Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study.

OBJECTIVE: Patients with gastric atrophy and intestinal metaplasia (IM) were at risk for gastric cancer, necessitating an accurate risk assessment. We aimed to establish and validate a diagnostic approach for gastric biopsy specimens using deep learning and OLGA/OLGIM for individual gastric cancer risk classification. METHODS: In this study, we prospectively enrolled 545 patients suspected of atrophic gastritis during endoscopy from 13 tertiary hospitals between December 22, 2017, to September 25, 2020, with a total of 2725 whole-slide images (WSIs). Patients were randomly divided into a training set (n = 349), an internal validation set (n = 87), and an external validation set (n = 109). Sixty patients from the external validation set were randomly selected and divided into two groups for an observer study, one with the assistance of algorithm results and the other without. We proposed a semi-supervised deep learning algorithm to diagnose and grade IM and atrophy, and we compared it with the assessments of 10 pathologists. The model's performance was evaluated based on the area under the curve (AUC), sensitivity, specificity, and weighted kappa value. RESULTS: The algorithm, named GasMIL, was established and demonstrated encouraging performance in diagnosing IM (AUC 0.884, 95% CI 0.862-0.902) and atrophy (AUC 0.877, 95% CI 0.855-0.897) in the external test set. In the observer study, GasMIL achieved an 80% sensitivity, 85% specificity, a weighted kappa value of 0.61, and an AUC of 0.953, surpassing the performance of all ten pathologists in diagnosing atrophy. Among the 10 pathologists, GasMIL's AUC ranked second in OLGA (0.729, 95% CI 0.625-0.833) and fifth in OLGIM (0.792, 95% CI 0.688-0.896). With the assistance of GasMIL, pathologists demonstrated improved AUC (p = 0.013), sensitivity (p = 0.014), and weighted kappa (p = 0.016) in diagnosing IM, and improved specificity (p = 0.007) in diagnosing atrophy compared to pathologists working alone. CONCLUSION: GasMIL shows the best overall performance in diagnosing IM and atrophy when compared to pathologists, significantly enhancing their diagnostic capabilities.

Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography.

BACKGROUND: Lymph node (LN) metastasis is significantly associated with worse prognosis for patients with intrahepatic cholangiocarcinoma (ICC). Improvement in preoperative assessment on LN metastasis helps in treatment decision-making. We aimed to investigate the role of radiomics-based method in predicting LN metastasis for patients with ICC. METHODS: A total of 296 patients with ICC who underwent curative-intent hepatectomy and lymphadenectomy at two centers in China were analyzed. Radiomic features, including histogram- and wavelet-based features, shape and size features, and texture features were extracted from four-phase computerized tomography (CT) images. The clinical and conventional radiological variables which were independently associated with LN metastasis were also identified. A combined nomogram predicting LN metastasis was developed, and its performance was determined by discrimination, calibration, and stratification of long-term prognosis. The results were validated by the internal and external validation cohorts. RESULTS: Twenty-four radiomic features were selected into the nomogram. The established nomogram demonstrated good discrimination and calibration, with areas under the curve (AUCs) of 0.98 [95% confidence interval (CI) 0.96-0.99], 0.93 (0.88-0.98), and 0.89 (0.81-0.96) in the training and two validation cohorts, respectively. The 5-year overall survival (OS) and recurrence-free survival (RFS) rates of patients with high risk of LN metastasis as grouped by nomogram were poorer than those of patients with low risk in the training cohort (OS 28.8% versus 53.9%, p < 0.001; RFS 26.3% versus 44.2%, p = 0.001). Similar results were observed in the two validation cohorts. CONCLUSIONS: Radiomics-based method provided accurate prediction of LN metastasis and prognostic assessment for ICC patients, and might aid the preoperative surgical decision.

Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma.

OBJECTIVE. The objective of our study was to preoperatively predict fat-poor angiomyolipoma (fp-AML) and renal cell carcinoma (RCC) by conducting quantitative analysis of contrast-enhanced CT images. MATERIALS AND METHODS. One hundred fifteen patients with a pathologic diagnosis of fp-AML or RCC from a single institution were randomly allocated into a train set (tumor size: mean ± SD, 4.50 ± 2.62 cm) and test set (tumor size: 4.32 ± 2.73 cm) after data augmentation. High-dimensional histogram-based features, texture-based features, and Laws features were first extracted from CT images and were then combined as different combinations sets to construct a logistic prediction model based on the least absolute shrinkage and selection operator procedure for the prediction of fp-AML and RCC. Prediction performances were assessed by classification accuracy, area under the ROC curve (AUC), positive predictive value, negative predictive value, true-positive rate, and false-positive rate (FPR). In addition, we also investigated the effects of different gray-scales of quantitative features on prediction performances. RESULTS. The following combination sets of features achieved satisfying performances in the test set: histogram-based features (mean AUC = 0.8492, mean classification accuracy = 91.01%); histogram-based features and texture-based features (mean AUC = 0.9244, mean classification accuracy = 91.81%); histogram-based features and Laws features (mean AUC = 0.8546, mean classification accuracy = 88.76%); and histogram-based features, texture-based features, and Laws features (mean AUC = 0.8925, mean classification accuracy = 90.36%). The different quantitative gray-scales did not have an obvious effect on prediction performances. CONCLUSION. The integration of histogram-based features with texture-based features and Laws features provided a potential biomarker for the preoperative diagnosis of fp-AML and RCC. The accurate diagnosis of benign or malignant renal masses would help to make the clinical decision for radical surgery or close follow-up.

Radiomics Analysis of DTI Data to Assess Vision Outcome After Intravenous Methylprednisolone Therapy in Neuromyelitis Optic Neuritis.

BACKGROUND: Neuromyelitis optica-optic neuritis (NMO-ON) patients are routinely treated with intravenous methylprednisolone (IVMP). For the patients nonresponsive to IVMP, more effective but aggressive therapy of plasma exchange (PE) should be employed instead of IVMP in the first line. PURPOSE: To assess the visual outcomes of NMO-ON patients after IVMP by radiomics analysis of whole brain diffusion tensor imaging (DTI) data. STUDY TYPE: Retrospective. POPULATION: In all, 57 NMO-ON patients receiving IVMP therapy for 3 days. FIELD STRENGTH/SEQUENCE: 3.0T; DTI images acquired by a single-shot echo planar image sequence; T1 images acquired by 3D fast spoiled gradient echo (3D-FSPGR) MRI. ASSESSMENT: In all, 200 DTI measures were extracted from the DTI data and employed as features to construct a radiomics assessment model for visual outcomes of NMO-ON patients after IVMP. The assessment performance was evaluated by area under the receiver operating characteristic curve (AUC), classification accuracy (ACC), sensitivity, specificity, and positive and negative predicted values (PPV and NPV). The selected DTI measures would reveal the white matter impairments related to visual recovery of NMO-ON patients. STATISTICAL TESTS: The relationship between the selected DTI measures and the clinical visual characteristics were investigated by Pearson correlation, Spearman's rank correlation, and one-way analysis of variance analysis. RESULTS: The radiomics model obtained an ACC of 73.68% (P = 0.002), AUC of 0.7931, sensitivity of 0.6207, specificity of 0.8571, PPV of 0.8182, and NPV of 0.6857 in assessing visual outcomes of the NMO-ON patients after IVMP treatment. The selected DTI measures revealed white matter impairments related to the visual outcomes in the white matter tracts of vision-relevant regions, motor-related regions, and corpus callosum. The white matter impairments were found significantly correlated with the disease duration and the length of lesions in the optic nerve. DATA CONCLUSION: Radiomics analysis of DTI data has great potential in assessing visual outcomes of NMO-ON patients after IVMP therapy. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:1365-1373.

Axonal chronic injury in treatment-naïve HIV+ adults with asymptomatic neurocognitive impairment and its relationship with clinical variables and cognitive status.

BACKGROUND: HIV is a neurotropic virus, and it can bring about neurodegeneration and may even result in cognitive impairments. The precise mechanism of HIV-associated white matter (WM) injury is unknown. The effects of multiple clinical contributors on WM impairments and the relationship between the WM alterations and cognitive performance merit further investigation. METHODS: Diffusion tensor imaging (DTI) was performed in 20 antiretroviral-naïve HIV-positive asymptomatic neurocognitive impairment (ANI) adults and 20 healthy volunteers. Whole-brain analysis of DTI metrics between groups was conducted by employing tract-based spatial statistics (TBSS), including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). DTI parameters were correlated with clinical variables (age, CD4+ cell count, CD4+/CD8+ ratio, plasma viral load and duration of HIV infection) and multiple cognitive tests by using multilinear regression analyses. RESULTS: DTI quantified diffusion alterations in the corpus callosum and corona radiata (MD increased significantly, P < 0.05) and chronic axonal injury in the corpus callosum, corona radiata, internal capsule, external capsule, posterior thalamic radiation, sagittal stratum, and superior longitudinal fasciculus (AD increased significantly, P < 0.05). The impairments in the corona radiata had significant correlations with the current CD4+/CD8+ ratios. Increased MD or AD values in multiple white matter structures showed significant associations with many cognitive domain tests. CONCLUSIONS: WM impairments are present in neurologically asymptomatic HIV+ adults, periventricular WM (corpus callosum and corona radiata) are preferential occult injuries, which is associated with axonal chronic damage rather than demyelination. Axonopathy may exist before myelin injury. DTI-TBSS is helpful to explore the WM microstructure abnormalities and provide a new perspective for the investigation of the pathomechanism of HIV-associated WM injury.

Identifying the white matter impairments among ART-naïve HIV patients: a multivariate pattern analysis of DTI data.

OBJECTIVE: To identify the white matter (WM) impairments of the antiretroviral therapy (ART)-naïve HIV patients by conducting a multivariate pattern analysis (MVPA) of Diffusion Tensor Imaging (DTI) data METHODS: We enrolled 33 ART-naïve HIV patients and 32 Normal controls in the current study. Firstly, the DTI metrics in whole brain WM tracts were extracted for each subject and feed into the Least Absolute Shrinkage and Selection Operators procedure (LASSO)-Logistic regression model to identify the impaired WM tracts. Then, Support Vector Machines (SVM) model was constructed based on the DTI metrics in the impaired WM tracts to make HIV-control group classification. Pearson correlations between the WM impairments and HIV clinical statics were also investigated. RESULTS: Extensive HIV-related impairments were observed in the WM tracts associated with motor function, the corpus callosum (CC) and the frontal WM. With leave-one-out cross validation, accuracy of 83.08% (P=0.002) and the area under the Receiver Operating Characteristic curve of 0.9110 were obtained in the SVM classification model. The impairments of the CC were significantly correlated with the HIV clinic statics. CONCLUSION: The MVPA was sensitive to detect the HIV-related WM changes. Our findings indicated that the MVPA had considerable potential in exploring the HIV-related WM impairments. KEY POINTS: • WM impairments along motor pathway were detected among the ART-naïve HIV patients • Prominent HIV-related WM impairments were observed in CC and frontal WM • The impairments of CC were significantly related to the HIV clinic statics • The CC might be susceptible to immune dysfunction and HIV replication • Multivariate pattern analysis had potential for studying the HIV-related white matter impairments.

Longitudinal assessment of fractional anisotropy alterations caused by simian immunodeficiency virus infection: a preliminary diffusion tensor imaging study.

Previous diffusion tensor imaging (DTI) studies found that human immunodeficiency virus (HIV) infection led to white matter (WM) microstructure degeneration. Most of the DTI studies were cross-sectional and thus merely investigated only one specific point in the disease. In order to systematically study the WM impairments caused by HIV infection, more longitudinal studies are needed. However, longitudinal studies on HIV patients are very difficult to conduct. To address this question, we employed the simian immunodeficiency virus (SIV)-infected rhesus monkeys model to carry out a longitudinal DTI study. We aimed to longitudinally access the WM abnormalities of SIV-infected rhesus monkeys by studying the fractional anisotropy (FA) alterations with Tract Based Spatial Statistic (TBSS) analysis. Four rhesus monkeys inoculated intravenously with SIVmac239 were utilized in the study. DTI scans and peripheral blood CD4(+) and CD8(+) T cell counts were acquired prior to virus inoculation (as the baseline) and in the 12th and 24th week postvirus inoculation. Significant FA alterations were found in the two areas of the inferotemporal regions (iTE), respectively located in the ventral subregion of posterior iTE (iTEpv) and the dorsal subregion of iTE (iTEpd). The decreased FA values in iTEpd were found significantly negatively correlated with the elevated peripheral blood CD4(+)/CD8(+) ratios. It might suggest that WM in iTEpd was still impaired even though the immune dysfunction alleviated temporally.

Gray and white matter alterations in early HIV-infected patients: Combined voxel-based morphometry and tract-based spatial statistics.

PURPOSE: To investigate both the gray matter (GM) and whiter matter (WM) alterations in a homogeneous cohort of early HIV-infected patients by combining voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS). MATERIALS AND METHODS: Twenty-six HIV and 26 control subjects enrolled in this study with 3D T1 and diffusion-tensor imaging acquired on a 3.0T Siemens scanner. Group differences in regional GM were assessed using VBM analysis, while differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and relative anisotropy (RD) of WM were evaluated using TBSS analysis. After that, interactions between GM changes and white matter alterations were investigated by using a correlation analysis. RESULTS: The HIV-infected patients displayed decreased GM volume, mainly located in the bilateral frontal cortices, bilateral anterior cingulate cortex, and left supplementary motor area (P < 0.05, false discovery rate-corrected). Meanwhile, the patient group showed decreased FA in the genu of capsule callosum, body of capsule callosum, and bilateral anterior corona radiate (P < 0.05, family wise error [FEW]-corrected). Areas of increased MD, RD, and AD in HIV patients were more extensive and observed in most skeleton locations (P < 0.05, FEW-corrected). The interaction analysis in the patient group revealed that there were no significant correlations between GM changes and WM alterations (P > 0.05). CONCLUSION: Our results indicate that structural brain alterations occurred early in HIV-infected patients. The current study may shed further light on the potential brain effects of HIV. J. Magn. Reson. Imaging 2016;43:1474-1483.

Space-Specific Mixing Excitation for High-SNR Spatial Encoding in Magnetic Particle Imaging.

OBJECTIVE: Magnetic Particle Imaging (MPI) is a radiation-free tracer-based imaging technology that visualizes the spatial distribution of superparamagnetic iron oxide nanoparticles. Conventional spatial encoding methods in MPI rely on a gradient magnetic field with a constant gradient strength to generate a field-free point or line for spatial scanning. However, increasing the gradient strength can enhance theoretical spatial resolution but also lead to a decrease in the Signal-to-Noise Ratio (SNR) and sensitivity of the imaging system. This poses a technical challenge in balancing spatial resolution and sensitivity, necessitating intricate hardware design. METHODS: To address this, we present a Space-Specific Mixing Excitation (SSME) technique for achieving high-SNR spatial encoding in MPI. By utilizing a dual-frequency excitation magnetic field with a non-homogeneous field strength, magnetic particles at each position generate unique intermodulation responses. By performing multi-channel acquisitions across the entire field of view, high SNR MPI signals can be acquired. When combined with reconstruction techniques based on system matrix, multi-dimensional SSME-MPI can be achieved. RESULTS: The effectiveness of the proposed method was validated through phantom and in vivo imaging experiments. The results demonstrate significant improvements in sensitivity (3.6-fold improvement) and spatial resolution (better than 1 mm) without any hardware modifications. CONCLUSION: These findings demonstrate the capability of SSME to enhance both the spatial resolution and sensitivity of MPI. SIGNIFICANCE: This method provides a solution to the ongoing challenge of balancing spatial resolution and sensitivity in MPI, potentially facilitating the implementation of MPI in a wider range of medical applications.

Parkinson's severity diagnosis explainable model based on 3D multi-head attention residual network.

The severity evaluation of Parkinson's disease (PD) is of great significance for the treatment of PD. However, existing methods either have limitations based on prior knowledge or are invasive methods. To propose a more generalized severity evaluation model, this paper proposes an explainable 3D multi-head attention residual convolution network. First, we introduce the 3D attention-based convolution layer to extract video features. Second, features will be fed into LSTM and residual backbone networks, which can be used to capture the contextual information of the video. Finally, we design a feature compression module to condense the learned contextual features. We develop some interpretable experiments to better explain this black-box model so that it can be better generalized. Experiments show that our model can achieve state-of-the-art diagnosis performance. The proposed lightweight but effective model is expected to serve as a suitable end-to-end deep learning baseline in future research on PD video-based severity evaluation and has the potential for large-scale application in PD telemedicine. The source code is available at https://github.com/JackAILab/MARNet.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

DSIL-DDI: A Domain-Invariant Substructure Interaction Learning for Generalizable Drug-Drug Interaction Prediction.

Drug-drug interactions (DDIs) trigger unexpected pharmacological effects in vivo, often with unknown causal mechanisms. Deep learning methods have been developed to better understand DDI. However, learning domain-invariant representations for DDI remains a challenge. Generalizable DDI predictions are closer to reality than source domain predictions. For existing methods, it is difficult to achieve out-of-distribution (OOD) predictions. In this article, focusing on substructure interaction, we propose DSIL-DDI, a pluggable substructure interaction module that can learn domain-invariant representations of DDIs from source domain. We evaluate DSIL-DDI on three scenarios: the transductive setting (all drugs in test set appear in training set), the inductive setting (test set contains new drugs that were not present in training set), and OOD generalization setting (training set and test set belong to two different datasets). The results demonstrate that DSIL-DDI improve the generalization and interpretability of DDI prediction modeling and provides valuable insights for OOD DDI predictions. DSIL-DDI can help doctors ensuring the safety of drug administration and reducing the harm caused by drug abuse.

The default mode network is affected in the early stage of simian immunodeficiency virus infection: a longitudinal study.

Acquired immune deficiency syndrome infection can lead to cognitive dysfunction represented by changes in the default mode network. Most recent studies have been cross-sectional and thus have not revealed dynamic changes in the default mode network following acquired immune deficiency syndrome infection and antiretroviral therapy. Specifically, when brain imaging data at only one time point are analyzed, determining the duration at which the default mode network is the most effective following antiretroviral therapy after the occurrence of acquired immune deficiency syndrome. However, because infection times and other factors are often uncertain, longitudinal studies cannot be conducted directly in the clinic. Therefore, in this study, we performed a longitudinal study on the dynamic changes in the default mode network over time in a rhesus monkey model of simian immunodeficiency virus infection. We found marked changes in default mode network connectivity in 11 pairs of regions of interest at baseline and 10 days and 4 weeks after virus inoculation. Significant interactions between treatment and time were observed in the default mode network connectivity of regions of interest pairs area 31/V6.R and area 8/frontal eye field (FEF). L, area 8/FEF.L and caudal temporal parietal occipital area (TPOC).R, and area 31/V6.R and TPOC.L. ART administered 4 weeks after infection not only interrupted the progress of simian immunodeficiency virus infection but also preserved brain function to a large extent. These findings suggest that the default mode network is affected in the early stage of simian immunodeficiency virus infection and that it may serve as a potential biomarker for early changes in brain function and an objective indicator for making early clinical intervention decisions.

Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study.

Background: This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making. Methods: A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model. Anatomic information from CT images was first obtained automatically using a U-Net-based multi-organ segmentation model, and metabolic information from PET images was subsequently extracted using a gradient-based approach. AI-CAD was developed in the training cohort and externally validated in two validation cohorts. Results: The AI-CAD achieved an accuracy of 0.744 for predicting pathological LNM in the external cohort and a good agreement with a human expert in two external validation cohorts (kappa = 0.674 and 0.587, p < 0.001). With the aid of AI-CAD, the human expert's diagnostic performance for LNM was significantly improved (accuracy [95% confidence interval]: 0.712 [0.669-0.758] vs. 0.833 [0.797-0.865], specificity [95% confidence interval]: 0.697 [0.636-0.753] vs. 0.891 [0.851-0.928]; p < 0.001) among patients underwent lymphadenectomy in the external validation cohorts. Conclusions: The AI-CAD could aid in preoperative diagnosis of LNM in ESCC patients and thereby support clinical treatment decision-making.

MRI radiomics in overall survival prediction of local advanced cervical cancer patients tread by adjuvant chemotherapy following concurrent chemoradiotherapy or concurrent chemoradiotherapy alone.

OBJECTIVES: To build radiomics based OS prediction tools for local advanced cervical cancer (LACC) patients treated by concurrent chemoradiotherapy (CCRT) alone or followed by adjuvant chemotherapy (ACT). And, to construct adjuvant chemotherapy decision aid. METHODS: 83 patients treated by ACT following CCRT and 47 patients treated by CCRT were included in the ACT cohort and non-ACT cohort. Radiomics features extracted from primary tumor area of T2-weighted MRI. Two radiomics models were built for ACT and non-ACT cohort in prediction of 3 years overall survival (OS). Elastic Net Regression was applied to the the ACT cohort, meanwhile least absolute shrinkage and selection operator plus support vector machine was applied to the non-ACT cohort. Cox regression models was used in clinical features selection and OS predicting nomograms building. RESULT: The two radiomics models predicted the 3 years OS of two cohorts. The receiver operator characteristics analysis was used to evaluate the 3 years OS prediction performance of the two radiomics models. The area under the curve of ACT and non-ACT cohort model were 0.832 and 0.879, respectively. Patients were stratified into low-risk group and high-risk group determined by radiomics models and nomograms, respectively. And, the low-risk group patients present significantly increased OS, progression-free survival, local regional control, and metastasis free survival compare with high-risk group (P < 0.05). Meanwhile the prognosis prediction performance of radiomics model and nomogram is superior to the prognosis prediction performance of Figo stage. CONCLUSION: The two radiomics model and the two nomograms is a prognosis predictor of LACC patients treated by CCRT alone or followed by ACT.

A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods.

BACKGROUND: The present study aimed to explore the difference in the brain function and structure between patients with major depressive disorder (MDD) and healthy controls (HCs) using two-center and multi-modal MRI data, which would be helpful to investigate the pathogenesis of MDD. METHODS: The subjects were collected from two hospitals. One including 140 patients with MDD and 138 HCs was used as primary cohort. Another one including 29 patients with MDD and 52 HCs was used as validation cohort. Functional and structural magnetic resonance images (MRI) were acquired to extract four types of features: functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV). Then classifiers using different combinations among the four types of selected features were respectively built to discriminate patients from HCs. Different templates were applied and the results under different templates were compared. RESULTS: The classifier built with the combination of FC, ALFF, and GMV under the AAL template discriminated patients from HCs with the best performance (AUC=0.916, ACC=84.8%). The regions selected in all the different templates were mainly located in the default mode network, affective network, prefrontal cortex. LIMITATIONS: First, the sample size of the validation cohort was limited. Second, diffusion tensor imaging data were not collected. CONCLUSION: The performance of classifier was improved by using multi-modal MRI imaging. Different templates would be suitable for different types of analysis. The regions selected in all the different templates are possibly the core regions to investigate the pathophysiology of MDD.

Attention-Based Multi-Scale Generative Adversarial Network for synthesizing contrast-enhanced MRI.

In clinical practice, about 35% of MRI scans are enhanced with Gadolinium - based contrast agents (GBCAs) worldwide currently. Injecting GBCAs can make the lesions much more visible on contrast-enhanced scans. However, the injection of GBCAs is high-risk, time-consuming, and expensive. Utilizing a generative model such as an adversarial network (GAN) to synthesize the contrast-enhanced MRI without injection of GBCAs becomes a very promising alternative method. Due to the different features of the lesions in contrast-enhanced images while the single-scale feature extraction capabilities of the traditional GAN, we propose a new generative model that a multi-scale strategy is used in the GAN to extract different scale features of the lesions. Moreover, an attention mechanism is also added in our model to learn important features automatically from all scales for better feature aggregation. We name our proposed network with an attention-based multi-scale contrasted-enhanced-image generative adversarial network (AMCGAN). We examine our proposed AMCGAN on a private dataset from 382 ankylosing spondylitis subjects. The result shows our proposed network can achieve state-of-the-art in both visual evaluations and quantitative evaluations than traditional adversarial training.Clinical Relevance-This study provides a safe, convenient, and inexpensive tool for the clinical practices to get contrast-enhanced MRI without injection of GBCAs.

Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study.

BACKGROUND: Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics. METHODS: We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months' postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram. FINDINGS: DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM (P < 0·05). INTERPRETATION: MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT. FUNDING: The funding bodies that contributed to this study are listed in the Acknowledgements section.

Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma.

PURPOSE: For timely treatment of extrahepatic metastasis and macrovascular invasion (aggressive progressive disease [PD]) in hepatocellular carcinoma, models aimed at stratifying the risks of subsequent aggressive PD should be constructed. PATIENTS AND METHODS: After dividing 332 patients from five hospitals into training (n = 236) and validation (n = 96) datasets, non-invasive models, including clinical/semantic factors (ModelCS), deep learning radiomics (ModelD), and both (ModelCSD), were constructed to stratify patients according to the risk of aggressive PD. We examined the discrimination and calibration; similarly, we plotted a decision curve and devised a nomogram. Furthermore, we performed analyses of subgroups who received different treatments or those in different disease stages and compared time to aggressive PD and overall survival in the high- and low-risk subgroups. RESULTS: Among the constructed models, ModelCSD, combining clinical/semantic factors and deep learning radiomics, outperformed ModelCS and ModelD (areas under the curve [AUCs] for the training dataset: 0.741, 0.815, and 0.856; validation dataset: 0.780, 0.836, and 0.862), with statistical difference per the net reclassification improvement, the integrated discrimination improvement, and/or the DeLong test in both datasets. Besides, ModelCSD had the best calibration and decision curves. The performance of ModelCSD was not affected by treatment types (AUC: resection = 0.839; transarterial chemoembolization = 0.895; p = 0.183) or disease stages (AUC: BCLC [Barcelona Clinic Liver Cancer] stage 0 and A = 0.827; BCLC stage AB &B = 0.861; p = 0.537). Moreover, the high-risk group had a significantly shorter median time to aggressive PD than the low-risk group (training dataset hazard ratio [HR] = 0.108, p < 0.001; validation dataset HR = 0.058, p < 0.001) and poorer overall survival (training dataset HR = 0.357, p < 0.001; validation dataset HR = 0.204, p < 0.001). CONCLUSION: Our deep learning-based model successfully stratified the risks of aggressive PD. In the high-risk population, current guideline indicates that first-line treatments are insufficient to prevent extrahepatic metastasis and macrovascular invasion and ensure survival benefits, so more therapies may be explored for these patients.