Development and validation of a radiomics-based prediction pipeline for the response to stereotactic radiosurgery therapy in brain metastases.

OBJECTIVES: The first treatment strategy for brain metastases (BM) plays a pivotal role in the prognosis of patients. Among all strategies, stereotactic radiosurgery (SRS) is considered a promising therapy method. Therefore, we developed and validated a radiomics-based prediction pipeline to prospectively identify BM patients who are insensitive to SRS therapy, especially those who are at potential risk of progressive disease. METHODS: A total of 337 BM patients (277, 30, and 30 in the training set, internal validation set, and external validation set, respectively) were enrolled in the study. 19,377 radiomics features (3 masks × 3 MRI sequences × 2153 features) extracted from 9 ROIs were filtered through LASSO and Max-Relevance and Min-Redundancy (mRMR) algorithms. The selected radiomics features were combined with 4 clinical features to construct a two-stage cascaded model for the prediction of BM patients' response to SRS therapy using SVM and an ensemble learning classifier. The performance of the model was evaluated by its accuracy, specificity, sensitivity, and AUC curve. RESULTS: Radiomics features were integrated with the clinical features of patients in our optimal model, which showed excellent discriminative performance in the training set (AUC: 0.95, 95% CI: 0.88-0.98). The model was also verified in the internal validation set and external validation set (AUC 0.93, 95% CI: 0.76-0.95 and AUC 0.90, 95% CI: 0.73-0.93, respectively). CONCLUSIONS: The proposed prediction pipeline could non-invasively predict the response to SRS therapy in patients with brain metastases thus assisting doctors to precisely designate individualized first treatment decisions. CLINICAL RELEVANCE STATEMENT: The proposed prediction pipeline combines the radiomics features of multi-modal MRI with clinical features to construct machine learning models that noninvasively predict the response of patients with brain metastases to stereotactic radiosurgery therapy, assisting neuro-oncologists to develop personalized first treatment plans. KEY POINTS: • The proposed prediction pipeline can non-invasively predict the response to SRS therapy. • The combination of multi-modality and multi-mask contributes significantly to the prediction. • The edema index also shows a certain predictive value.

A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly.

BACKGROUND: The value of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied extensively. We assess the value of radiomics features from the adrenal gland and periadrenal fat CT images in predicting COVID-19 disease exacerbation. METHODS: A total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed a 3D V-net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted. RESULTS: The auto-segmentation framework yielded a dice value 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.717, 0.716, 0.736, 0.760, and 0.833 in the validation set. FM and RN had better predictive efficacy than CM (P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was between 0.4 and 0.8 in the validation set or between 0.3 and 0.7 in the test set, it could gain more net benefits using RN than FM and CM. CONCLUSIONS: Radiomics features extracted from the adrenal gland and periadrenal fat CT images are related to disease exacerbation in patients with COVID-19.

Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection.

OBJECTIVES: The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection. METHODS: A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated. RESULTS: The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice. CONCLUSIONS: The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling. KEY POINTS: • Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups. • Radiomics can improve the prognostic value of TNM staging system. • Radiomics may facilitate personalized treatment of gastric cancer patients.

[Preparation of angiopep-2-modified FITC-labeled neurotoxin nanoparticles and in vitro release characteristics].

In order to prepare angiopep-2 modified fluorescein isothiocyanate-labeled neurotoxin nanoparticles( ANG-NPs/FITCNT),emulsion/solvent evaporation method was used with m PEG-PLA and ANG-PEG-PLA( in proper proportions) as carriers and with FITC-NT as drug. With particle size and encapsulation efficiency as comprehensive indexes,the effects of different ultrasound power and ultrasound time combinations on the process were investigated. The in vitro release characteristics of nanoparticles in PBS buffer at p H 7. 4 and p H 6. 5 were investigated by dialysis method. The results indicated that the optimum process for preparing ANG-NPs/FITC-NT was as follows: ultrasonic power 90 W,ultrasonic time 30 s. In such optimal process,ANG-NPs/FITC-NT were well-shaped under the transmission electron microscope,with an average particle size of( 123. 9±0. 5) nm,Zeta potential of(-10. 5±0. 5) m V,encapsulation efficiency of( 68. 1±0. 4) %,and the drug loading of( 0. 82±0. 01) %. The in vitro drug release profiles of the nanoparticles in PBS buffer at p H 7. 4 and p H 6. 5 were both consistent with Ritger-Peppas equation,ln Q = 0. 508 8 lnt-2. 285 0,r = 0. 961 5( p H 7. 4) and ln Q= 0. 449 9 lnt-1. 855 3,r = 0. 970 3( p H 6. 5),respectively. The experiment results proved that the nanoparticles prepared by emulsion/solvent evaporation method had uniform particle size,high encapsulation efficiency and in vitro sustained release characteristic,which might be a potential carrier for NT intracerebral drug delivery.

SaB-Net: Self-attention backward network for gastric tumor segmentation in CT images.

Gastric cancer is a significant contributor to cancer-related fatalities globally. The automated segmentation of gastric tumors has the potential to analyze the medical condition of patients and enhance the likelihood of surgical treatment success. However, the development of an automatic solution is challenged by the heterogeneous intensity distribution of gastric tumors in computed tomography (CT) images, the low-intensity contrast between organs, and the high variability in the stomach shapes and gastric tumors in different patients. To address these challenges, we propose a self-attention backward network (SaB-Net) for gastric tumor segmentation (GTS) in CT images by introducing a self-attention backward layer (SaB-Layer) to feed the self-attention information learned at the deep layer back to the shallow layers. The SaB-Layer efficiently extracts tumor information from CT images and integrates the information into the network, thereby enhancing the network's tumor segmentation ability. We employed datasets from two centers, one for model training and testing and the other for external validation. The model achieved dice scores of 0.8456 on the test set and 0.8068 on the external verification set. Moreover, we validated the model's transfer learning ability on a publicly available liver cancer dataset, achieving results comparable to state-of-the-art liver cancer segmentation models recently developed. SaB-Net has strong potential for assisting in the clinical diagnosis of and therapy for gastric cancer. Our implementation is available at https://github.com/TyrionJ/SaB-Net.

Determination and pharmacokinetic study of the diacid metabolite of norcantharidin in beagle plasma by use of liquid chromatography-tandem mass spectrometry.

Because norcantharidin (NCTD) is unstable and subject to ring opening and hydrolysis, the diacid metabolite of norcantharidin (DM-NCTD) is the stable form of NCTD found in normal saline solution. Conversion of NCTD to DM-NCTD is almost 100%, making it possible to determine and investigate the pharmacokinetics of DM-NCTD converted from NCTD. In this paper, a sensitive, simple and selective liquid chromatographic-tandem mass spectrometric method was developed and validated for determination of DM-NCTD in beagle plasma. DM-NCTD was detected in multiple-reaction monitoring (MRM) mode by using the dehydrated ion 169.3 as precursor ion and its product ion 123.1 as the detected ion. Ribavirin was used as internal standard and detected in MRM mode by use of precursor ions, resulting in a product ion transition of m/z 267.1 → 135.1. This method was successfully used for a pharmacokinetic study of DM-NCTD in beagles after intravenous administration of DM-NCTD in normal saline solution at doses of 0.39, 0.78, and 1.6 mg kg(-1). DM-NCTD had dose-dependent kinetics across the dosage range investigated, with enhanced T(1/2α) and AUC(0-12) and apparently decreasing V(d) and CL with increasing dosage. After single-dose administration, T(1/2α) ranged from 0.20 to 0.55 h, AUC(0-12) from 1.81 to 43.6 µg mL(-1) h(-1), V(d) from 228 to 55.9 mL kg(-1), and CL from 220 to 36.5 mL kg(-1) h(-1) (P < 0.01). The results indicated nonlinear pharmacokinetic behavior of DM-NCTD in beagles, suggesting that the risk of DM-NCTD in normal saline solution intoxication may be non-proportionally increased at higher doses.

Case Report: Multidisciplinary management of primary inferior vena cava leiomyosarcoma: a comprehensive case study.

Introduction: Primary Inferior vena cava (IVC) leiomyosarcoma, a rare malignant tumor, presents unique challenges in diagnosis and treatment due to its rarity and the lack of consensus on surgical and adjuvant therapy approaches. Case Report: A 39-year-old female patient presented with lower limb swelling and mild fatigue. Contrast-enhanced CT identified a tumor mass within the dilated IVC. Abdominal MRI revealed primary IVC leiomyosarcoma extending into the right hepatic vein. A multidisciplinary consultation established a diagnosis and devised a treatment plan, opting for Ex-vivo Liver Resection and Auto-transplantation (ELRA), tumor resection and IVC reconstruction. Pathological examination confirmed primary IVC leiomyosarcoma. Postoperatively, the patient underwent a comprehensive treatment strategy that included radiochemotherapy, immunotherapy, targeted therapy, and PRaG therapy (PD-1 inhibitor, Radiotherapy, and Granulocyte-macrophage colony-stimulating factor). Despite the tumor's recurrence and metastasis, the disease progression was partially controlled. Conclusion: This case report emphasizes the complexities of diagnosing and treating IVC leiomyosarcoma and highlights the potential benefits of employing ELRA, IVC reconstruction, and PRaG therapy. Our study may serve as a valuable reference for future investigations addressing the management of this rare disease.

Evaluation of condylar osseous changes using a wireless detector with proton density-weighted imaging sequences.

Background: Cone-beam computed tomography (CBCT) is the gold standard for evaluating condylar osseous changes. However, the radiation risk and low soft-tissue resolution of CBCT make it unsuitable for evaluating soft tissue such as the articular disc and lateral pterygoid muscle. This study aimed to qualitatively and quantitatively evaluate the feasibility and advantages of using wireless detectors (WD) with proton density-weighted imaging (PDWI) sequences to image condyle changes in patients with temporomandibular disorders (TMD). Methods: This study involved 20 patients (male =8, female =12; mean age 31.65 years, SD 12.68 years) with TMD. All participants underwent a closed oblique sagittal PDWI scan with head/neck coupling coiling (HNCC) and wireless detector-HNCC (WD-HNCC) on a 3.0 T magnetic resonance imaging (MRI) scanner. Subsequently, the changes in the condyle bones in the scanned images for the 2 image types were scored subjectively and compared qualitatively. The contrast-to-noise ratio (CNR) of the 2 types of scanned images was compared quantitatively. The comparison of CNR differences between the 2 types of images was performed using the paired t-test. The kappa statistic was used to test the consistency of quantitative analyses of MRI images between observers. The subjective scores of condylar osseous changes in the 2 types of images were compared by paired rank-sum test. A P value <0.05 was considered statistically significant. Results: A total of 40 condyles from 20 patients were scanned. Among them, 8 condyles showed no bone changes, and the other 32 condyles demonstrated condylar osseous changes of varying degrees and nature. These 32 condyles were used in the subsequent analysis. As compared to images acquired by HNCC in the PDWI sequence, the WD-HNCC images more clearly showed mandibular osteophyte, bone cortical erosion, subcortical cystic focus, and bone cortical hyperplasia and thickening. In addition, the WD-HNCC was demonstrated to improve image CNR by 158.9% compared to HNCC (28.17±16.01 vs. 10.88±6.53; t=8.63; P=0.001). Conclusions: WD-HNCC in PDWI sequences is suitable for imaging the condylar bone changes of patients with TMD and significantly improves the image quality.

TGMIL: A hybrid multi-instance learning model based on the Transformer and the Graph Attention Network for whole-slide images classification of renal cell carcinoma.

BACKGROUND AND OBJECTIVES: The pathological diagnosis of renal cell carcinoma is crucial for treatment. Currently, the multi-instance learning method is commonly used for whole-slide image classification of renal cell carcinoma, which is mainly based on the assumption of independent identical distribution. But this is inconsistent with the need to consider the correlation between different instances in the diagnosis process. Furthermore, the problem of high resource consumption of pathology images is still urgent to be solved. Therefore, we propose a new multi-instance learning method to solve this problem. METHODS: In this study, we proposed a hybrid multi-instance learning model based on the Transformer and the Graph Attention Network, called TGMIL, to achieve whole-slide image of renal cell carcinoma classification without pixel-level annotation or region of interest extraction. Our approach is divided into three steps. First, we designed a feature pyramid with the multiple low magnifications of whole-slide image named MMFP. It makes the model incorporates richer information, and reduces memory consumption as well as training time compared to the highest magnification. Second, TGMIL amalgamates the Transformer and the Graph Attention's capabilities, adeptly addressing the loss of instance contextual and spatial. Within the Graph Attention network stream, an easy and efficient approach employing max pooling and mean pooling yields the graph adjacency matrix, devoid of extra memory consumption. Finally, the outputs of two streams of TGMIL are aggregated to achieve the classification of renal cell carcinoma. RESULTS: On the TCGA-RCC validation set, a public dataset for renal cell carcinoma, the area under a receiver operating characteristic (ROC) curve (AUC) and accuracy of TGMIL were 0.98±0.0015,0.9191±0.0062, respectively. It showcased remarkable proficiency on the private validation set of renal cell carcinoma pathology images, attaining AUC of 0.9386±0.0162 and ACC of 0.9197±0.0124. Furthermore, on the public breast cancer whole-slide image test dataset, CAMELYON 16, our model showed good classification performance with an accuracy of 0.8792. CONCLUSIONS: TGMIL models the diagnostic process of pathologists and shows good classification performance on multiple datasets. Concurrently, the MMFP module efficiently diminishes resource requirements, offering a novel angle for exploring computational pathology images.

Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones in vivo.

BACKGROUND: Urolithiasis is a global disease with a high incidence and recurrence rate, and stone composition is closely related to the choice of treatment and preventive measures. Calcium oxalate monohydrate (COM) is the most common in clinical practice, which is hard and difficult to fragment. Preoperative identification of its components and selection of effective surgical methods can reduce the risk of patients having a second operation. Methods that can be used for stone composition analysis include infrared spectroscopy, X-ray diffraction, and polarized light microscopy, but they are all performed on stone specimens in vitro after surgery. This study aimed to design and develop an artificial intelligence (AI) model based on unenhanced computed tomography (CT) images of the urinary tract, and to investigate the predictive ability of the model for COM stones in vivo preoperatively, so as to provide surgeons with more accurate diagnostic information. METHODS: Preoperative unenhanced CT images of patients with urinary calculi whose components were determined by infrared spectroscopy in a single center were retrospectively analyzed, including 337 cases of COM stones and 170 of non-COM stones. All images were manually segmented and the image features were extracted, and randomly divided into the training and testing sets in a ratio of 7:3. The least absolute shrinkage and selection operation algorithm (LASSO) was used to construct the AI model, and classification of the training and testing sets was carried out. RESULTS: A total of 1,218 radiomics imaging features were extracted, and 8 features with non-zero coefficients were finally obtained. The sensitivity, specificity and accuracy of the AI model were 90.5%, 84.3% and 88.5% for the training set, and 90.1%, 84.3% and 88.3% for the testing set. The area under the curve was 0.935 for the training set and 0.933 for the testing set. CONCLUSIONS: The AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones in vivo preoperatively, and the model has high sensitivity, specificity and accuracy.

An HIV-infected patient with coronavirus disease 2019 has a favourable prognosis: a case report.

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly, which now has turned into a pandemic. The new emerging infectious disease has raised many challenges and uncertainties regarding disease management and prognosis in immunocompromised patient populations. The risk of COVID-19 among people living with human immunodeficiency virus (HIV) has different opinions. Some scholars speculated that patients with HIV may be at decreased risk for complications of COVID-19 because HIV antiretroviral medications may have activity against coronaviruses such as SARS-CoV-2. But others have the opposite because of the immunosuppression for HIV patients. Here we reported a case of HIV-infected patient confirmed with COVID-19 and had a favourable prognosis. The patient was a 24-year-old male who was diagnosed with HIV infection 2 years ago and then followed a regular antiretroviral therapy (ART). After infected with COVID-19, the patient had no other clinical symptoms and laboratory abnormalities throughout the course of the disease except presented with fever for a short-term (2 days), and no secondary infection or exacerbation occurred after admission in hospital. Follow-up chest CT showed that the lung lesions disappeared within a short period of time. After standard treatment by 9 days, the patient was cured and discharged. This report highlights the importance of ART for HIV-infected persons, and with regular ART for HIV patients may reduce adverse consequences after infection with COVID-19.

Toward human intervention-free clinical diagnosis of intracranial aneurysm via deep neural network.

Intracranial aneurysm (IA) is an enormous threat to human health, which often results in nontraumatic subarachnoid hemorrhage or dismal prognosis. Diagnosing IAs on commonly used computed tomographic angiography (CTA) examinations remains laborious and time consuming, leading to error-prone results in clinical practice, especially for small targets. In this study, we propose a fully automatic deep-learning model for IA segmentation that can be applied to CTA images. Our model, called Global Localization-based IA Network (GLIA-Net), can incorporate the global localization prior and generates the fine-grain three-dimensional segmentation. GLIA-Net is trained and evaluated on a big internal dataset (1,338 scans from six institutions) and two external datasets. Evaluations show that our model exhibits good tolerance to different settings and achieves superior performance to other models. A clinical experiment further demonstrates the clinical utility of our technique, which helps radiologists in the diagnosis of IAs.

Accurate intravoxel incoherent motion parameter estimation using Bayesian fitting and reduced number of low b-values.

PURPOSE: Intravoxel incoherent motion (IVIM) magnetic resonance imaging is a potential noninvasive technique for the diagnosis of brain tumors. However, perfusion-related parameter mapping is a persistent problem. The purpose of this paper is to investigate the IVIM parameter mapping of brain tumors using Bayesian fitting and low b-values. METHODS: Bayesian shrinkage prior (BSP) fitting method and different low b-value distributions were used to estimate IVIM parameters (diffusion D, pseudo-diffusion D*, and perfusion fraction F). The results were compared to those obtained by least squares (LSQ) on both simulated and in vivo brain data. Relative error (RE) and reproducibility were used to evaluate the results. The differences of IVIM parameters between brain tumor and normal regions were compared and used to assess the performance of Bayesian fitting in the IVIM application of brain tumor. RESULTS: In tumor regions, the value of D* tended to be decreased when the number of low b-values was insufficient, especially with LSQ. BSP required less low b-values than LSQ for the correct estimation of perfusion parameters of brain tumors. The IVIM parameter maps of brain tumors yielded by BSP had smaller variability, lower RE, and higher reproducibility with respect to those obtained by LSQ. Obvious differences were observed between tumor and normal regions in parameters D (P < 0.05) and F (P < 0.001), especially F. BSP generated fewer outliers than LSQ, and distinguished better tumors from normal regions in parameter F. CONCLUSIONS: Intravoxel incoherent motion parameters clearly allow brain tumors to be differentiated from normal regions. Bayesian fitting yields robust IVIM parameter mapping with fewer outliers and requires less low b-values than LSQ for the parameter estimation.

Preoperative CT Radiomics Predicting the SSIGN Risk Groups in Patients With Clear Cell Renal Cell Carcinoma: Development and Multicenter Validation.

Objective: The stage, size, grade, and necrosis (SSIGN) score can facilitate the assessment of tumor aggressiveness and the personal management for patients with clear cell renal cell carcinoma (ccRCC). However, this score is only available after the postoperative pathological evaluation. The aim of this study was to develop and validate a CT radiomic signature for the preoperative prediction of SSIGN risk groups in patients with ccRCC in multicenters. Methods: In total, 330 patients with ccRCC from three centers were classified into the training, external validation 1, and external validation 2 cohorts. Through consistent analysis and the least absolute shrinkage and selection operator, a radiomic signature was developed to predict the SSIGN low-risk group (scores 0-3) and intermediate- to high-risk group (score ≥ 4). An image feature model was developed according to the independent image features, and a fusion model was constructed integrating the radiomic signature and the independent image features. Furthermore, the predictive performance of the above models for the SSIGN risk groups was evaluated with regard to their discrimination, calibration, and clinical usefulness. Results: A radiomic signature consisting of sixteen relevant features from the nephrographic phase CT images achieved a good calibration (all Hosmer-Lemeshow p > 0.05) and favorable prediction efficacy in the training cohort [area under the curve (AUC): 0.940, 95% confidence interval (CI): 0.884-0.973] and in the external validation cohorts (AUC: 0.876, 95% CI: 0.811-0.942; AUC: 0.928, 95% CI: 0.844-0.975, respectively). The radiomic signature performed better than the image feature model constructed by intra-tumoral vessels (all p < 0.05) and showed similar performance with the fusion model integrating radiomic signature and intra-tumoral vessels (all p > 0.05) in terms of the discrimination in all cohorts. Moreover, the decision curve analysis verified the clinical utility of the radiomic signature in both external cohorts. Conclusion: Radiomic signature could be used as a promising non-invasive tool to predict SSIGN risk groups and to facilitate preoperative clinical decision-making for patients with ccRCC.

A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma.

Objective: To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC). Methods: In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC patients from second hospital served as the independent validation cohort. Radiomic features were extracted from corticomedullary and nephrographic phase contrast-enhanced computed tomography (CT) images. A radiomics signature based on optimal features selected by consistency analysis and the least absolute shrinkage and selection operator was developed. An image features model was constructed based on independent image features according to visual assessment. By integrating the radiomics signature and independent image features, a radiomics nomograph was constructed. The predictive performance of the above models was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the nomogram was assessed using calibration curve and decision curve analysis. Results: Thirty-seven features were used to establish a radiomics signature, which demonstrated better predictive performance than did the image features model constructed using tumor size and intratumoral vessels in the training and validation cohorts (p <0.05). The radiomics nomogram demonstrated satisfactory discrimination in the training (area under the ROC curve [AUC] 0.93 [95% CI 0.87-0.96]) and validation (AUC 0.87 [95% CI 0.79-0.93]) cohorts and good calibration (Hosmer-Lemeshow p>0.05). Decision curve analysis verified that the radiomics nomogram had the best clinical utility compared with the other models. Conclusion: The radiomics nomogram developed in the present study is a promising tool to predict tumor necrosis and facilitate preoperative clinical decision-making for patients with ccRCC.

A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study.

BACKGROUND AND PURPOSE: Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for OS based on computed tomography (CT) images. MATERIALS AND METHODS: We retrospectively collected 640 patients from three independent centers, which were divided into a training cohort (center 1 and center 2, n = 518) and an external validation cohort (center 3, n = 122). We developed a DL model based on the architecture of residual convolutional neural network. We augmented the size of training dataset by image transformations to avoid overfitting. We also developed radiomics and clinical models for comparison. The performance of the three models were comprehensively assessed. RESULTS: Totally 518 patients were prepared by data augmentation and fed into DL model. The trained DL model significantly classified patients into high-risk and low-risk groups in training cohort (P-value <0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort (P-value <0.001, C-index: 0.78, HR: 11.76). Radiomics model was developed with selected 24 features and clinical model was developed with three significant clinical variables (P-value <0.05). The comparison illustrated DL model had the best performance for risk prediction of OS according to the C-index (training: DL vs Clinical vs Radiomics = 0.82 vs 0.73 vs 0.66; external validation: 0.78 vs 0.71 vs 0.72). CONCLUSION: The DL model is a powerful model for risk assessment, and potentially serves as an individualized recommender for decision-making in GC patients.

Estimation of intravoxel incoherent motion parameters using low b-values.

Intravoxel incoherent motion (IVIM) imaging is a magnetic resonance imaging (MRI) technique widely used in clinical applications for various organs. However, IVIM imaging at low b-values is a persistent problem. This paper aims to investigate in a systematic and detailed manner how the number of low b-values influences the estimation of IVIM parameters. To this end, diffusion-weighted (DW) data with different low b-values were simulated to get insight into the distributions of subsequent IVIM parameters. Then, in vivo DW data with different numbers of low b-values and different number of excitations (NEX) were acquired. Finally, least-squares (LSQ) and Bayesian shrinkage prior (BSP) fitting methods were implemented to estimate IVIM parameters. The influence of the number of low b-values on IVIM parameters was analyzed in terms of relative error (RE) and structural similarity (SSIM). The results showed that the influence of the number of low b-values on IVIM parameters is variable. LSQ is more dependent on the number of low b-values than BSP, but the latter is more sensitive to noise.