Deregulation of immune response contributing to fulminant hepatitis in HEV infected pregnant women.

Hepatitis E virus (HEV) infection in pregnant women is associated with a wide spectrum of adverse consequences for both mother and fetus. The high mortality in this population appears to be associated with hormonal changes and consequent immunological changes. This study conducted an analysis of immune responses in pregnant women infected with HEV manifesting varying severity. Data mining analysis of the GSE79197 was utilized to examine differentially biological functions in pregnant women with HEV infection (P-HEV) versus without HEV infection (P-nHEV), P-HEV progressing to ALF (P-ALF) versus P-HEV, and P-HEV versus non-pregnant women with HEV infection (nP-HEV). We found cellular response to interleukin and immune response-regulating signalings were activated in P-HEV compared with P-nHEV. However, there was a significant decrease of immune responses, such as T cell activation, leukocyte cell-cell adhesion, regulation of lymphocyte activation, and immune response-regulating signaling pathway in P-ALF patient than P-HEV patient. Compared with nP-HEV, MHC protein complex binding function was inhibited in P-HEV. Further microRNA enrichment analysis showed that MAPK and T cell receptor signaling pathways were inhibited in P-HEV compared with nP-HEV. In summary, immune responses were activated during HEV infection while being suppressed when developing ALF during pregnancy, heightening the importance of immune mediation in the pathogenesis of severe outcome in HEV infected pregnant women.

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.

Background: In low-dose computed tomography (LDCT) lung cancer screening, soft tissue is hardly appreciable due to high noise levels. While deep learning-based LDCT denoising methods have shown promise, they typically rely on structurally aligned synthesized paired data, which lack consideration of the clinical reality that there are no aligned LDCT and normal-dose CT (NDCT) images available. This study introduces an LDCT denoising method using clinically structure-unaligned but paired data sets (LDCT and NDCT scans from the same patients) to improve lesion detection during LDCT lung cancer screening. Methods: A cohort of 64 patients undergoing both LDCT and NDCT was randomly divided into training (n=46) and testing (n=18) sets. A two-stage training approach was adopted. First, Gaussian noise was added to NDCT data to create simulated LDCT data for generator training. Then, the model was trained on a clinically structure-unaligned paired data set using a Wasserstein generative adversarial network (WGAN) framework with the initial generator weights obtained during the first stage of training. An attention mechanism was also incorporated into the network. Results: Validated on a clinical CT data set, our proposed method outperformed other available methods [CycleGAN, Pixel2Pixel, block-matching and three-dimensional filtering (BM3D)] in noise removal and detail retention tasks in terms of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE) metrics. Compared with the results produced by BM3D, our method yielded an average improvement of approximately 7% in terms of the three evaluation indicators. The probability density profile of the denoised CT output produced using our method best fit the reference NDCT scan. Additionally, our two-stage model outperformed the one-stage WGAN-based model in both objective and subjective evaluations, further demonstrating the higher effectiveness of our two-stage training approach. Conclusions: The proposed method performed the best in removing noise from LDCT scans and exhibited good detail retention, which could potentially enhance the lesion detection and characterization effects obtained for soft tissues in the scanning scope of LDCT lung cancer screening.

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images.

Objective.Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion.Approach.In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices.Main results.To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods.Significance.The Voronoi diagram in the paper serves as prior information for the active contour, providing positional information for individual cells. Moreover, the active contour model achieves precise segmentation results while offering mathematical interpretability.

Spectral CT assists differentiation of osteoblastic bone metastasis from bone island in newly diagnosed cancer patients.

OBJECTIVES: To investigate measurements derived from plain and enhanced spectral CT in differentiating osteoblastic bone metastasis (OBM) from bone island (BI). MATERIALS AND METHODS: From January to November 2020, 73 newly diagnosed cancer patients with 201 bone lesions (OBM = 92, BI = 109) having received spectral CT were retrospectively enrolled. Measurements including CT values of 40-140 keV, slope of the spectral curve, effective atomic number (Zeff), water (calcium) density, calcium (water) density, and Iodine (calcium) density were derived from manually segmented lesions on plain and enhanced spectral CT, and then analyzed using Student t-test and Pearson's correlation. Multivariate analysis was performed to build models (plain spectral model, enhanced spectral CT model, and combined model) for the discrimination of OBM and BI with performance evaluated using receiver operator characteristics curve and DeLong test. RESULTS: All features were significantly different between the BI group and OBM group (all p < 0.05), highly correlated with the corresponding features between plain and enhanced spectral CT both in OBM (r: 0.392-0.763) and BI (r: 0.430-0.544). As for the model performance, the combined model achieved the best performance (AUC = 0.925, 95% CI: 0.879 to 0.957), which significantly outperformed the plain spectral CT model (AUC = 0.815, 95% CI: 0.754 to 0.866, p < 0.001) and enhanced spectral CT model (AUC = 0.901, 95% CI: 0.852 to 0.939, p = 0.024) in differentiating OBM and BI. CONCLUSION: In addition to plain spectral CT measurements, enhanced spectral CT measurements would further significantly benefit the differential diagnosis. CLINICAL RELEVANCE STATEMENT: Measurements derived either from plain or enhanced spectral CT could provide additional valuable information to improve the differential diagnosis between OBM and BI in newly diagnosed cancer patients. KEY POINTS: • We intend to investigate plain and enhanced spectral CT measurements in differentiating OBM from BI. • Both plain and enhanced spectral CT help in discriminating OBM and BI in newly diagnosed cancer patients. • Enhanced spectral CT measurements further improve plain spectral CT measurements-based differential diagnosis.

Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions.

OBJECTIVES: To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications. MATERIALS AND METHODS: From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes. RESULTS: The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970). CONCLUSION: In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity. CLINICAL RELEVANCE STATEMENT: Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions. KEY POINTS: • Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.

Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma.

BACKGROUND: Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC. METHODS: Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio. Recurrence was defined as cytology/pathology-proven disease or morphological evidence of lesions on imaging examinations within 5 years after surgery. Radiomics features were extracted from manually segmented tumor on CT images and were then selected using four different feature selection methods sequentially. Multivariate logistic regression analysis was conducted to identify clinical features associated with recurrence. Radiomics, clinical, and combined models were constructed separately using logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and neural network (NN), respectively. Receiver operating characteristic analysis was performed to evaluate the model performance in predicting recurrence. A nomogram was established based on all relevant features, with its reliability and reproducibility verified using calibration curves and decision curve analysis (DCA). RESULTS: Eighty-nine patients with PTC experienced recurrence. A total of 1218 radiomics features were extracted from each segmentation. Five radiomics and six clinical features were related to recurrence. Among the 4 radiomics models, the LR-based and SVM-based radiomics models outperformed the NN-based radiomics model (P = 0.032 and 0.026, respectively). Among the 4 clinical models, only the difference between the area under the curve (AUC) of the LR-based and NN-based clinical model was statistically significant (P = 0.035). The combined models had higher AUCs than the corresponding radiomics and clinical models based on the same classifier, although most differences were not statistically significant. In the validation cohort, the combined models based on the LR, SVM, KNN, and NN classifiers had AUCs of 0.746, 0.754, 0.669, and 0.711, respectively. However, the AUCs of these combined models had no significant differences (all P > 0.05). Calibration curves and DCA indicated that the nomogram have potential clinical utility. CONCLUSIONS: The combined model may have potential for better prediction of PTC recurrence than radiomics and clinical models alone. Further testing with larger cohort may help reach statistical significance.

A classifier-combined method for grading breast cancer based on Dempster-Shafer evidence theory.

Background: Preoperative non-invasive histologic grading of breast cancer is essential. This study aimed to explore the effectiveness of a machine learning classification method based on Dempster-Shafer (D-S) evidence theory for the histologic grading of breast cancer. Methods: A total of 489 contrast-enhanced magnetic resonance imaging (MRI) slices with breast cancer lesions (including 171 grade Ⅰ, 140 grade Ⅱ, and 178 grade Ⅲ lesions) were used for analysis. All the lesions were segmented by two radiologists in consensus. For each slice, the quantitative pharmacokinetic parameters based on a modified Tofts model and the textural features of the segmented lesion on the image were extracted. Principal component analysis was then used to reduce feature dimensionality and obtain new features from the pharmacokinetic parameters and texture features. The basic confidence assignments of different classifiers were combined using D-S evidence theory based on the accuracy of three classifiers: support vector machine (SVM), Random Forest, and k-nearest neighbor (KNN). The performance of the machine learning techniques was evaluated in terms of accuracy, sensitivity, specificity, and the area under the curve. Results: The three classifiers showed varying accuracy across different categories. The accuracy of using D-S evidence theory in combination with multiple classifiers reached 92.86%, which was higher than that of using SVM (82.76%), Random Forest (78.85%), or KNN (87.82%) individually. The average area under the curve of using the D-S evidence theory combined with multiple classifiers reached 0.896, which was larger than that of using SVM (0.829), Random Forest (0.727), or KNN (0.835) individually. Conclusions: Multiple classifiers can be effectively combined based on D-S evidence theory to improve the prediction of histologic grade in breast cancer.

A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.

Background: Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition. Methods: A fully automatic segmentation deep learning model was designed based on the attention mechanism and using U-Net as the framework. Subcutaneous fat, skeletal muscle, and visceral fat were manually segmented by two experts to serve as ground truth labels. The performance of the model was evaluated using Dice similarity coefficients (DSCs) and Hausdorff distance at 95th percentile (HD95). Results: The mean DSC for subcutaneous fat and skeletal muscle were high for both the enhanced CT test set (0.93±0.06 and 0.96±0.02, respectively) and the plain CT test set (0.90±0.09 and 0.95±0.01, respectively). Nevertheless, the model did not perform well in the segmentation performance of visceral fat, especially for the enhanced CT test set. The mean DSC for the enhanced CT test set was 0.87±0.11, while the mean DSC for the plain CT test set was 0.92±0.03. We discuss the reasons for this result. Conclusions: This work demonstrates a method for the automatic outlining of subcutaneous fat, skeletal muscle, and visceral fat areas at L3.

Utility of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) to predict prognosis and survival risk in laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy.

PURPOSE: To investigate the predictive power of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) in prognosis and survival risk of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy. MATERIALS AND METHODS: Forty-five patients with laryngeal or hypopharyngeal squamous cell carcinoma were retrospectively enrolled. All patients had undergone pretreatment IVIM examination, subsequently, mean apparent diffusion coefficient (ADCmean), maximum ADC (ADCmax), minimum ADC (ADCmin) and ADCrange (ADCmax - ADCmean) by mono-exponential model, true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f) by bi-exponential model, distributed diffusion coefficient (DDC), and diffusion heterogeneity index (α) by stretched exponential model were measured. Survival data were collected for 5 years. RESULTS: Thirty-one cases were in the treatment failure group and fourteen cases were in the local control group. Significantly lower ADCmean, ADCmax, ADCmin, D, f, and higher D* values were observed in the treatment failure group than in the local control group (p < 0.05). D* had the greatest AUC of 0.802, with sensitivity and specificity of 77.4 and 85.7% when D* was 38.85 × 10-3 mm2/s. Kaplan-Meier survival analysis showed that the curves of N stage, ADCmean, ADCmax, ADCmin, D, D*, f, DDC, and α values were significant. Multivariate Cox regression analysis showed ADCmean and D* were independently correlated with progression-free survival (PFS) (hazard ratio [HR] = 0.125, p = 0.001; HR = 1.008, p = 0.002, respectively). CONCLUSION: The pretreatment parameters of mono-exponential and bi-exponential models were significantly correlated with prognosis of LHSCC, ADCmean and D* values were independent factors for survival risk prediction.

Breast Amide Proton Transfer Imaging at 3 T: Diagnostic Performance and Association With Pathologic Characteristics.

BACKGROUND: Amide proton transfer (APT) imaging has been increasingly applied in tumor characterization. However, its value in evaluating breast cancer remains undetermined. PURPOSE: To assess the diagnostic performance of APT imaging in breast cancer and its association with prognostic histopathologic characteristics. STUDY TYPE: Prospective. SUBJECTS: Eighty-four patients with breast lesions. FIELD STRENGTH/SEQUENCE: A 3.0 T/single-shot fast spin echo APT imaging. ASSESSMENT: APTw signal in breast lesion was quantified. Lesion malignancy, T stage, grades, Ki-67 index, molecular biomarkers (estrogen receptor [ER] expression, progesterone receptor [PR] expression, human epidermal growth factor receptor [HER-2] expression), molecular subtypes (luminal A, luminal B, triple negative, and HER-2 enriched) were determined. STATISTICAL TESTS: Student t-test, one-way analysis of variance, receiver operating characteristic analysis, and Pearson's correlation with P < 0.05 as statistical significance. RESULTS: APTw signal was significantly higher in malignant lesions (1.55% ± 1.24%) than in benign lesions (0.54% ± 1.13%), and in grade III lesions than in grade II lesions (1.65% ± 0.84% vs. 0.96% ± 0.96%), and in T2- (1.57% ± 0.64%) and T3-stage lesions (1.54% ± 0.63%) than in T1-stage lesions (0.81% ± 0.64%) for invasive breast carcinoma of no special type. APTw signal significantly correlated with Ki-67 index (r = 0.364) but showed no significant difference in groups of ER (P = 0.069), PR (P = 0.069), HER-2 (P = 0.961), and among molecular subtypes (P = 0.073). DATA CONCLUSION: APT imaging shows potential in differentiating breast lesion malignancy and associates with prognosis-related tumor grade, T stage, and proliferative activity. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Added value of histogram analysis of ADC in predicting radiation-induced temporal lobe injury of patients with nasopharyngeal carcinoma treated by intensity-modulated radiotherapy.

BACKGROUND: This study evaluated the predictive potential of histogram analysis derived from apparent diffusion coefficient (ADC) maps in radiation-induced temporal lobe injury (RTLI) of nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). RESULTS: Pretreatment diffusion-weighted imaging (DWI) of the temporal lobes of 214 patients with NPC was retrospectively analyzed to obtain ADC histogram parameters. Of the 18 histogram parameters derived from ADC maps, 7 statistically significant variables in the univariate analysis were included in the multivariate logistic regression analysis. The final best prediction model selected by backward stepwise elimination with Akaike information criteria as the stopping rule included kurtosis, maximum energy, range, and total energy. A Rad-score was established by combining the four variables, and it provided areas under the curve (AUCs) of 0.95 (95% confidence interval [CI] 0.91-0.98) and 0.89 (95% CI 0.81-0.97) in the training and validation cohorts, respectively. The combined model, integrating the Rad-score with the T stage (p = 0.02), showed a favorable prediction performance in the training and validation cohorts (AUC = 0.96 and 0.87, respectively). The calibration curves showed a good agreement between the predicted and actual RTLI occurrences. CONCLUSIONS: Pretreatment histogram analysis of ADC maps and their combination with the T stage showed a satisfactory ability to predict RTLI in NPC after IMRT.

PPA1, an energy metabolism initiator, plays an important role in the progression of malignant tumors.

Inorganic pyrophosphatase (PPA1) encoded by PPA1 gene belongs to Soluble Pyrophosphatases (PPase) family and is expressed widely in various tissues of Homo sapiens, as well as significantly in a variety of malignancies. The hydrolysis of inorganic pyrophosphate (PPi) to produce orthophosphate (Pi) not only dissipates the negative effects of PPi accumulation, but the energy released by this process also serves as a substitute for ATP. PPA1 is highly expressed in a variety of tumors and is involved in proliferation, invasion, and metastasis during tumor development, through the JNK/p53, Wnt/ß-catenin, and PI3K/AKT/GSK-3ß signaling pathways. Because of its remarkable role in tumor development, PPA1 may serve as a biological target for adjuvant therapy of tumor malignancies. Further, PPA1 is a potential biomarker to predict survival in patients with cancer, where the assessment of its transcriptional regulation can provide an in-depth understanding. Herein, we describe the signaling pathways through which PPA1 regulates malignant tumor progression and provide new insights to establish PPA1 as a biomarker for tumor diagnosis.

Induction TPF followed by concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in locally advanced hypopharyngeal cancer: a preliminary analysis of a randomized phase 2 trial.

PURPOSE: Concurrent chemoradiotherapy (CCRT) is a standard treatment choice for locally advanced hypopharyngeal carcinoma. The aim of this study was to investigate whether induction chemotherapy (IC) followed by CCRT is superior to CCRT alone to treat locally advanced hypopharyngeal carcinoma. METHODS AND MATERIALS: Patients (n = 142) were randomized to receive two cycles of paclitaxel/cisplatin/5-fluorouracil (TPF) IC followed by CCRT or CCRT alone. The primary end point was overall survival (OS). The secondary end points included the larynx-preservation rate, progression-free survival (PFS), distant metastasis-free survival (DMFS), and toxicities. RESULTS: Ultimately, 113 of the 142 patients were analyzed. With a median follow-up of 45.6 months (interquartile range 26.8-57.8 months), the 3-year OS was 53.1% in the IC + CCRT group compared with 54.8% in the CCRT group (hazard ratio, 1.004; 95% confidence interval, 0.573-1.761; P = 0.988). There were no statistically significant differences in PFS, DMFS, and the larynx-preservation rate between the two groups. The incidence of grade 3-4 hematological toxicity was much higher in the IC+ CCRT group than in the CCRT group (54.7% vs. 10%, P < 0.001). CONCLUSIONS: Adding induction TPF to CCRT did not improve survival and the larynx-preservation rate in locally advanced hypopharyngeal cancer, but caused a higher incidence of acute hematological toxicities. TRIAL REGISTRATION: ClinicalTrials.gov , number NCT03558035. Date of first registration, 15/06/2018.

Segmentation-guided Denoising Network for Low-dose CT Imaging.

BACKGROUND: To reduce radiation exposure and improve diagnosis in low-dose computed tomography, several deep learning (DL)-based image denoising methods have been proposed to suppress noise and artifacts over the past few years. However, most of them seek an objective data distribution approximating the gold standard and neglect structural semantic preservation. Moreover, the numerical response in CT images presents substantial regional anatomical differences among tissues in terms of X-ray absorbency. METHODS: In this paper, we introduce structural semantic information for low-dose CT imaging. First, the regional segmentation prior to low-dose CT can guide the denoising process. Second the structural semantical results can be considered as evaluation metrics on the estimated normal-dose CT images. Then, a semantic feature transform is engaged to combine the semantic and image features on a semantic fusion module. In addition, the structural semantic loss function is introduced to measure the segmentation difference. RESULTS: Experiments are conducted on clinical abdomen data obtained from a clinical hospital, and the semantic labels consist of subcutaneous fat, muscle and visceral fat associated with body physical evaluation. Compared with other DL-based methods, the proposed method achieves better performance on quantitative metrics and better semantic evaluation. CONCLUSIONS: The quantitative experimental results demonstrate the promising performance of the proposed methods in noise reduction and structural semantic preservation. While, the proposed method may suffer from several limitations on abnormalities, unknown noise and different manufacturers. In the future, the proposed method will be further explored, and wider applications in PET/CT and PET/MR will be sought.

Prediction model based on preoperative CT findings for carotid artery invasion in patients with head and neck masses.

Objectives: To investigate the performance of a model in predicting carotid artery (CA) invasion in patients with head and neck masses using computed tomography (CT). Methods: This retrospective study included patients with head and neck masses who underwent CT and surgery between January 2013 and July 2021. Patient characteristics and ten CT features were assessed by two radiologists. The patients were randomly allocated to a training cohort (n=106) and a validation cohort (n=109). Independent risk factors for CA invasion were assessed by univariate and multivariate logistic regression analyses. The predictive model was established as a nomogram using the training cohort. In addition, the calibration, discrimination, reclassification, and clinical application of the model were assessed in the validation cohort. Results: A total of 215 patients were evaluated, including 54 patients with CA invasion. Vascular wall deformation (odds ratio [OR], 7.17; p=0.02) and the extent of encasement to the CA (OR, 1.02; p<0.001) were independent predictors of CA invasion in the multivariable analysis in the training cohort. The performance of the model was similar between the training and validation cohort, with an area under the receiver operating characteristic curve of 0.93 (95% confidence intervals [CI], 0.88-0.98) and 0.88 (95% CI, 0.80-0.96) (p=0.07), respectively. The calibration curve showed a good agreement between the predicted and actual probabilities. Conclusion: A predictive model for carotid artery invasion can be defined based on features that come from patient characteristics and CT data to help in improve surgical planning and invasion evaluation.

Magnetic resonance imaging-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma after intensity-modulated radiotherapy.

BACKGROUND: To develop a model based on magnetic resonance imaging (MRI) radiomics and clinical features for predicting radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). METHODS: Two hundred and sixteen patients with NPC were retrospectively included. Radiomics features were extracted and selected. The logistic regression analysis was performed for prediction models construction. The area under the receiver operating characteristic curve (AUC) was calculated for performance evaluation. RESULTS: Three radiomics features were selected to construct the radiomics signature (AUC of 0.94 and 0.92). The clinical-radiomics model, integrating radiomics signature with T classification, achieved higher predictive performance in the training and validation cohorts (AUC of 0.95 and 0.93), as well as improved accuracy of the classification of RTLI outcomes (net reclassification improvement: 0.711; 95% CI: 0.57-0.86; p < 0.001). CONCLUSIONS: The clinical-radiomics model and radiomics signature both showed great performance in predicting RTLI in patients with NPC.

Improved GAN: Using a transformer module generator approach for material decomposition.

Dual-energy computed tomography (CT) can be used for material decomposition, allowing for the precise quantitative mapping of body substances; this has a wide range of clinical applications, including disease diagnosis, treatment response evaluation and prognosis prediction. However, dual-energy CT has not yet become the mainstream technique in most clinical settings due to its limited accessibility. To fully take advantage of material quantification, researchers have attempted to use deep learning to generate material decomposition maps from conventional single-energy CT images, mainly by synthesizing another single-energy CT image from a conventional single-energy CT image to form a dual-energy CT image first and then generate material decomposition maps. This is not a straightforward process, and it potentially introduces many inaccuracies after multiple steps. In this work, we proposed a generative adversarial network (GAN) framework as the base and improved its generator; this approach combines convolutional neural networks (CNNs) and a transformer module to directly generate material decomposition maps from conventional single-energy CT images. Our model pays attention to both local and global information. Then, we compared our method with 6 competitive deep learning methods on water (calcium) and calcium (water) substrate density image datasets. The average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the water (calcium) substrate density images were 32.7207, 0.9685, 0.0323, and 0.0555, respectively. Furthermore, the average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the calcium (water) substrate density images were 30.2823, 0.9449, 0.0652, and 0.0715, respectively. Our model achieved better performance and stronger stability than competing approaches.

Sarcopenia Was a Poor Prognostic Predictor for Patients With Advanced Lung Cancer Treated With Immune Checkpoint Inhibitors.

Background: It remains not well known whether skeletal muscle mass (SMM) loss has any impact on the effectiveness of immune checkpoint inhibitors (ICIs) in patients with advanced lung cancer. We aimed to evaluate the association between SMM and clinical outcome of patients with advanced lung cancer receiving ICIs as first line or second line. Materials and Methods: From March 1st, 2019 to March 31st, 2021 at our hospital, 34 patients with advanced lung cancer treated with first-line or second-line ICIs were enrolled retrospectively. The estimation of skeletal muscle index (SMI) for sarcopenia was assessed at the level of the third lumbar vertebra (L3) on computed tomography (CT) images obtained within 4 weeks before initiation of ICIs treatment. The impact of sarcopenia (low SMI) on progression free survival (PFS) was analyzed using Kaplan-Meier method and log-rank tests. The effect of various variables on PFS was evaluated using Cox proportional hazards regression model with univariate and multivariate analysis. The impact on treatment response including objective response rate (ORR) and disease control rate (DCR) and immunotherapy related adverse events (irAEs) between patients with and without sarcopenia was compared by the chi-squared test. The comparison of SMI value between patients with objective response (OR), disease control (DC) and those without OR and DC was used student t-test or Mann-Whitney U test. Results: Both in univariate and multivariate analysis, sarcopenia and treatment lines were the predictive factors for PFS (p < 0.05). Patients with sarcopenia had significantly shorter PFS than that of non-sarcopenic ones [6.57 vs. 16.2 months, hazard ratios (HR) = 2.947 and 3.542, and 95% confidence interval (CI): 1.123-13.183 and 1.11-11.308, p = 0.022 and 0.033]. No significant difference in ORR and irAEs was found. Patients with sarcopenia had lower DCR than those without sarcopenia. The mean SMI value of DCR group and non-DCR group was 32.94 ± 5.49 and 44.77 ± 9.06 cm2/m2, respectively (p = 0.008). Conclusion: Sarcopenia before immunotherapy might be a significant predictor for poor prognosis including shorter PFS and lower DCR in patients with advanced lung cancer treated with ICIs as first line or second line.

A MRI-based radiomics model predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

OBJECTIVES: To develop and validate a radiomics-based model for predicting radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) by pretreatment MRI of the temporal lobe. METHODS: A total of 216 patients with diagnosed NPC were retrospectively reviewed. Patients were randomly allocated to the training (n = 136) and the validation cohort (n = 80). Radiomics features were extracted from pretreatment contrast-enhanced T1- or fat-suppressed T2 weighted MRI. A radiomics signature was generated by the least absolute shrinkage and selection operator (LASSO) regression algorithm, Pearson correlation analysis, and univariable logistic analysis. Clinical features were selected with logistic regression analysis. Multivariable logistic regression analysis was conducted to develop three models for RTLI prediction in the training cohort: namely radiomics signature, clinical variables, and clinical-radiomics parameters. A radiomics nomogram was used and assessed with respect to calibration, discrimination, reclassification, and clinical application. RESULTS: The radiomics signature, composed of two radiomics features, was significantly associated with RTLI. The proposed radiomics model demonstrated favorable discrimination in both the training (AUC, 0.89) and the validation cohort (AUC, 0.92), outperforming the clinical prediction model (p < 0.05). Combining radiomics and clinical features, higher AUCs were achieved (AUC, 0.93 and 0.95), as well as a better calibration and improved accuracy of the prediction of RTLI. The clinical-radiomics model showed also excellent performance in predicting RTLI in different clinical-pathologic subgroups. CONCLUSION: A radiomics model derived from pretreatment MRI of the temporal lobe showed persuasive performance for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma. KEY POINTS: • Radiomics features from pretreatment MRI are associated with radiation-induced temporal lobe injury in nasopharyngeal carcinoma. • The radiomics model shows better predictive performance than a clinical model and was similar to a clinical-radiomics model. • A clinical-radiomics model shows excellent performance in the prediction of radiation-induced temporal lobe injury in different clinical-pathologic subgroups.

Demonstration of fast and equilibrium human muscle creatine CEST imaging at 3 T.

PURPOSE: Creatine chemical exchange saturation transfer (CrCEST) MRI is used increasingly in muscle imaging. However, the CrCEST measurement depends on the RF saturation duration (Ts) and relaxation delay (Td), and it is challenging to compare the results of different scan parameters. Therefore, this study aims to evaluate the quasi-steady-state (QUASS) CrCEST MRI on clinical 3T scanners. METHODS: T1 and CEST MRI scans of Ts/Td of 1 s/1 s and 2 s/2 s were obtained from a multi-compartment creatine phantom and 5 healthy volunteers. The CrCEST effect was quantified with asymmetry analysis in the phantom, whereas 5-pool Lorentzian fitting was applied to isolate creatine from phosphocreatine, amide proton transfer, combined magnetization transfer and nuclear Overhauser enhancement effects, and direct water saturation in four major muscle groups of the lower leg. The routine and QUASS CrCEST measurements were compared under two different imaging conditions. Paired Student's t-test was performed with p-values less than 0.05 considered statistically significant. RESULTS: The phantom study showed a substantial influence of Ts/Td on the routine CrCEST quantification (p = 0.02), and such impact was mitigated with the QUASS algorithm (p = 0.20). The volunteer experiment showed that the routine CrCEST, amide proton transfer, and combined magnetization transfer and nuclear Overhauser enhancement effects increased significantly with Ts and Td (p < 0.05) and were significantly smaller than the corresponding QUASS indices (p < 0.01). In comparison, the QUASS CrCEST MRI showed little dependence on Ts and Td, indicating its robustness and accuracy. CONCLUSION: The QUASS CrCEST MRI is feasible to provide fast and accurate muscle creatine imaging.