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.

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.

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.

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.

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.

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.

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.

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.

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.

Obliteration of portal venules contributes to portal hypertension in biliary cirrhosis.

The effects of the obliteration of portal venules (OPV) in cirrhotic portal hypertension are poorly understood. To investigate its contribution to portal hypertension in biliary cirrhosis and its underlying mechanism, we evaluated OPV using two-dimensional (2D) histopathology in liver explants from patients with biliary atresia (BA, n = 63), primary biliary cholangitis (PBC, n = 18), and hepatitis B-related cirrhosis (Hep-B-cirrhosis, n = 35). Then, three-dimensional (3D) OPV was measured by X-ray phase-contrast CT in two parallel models in rats following bile duct ligation (BDL) or carbon tetrachloride (CCl4) administration, representing biliary cirrhosis and post-necrotic cirrhosis, respectively. The portal pressure was also measured in the two models. Finally, the effects of proliferative bile ducts on OPV were investigated. We found that OPV was significantly more frequent in patients with biliary cirrhosis, including BA (78.57 ± 16.45%) and PBC (60.00 ± 17.15%), than that in Hep-B-cirrhotic patients (29.43 ± 14.94%, p < 0.001). OPV occurred earlier, evidenced by the paired liver biopsy at a Kasai procedure (KP), and was irreversible even after a successful KP in the patients with BA. OPV was also significantly more frequent in the BDL models than in the CCl4 models, as shown by 2D and 3D quantitative analysis. Portal pressure was significantly higher in the BDL model than that in the CCl4 model. With the proliferation of bile ducts, portal venules were compressed and irreversibly occluded, contributing to the earlier and higher portal pressure in biliary cirrhosis. OPV, as a pre-sinusoidal component, plays a key role in the pathogenesis of portal hypertension in biliary cirrhosis. The proliferated bile ducts and ductules gradually take up the 'territory' originally attributed to portal venules and compress the portal venules, which may lead to OPV in biliary cirrhosis. © 2024 The Pathological Society of Great Britain and Ireland.

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.

Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study.

PURPOSE: To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment. MATERIAL AND METHODS: A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis. RESULTS: The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p < 0.0001). CONCLUSIONS: This radiomics model resulted as a powerful, non-invasive auxiliary tool for accurately predicting prostate cancer aggressiveness. The radiomics model could reduce unnecessary biopsies and help improve the diagnostic performance of radiologists' PI-RADS. Yet, prospective studies are still needed to validate the radiomics models further. CRITICAL RELEVANCE STATEMENT: The radiomics model with MRI may help to accurately screen out clinically significant prostate cancer, thereby assisting physicians in making individualized treatment plans. KEY POINTS: • The diagnostic performance of the radiomics model using the Random Forest algorithm is comparable to the Prostate Imaging Reporting and Data System (PI-RADS) obtained by radiologists. • The performance of the adjusted Prostate Imaging Reporting and Data System (PI-RADS) was improved, which implied that the radiomics model could be a potential radiological assessment tool. • The radiomics model lowered the percentage of equivocal cases. Moreover, the Rad-scores can be used to characterize prostate cancer aggressiveness.

The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy.

BACKGROUND: The detection of local recurrence for prostate cancer (PCa) patients following radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was to construct and verify machine learning models with three different algorithms based on post-operative mpMRI for predicting local recurrence of PCa after RP and explore their potential clinical value compared with the Prostate Imaging for Recurrence Reporting (PI-RR) score of expert-level radiologists. METHODS: A total of 176 patients were retrospectively enrolled and randomly divided into training (n = 123) and testing (n = 53) sets. The PI-RR assessments were performed by two expert-level radiologists with access to the operative histopathological and pre-surgical clinical results. The radiomics models to predict local recurrence were built by utilizing three different algorithms (i.e., support vector machine [SVM], linear discriminant analysis [LDA], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]). The combined model integrating radiomics features and PI-RR score was developed using the most effective classifier. The classification performances of the proposed models were assessed by receiver operating characteristic (ROC) curve analysis. RESULTS: There were no significant differences between the training and testing sets concerning age, prostate-specific antigen (PSA), Gleason score, T-stage, seminal vesicle invasion (SVI), perineural invasion (PNI), and positive surgical margins (PSM). The radiomics model based on LR-LASSO exhibited superior performance than other radiomics models, with an AUC of 0.858 in the testing set; the PI-RR yielded an AUC of 0.833, and there was no significant difference between the best radiomics model and the PI-RR score. The combined model achieved the best predictive performance with an AUC of 0.924, and a significant difference was observed between the combined model and PI-RR score. CONCLUSIONS: Our radiomics model is an effective tool to predict PCa local recurrence after RP. By integrating radiomics features with the PI-RR score, our combined model exhibited significantly better predictive performance of local recurrence than expert-level radiologists' PI-RR assessment.

Prognostic significance of pan-immune-inflammation value (PIV) in nasopharyngeal carcinoma patients.

Background: Blood-based immune-inflammation indexes have been widely used to predict survival in a variety of cancers. In this research, we seeked to evaluate a novel immune-inflammation marker, named the pan-immune-inflammation value (PIV), in patients with nasopharyngeal carcinoma (NPC) undergoing definitive radiotherapy. Methods: A group of 377 patients with NPC was retrospectived analyzed. Clinical data and laboratory data were collected. Receiver operating characteristic (ROC) curve analysis was performed in order to determine the optimal PIV cut-off value. Survival curves were estimated by Kaplan-Meier method, and prognostic variables were identified using a Cox regression model. Additionally, we developed a nomogram and assessed its acuracy using the concordance index (C-index) and a calibration curve. Results: The optimal PIV cut-off value was 146.24 according to ROC analysis. High PIV was related to poorer Eastern Cooperative Oncology Group Performance Status (ECOG PS) score (p = 0.017), more advanced T (p＜0.001) and clinical stages (p = 0.024). In univariate analysis, older Age, poorer ECOG PS, higher Epstein-Barr virus DNA (EBV-DNA), advanced T, N and clinical stage, and higher PIV levels were related to patients' poorer overall survival (OS). Poorer ECOG PS, higher EBV-DNA, later T stage, later clinical stage, and higher PIV were associated with patients' poorer progression free survival (PFS). Male sex and later T stage were associated with patients' poorer locoregional recurrence free survival (LRRFS). Poorer ECOG PS, higher EBV-DNA, later T stage, later clinical stage, and higher PIV were associated with patients' poorer distant metastasis free survival (DMFS). Multivariate analysis demonstrated that PIV was an independent prognostic index for OS (HR 2.231, 95 % CI 1.241-4.011, P = 0.007), PFS (HR 1.664, 95 % CI 1.003-2.760, P = 0.049), and DMFS(HR 2.081, 95 % CI 1.071-4.044, P = 0.031). Nomogram C-indexes for the nomogram of OS were 0.684, and PFS were 0.62, respectively. Survival predictions and actual survival were consistent according to the calibration curve. Conclusions: Pre-treatment PIV is a promising biomarker for predicting survival in patients with NPC.

Prognostic value of native T1 and extracellular volume in patients with immunoglubin light-chain amyloidosis.

BACKGROUND: Cardiac involvement in patients with immunoglubin light-chain amyloidosis (AL) is a major determinant of treatment choice and prognosis, and early identification of high-risk patients can initiate intensive treatment strategies to achieve better survival. This study aimed to investigate the prognostic value of native T1 and ECV in patients with AL-cardiac amyloidosis (CA). METHODS: A total of 38 patients (mean age 59 ± 11 years) with AL diagnosed histopathologically from July 2017 to October 2021 were collected consecutively. All patients were performed 3.0-T cardiac magnetic resonance (CMR) including cine, T1 mapping, and late gadolinium enhancement (LGE). Pre- and post-contrast T1 mapping images were transferred to a dedicated research software package (CVI42 v5.11.3) to create parametric T1 and ECV values. In addition, clinical and laboratory data of all patients were collected, and patients or their family members were regularly followed up by telephone every 3 months. The starting point of follow-up was the time of definitive pathological diagnosis, and the main endpoint was all-cause death. Kaplan-Meier analysis and Cox proportional risk model were used to evaluate the association between native T1 and ECV and death in patients with CA. RESULTS: After a median follow-up of 27 (16, 37) months, 12 patients with CA died. Kaplan-Meier analysis showed that elevated native T1 and ECV were closely associated with poor prognosis in patients with CA. The survival rate of patients with ECV > 44% and native T1 > 1389ms were significantly lower than that of patients with ECV ≤ 44% and native T1 ≤ 1389ms (Log-rank P < 0.001), and was not associated with the presence of LGE. After adjusting for clinical risk factors and CMR measurements in a stepwise multivariate Cox regression model, ECV [risk ratio (HR):1.37, 95%CI: 1.09-1.73, P = 0.008] and native T1 (HR:1.01, 95%CI: 1.00-1.02, P = 0.037) remained independent predictors of all-cause mortality in patients with CA. CONCLUSIONS: Both native T1 and ECV were independently prognostic for mortality in patients with CA, and can be used as important indicators for clinical prognosis assessment of AL.

Trans-3, 5, 4'-trimethoxystilbene restrains non-small-cell lung carcinoma progression via suppressing M2 polarization through inhibition of m6A modified circPACRGL-mediated Hippo signaling.

BACKGROUND: Non-small-cell lung carcinoma (NSCLC) accounts for ∼85% of all lung carcinomas. Trans-3,5,4'-trimethoxystilbene (TMS) shows strong anti-tumor activity and induces tumor cell apoptosis. However, its function and mechanism in NSCLC still require investigation. METHODS: PMA was used to treated THP-1 cells for macrophage differentiation. The abundance and m6A modification of circPACRGL were examined with qRT-PCR and MeRIP. Colony forming, transwell, wound healing, and Western blotting assays were applied to analyze proliferation, invasion, migration, and EMT. Macrophage polarization was determined through flow cytometry analysis of M1 and M2 markers. The interplay between circPACRGL, IGF2BP2 and YAP1 was validated by RNA pull-down and RIP assays. Mice received subcutaneous injection of NSCLC cells as a mouse model of subcutaneous tumor. RESULTS: CircPACRGL was upregulated in NSCLC cells, but it was reduced by TMS treatment. CircPACRGL depletion blocked proliferation, migration, and invasion in H1299 and H1975 cells. TMS suppressed these malignant behaviors, but it was abolished by circPACRGL overexpression. In addition, NSCLC-derived exosomes delivered circPACRGL into THP-1 cells to promote its M2 polarization, but TMS inhibited these effects by downregulating exosomal circPACRGL. Mechanically, exosomal circPACRGL bound to IGF2BP2 to improve the stability of YAP1 mRNA and regulate Hippo signaling in polarized THP-1 cells. TMS inhibited NSCLC growth via suppressing Hippo signaling and M2 polarization in vivo. CONCLUSION: TMS restrains M2 polarization and NSCLC progression by reducing circPACRGL and inhibiting exosomal circPACRGL-mediated Hippo signaling. Thus, these findings provide a novel mechanism underlying NSCLC progression and potential therapeutic targets.

Quantitative assessment of bone marrow infiltration and characterization of tumor burden using dual-layer spectral CT in patients with multiple myeloma.

BACKGROUND: The aim of the study was to evaluate whether virtual calcium subtraction (VNCa) image extracted from dual-layer spectral CT could estimate bone marrow (BM) infiltration with MRI as the reference standard and characterize tumor burden in patients with multiple myeloma (MM). PATIENTS AND METHODS: Forty-seven patients with newly diagnosed MM were retrospectively enrolled. They had undergone whole-body low-dose dual-layer spectral CT (DLCT) and whole-body MRI within one week. VNCa images with calcium-suppressed (CaSupp) indices ranging from 25 to 95 at an interval of 10 and apparent diffusion coefficient (ADC) maps were quantitatively analyzed on vertebral bodies L1-L5 at the central slice of images. The optimal combination was selected by correlation analysis between CT numbers and ADC values. Then, it was used to characterize tumor burden by correlation analysis and receiver operating characteristic (ROC) curves analysis, including plasma cell infiltration rate (PCIR), high serum-free light chains (SFLC) ratio and the high-risk cytogenetic (HRC) status. RESULTS: The most significant quantitative correlation between CT numbers of VNCa images and ADC values could be found at CaSupp index 85 for averaged L1-L5 (r = 0.612, p < 0.001). It allowed quantitative evaluation of PCIR (r = 0.835, p < 0.001). It could also anticipate high SFLC ratio and the HRC status with area under the curve (AUC) of 0.876 and 0.760, respectively. CONCLUSIONS: The VNCa measurements of averaged L1-L5 showed the highest correlation with ADC at CaSupp index 85. It could therefore be used as additional imaging biomarker for non-invasive assessment of tumor burden if ADC is not feasible.