accumulate in ER with high photo-stability. Importantly, HM has ability for in situ tracing the changes of ER polarity with ratiometric behavior during the ER stress process with the stimulation of tunicamycin, dithiothreitol and hypoxia, suggesting that HM is an effective molecule tool for monitoring the variations of ER polarity.

Sparse-view cone-beam computed tomography iterative reconstruction based on new multi-gradient direction total variation.

AIM: The accurate reconstruction of cone-beam computed tomography (CBCT) from sparse projections is one of the most important areas for study. The compressed sensing theory has been widely employed in the sparse reconstruction of CBCT. However, the total variation (TV) approach solely uses information from the i-coordinate, j-coordinate, and k-coordinate gradients to reconstruct the CBCT image. MATERIALS AND METHODS: It is well recognized that the CBCT image can be reconstructed more accurately with more gradient information from different directions. Thus, this study introduces a novel approach, named the new multi-gradient direction total variation minimization method. The method uses gradient information from the ij-coordinate, ik-coordinate, and jk-coordinate directions to reconstruct CBCT images, which incorporates nine different types of gradient information from nine directions. RESULTS: This study assessed the efficacy of the proposed methodology using under-sampled projections from four different experiments, including two digital phantoms, one patient's head dataset, and one physical phantom dataset. The results indicated that the proposed method achieved the lowest RMSE index and the highest SSIM index. Meanwhile, we compared the voxel intensity curves of the reconstructed images to assess the edge structure preservation. Among the various methods compared, the curves generated by the proposed method exhibited the highest level of consistency with the gold standard image curves. CONCLUSION: In summary, the proposed method showed significant potential in enhancing the quality and accuracy of CBCT image reconstruction.

A pH-response-based fluorescent probe for detecting the mitophagy process by tracing changes in colocalization coefficients.

Mitochondria are not only the center of energy metabolism but also involved in regulating cellular activities. Quality and quantity control of mitochondria is therefore essential. Mitophagy is a lysosome-dependent process to clear dysfunctional mitochondria, and abnormal mitophagy can cause metabolic disorders. Therefore, it is necessary to monitor the mitophagy in living cells on a real-time basis. Herein, we developed a pH-responsive fluorescent probe MP for the detection of the mitophagy process using real-time tracing colocalization coefficients. Probe MP showed good pH responses with high selectivity and sensitivity in spectral testing. Probe MP is of positive charge, which is beneficial for accumulating into mitochondrial in living cells. Cells exhibited pH-dependent fluorescence when they were treated with different pH media. Importantly, the changes in the colocalization coefficient between probe MP and Lyso Tracker® Deep Red from 0.4 to 0.8 were achieved in a real-time manner during the mitophagy stimulated by CCCP, starvation and rapamycin. Therefore, combined with the parameter of the colocalization coefficient, probe MP is a potential molecular tool for the real-time tracing of mitophagy to further explore the details of mitophagy.

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.

Background: Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images. Methods: The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentation (Ta) method is used to enhance the meta-training process by augmenting the number of tasks through image rotation and improving the feature-extraction capability. To implement the expansion of the meta-training tasks, various task instances can be sampled during the meta-training stage. Ultimately, the proposed Ta method was introduced to optimize the initialization parameters and enhance the meta-generalization performance of the model. The DaTa-ML model showed promising results by effectively addressing the challenges associated with few-shot DR classification. Results: The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 blindness detection data set was used to evaluate the DaTa-ML model. The results showed that with only 1% of the training data (5-way, 20-shot) and a single update step (training time reduced by 90%), the DaTa-ML model had an accuracy rate of 89.6% on the test data, which is a 1.7% improvement over the transfer-learning method [i.e., residual neural network (ResNet)50 pre-trained on ImageNet], and a 16.8% improvement over scratch-built models (i.e., ResNet50 without pre-trained weights), despite having fewer trainable parameters (the parameters used by the DaTa-ML model are only 0.47% of the ResNet50 parameters). Conclusions: The DaTa-ML model provides a more efficient DR classification solution with little annotated data and has significant advantages over state-of-the-art methods. Thus, it could be used to guide and assist ophthalmologists to determine the severity of DR.

Hierarchical-Instance Contrastive Learning for Minority Detection on Imbalanced Medical Datasets.

Deep learning methods are often hampered by issues such as data imbalance and data-hungry. In medical imaging, malignant or rare diseases are frequently of minority classes in the dataset, featured by diversified distribution. Besides that, insufficient labels and unseen cases also present conundrums for training on the minority classes. To confront the stated problems, we propose a novel Hierarchical-instance Contrastive Learning (HCLe) method for minority detection by only involving data from the majority class in the training stage. To tackle inconsistent intra-class distribution in majority classes, our method introduces two branches, where the first branch employs an auto-encoder network augmented with three constraint functions to effectively extract image-level features, and the second branch designs a novel contrastive learning network by taking into account the consistency of features among hierarchical samples from majority classes. The proposed method is further refined with a diverse mini-batch strategy, enabling the identification of minority classes under multiple conditions. Extensive experiments have been conducted to evaluate the proposed method on three datasets of different diseases and modalities. The experimental results show that the proposed method outperforms the state-of-the-art methods.

Home-based tele-rehabilitation versus hospital-based outpatient rehabilitation for pain and function after initial total knee arthroplasty: A systematic review and meta-analysis.

BACKGROUND: This systematic review and meta-analysis aims to compare the effectiveness of home-based tele-rehabilitation programs with hospital-based rehabilitation programs in improving pain and function at various time points (≤6 weeks, ≤14 weeks, and ≤ 52 weeks) following the initial total knee arthroplasty. METHODS: This study used PRISMA and AMSTAR reporting guidelines. We systematically searched 5 databases (PubMed, Embase, Web of Science, Cochrane Library, and Medline) to identify randomized controlled trials published from January 1, 2019, to January 1, 2023. The primary outcomes were pain, knee injury and osteoarthritis outcome score, and mobility (knee range of motion). RESULTS: We included 9 studies involving 1944 patients. Low-quality evidence showed hospital-based rehabilitation was better than home-based tele-rehabilitation in knee injury and osteoarthritis outcome score (mean difference [MD], -2.62; 95% confidence interval [CI], -4.65 to -0.58; P = .01) at ≤ 14 weeks after total knee arthroplasty. Based on low-quality evidence, home-based tele-rehabilitation was better than hospital-based rehabilitation in knee range of motion (MD, 2.00; 95% CI, 0.60 to 3.40; P = .005). There was no significant difference between hospital-based rehabilitation and home-based tele-rehabilitation in knee pain at ≤ 6 weeks (MD, 0.18; 95% CI, -0.07 to 0.42; P = .16), 14 weeks (MD, 0.12; 95% CI, -0.26 to 0.49; P = .54), and ≤ 52 weeks (MD, 0.16; 95% CI, -0.11 to 0.43; P = .24). CONCLUSION: Home-based tele-rehabilitation and hospital-based rehabilitation programs showed comparable long-term outcomes in pain, mobility, physical function, and patient-reported health status after primary total knee arthroplasty. Considering the economic costs, home-based tele-rehabilitation programs are recommended as a viable alternative to hospital-based rehabilitation programs.

Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.

Background: Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC. Methods: In total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared. Results: The machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003). Conclusions: The machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.

OBJECTIVE: To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL). METHODS: A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts. RESULTS: The DLSPFS and DLSOS exhibited significant associations with PFS and OS, respectively (P<0.05) in the training and validation cohorts. The multiparametric models that incorporated DLSs demonstrated superior efficacy in predicting PFS (C-index: 0.866) and OS (C-index: 0.835) compared to competing models in training cohorts. In external validation cohorts, the C-indices for PFS and OS were 0.760 and. 0.770 and 0.748 and 0.766, respectively, indicating the reliable validity of the multiparametric models. The calibration curves displayed good consistency, and the decision curve analysis (DCA) confirmed that the multiparametric models offered more net clinical benefits. CONCLUSIONS: The DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.

Computational fluid dynamics can detect changes in airway resistance for patients after COVID-19 infection.

Infection with COVID-19 can cause severe complication in the respiratory system, which may be related to increased respiratory resistance. Computational fluid dynamics(CFD) was used in this study to calculate the airway resistance based on the airway anatomy and a common air flowrate. The correlation between airway resistance and COVID-19 prognosis was then investigated. A total of 23 COVID-19 patients with 54 CT scans were grouped into the good prognosis and bad prognosis group based on whether the CT scan shows significant decrease in the pneumonia volume after one week treatment and retrospectively analyzed. A baseline group of 8 healthy people with the same age and gender ratio is enrolled for comparison. Results show that the airway resistance at admission is significantly higher for COVID-19 patients with poor prognosis than those with good prognosis and the baseline(0.063 ± 0.055 vs 0.029 ± 0.011 vs 0.017 ± 0.006 Pa/(ml/s),p = 0.01). In the left superior lobe (r = 0.3974,p = 0.01),left inferior lobe (r = 0.4843,p < 0.01), the right inferior lobe (r = 0.5298,p < 0.0001), the airway resistance was significantly correlated with the degree of pneumonia infection. It is concluded that for COVID-19 patients', airway resistance at admission is closely associated with their prognosis, and has the clinical potential to be used as an index for patients' diagnosis.

Anatomically Guided Cross-Domain Repair and Screening for Ultrasound Fetal Biometry.

Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal anatomy is a crucial prerequisite. Although deep neural network-based models have achieved encouraging results on this task, inevitable distribution shifts in ultrasound images can still result in severe performance drop in real world deployment scenarios. In this article, we propose a complete ultrasound fetal examination system to deal with this troublesome problem by repairing and screening the anatomically implausible results. Our system consists of three main components: A routine segmentation network, a fetal anatomical key points guided repair network, and a shape-coding based selective screener. Guided by the anatomical key points, our repair network has stronger cross-domain repair capabilities, which can substantially improve the outputs of the segmentation network. By quantifying the distance between an arbitrary segmentation mask to its corresponding anatomical shape class, the proposed shape-coding based selective screener can then effectively reject the entire implausible results that cannot be fully repaired. Extensive experiments demonstrate that our proposed framework has strong anatomical guarantee and outperforms other methods in three different cross-domain scenarios.

Self-supervised anomaly detection, staging and segmentation for retinal images.

Unsupervised anomaly detection (UAD) is to detect anomalies through learning the distribution of normal data without labels and therefore has a wide application in medical images by alleviating the burden of collecting annotated medical data. Current UAD methods mostly learn the normal data by the reconstruction of the original input, but often lack the consideration of any prior information that has semantic meanings. In this paper, we first propose a universal unsupervised anomaly detection framework SSL-AnoVAE, which utilizes a self-supervised learning (SSL) module for providing more fine-grained semantics depending on the to-be detected anomalies in the retinal images. We also explore the relationship between the data transformation adopted in the SSL module and the quality of anomaly detection for retinal images. Moreover, to take full advantage of the proposed SSL-AnoVAE and apply towards clinical usages for computer-aided diagnosis of retinal-related diseases, we further propose to stage and segment the anomalies in retinal images detected by SSL-AnoVAE in an unsupervised manner. Experimental results demonstrate the effectiveness of our proposed method for unsupervised anomaly detection, staging and segmentation on both retinal optical coherence tomography images and color fundus photograph images.

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.

Objectives: To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT). Methods: Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC). Results: There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model. Conclusion: MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

Experimental Boson Sampling Enabling Cryptographic One-Way Function.

Boson sampling is a computational problem, which is commonly believed to be a representative paradigm for attaining the milestone of quantum advantage. So far, massive efforts have been made to the experimental large-scale boson sampling for demonstrating this milestone, while further applications of the machines remain a largely unexplored area. Here, we investigate experimentally the efficiency and security of a cryptographic one-way function that relies on coarse-grained boson sampling, in the framework of a photonic boson-sampling machine fabricated by a femtosecond laser direct writing technique. Our findings demonstrate that the implementation of the function requires moderate sample sizes, which can be over 4 orders of magnitude smaller than the ones predicted by the Chernoff bound; whereas for numbers of photons n≥3 and bins d∼poly(m,n), the same output of the function cannot be generated by nonboson samplers. Our Letter is the first experimental study that deals with the potential applications of boson sampling in the field of cryptography and paves the way toward additional studies in this direction.

Musculoskeletal Ultrasound Image-Based Radiomics for the Diagnosis of Achilles Tendinopathy in Skiers.

OBJECTIVES: Our study aimed to develop and validate an efficient ultrasound image-based radiomic model for determining the Achilles tendinopathy in skiers. METHODS: A total of 88 feet of skiers clinically diagnosed with unilateral chronic Achilles tendinopathy and 51 healthy feet were included in our study. According to the time order of enrollment, the data were divided into a training set (n = 89) and a test set (n = 50). The regions of interest (ROIs) were segmented manually, and 833 radiomic features were extracted from red, green, blue color channels and grayscale of ROIs using Pyradiomics, respectively. Three feature selection and three machine learning modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. Finally, the area under the receiver operating characteristic curve (AUC), consistency analysis, and decision analysis were used to evaluate the diagnostic performance. RESULTS: By comparing nine radiomics analysis strategies of three color channels and grayscale, the radiomic model under the green channel obtained the best diagnostic performance, using the Random Forest selection and Support Vector Machine modeling, which was selected as the final machine learning model. All the selected radiomic features were significantly associated with the Achilles tendinopathy (P < .05). The radiomic model had a training AUC of 0.98, a test AUC of 0.99, a sensitivity of 0.90, and a specificity of 1, which could bring sufficient clinical net benefits. CONCLUSIONS: Ultrasound image-based radiomics achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of Achilles tendinopathy.

Prognostic Value of Left Atrial Reservoir Strain in Left Ventricular Myocardial Noncompaction: A 3.0 T Cardiac Magnetic Resonance Feature Tracking Study.

BACKGROUND: The relationship of left atrial (LA) strain to high-risk heart failure (HF) events in patients with left ventricular myocardial noncompaction (LVNC) remains to be thoroughly investigated. PURPOSE: To evaluate the LA performance in patients with LVNC, and to investigate the prognostic value of LA phasic strain on high-risk HF events, and its influencing factors. STUDY TYPE: Retrospective. POPULATION: A total of 95 LVNC patients (74 with LA enlargement [LAE] and 21 without LAE) and 50 healthy controls. FIELD STRENGTH/SEQUENCE: A 3.0 T, balanced steady-state free-precession cine imaging. ASSESSMENT: LA longitudinal strains were measured by cardiac MRI feature tracking technique. LA volume index (LAVI) and LA ejection fraction (LAEF) were calculated. Their intraobserver and interobserver reproducibility were evaluated. The primary outcome was high-risk HF events, a composite of first HF hospitalization, hospitalization for worsening HF and death from HF. STATISTICAL TESTS: Student's t/Mann-Whitney U, one-way analysis of variance/Kruskal-Wallis, Chi-squared, receiver operating characteristic, Kaplan-Meier, log-rank, Cox regression, Pearson and Spearman correlation and linear regression analyses were performed. The significance threshold was set at P < 0 .05. RESULTS: LAEF and LA longitudinal strains decreased in LVNC patients irrespective of the presence of LAE. During a median follow-up of 32.17 months, high-risk HF occurred in 13 (13.68%) patients. Patients with increased LAVI, decreased LAEF and decreased LA longitudinal strain had significantly higher risks of high-risk HF events. In patients with LVNC, LA reservoir strain (εs) was independently associated with high-risk HF (hazard ratio = 23.208 [95% CI: 2.993-179.967]). LV global longitudinal strain (LV GLS) (ß = -1.783 [95% CI: -2.493 to -1.073]) was significantly and independently associated with εs. Intraobserver and interobserver reproducibility was excellent for LAVI, LAEF, and LA strain. CONCLUSION: In patients with LVNC, εs was an independent predictor for high-risk HF events. LV GLS was an independent determinant of εs in LVNC. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 4.

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study.

Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration. Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis. Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05). Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management.

Direct Observation of Dynamically Localized Quantum Optical States.

Quantum-correlated biphoton states play an important role in quantum communication and processing, especially considering the recent advances in integrated photonics. However, it remains a challenge to flexibly transport quantum states on a chip, when dealing with large-scale sophisticated photonic designs. The equivalence between certain aspects of quantum optics and solid-state physics makes it possible to utilize a range of powerful approaches in photonics, including topologically protected boundary states, graphene edge states, and dynamic localization. Optical dynamic localization allows efficient protection of classical signals in photonic systems by implementing an analogue of an external alternating electric field. Here, we report on the observation of dynamic localization for quantum-correlated biphotons, including both the generation and the propagation aspects. As a platform, we use sinusoidal waveguide arrays with cubic nonlinearity. We record biphoton coincidence count rates as evidence of robust generation of biphotons and demonstrate the dynamic localization features in both spatial and temporal space by analyzing the quantum correlation of biphotons at the output of the waveguide array. Experimental results demonstrate that various dynamic modulation parameters are effective in protecting quantum states without introducing complex topologies. Our Letter opens new avenues for studying complex physical processes using photonic chips and provides an alternative mechanism of protecting communication channels and nonclassical quantum sources in large-scale integrated quantum optics.

Deep learning in bladder cancer imaging: A review.

Deep learning (DL) is a rapidly developing field in machine learning (ML). The concept of deep learning originates from research on artificial neural networks and is an upgrade of traditional neural networks. It has achieved great success in various domains and has shown potential in solving medical problems, particularly when using medical images. Bladder cancer (BCa) is the tenth most common cancer in the world. Imaging, as a safe, noninvasive, and relatively inexpensive technique, is a powerful tool to aid in the diagnosis and treatment of bladder cancer. In this review, we provide an overview of the latest progress in the application of deep learning to the imaging assessment of bladder cancer. First, we review the current deep learning approaches used for bladder segmentation. We then provide examples of how deep learning helps in the diagnosis, staging, and treatment management of bladder cancer using medical images. Finally, we summarize the current limitations of deep learning and provide suggestions for future improvements.

A fast fluorescent probe for tracing endoplasmic reticulum-located carboxylesterase in living cells.

Carboxylesterase (CEs), mainly localized in endoplasmic reticulum (ER), are responsible for hydrolyzing compounds containing various ester bonds. They have been closely associated with drug metabolism and cellular homeostasis. Although some CE fluorescent probes have been developed, there are still a lack of probes that could target to the ER. Here, we developed a novel fluorescent probe CR with a specific ER anchor for monitoring CEs. In CR, p-toluenesulfonamide was chosen for precise ER targeting. A simple acetyl moiety was used as the CE response site and fluorescence modulation unit. During the spectral tests, CR displayed a fast response speed (within 10 s) towards CEs. In addition, it showed high sensitivity [limit of detection (LOD) = 5.1 × 10-3 U/ml] and high selectivity with CEs. In biological imaging, probe CR could especially locate in the ER in HepG2 cells. After cells were treated with orilistat, CR succeeded in monitoring the changes in the CEs. Importantly, CR also had the ability to trace the changes in CEs in a tunicamycin-induced ER stress model. Therefore, probe CR could be a powerful molecular tool for further investigating the functions of CEs in the ER.