Liver cancer-specific mutations in functional domains of ADAR2 lead to the elevation of coding and non-coding RNA editing in multiple tumor-related genes.

Mutation is the major cause of phenotypic innovations. Apart from DNA mutations, the alteration on RNA such as the ADAR-mediated A-to-I RNA editing could also shape the phenotype. These two layers of variations have not been systematically combined to study their collective roles in cancers. We collected the high-quality transcriptomes of ten hepatocellular carcinoma (HCC) and the matched control samples. We systematically identified HCC-specific mutations in the exonic regions and profiled the A-to-I RNA editome in each sample. All ten HCC samples had mutations in the CDS of ADAR2 gene (dsRNA-binding domain or catalytic domain). The consequence of these mutations converged to the elevation of ADAR2 efficiency as reflected by the global increase of RNA editing levels in HCC. The up-regulated editing sites (UES) were enriched in the CDS and UTR of oncogenes and tumor suppressor genes (TSG), indicating the possible roles of these target genes in HCC oncogenesis. We present the mutation-ADAR2-UES-oncogene/TSG-HCC axis that explains how mutations at different layers would finally lead to abnormal phenotype. In the light of central dogma, our work provides novel insights into how to fully take advantage of the transcriptome data to decipher the consequence of mutations.

Smart Chemical Oxidative Polymerization Strategy To Construct Au@PPy Core-Shell Nanoparticles for Cancer Diagnosis and Imaging-Guided Photothermal Therapy.

Imaging-guided photothermal therapy (PTT) in a single nanoscale platform has aroused extensive research interest in precision medicine, yet only a few methods have gained wide acceptance. Thus, it remained an urgent need to facilely develop biocompatible and green probes with excellent theranostic capacity for superior biomedical applications. In this study, a smart chemical oxidative polymerization strategy was successfully developed for the synthesis of Au@PPy core-shell nanoparticles with polyvinyl alcohol (PVA) as the hydrophile. In the reaction, the reactant tetrachloroauric acid (HAuCl4) was reduced by pyrrole to fabricate a gold (Au) core, and pyrrole was oxidized to deposit around the Au core to form a polypyrrole (PPy) shell. The as-synthesized Au@PPy nanoparticles showed a regular core-shell morphology and good colloidal stability. Relying on the high X-ray attenuation of Au and strong near-infrared (NIR) absorbance of PPy and Au, Au@PPy nanoparticles exhibited excellent performance in blood pool/tumor imaging and PTT treatment by a series of in vivo experiments, in which tumor could be precisely positioned and thoroughly eradicated. Hence, the facile chemical oxidative polymerization strategy for constructing monodisperse Au@PPy core-shell nanoparticles with potential for cancer diagnosis and imaging-guided photothermal therapy shed light on an innovative design concept for the facile fabrication of biomedical materials.

Predictive value of 18F-FDG PET/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner/center study with external validation.

PURPOSE: To develop and validate the predictive value of an 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) model for breast cancer neoadjuvant chemotherapy (NAC) efficacy based on the tumor-to-liver ratio (TLR) radiomic features and multiple data pre-processing methods. METHODS: One hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent 18F-FDG PET/CT imaging before NAC treatment, and CT and PET images volume of interest (VOI) segmentation by manual segmentation and semi-automated absolute threshold segmentation, respectively. Then, feature extraction of VOI was performed with the pyradiomics package. A total of 630 models were created based on the source of radiomic features, the elimination of the batch effect approach, and the discretization method. The differences in data pre-processing approaches were compared and analyzed to identify the best-performing model, which was further tested by the permutation test. RESULTS: A variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test. CONCLUSION: It is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.

Post-Modification of a Robust Covalent Organic Framework for Efficient Sequestration of 99 TcO4 - /ReO4.

Nuclear industry spent fuel reprocessing and some radioactive contamination sites often involve high acidity and salinity environments. Currently developed and reported sorbents in 99 TcO4 - sequestration from the nuclear waste are unstable and show low adsorption efficiency in harsh conditions. To address this issue, we developed a post-synthetic modification strategy by grafting imidazole-based ionic liquids (ILs) onto the backbone of covalent organic framework (COF) via vinyl polymerization. The resultant COF-polyILs sorbent exhibits fast adsorption kinetics (<5 min) and good sorption capacity (388 mg g-1 ) for ReO4 - (a nonradioactive surrogate of 99 TcO4 - ). Outstandingly, COF-polyILs composite shows superior ReO4 - removal even under highly acidic conditions and in the presence of excess competing ions of Hanford low-level radioactive waste stream, benefiting from the stable covalent bonds between the COF and polyILs, mass of imidazole rings, and hydrophobic pores in COF.

18F-FDG-PET/CT-based machine learning model evaluates indeterminate adrenal nodules in patients with extra-adrenal malignancies.

BACKGROUND: To assess the value of an 18F-FDG-positron emission tomography/computed tomography (PET/CT)-based machine learning model for distinguishing between adrenal benign nodules (ABNs) and adrenal metastases (AMs) in patients with indeterminate adrenal nodules and extra-adrenal malignancies. METHODS: A total of 303 patients who underwent 18F-FDG-PET/CT with indeterminate adrenal nodules and extra-adrenal malignancies from March 2015 to June 2021 were included in this retrospective study (training dataset (n = 182): AMs (n = 97), ABNs (n = 85); testing dataset (n = 121): AMs (n = 68), ABNs (n = 55)). The clinical and PET/CT imaging features of the two groups were analyzed. The predictive model and simplified scoring system for distinguishing between AMs and ABNs were built based on clinical and PET/CT risk factors using multivariable logistic regression in the training cohort. The performances of the predictive model and simplified scoring system in both the training and testing cohorts were evaluated by the areas under the receiver operating characteristic curves (AUCs) and calibration curves. The comparison of AUCs was evaluated by the DeLong test. RESULTS: The predictive model included four risk factors: sex, the ratio of the maximum standardized uptake value (SUVmax) of adrenal lesions to the mean liver standardized uptake value, the value on unenhanced CT (CTU), and the clinical stage of extra-adrenal malignancies. The model achieved an AUC of 0.936 with a specificity, sensitivity and accuracy of 0.918, 0.835, and 0.874 in the training dataset, respectively, while it yielded an AUC of 0.931 with a specificity, sensitivity, and accuracy of 1.00, 0.735, and 0.851 in the testing dataset, respectively. The simplified scoring system had comparable diagnostic value to the predictive model in both the training (AUC 0.938, sensitivity: 0.825, specificity 0.953, accuracy 0.885; P = 0.5733) and testing (AUC 0.931, sensitivity 0.735, specificity 1.000, accuracy 0.851; P = 1.00) datasets. CONCLUSIONS: Our study showed the potential ability of a machine learning model and a simplified scoring system based on clinical and 18F-FDG-PET/CT imaging features to predict AMs in patients with indeterminate adrenal nodules and extra-adrenal malignancies. The simplified scoring system is simple, convenient, and easy to popularize.

MOFs-Derived Fe-N Codoped Carbon Nanoparticles as O2-Evolving Reactor and ROS Generator for CDT/PDT/PTT Synergistic Treatment of Tumors.

Metal-organic frameworks (MOFs) derivatives had been widely explored in electronic and environmental fields, but rarely evaluated in the biomedical applications. Herein, Fe-N codoped carbon (FeNC) nanoparticles were synthesized and characterized via facile pyrolysis of precursor ZIF-8 (Fe/Zn) nanoparticles, and their potential applications in tumor therapy were assessed in this investigation both in vitro and in vivo. After PAA (sodium polyacrylate) modification, the FeNC@PAA nanoparticles were able to initiate a Fe-based Fenton-like reaction to generate ·OH and O2 for chemodynamic therapy (CDT) and O2 evolution. Meanwhile, the porphyrin-like metal center in the FeNC@PAA nanoparticles could be used as a photosensitizer for photodynamic therapy (PDT) of tumors, which could be enhanced by O2 generated in CDT. Furthermore, the FeNC@PAA nanoparticles were also found to be effective in photothermal therapy (PTT) with a photothermal conversion efficiency of 29.15%, owing to a high absorbance in the near-infrared region (NIR). In conclusion, the synthesized FeNC@PAA nanoparticles exhibited promising applications in O2 evolution and CDT/PDT/PTT synergistic treatment of tumors.

Prediction of mediastinal lymph node metastasis based on 18F-FDG PET/CT imaging using support vector machine in non-small cell lung cancer.

OBJECTIVE: The purpose of this study was to develop a classification method based on support vector machine (SVM) to improve the diagnostic performance of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) to detect the lymph node (LN) metastasis in non-small cell lung cancer (NSCLC). METHOD: Two hundred nineteen lymph nodes (37 metastatic) from 71 patients were evaluated in this study. SVM models were developed with 7 LN features. The area under the curve (AUC) and accuracy of 9 models were compared to select the best model. The best SVM model was simplified on the basis of the feature weights and value distribution to further suit the clinical application. RESULTS: The maximum, minimum, and mean accuracy of the best model was 91.89% (68/74, 95% CI 83.11~96.54%), 66.22% (49/74, 95% CI 54.85~75.98%), and 80.09% (59,266/74,000, 95% CI 70.27~89.19%), respectively, with an AUC of 0.94, 0.66, and 0.81, respectively. The best SVM model was finally simplified into a score rule: LNs with scores more than 3.0 were considered as malignant ones, whereas LNs with scores less than 1.5 tended to be benign ones. For the LNs with scores within a range of 1.5-3.0, metastasis was suspected. CONCLUSION: An SVM model based on 18F-FDG PET/CT images was able to predict the metastatic LNs for patients with NSCLC. The ratio of the maximum of standard uptake value of LNs to aortic arch played a major role in the model. After simplification, the model could be transferred into a scoring method which may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier. KEY POINTS: • The SVM model based on 18F-FDG PET/CT features may help clinicians to make a decision for metastatic mediastinal lymph nodes in patients with NSCLC. • The SURblood plays a major role in the SVM model. • The score rule based on the SVM model simplified the complexity of the model and may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.

Preliminary and Comparative Experiment Study Between 18F-Flurpiridaz and 13N-NH3·H2O Myocardial Perfusion Imaging With PET/CT in Miniature Pigs.

OBJECTVES: To comparatively explore the differences between 18F-Flurpiridaz and 13N-NH3·H2O PET/CT myocardial perfusion imaging in miniature pigs. METHODS: Ten Bama minipigs were divided into normal group and myocardial infarction group. The changes of the ratio of left ventricular myocardium to main organs with time were calculated and the best imaging time was confirmed for 18F-Flurpiridaz imaging in normal group. The image quality score, summed rest score(SRS), Extend, total perfusion deficit(TPD) and left ventricle ejection fraction(LVEF) were respectively compared for 18F-Flurpiridaz and 13N-NH3·H2O in infarction group. RESULTS: 18F-Flurpiridaz was rapid distributed in myocardium, and the background counts of cardiac cavity were very low, and no obvious interference extracardiac radioactivity was observed. The radioactive ratio of the left ventricular myocardium to cardiac blood pool and adjacent liver were high. Compared with 13N-NH3·H2O, there were no significant differences in functional parameters, including SRS, Extend, TPD and LVEF. CONCLUSION: The results preliminaryly show that 18F-FIurpiridaz is a promising positron MPI agent with good image quality, ability of accurately evaluating cardiac function, and also convenience for application.

Enhanced glucose metabolism mediated by CD147 contributes to immunosuppression in hepatocellular carcinoma.

From a metabolic perspective, cancer may be considered as a metabolic disease characterized by reprogrammed glycolytic metabolism. The aim of the present study was to investigate CD147-mediated glucose metabolic regulation in hepatocellular carcinoma (HCC) and its contribution to altered immune responses in the tumor microenvironment. Several HCC cell lines and corresponding nude mice xenografts models differing in CD147 expressions were established to directly investigate the role of CD147 in the reprogramming of glucose metabolism, and to determine the underlying molecular mechanisms. Immunohistochemistry (IHC) analyses and flow cytometry were used to identify the relationship between reprogrammed glycolysis and immunosuppression in HCC. Upregulated CD147 expressions were found to be associated with enhanced expressions of GLUT1, MCT1 in HCC tumorous tissues. CD147 promoted the glycolytic metabolism in HCC cell lines in vitro via the PI3K/Akt/mTOR signaling pathway. A positive correlation existed between a profile of immunosuppressive lymphocytes infiltration and CD147 expression in HCC tissues. Accumulation of FOXP3-expressing regulatory T cells was induced under a stimulation with lactate in vitro. In conclusion, CD147 promoted glycolytic metabolism in HCC via the PI3K/Akt/mTOR signaling pathway, and was related to immunosuppression in HCC.

New insight on the correlation of metabolic status on 18F-FDG PET/CT with immune marker expression in patients with non-small cell lung cancer.

BACKGROUND: Metabolic information obtained through 18F-flurodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is used to evaluate malignancy by calculating the glucose uptake rate, and these parameters play important roles in determining the prognosis of non-small cell lung cancer (NSCLC). The expression of immune-related markers in tumor tissue reflects the immune status in the tumor microenvironment. However, there is lack of reports on the association between metabolic variables and intra-tumor immune markers. Herein, we investigate the correlation between metabolic status on 18F-FDG PET/CT and intra-tumor immunomarkers' expression in NSCLC patients. METHODS: From April 2008 to August 2014, 763 patients were enrolled in the analysis to investigate the role of maximum standardized uptake value (SUVmax) in lung cancer. One hundred twenty-two tumor specimens were analyzed by immunohistochemistry (IHC) to intra-tumor immune cells and programmed death protein ligand 1(PD-L1) expression on tumor cells. The correlation between metabolic variables and the expression of tissue immune markers were analyzed. RESULTS: SUVmax values have significant variations in different epidermal growth factor receptor (EGFR) statuses (wild type vs mutant type), high/low neutrophil-to-lymphocyte ratio (NLR) groups, and high/low platelets-to-lymphocyte ratio (PLR) groups (p < 0.001, p < 0.001, p = 0.003, respectively). SUVmax was an independent prognostic factor in lung cancer patients (p = 0.013). IHC demonstrated a statistically significant correlation between SUVmax and the expression of CD8 tumor-infiltrating lymphocytes (p = 0.015), CD163 tumor-associated macrophages (TAMs) (p = 0.003), and Foxp3-regulatory T cells (Tregs) (p = 0.004), as well as PD-1 and PD-L1 (p = 0.003 and p = 0.012, respectively). With respect to patient outcomes, disease stage, BMI, SUVmax, metabolic tumor volume (MTV), TLG (tumor lesion glycolysis), CD163-TAMs, CD11c-dendritic cells (DCs), PD-L1, and Tregs showed a statistically significant correlation with progression-free survival (PFS) (p < 0.001, 0.023, < 0.001, 0.007, 0.005, 0.004, 0.008, 0.048, and 0.014, respectively), and disease stage, SUVmax, MTV, TLG, CD163-TAMs, CD11c-DCs, and PD-L1 showed a statistically significant correlation with overall survival (OS) (p < 0.001, < 0.001, 0.014, 0.012, < 0.001, 0.001, and < 0.001, respectively). CONCLUSION: This study revealed an association between metabolic variable and immune cell expression in the tumor microenvironment and suggests that SUVmax on 18F-FDG PET/CT could be a potential predictor for selecting candidates for immunotherapy.

Combination of baseline total metabolic tumor volume measured on FDG-PET/CT and ß2-microglobulin have a robust predictive value in patients with primary breast lymphoma.

The aim was to build a prognostic model to stratify patients at diagnosis into different risk categories. We investigated the prognostic value of functional PET parameters and clinical features in 64 primary breast lymphoma (PBL) patients. With a median follow-up of 60 months, 5-year progression-free survival (PFS) and overall survival (OS) was 62.5% and 73.4%. In multivariate analysis, baseline total metabolic tumor volume (TMTV0) and ß2-microglobulin remained more reliable predictors of survival than other prognostic factors. The optimal TMTV0 cut-off value was 90 cm3 . Among 29 patients with high TMTV0, 5-year PFS and OS were 44.8% and 62.1%, respectively, while 5-year PFS and OS of 35 patients with low TMTV0 were 74.3% and 85.7%, respectively. TMTV0 combined with ß2-microglobulin identified three groups with very different prognosis, including low-risk group with low TMTV0 and ß2-microglobulin≤normal (n = 30), intermediate-risk group with high TMTV0 or ß2-microglobulin>normal (n = 20), and high-risk group with high TMTV0 and ß2-microglobulin>normal (n = 14). In the three groups, 5-year PFS rates were 80%, 55% and 28.6% (P = .003), and 5-year OS rates were 90%, 65%, and 50% (P = .023) respectively. We established a new prognostic model through TMTV0 and ß2-microglobulin, and can divide PBL at diagnosis into different risk categories.

CD147-mediated glucose metabolic regulation contributes to the predictive role of 18 F-FDG PET/CT imaging for EGFR-TKI treatment sensitivity in NSCLC.

The aim of this study is to investigate the role of CD147 in glucose metabolic regulation and its association with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment sensitivity prediction using 18 F-fluorodeoxyglucose (18 F-FDG) PET/CT imaging in non-small cell lung cancer (NSCLC). In this study, four human NSCLC cell lines with different EGFR-TKI responses were used to detect p-EGFR/EGFR and CD147 expression via Western blotting and flow cytometric analyses. Radioactive uptake of 18 F-FDG by established stable NSCLC cell lines (HCC827, H1975) with different levels of CD147 expression and the corresponding xenografts was assessed through γ-radioimmunoassays in vitro and micro-PET/CT imaging in vivo to study the role of CD147 in glucose metabolic reprogramming. Correlation analyses were performed to investigate the association between CD147 expression and PD-L1 expression in stable NSCLC cell lines. Higher CD147 expression was found in EGFR-TKI-sensitive NSCLC cell lines than in relatively resistant NSCLC cell lines (HCC827>PC9>A549>H1975). CD147 could promote 18 F-FDG uptake by HCC827 and H1975 cells in vitro and in vivo through an EGFR-initiated Akt/mTOR-dependent signaling pathway. Programmed cell death-ligand 1 (PD-L1) expression was positively correlated with CD147 expression in human NSCLC cell lines. EGFR-TKI treatment sensitivity prediction in NSCLC using 18 F-FDG PET/CT imaging significantly correlated with CD147-mediated glucose metabolic regulation via the Akt/mTOR-dependent pathway. Moreover, PD-L1 expression in NSCLC cell lines could be regulated by CD147, suggesting a potential immunosuppression induced by the upregulation of tumor glucose metabolism.

Multivariate analysis of clinicopathological and prognostic significance of miRNA 106b~25 cluster in gastric cancer.

BACKGROUND: miRNA 106b~25 cluster were demonstrated to be an oncogene. In previous study, we had analyzed the diagnostic significance of miRNA 106b~25 based on its carcinogenesis effect. The significance of miRNA 106b~25 for prognosis of gastric cancer were not researched. METHODS: We applied multivariate analysis of PCA, PLS-DA and Cox Regression for clinicopathological features and survival time to explore the significance of miRNA 106b~25 expression in plasma and cancer tissues for gastric cancer. RESULTS: The expression of miRNA 106b, miRNA 93 and miRNA 25 in plasma were positively correlated with their expression in tumor tissues. Via PCA analysis, it was found that miRNA 106b~25 expression in plasma and tumor, T, N and TNM stage were correlated with each other. Via PLS-DA analysis, we identified that T, N and TNM stage were important factors for miRNA 106b~25 expression both in plasma and tumor (all VIP value > 1.2). According to loading weights of variables for the first and second components, it was found that the importance of the miRNA 106b~25s expression carried with the progressed stage of gastric cancer. In the survival analysis, COX regression showed that T stage, plasma miRNA 106b and tumor miRNA 93 were significant risk factors for overall survival [HR: 0.400 (0.205-0.780); P = 0.007; HR: 0.371 (0.142-0.969), P = 0.043; 0.295 (0.134-0.650), P = 0.002]. CONCLUSION: Plasma and tumor miRNA 106b~25 expression correlated with T, N and TNM stage. Increased miRNA 106b~25 expression was important characters carried with gastric cancer progression. T stage, plasma miRNA106b and tumor miRNA 93 significant risk factors for overall survival.

Application and Indication of Carcinoembryonic Antigen Triggered 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Scanning in the Detection of Relapse of Colorectal Cancer Patients After Curative Therapy.

OBJECTIVE: This study aimed to explore the characteristics of patients with colorectal cancer (CRC) following curative therapy that may benefit from fluorine-18-2-uoro-2-deoxy-D-glucose positron emission tomography/computed tomography (F-FDG PET/CT) scanning, evaluate the application of carcinoembryonic antigen (CEA)-triggered F-FDG PET/CT scanning, and provide referential indicators. METHODS: This retrospective study included 56 CRC patients who received a PET/CT scan as a primary examination because of rising CEA levels after curative therapy and who had not received any other radiological examinations previously. RESULTS: The rate of recurrence or metastasis was 75.0% by PET/CT scan but was 69.6% with follow-up treatment. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 94.9%, 70.6%, 87.5%, 88.1%, and 85.7%, respectively. TNM (tumor, node, metastasis) stage, body mass index, and CEA level were significant prognostic factors. CONCLUSIONS: Positron emission tomography/CT can be selectively applied as a primary examination in CRC patients with asymptomatic elevation of CEA. High CEA levels, increased body mass index, and advanced TNM staging are risk factors for relapse.

Predictive efficacy of (11)C-PD153035 PET imaging for EGFR-tyrosine kinase inhibitor sensitivity in non-small cell lung cancer patients.

To determine the correlation of (11)C-PD153035 uptake with epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) sensitivity and phosphorylated EGFR (pEGFR) expression in non-small cell lung cancer (NSCLC) cell lines with different EGFR-TKI sensitivities and in their corresponding xenografts. Four human NSCLC cell lines (HCC827, PC9, A549, and H1975) in the logarithmic phase were co-incubated with (11)C-PD153035 to analyze the correlation of (11)C-PD153035 uptake with EGFR-TKI sensitivity, and EGFR/pEGFR expression. Nude mice xenograft models bearing the four NSCLCs were prepared. (11)C-PD153035 positron-emission tomography (PET)-computed tomography (CT) was used to image the xenografts and observe radioactive uptakes. Correlation of the in vivo uptakes with EGFR-TKI sensitivity, and EGFR/pEGFR expression was analyzed. HCC827 and PC9 cells, which were highly sensitive to EGFR-TKIs, exhibited higher (11)C-PD153035 uptakes than the other cells. A549 cells, which were moderately sensitive to EGFR-TKIs, showed higher uptake than the EGFR-TKI-resistant H1975 cells, which showed little or no uptake. Radioactive uptakes were positively correlated with pEGFR expression in all cells. PET-CT showed that radioactivity was highest in HCC827 xenografts. The radioactivity in PC9 xenografts was higher than that in A549 and H1975 xenografts. Tumor vs. non-tumor tissue ratio values were positively correlated with pEGFR expression in HCC827 and PC9 xenografts, but not in A549 and H1975 xenografts. In conclusion, (11)C-PD153035 can serve as an EGFR imaging agent in vitro and in vivo, and predicts sensitivity to EGFR-TKIs. This will provide an experimental basis for clinical applications of (11)C-PD153035 and individualized NSCLC therapy.

[Diagnostic value of (18)F-fluorodeoxyglucose positron emission tomography/ computed tomography for N-and M-staging of malignant melanoma].

OBJECTIVE: To evaluate the effectiveness and advantages of positron emission tomography/computed tomography (PET/CT) for N and M staging of malignant melanoma. METHODS: A total of 114 patients (69 males, 45 females) with AJCC stage II-IV malignant melanoma were recruited. The diagnostic accuracy for N- and M-staging was determined for CT alone, PET alone and PET/CT. RESULTS: PET/CT imaging provided significantly more accurate interpretations regarding overall N- and M-staging than PET or CT alone (97.4% vs 94.7% and 88.6%). Overall N- and M-stage was correctly determined by PET/CT (98.2%) versus PET (93.9%) and CT (83.3%) and the differences were significant (P < 0.01). CONCLUSION: The diagnostic performance of FDG-PET/CT is excellent for N- and M-staging of melanoma patients, especially for detecting or excluding distant metastases.

Elevated splenic 18F-fluorodeoxyglucose positron emission tomography/computed tomography activity is associated with 5-year risk of recurrence in non-metastatic invasive ductal carcinoma of the breast.

OBJECTIVE: To construct prediction models including baseline 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters of tumoural lesions and non-tumour lymphoid tissue for recurrence-free survival within 5 years (5y-RFS) after imaging examination in patients with invasive ductal carcinomas (IDCs) of the breast. METHODS: The study included 101 consecutive female patients. Univariable and multivariable Cox regression were used to identify clinicopathological and metabolic parameters associated with risk of recurrence. Four prediction models based on the results of multivariable analysis were constructed and visualized as nomograms. Performance of each nomogram was evaluated using the concordance index (C-index), integrated discrimination improvement, decision curve analysis (DCA), and calibration curve. RESULTS: N3 status, total metabolic tumour volume, the maximum standardized uptake value of spleen, and spleen-to-liver ratio were significant predictors of 5y-RFS. The nomogram including all significant predictors demonstrated superior predictive performance for 5y-RFS, with a C-index of 0.907 (95% CI, 0.833-0.981), greatest net benefit on DCA, good accuracy on calibration curves, and excellent risk stratification on Kaplan-Meier curves. CONCLUSIONS: The model that included metabolic parameters of the spleen had the best performance for predicting 5y-RFS in patients with IDCs of the breast. This model may guide personalized treatment decisions and inform patients and clinicians about prognosis. ADVANCES IN KNOWLEDGE: This research identifies 18F-FDG PET/CT metabolic parameters of non-tumour lymphoid tissue as predictors of recurrence in breast cancer.

A comprehensive analysis of imaging features and clinical characteristics to differentiate malignant from non-malignant mammographic architectural distortion.

Background: Mammographic architectural distortion (AD) is usually subtle and has variable presentations and causes, which poses a diagnostic challenge for breast radiologists and consequently a complex decision-making challenge for clinicians and patients. Presently, there is no reliable imaging standard to differentiate between malignant and benign ADs preoperatively. This study aimed to perform a comprehensive analysis of detailed mammographic and ultrasonographic features and clinical characteristics to enhance the diagnostic and differential efficacy for AD lesions. The findings have the potential to boost the diagnostic confidence of breast radiologists when encountering with AD lesions and could be instrumental in refining clinical management strategies for ADs. Methods: This retrospective study included consecutive female patients with ADs on screening or diagnostic mammography from January 6, 2015, to December 28, 2018. The patient's clinical data, mammographic and ultrasonographic or "second look" ultrasonographic findings, and pathological results were reviewed. The continuous variables were analyzed using the t-test. The categorical variables were assessed using the Chi-square test or two-tailed Fisher's exact test. Logistic regression analyses were conducted to evaluate potential risk factors for pathologically proven malignant ADs. Machine learning model based on multimodal clinical and imaging features was constructed using R software. Results: Ultimately, 344 patients with 346 AD lesions were enrolled in the study (mean age: 47.40±10.07 years; range, 19-84 years). Of the ADs, 228 were malignant and 118 were non-malignant. Palpable AD on mammography was more likely to indicate malignancy than non-palpable AD (83.43% vs. 49.15%, P<0.001). AD associated with other mammographic findings was more likely to be malignant than pure AD (73.58% vs. 59.36%, P=0.005). Ultrasonography (US) correlates were observed in 345 of these 346 AD lesions. Among these US correlates, 63 (18.26%, 63/345) were detected by "second look" ultrasound. For the US correlates, the mammographic ADs that appeared as non-mass-like hypoechoic areas and masses on US were more likely to be malignant than those that appeared as other abnormalities (P<0.001). The sensitivity, specificity and diagnostic accuracy of the eXtreme Gradient Boosting (XGBoost) model based on clinical and comprehensive imaging features in differentiation of AD lesions in the validation set were 66.46%, 94.23% and 78.9%, respectively, and the AUC was 0.886 (95% confidence interval: 0.825-0.947). Conclusions: The application of mammograms-guided "second-look" ultrasound could enhance the detection of US correlates, particularly non-mass-like features. The comprehensive analysis based on clinical and multimodal imaging features could be beneficial in improving the diagnostic and differential efficacy for AD lesions detected on mammography and instrumental in refining clinical management strategies for ADs.

Optimized gated a SPECT-derived myocardial salvage index: its prognostic significance in predicting major adverse cardiac events following acute myocardial infarction percussion.

PURPOSE: Estimate myocardial salvage index (MSI) using a single-gated Single-Photon Emission Computed Tomography (SPECT) myocardial perfusion imaging (GSMPI) early after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) and compare its predictive value with the traditional method especially for post-PCI left ventricular ejection fraction (LVEF) improvement and major adverse cardiac events (MACEs). METHODS: GSMPI was performed in 62 patients with AMI early after PCI (3-10 days). The MSI and the conventional parameters were obtained, including total perfusion deficit, LVEF, peak ejection rate (PER), and peak filling rate (PFR). The new calculation method (scoring evaluation method means the extent of abnormality is the percentage of the total scores of abnormal segments divided by the sum of the maximum scores of all myocardial segments using 4-point and 5-point scale semi-quantitative scoring method) and the reference method (number evaluation method means the extent of abnormality is the percentage of the number of abnormal segments divided by the total number of myocardial segments) were applied to acquire the MSI. We compared the predictive ability of the 2 methods based on the area under the receiver operating characteristic curve for LVEF improvement 6 months after PCI using MSI. The Kaplan-Meier method was used for depicting survival curves for predicting MACEs by the 2 methods. Cox proportional-hazards regression was applied to confirm the independent predictors of MACEs. RESULTS: The MSI obtained by the new method indicated stronger prognostic significance in LVEF improvement [area under the curve (AUC): 0.793, 95% confidence interval (CI) 0.620-0.912, P < .001] compared with the reference method (AUC: 0.634, 95%CI 0.452-0.792, P = .187). Delong's test revealed a statistically significant difference in AUCs between the 2 methods (P < .05, 95%CI 0.003-0.316). The diagnostic value of the scoring evaluation method was higher than that of the number evaluation method. The Cox prevalence of MACEs was substantially higher in the < median MSI group than in the ≥ median MSI group (hazard ratio: 0.172; 95% CI 0.041-0.724; P < .05] using the new method, whereas no considerable differences were observed between the 2 groups using the reference method (P = .12). Further, the multivariate Cox regression analysis revealed that MSI was an independent indicator for predicting MACEs (P < .05). CONCLUSION: The MSI obtained from a simple GSMPI early after PCI, using the scoring evaluation method, was a reliable prognostic indicator for predicting LVEF improvement and MACEs in AMI. It remarkably improved the prognostic value compared with the previous reference methods.

A predictive radiotranscriptomics model based on DCE-MRI for tumor immune landscape and immunotherapy in cholangiocarcinoma.

This study aims to determine the predictive role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived radiomic model in tumor immune profiling and immunotherapy for cholangiocarcinoma. To perform radiomic analysis, immune related subgroup clustering was first performed by single sample gene set enrichment analysis (ssGSEA). Second, a total of 806 radiomic features for each phase of DCE-MRI were extracted by utilizing the Python package Pyradiomics. Then, a predictive radiomic signature model was constructed after a three-step features reduction and selection, and receiver operating characteristic (ROC) curve was employed to evaluate the performance of this model. In the end, an independent testing cohort involving cholangiocarcinoma patients with anti-PD-1 Sintilimab treatment after surgery was used to verify the potential application of the established radiomic model in immunotherapy for cholangiocarcinoma. Two distinct immune related subgroups were classified using ssGSEA based on transcriptome sequencing. For radiomic analysis, a total of 10 predictive radiomic features were finally identified to establish a radiomic signature model for immune landscape classification. Regarding to the predictive performance, the mean AUC of ROC curves was 0.80 in the training/validation cohort. For the independent testing cohort, the individual predictive probability by radiomic model and the corresponding immune score derived from ssGSEA was significantly correlated. In conclusion, radiomic signature model based on DCE-MRI was capable of predicting the immune landscape of chalangiocarcinoma. Consequently, a potentially clinical application of this developed radiomic model to guide immunotherapy for cholangiocarcinoma was suggested.