Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer.

OBJECTIVE: To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC). MATERIALS AND METHODS: A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy. RESULTS: The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035). CONCLUSION: Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies. CLINICAL RELEVANCE STATEMENT: The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making. KEY POINTS: Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.

Adaptive orthogonal directional total variation with kernel regression for CT image denoising.

BACKGROUND: Low-dose computed tomography (CT) has been successful in reducing radiation exposure for patients. However, the use of reconstructions from sparse angle sampling in low-dose CT often leads to severe streak artifacts in the reconstructed images. OBJECTIVE: In order to address this issue and preserve image edge details, this study proposes an adaptive orthogonal directional total variation method with kernel regression. METHODS: The CT reconstructed images are initially processed through kernel regression to obtain the N-term Taylor series, which serves as a local representation of the regression function. By expanding the series to the second order, we obtain the desired estimate of the regression function and localized information on the first and second derivatives. To mitigate the noise impact on these derivatives, kernel regression is performed again to update the first and second derivatives. Subsequently, the original reconstructed image, its local approximation, and the updated derivatives are summed using a weighting scheme to derive the image used for calculating orientation information. For further removal of stripe artifacts, the study introduces the adaptive orthogonal directional total variation (AODTV) method, which denoises along both the edge direction and the normal direction, guided by the previously obtained orientation. RESULTS: Both simulation and real experiments have obtained good results. The results of two real experiments show that the proposed method has obtained PSNR values of 34.5408 dB and 29.4634 dB, which are 1.2392-5.9333 dB and 2.828-6.7995 dB higher than the contrast denoising algorithm, respectively, indicating that the proposed method has good denoising performance. CONCLUSIONS: The study demonstrates the effectiveness of the method in eliminating strip artifacts and preserving the fine details of the images.

Radiomics model based on dual-energy CT can determine the source of thrombus in strokes with middle cerebral artery occlusion.

PURPOSE: To develop thrombus radiomics models based on dual-energy CT (DECT) for predicting etiologic cause of stroke. METHODS: We retrospectively enrolled patients with occlusion of the middle cerebral artery who underwent computed tomography (NCCT) and DECT angiography (DECTA). 70 keV virtual monoenergetic images (simulate conventional 120kVp CTA images) and iodine overlay maps (IOM) were reconstructed for analysis. Five logistic regression radiomics models for predicting cardioembolism (CE) were built based on the features extracted from NCCT, CTA and IOM images. From these, the best one was selected to integrate with clinical information for further construction of the combined model. The performance of the different models was evaluated and compared using ROC curve analysis, clinical decision curves (DCA), calibration curves and Delong test. RESULTS: Among all the radiomic models, model NCCT+IOM performed the best, with AUC = 0.95 significantly higher than model NCCT, model CTA, model IOM and model NCCT+CTA in the training set (AUC = 0.88, 0.78, 0.90,0.87, respectively, P < 0.05), and AUC = 0.92 in the testing set, significantly higher than model CTA (AUC = 0.71, P < 0.05). Smoking and NIHSS score were independent predictors of CE (P < 0.05). The combined model performed similarly to the model NCCT+IOM, with no statistically significant difference in AUC either in the training or test sets. (0.96 vs. 0.95; 0.94 vs. 0.92, both P > 0.05). CONCLUSION: Radiomics models constructed based on NCCT and IOM images can effectively determine the source of thrombus in stroke without relying on clinical information.

The Value of Radiomics Features Based on HRCT in Predicting whether the Lung Sub-Centimeter Pure Ground Glass Nodule is Benign or Malignant.

OBJECTIVE: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant. METHODS: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model. RESULTS: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model. CONCLUSION: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics.

Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

Central nervous system efficacy of aumolertinib versus gefitinib in patients with untreated, EGFR-mutated, advanced non-small cell lung cancer: data from a randomized phase III trial (AENEAS).

BACKGROUND: The initial randomized, double-blinded, actively controlled, phase III ANEAS study (NCT03849768) demonstrated that aumolertinib showed superior efficacy relative to gefitinib as first-line therapy in epidermal growth factor receptor (EGFR)-mutated advanced non-small cell lung cancer (NSCLC). Metastatic disease in the central nervous system (CNS) remains a challenge in the management of NSCLC. This study aimed to compare the efficacy of aumolertinib versus gefitinib among patients with baseline CNS metastases in the ANEAS study. METHODS: Eligible patients were enrolled and randomly assigned in a 1:1 ratio to orally receive either aumolertinib or gefitinib in a double-blinded fashion. Patients with asymptomatic, stable CNS metastases were included. Follow-up imaging of the same modality as the initial CNS imaging was performed every 6 weeks for 15 months, then every 12 weeks. CNS response was assessed by a neuroradiological blinded, independent central review (neuroradiological-BICR). The primary endpoint for this subgroup analysis was CNS progression-free survival (PFS). RESULTS: Of the 429 patients enrolled and randomized in the ANEAS study, 106 patients were found to have CNS metastases (CNS Full Analysis Set, cFAS) at baseline by neuroradiological-BICR, and 60 of them had CNS target lesions (CNS Evaluable for Response, cEFR). Treatment with aumolertinib significantly prolonged median CNS PFS compared with gefitinib in both cFAS (29.0 vs. 8.3 months; hazard ratio [HR] = 0.31; 95% confidence interval [CI], 0.17-0.56; P < 0.001) and cEFR (29.0 vs. 8.3 months; HR = 0.26; 95% CI, 0.11-0.57; P < 0.001). The confirmed CNS overall response rate in cEFR was 85.7% and 75.0% in patients treated with aumolertinib and gefitinib, respectively. Competing risk analysis showed that the estimated probability of CNS progression without prior non-CNS progression or death was consistently lower with aumolertinib than with gefitinib in patients with and without CNS metastases at baseline. No new safety findings were observed. CONCLUSIONS: These results indicate a potential advantage of aumolertinib over gefitinib in terms of CNS PFS and the risk of CNS progression in patients with EGFR-mutated advanced NSCLC with baseline CNS metastases. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT03849768.

Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease.

OBJECTIVE: To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD). MATERIALS AND METHODS: The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic. RESULTS: Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p < 0.05). The clinical radiomics model had significant higher C-index than clinical model (p = 0.030). There were no significant differences in C-index between clinical or clinical radiomics model and radiomics model (p values were 0.796 and 0.147 respectively). The AUC increased from 0.674 for clinical model to 0.721 for radiomics model, 0.759 for clinical radiomics model and 0.773 for combined model. CONCLUSION: Radiomics analysis of lesion-specific PCAT is useful in predicting MACE. Combination of lesion-specific Rad-score and CT-FFR shows incremental value over traditional risk factors.

Neural Progenitor Cell-Mediated Magnetic Nanoparticles for Magnetic Resonance Imaging and Photothermal Therapy of Glioma.

Glioma is the most common primary malignant tumor in the brain. The diagnostic accuracy and treatment efficiency of glioma are facing great challenges due to the presence of the blood-brain barrier (BBB) and the high infiltration of glioma. There is an urgent need to explore the combination of diagnostic and therapeutic approaches to achieve a more accurate diagnosis, as well as guidance before and after surgery. In this work, we induced human induction of pluripotent stem cell into neural progenitor cells (NPCs) and synthesized nanoprobes labeled with enhanced green fluorescent protein (EGFP, abbreviated as MFe3O4-labeled EGFP-NPCs) for photothermal therapy. Nanoprobes carried by NPCs can effectively penetrate the BBB and target glioma for the purpose of magnetic resonance imaging and guiding surgery. More importantly, MFe3O4-labeled EGFP-NPCs can effectively induce local photothermal therapy, conduct preoperative tumor therapy, and inhibit the recurrence of postoperative glioma. This work shows that MFe3O4-labeled EGFP-NPCs is a promising nanoplatform for glioma diagnosis, accurate imaging-guided surgery, and effective photothermal therapy.

A 32-Month Follow-Up Study of the Effect of APOE ε4 on the Whole Brain Connection in Young Healthy Individuals.

APOE ε4 is risk for cognitive decline even in normal aging, but its effect on the whole-brain functional connectivity (FC) among time in young adults remain elusive. This study aimed to validate the time-by-APOE ε4 interaction on brain FC of this specific population. Longitudinal changes in neuropsychological assessments and resting-state functional magnetic resonance imaging in 26 ε4 carriers and 26 matched non-ε4 carriers were measured for about 3 years. Whole-brain FC was calculated, and a full factorial design was used to compare the difference among groups. Two-sample t test was used for post-hoc analysis. Pearson's correlation analysis was conducted to investigate the relationships between FC and cognitive tests. Of 26 specially appointed ROIs, left superior temporal gyrus (TG) was most sensitive to the effect of time-by-gene interaction. Specifically, the alteration of FC was distributed between the left TG and right TG with GRF correction (voxel-P < 0.001, cluster-P < 0.05), and decreased in ε4 carriers while increased in non-ε4. The main effect of gene showed ε4 carriers has lower FC between left TG and right middle frontal gyrus as compared with non-ε4 both at baseline and follow-up study; ε4 carriers has lower FC between left TG and right supramarginal as compared with non-ε4 at baseline, but no difference in follow-up study. The time-by-APOE ε4 interaction on brain FC was demonstrated at a young age, and left TG was the earliest affected brain regions. The young adult ε4 carriers experience decreased FC among time in the absence overt clinical symptoms.

Finotonlimab with chemotherapy in recurrent or metastatic head and neck cancer: a randomized phase 3 trial.

Immunotherapy combined with chemotherapy regimen has been shown to be effective in recurrent or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC). However, due to the small number of patients, its efficacy remains controversial in Asian populations, particularly in mainland China. Here a randomized, double-blind phase 3 trial evaluated the efficacy and safety of finotonlimab (SCT-I10A), a programmed cell death 1 (PD-1) monoclonal antibody, combined with cisplatin plus 5-fluorouracil (C5F) for the first-line treatment of R/M HNSCC. Eligible patients (n = 370) were randomly 2:1 assigned to receive finotonlimab plus C5F (n = 247) or placebo plus C5F (n = 123). The primary endpoint was overall survival (OS). In the finotonlimab plus C5F group, OS was 14.1 months (95% confidence interval (CI) 11.1-16.4), compared with 10.5 months (95% CI 8.1-11.8) in the placebo plus C5F group. The hazard ratio was 0.73 (95% CI 0.57-0.95, P = 0.0165), meeting the predefined superiority criteria for the primary endpoint. Finotonlimab plus C5F showed significant OS superiority compared with C5F alone and acceptable safety profile with R/M HNSCC, supporting its use as a first-line treatment option for R/M HNSCC. These results validate the efficacy and safety of the combination of finotonlimab and C5F in Asian patients with R/M HNSCC. ClinicalTrials.gov identifier: NCT04146402 .

circ_PPAPDC1A promotes Osimertinib resistance by sponging the miR-30a-3p/ IGF1R pathway in non-small cell lung cancer (NSCLC).

BACKGROUND: Recent evidence has demonstrated that abnormal expression and regulation of circular RNA (circRNAs) are involved in the occurrence and development of a variety of tumors. The aim of this study was to investigate the effects of circ_PPAPDC1A in Osimertinib resistance in NSCLC. METHODS: Human circRNAs microarray analysis was conducted to identify differentially expressed (DE) circRNAs in Osimertinib-acquired resistance tissues of NSCLC. The effect of circ_PPAPDC1A on cell proliferation, invasion, migration, and apoptosis was assessed in both in vitro and in vivo. Dual-luciferase reporter assay, RT-qPCR, Western-blot, and rescue assay were employed to confirm the interaction between circ_PPAPDC1A/miR-30a-3p/IGF1R axis. RESULTS: The results revealed that circ_PPAPDC1A was significantly upregulated in Osimertinib acquired resistance tissues of NSCLC. circ_PPAPDC1A reduced the sensitivity of PC9 and HCC827 cells to Osimertinib and promoted cell proliferation, invasion, migration, while inhibiting apoptosis in Osimertinib-resistant PC9/OR and HCC829/OR cells, both in vitro and in vivo. Silencing circ_PPAPDC1A partially reversed Osimertinib resistance. Additionally, circ_PPAPDC1A acted as a competing endogenous RNA (ceRNA) by targeting miR-30a-3p, and Insulin-like Growth Factor 1 Receptor (IGF1R) was identified as a functional gene for miR-30a-3p in NSCLC. Furthermore, the results confirmed that circ_PPAPDC1A/miR-30a-3p/IGF1R axis plays a role in activating the PI3K/AKT/mTOR signaling pathway in NSCLC with Osimertinib resistance. CONCLUSIONS: Therefore, for the first time we identified that circ_PPAPDC1A was significantly upregulated and exerts an oncogenic role in NSCLC with Osimertinib resistance by sponging miR-30a-3p to active IGF1R/PI3K/AKT/mTOR pathway. circ_PPAPDC1A may serve as a novel diagnostic biomarker and therapeutic target for NSCLC patients with Osimertinib resistance.

Association between pretreatment emotional distress and immune checkpoint inhibitor response in non-small-cell lung cancer.

Emotional distress (ED), commonly characterized by symptoms of depression and/or anxiety, is prevalent in patients with cancer. Preclinical studies suggest that ED can impair antitumor immune responses, but few clinical studies have explored its relationship with response to immune checkpoint inhibitors (ICIs). Here we report results from cohort 1 of the prospective observational STRESS-LUNG study, which investigated the association between ED and clinical efficacy of first-line treatment of ICIs in patients with advanced non-small-cell lung cancer. ED was assessed by Patient Health Questionnaire-9 and Generalized Anxiety Disorder 7-item scale. The study included 227 patients with 111 (48.9%) exhibiting ED who presented depression (Patient Health Questionnaire-9 score ≥5) and/or anxiety (Generalized Anxiety Disorder 7-item score ≥5) symptoms at baseline. On the primary endpoint analysis, patients with baseline ED exhibited a significantly shorter median progression-free survival compared with those without ED (7.9 months versus 15.5 months, hazard ratio 1.73, 95% confidence interval 1.23 to 2.43, P = 0.002). On the secondary endpoint analysis, ED was associated with lower objective response rate (46.8% versus 62.1%, odds ratio 0.54, P = 0.022), reduced 2-year overall survival rate of 46.5% versus 64.9% (hazard ratio for death 1.82, 95% confidence interval 1.12 to 2.97, P = 0.016) and detriments in quality of life. The exploratory analysis indicated that the ED group showed elevated blood cortisol levels, which was associated with adverse survival outcomes. This study suggests that there is an association between ED and worse clinical outcomes in patients with advanced non-small-cell lung cancer treated with ICIs, highlighting the potential significance of addressing ED in cancer management. ClinicalTrials.gov registration: NCT05477979 .

Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.

Clear cell hepatocellular carcinoma: Gd-EOB-DTPA-enhanced MR imaging features and prognosis.

PURPOSE: To investigate imaging findings on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) and prognosis of clear cell hepatocellular carcinoma (CCHCC) comparing with non-otherwise specified hepatocellular carcinoma (NOS-HCC). METHODS: The clinical, pathological and MR imaging features of 42 patients with CCHCC and 84 age-matched patients with NOS-HCC were retrospectively analyzed from January 2015 to October 2021. Univariate and multivariate logistic regression and Cox regression analyses were performed to identify independent diagnostic and prognostic factors for CCHCC. Disease-free survival (DFS) and overall survival (OS) were determined by Kaplan-Meier analysis. RESULTS: CCHCC showed fat content more frequently (P < 0.001) and relatively higher Edmondson tumor grade (P = 0.001) compared with NOS-HCC. The lesion-to-muscle ratio (LMR) and lesion-to-liver ratio (LLR) of CCHCC on pre-enhancement T1-weighted imaging (pre-T1WI) (P = 0.001, P = 0.003) and hepatobiliary phase (HBP) (P = 0.007, P = 0.048) were significantly higher than those of NOS-HCC. The area under the curve (AUC) for fat content, LLR on pre-T1WI and their combination with better diagnostic performance in predicting CCHCC were 0.678, 0.666, and 0.750, respectively. There was no statistically significant difference in clinical outcomes between CCHCC and NOS-HCC. Multivariate Cox analysis confirmed that tumor size > 2 cm and enhancing capsule were independent prognostic factors for DFS and OS among CCHCC patients. CONCLUSION: Fat content and adjusted lesion signal intensity on pre-T1WI and HBP could be used to differentiate CCHCC from NOS-HCC. CCHCC had similar prognosis with NOS-HCC.

Multiscale X-ray phase-contrast CT unveils the evolution of bile infarct in obstructive biliary disease.

Bile infarct is a pivotal characteristic of obstructive biliary disease, but its evolution during the disease progression remains unclear. Our objective, therefore, is to explore morphological alterations of the bile infarct in the disease course by means of multiscale X-ray phase-contrast CT. Bile duct ligation is performed in mice to mimic the obstructive biliary disease. Intact liver lobes of the mice are scanned by phase-contrast CT at various resolution scales. Phase-contrast CT clearly presents three-dimensional (3D) images of the bile infarcts down to the submicron level with good correlation with histological images. The CT data illustrates that the infarct first appears on day 1 post-BDL, while a microchannel between the infarct and hepatic sinusoids is identified, the number of which increases with the disease progression. A 3D model of hepatic acinus is proposed, in which the infarct starts around the portal veins (zone I) and gradually progresses towards the central veins (zone III) during the disease process. Multiscale phase-contrast CT offers the comprehensive analysis of the evolutionary features of the bile infarct in obstructive biliary disease. During the course of the disease, the bile infarcts develop infarct-sinusoidal microchannels and gradually occupy the whole liver, promoting the disease progression.

Effects on brain structural and functional in deaf children after aerobic exercise training: a pilot cluster randomized controlled study.

Purpose: To examine effects of aerobic exercise interventions on brain via the structural Magnetic Resonance Imaging (MRI), as well as functional change during working memory (WM) task using fMRI in deaf children.Method: The study applied a cluster randomized controlled design. Twelve deaf children in the intervention group were required to complete an eleven-week aerobic exercise intervention, while other twelve age and gender matched deaf children in the control group were required to keep their normal daily life. Task fMRI images of each participant were acquired in the baseline and post intervention period. The surface-based morphometry (SBM) analysis and functional activation analysis were employed to probe the effects of 11-week aerobic exercise on cerebral structural and functional in deaf children, respectively.Results: The 11-week aerobic exercise intervention did not change brain structure in deaf children. However, behavior performance (reaction time and mean accuracy rate) presented significant improvements after the 11-week aerobic exercise intervention. Compared to the control group, the intervention group showed decreased reaction time in the 2-back (p < 0.001) and 2-0 back (p < 0.001), and increased mean accuracy rate during 2-back (p = 0.034). Furthermore, enhanced brain activations in the left supplementary motor cortex (p < 0.05, FDR-corrected) and left paracentral lobule (p < 0.05, FDR-corrected) were observed in the intervention group.Conclusion: 11-week aerobic exercise intervention may not be able to modulate brain structure in deaf children, but may have significantly positive effects on behavior performance and brain functional activation during WM task.

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning.

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.

Circ_PABPC1 promotes the malignancy of gastric cancer through interacting with ILK to activate NF-κB pathway.

BACKGROUND: Gastric cancer (GC) is a common cancer type with both high incidence and mortality. Recent studies have revealed an important role of circRNA in the development of GC. However, more experiments are needed to reveal the precise molecular mechanisms of circRNA in GC development. METHODS: Bioinformatics analysis was conducted to predict the potential role of circ_PABPC1 in GC and the target proteins of circ_PABPC1. Quantitative RT-PCR, Western blot and immunohistochemistry assays were conducted to detect the levels of circ_PABPC1, NF-κB p65, NF-κB p65 (Ser536) and ILK. MTT, Edu staining, cell scratch-wound and trans-well assays were carried out to detect cell proliferation, migration and invasion. The interaction between ILK and circ_PABPC1 was confirmed by RNA immunoprecipitation (RIP), RNA pull-down and fluorescence in situ hybridization assays. Genetically modified GC cells were injected into mice to evaluate the tumor growth performance. RESULTS: This study found that the high expression of circ_PABPC1 was associated with a poor prognosis of GC. The up-regulation of circ_PABPC1 promoted the proliferation, migration and invasion of GC cells. Circ_PABPC1 bound to ILK protein, thereby preventing the degradation of ILK. ILK mediated the effect of circ_PABPC1 on GC cells through activating NF-κB. CONCLUSION: circ_PABPC1 promotes the malignancy of GC cells through binding to ILK to activate NF-κB signaling pathway.

Uncertain prediction of deformable image registration on lung CT using multi-category features and supervised learning.

The assessment of deformable registration uncertainty is an important task for the safety and reliability of registration methods in clinical applications. However, it is typically done by a manual and time-consuming procedure. We propose a novel automatic method to predict registration uncertainty based on multi-category features and supervised learning. Three types of features, including deformation field statistical features, deformation field physiologically realistic features, and image similarity features, are introduced and calculated to train the random forest regressor for local registration uncertain prediction. Deformation field statistical features represent the numerical stability of registration optimization, which are correlated to the uncertainty of deformation fields; deformation field physiologically realistic features represent the biomechanical properties of organ motions, which mathematically reflect the physiological reality of deformation; image similarity features reflect the similarity between the warped image and fixed image. The multi-category features comprehensively reflect the registration uncertainty. The strategy of spatial adaptive random perturbations is also introduced to accurately simulate spatial distribution of registration uncertainty, which makes deformation field statistical features more discriminative to the uncertainty of deformation fields. Experiments were conducted on three publicly available thoracic CT image datasets. Seventeen randomly selected image pairs are used to train the random forest model, and 9 image pairs are used to evaluate the prediction model. The quantitative experiments on lung CT images show that the proposed method outperforms the baseline method for uncertain prediction of classical iterative optimization-based registration and deep learning-based registration with different registration qualities. The proposed method achieves good performance for registration uncertain prediction, which has great potential in improving the accuracy of registration uncertain prediction.

Nomograms based on the lymphocyte-albumin-neutrophil ratio (LANR) for predicting the prognosis of nasopharyngeal carcinoma patients after definitive radiotherapy.

Much evidence has accumulated to show that inflammation and nutritional status are associated with the prognosis of patients with various cancers. The present study was designed to explore the prognostic role of the LANR in NPC patients receiving definitive radiotherapy and to construct a nomogram for predicting patient survival. This study retrospectively reviewed 805 NPC patients (604 in the training cohort and 201 in the validation cohort) who received definitive radiotherapy between January 2013 and December 2019. The clinical data and pretreatment laboratory test data, including lymphocyte count, neutrophil count, and serum ALB concentration, were collected for all patients. The LANR was calculated as the albumin × lymphocyte/neutrophil ratio. Patients in the training cohort and validation cohort were categorized into high-LANR and low-LANR groups according to the corresponding cutoff values. The independent prognostic factors for overall survival (OS), progression-free survival (PFS), relapse-free survival (RFS), and metastasis-free survival (MFS) were evaluated by univariate and multivariate Cox regression analyses, and a nomogram was subsequently constructed. The performance of the nomogram was evaluated by the concordance index (C-index) and calibration curve. A low LANR (< 14.3) was independently associated with worse OS, PFS and MFS in NPC patients. A prognostic prediction nomogram was established based on T stage, N stage, Eastern Cooperative Oncology Group (ECOG) score, treatment modality, and LANR and was validated. The C-indices of the nomograms for OS and PFS in the training cohort were 0.729 and 0.72, respectively. The C-indices of the nomograms for OS and PFS in the validation cohort were 0.694 and 0.695, respectively. The calibration curve revealed good consistency between the actual survival and the nomogram prediction. Patients with NPC with low pretreatment LANR had a poor prognosis. The nomogram established on the basis of the LANR was efficient and clinically useful for predicting survival in NPC patients who underwent definitive radiotherapy.