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.

Novel CDK19-Targeted Radiotracers: A Potential Design Strategy to Improve the Pharmacokinetics and Tumor Uptake.

Cyclin-dependent kinase 19 (CDK19) is overexpressed in prostate cancer, making it an attractive target for both imaging and therapy. Since little is known about the optimized approach for radioligands of nuclear proteins, linker optimization strategies were used to improve pharmacokinetics and tumor absorption, including the adjustment of the length, flexibility/rigidity, and hydrophilicity/lipophilicity of linkers. Molecular docking was conducted for virtual screening and followed by IC50 determination. Both BALB/c mice and P-16 xenografts were used for tissue distribution and PET/CT imaging. The ligand 68Ga-10c demonstrated high absorption in tumor 5 min after injection and sustains long-term imaging within 3 h. Furthermore, 68Ga-10c exhibited slow clearance within the tumor and was predominantly metabolized in both the liver and kidneys, showing the potential to alleviate metabolic pressure and enhance tissue safety. Therefore, the linker optimization strategy is well suited for CDK19 and provides a reference for the radioactive ligands of other nuclear targets.

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.

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 PET/CT radiomics model for predicting distant metastasis in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy: a multicentric study.

OBJECTIVES: Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT. METHODS: Patients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups. RESULTS: In total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P < 0.001). CONCLUSIONS: The proposed PET/CT radiomics model can be used to predict distant metastasis in patients with early-stage NSCLC treated with SBRT and provide a reference for clinical decision-making. In this study, the model was established by combining CT and PET radiomics signatures in a moderate-quantity training cohort of early-stage NSCLC patients treated with SBRT and was successfully validated in independent cohorts. Physicians could use this easy-to-use model to assess the risk of distant metastasis after SBRT. Identifying subgroups of patients with different risk factors for distant metastasis is useful for guiding personalized treatment approaches.

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.

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.

Predictive value of radiomic signature based on 2-[18F]FDG PET/CT in HER2 status determination for primary breast cancer with equivocal IHC results.

PURPOSE: To evaluate the predictive power of 2-[18F]FDG PET/CT-derived radiomic signature in human epidermal growth factor receptor 2 (HER2) status determination for primary breast cancer (BC) with equivocal immunohistochemistry (IHC) results for HER2. METHODS: A total of 154 primary BC with equivocal IHC results for HER2 were retrospectively enrolled in the study. First, the following five conventional PET parameters (SUVmax, SUVmean, SUVpeak, MTV, TLG) were measured and compared between HER2-positive and HER2-negative cohorts. After quantitative radiomic features extraction and reduction, the least absolute shrinkage and selection operator (LASSO) algorithm was used to establish a radiomic signature model. Then, the area under the curve (AUCs) after a receiver operator characteristic (ROC) analysis, accuracy, sensitivity and specificity were calculated and used as the main outcomes. Finally, a total of 37 BC patients from an external institution were included to perform an external validation. RESULTS: All the five conventional PET parameters were unable to discriminate between HER2-positive and HER2-negative cohorts for BC (P = 0.104-0.544). Whereas, the developed radiomic signature model was potentially predictive of HER2 status with an of AUC 0.887 (95% confidence interval [CI], 0.824-0.950) in the training cohort and 0.766 (95% CI, 0.616-0.916) in the validation cohort, respectively. For external validation, the AUC for the external test cohort was 0.788 (95% CI, 0.633-0.944). CONCLUSIONS: Radiomic signature based on 2-[18F]FDG PET/CT images was capable of non-invasively predicting the HER2 status with a comparable ability to FISH assay, especially for those with equivocal IHC results for HER2.

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.

Time-Related Vascular Inflammatory Response to COVID-19 Assessed by 18F-FDG PET/CT in Follow-Up Tumor Patients.

Purpose: This study aimed to assess COVID-19's effects on vascular inflammatory response, by evaluating 18-Fluorodeoxyglucose (18F-FDG) uptake via positron emission tomography/computed tomography (PET/CT) in the artery of diffuse large B cell lymphoma (DLBCL) patients before and after infection with COVID-19. Patients and Methods: Thirty-five DLBCL patients administered the chemotherapy regimen R-CHOP and examined by oncological 18F-FDG PET/CT imaging twice from August 2022 to February 2023 for pre-treatment evaluation or assessment of treatment efficacy were enrolled. Seventeen patients were confirmed with COVID-19 within the study period. Arterial wall FDG uptake was semi-quantitatively analyzed as TBR (target-to-blood pool ratio) in 14 different vascular regions using oncological 18F-FDG PET/CT. Based on COVID-19 course and the two PET/CT scans, we further analyzed time-related FDG uptake for vascular walls in DLBCL patients with COVID-19. Results: Arterial TBRs were higher in the last PET/CT examination than previous ones in all patients with or without COVID-19. Besides the ascending aorta, ΔTBR (last PET/CT scanning's TBR minus previous PET/CT scanning's TBR) were not significantly different between the COVID-19 and Control groups. However, cases scanned ≤30 days from infection had remarkably higher ΔTBRs in comparison with those assessed >30 days post-infection in the COVID-19 group (p<0.05). A moderate inverse correlation was observed between ∆Global TBR (last PET/CT scanning's average TBR value minus previous PET/CT scanning's average TBR value) and time distance from COVID-19 onset to 18F-FDG PET/CT scan (Spearman's rho=-0.591, P=0.012). Interestingly, there were no differences of changes of TBR between different purpose of PET/CT examination group. Conclusion: This work firstly suggested vascular inflammation is elevated in the early post-COVID-19 phase in DLBCL cases compared with prolonged post-COVID-19 phase or controls. Increasing attention should be paid to these patients and the protection of their vascular function and complications in early COVID-19.

Small hyperattenuating adrenal nodules in patients with lung cancer: Differentiation of metastases from adenomas on biphasic contrast-enhanced computed tomography.

Objective: The objective of this study was to evaluate the value of biphasic contrast-enhanced computed tomography (CECT) in the differential diagnosis of metastasis and lipid-poor adenomas (LPAs) in lung cancer patients with unilateral small hyperattenuating adrenal nodule. Materials and methods: This retrospective study included 241 lung cancer patients with unilateral small hyperattenuating adrenal nodule (metastases, 123; LPAs, 118). All patients underwent plain chest or abdominal computed tomography (CT) scan and biphasic CECT scan, including arterial and venous phases. Qualitative and quantitative clinical and radiological characteristics of the two groups were compared using univariate analysis. An original diagnostic model was developed using multivariable logistic regression, and then, according to odds ratio (OR) of the risk factors of metastases, a diagnostic scoring model was developed. The areas under the receiver operating characteristic curves (AUCs) of the two diagnostic models were compared by DeLong test. Results: Compared with LAPs, metastases were older and showed more frequently irregular in shape and cystic degeneration/necrosis (all p < 0.05). Enhancement ratios on venous (ERV) and arterial (ERA) phase of LAPs were noticeably higher than that of metastases, whereas CT values in unenhanced phase (UP) of LPAs were noticeably lower than that of metastases (all p < 0.05). Compared with LAPs, the proportions of male and III/IV clinical stage and small-cell lung cancer (SCLL) were significantly higher for metastases (all p < 0.05). As for peak enhancement phase, LPAs showed relatively faster wash-in and earlier wash-out enhancement pattern than metastases (p < 0.001). Multivariate analysis revealed age ≥ 59.5 years (OR: 2.269; p = 0.04), male (OR: 3.511; p = 0.002), CT values in UP ≥ 27.5 HU (OR: 6.968; p < 0.001), cystic degeneration/necrosis (OR: 3.076; p = 0.031), ERV ≤ 1.44 (OR: 4.835; p < 0.001), venous phase or equally enhanced (OR: 16.907; p < 0.001 or OR: 14.036; p < 0.001), and clinical stage II or III or IV (OR: 3.550; p = 0.208 or OR: 17.535; p = 0.002 or OR: 20.241; p = 0.001) were risk factors for diagnosis of metastases. AUCs of the original diagnostic model and the diagnostic scoring model for metastases were 0.919 (0.883-0.955) and 0.914 (0.880-0.948), respectively. There was no statistical significance of AUC between the two diagnostic model (p = 0.644). Conclusions: Biphasic CECT performed well diagnostic ability in differentiating metastases from LAPs. The diagnostic scoring model is easy to popularize due to simplicity and convenience.

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.

Objectives: To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT). Methods: Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC). Results: There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model. Conclusion: MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

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.

Diagnostic Value of Multi-Mode Ultrasonic Flow Imaging Examination in Solid Renal Tumors of Different Sizes.

Purposes: To explore the value of Microflow Imaging (MFI) in renal solid tumors. Methods: A total of 195 patients with 199 lesions pathologically confirmed masses were retrospectively analyzed. The 199 masses were divided into the tumor ≤ 4 cm group (n = 104) and tumor > 4 cm group (n = 95). The diagnostic efficacy of Color Doppler Flow Imaging (CDFI), Power Doppler Imaging (PDI) and MFI in renal tumors sizes were compared by determining the Adler grade, vascular morphology and peripheral blood flow. Results: Among 199 tumors, 161 lesions were malignant and 38 lesions were benign. MFI in malignant tumor ≤ 4 cm demonstrated statistically significant differences in Adler grade and vascular morphology as compared to CDFI and PDI (p < 0.05). In malignant tumor > 4 cm group, MFI showed significant difference in vascular morphology compared with CDFI (p < 0.05). MFI showed a significant difference in the peripheral annular blood flow of malignant tumors when compared to CDFI and PDI (p < 0.05). In addition, the malignant tumors of the two sizes by MFI in peripheral annular blood flow detection showed significant difference (p < 0.05). The area under the curve of ROC by MFI in the tumor ≤ 4 cm was 0.771, which was higher than CDFI and PDI (p < 0.05), but no obvious difference among the tumor > 4 cm (p > 0.05). Conclusion: MFI provides a new method for the differential diagnosis of small renal carcinoma. Based on the convenience and non-radiation of MFI, we can choose MFI as an imaging diagnostic tool for patients who need long-term active surveillance (AS) follow-up.

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.

Radiomics approach based on biphasic CT images well differentiate "early stage" of adrenal metastases from lipid-poor adenomas: A STARD compliant article.

The aim of the study was to develop an optimal radiomics model based on abdominal contrast-enhanced computed tomography (CECT) for pre-operative differentiation of "early stage" adrenal metastases from lipid-poor adenomas (LPAs). This retrospective study included 188 patients who underwent abdominal CECT (training cohort: LPAs, 68; metastases, 64; validation cohort: LPAs, 29; metastases, 27). Abdominal CECT included plain, arterial, portal, and venous imaging. Clinical and CECT radiological features were assessed and significant features were selected. Radiomic features of the adrenal lesions were extracted from four-phase CECT images. Significant radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression. The clinical-radiological, unenhanced radiomics, arterial radiomics, portal radiomics, venous radiomics, combined radiomics, and clinical-radiological-radiomics models were established using a support vector machine (SVM). The DeLong test was used to compare the areas under the receiver operating characteristic curves (AUCs) of all models. The AUCs of the unenhanced (0.913), arterial (0.845), portal (0.803), and venous (0.905) radiomics models were all higher than those of the clinical-radiological model (0.788) in the testing dataset. The AUC of the combined radiomics model (incorporating plain and venous radiomics features) was further improved to 0.953, which was significantly higher than portal radiomics model (P = .033) and clinical-radiological model (P = .009), with the highest accuracy (89.13%) and a relatively stable sensitivity (91.67%) and specificity (86.36%). As the optimal model, the combined radiomics model based on biphasic CT images is effective enough to differentiate "early stage" adrenal metastases from LPAs by reducing the radiation dose.

A Phase IB Trial of Autologous Cytokine-Induced Killer Cells in Combination with Sintilimab, Monoclonal Antibody Against Programmed Cell Death-1, plus Chemotherapy in Patients with Advanced Non-Small-Cell Lung Cancer.

INTRODUCTION: Can the Cytokine-induced killer (CIK) cells in combination with immune checkpoint inhibitor further improve the efficacy of chemotherapy in non-small cell lung cancer (NSCLC) patients? What are the adverse reactions of this combination therapy? But these problems are not clear. Therefore, we conducted a phase 1b trial to evaluate the safety and efficacy of autologous CIK cells therapy combined with Sintilimab, antibody against programmed cell death-1, plus chemotherapy in untreated, advanced NSCLC patients. PATIENTS AND METHODS: Patients with stage IIIB/IIIC/IV NSCLC received Sintilimab, platinum-based doublet chemotherapy, and CIK cells every 3 weeks for 4 cycles, then maintenance treatment with Sintilimab in squamous and with Sintilimab plus pemetrexed in non-squamous NSCLC until disease progression or unacceptable toxicity or 2 years. The primary endpoints were safety and objective response rate (ORR). RESULTS: Thirty-four patients received the treatment. 94.1% of patients experienced treatment-related adverse events (TRAEs). Grade 3 or greater TRAEs occurred in 64.7% of patients. One (2.9%) patient died of grade 5 immune-related pneumonia. The ORR and DCR were 82.4% (95% CI, 65.5%-93.2%) and 100.0% (95% CI, 89.7%-100.0%), respectively. Objective responses were evaluated in 14 of 15 non-squamous patients (93.3%; 95% CI, 68.1%-99.8%) and in 14 of 19 squamous patients (73.7%; 95% CI, 48.8%-90.9%). Median PFS was 19.3 months (95% CI, 8.3 months to not available). CONCLUSION: Autologous CIK cells immunotherapy in combination with Sintilimab plus chemotherapy was well tolerable and showed encouraging efficacy in patients with previously untreated, advanced NSCLC (ClinicalTrials.gov number, NCT03987867).

Predictive Value of 18F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer.

BACKGROUND: To develop and validate a radiomics model based on 18F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31-82 years) with pathologically proven IDC and a preoperative 18F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661-0.929 (mean AUC, 0.817), and the accuracy range was 65.3-93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative 18F-FDG PET/CT radiomic features.

Combined Detection of RUNX3 and EZH2 in Evaluating Efficacy of Neoadjuvant Therapy and Prognostic Value of Middle and Low Locally Advanced Rectal Cancer.

Objective: This article investigated whether Runt-Related Transcription Factor 3 (RUNX3) and enhancer of zeste homolog 2 (EZH2) can be used to evaluate the clinical efficacy of neoadjuvant therapy and prognosis of locally advanced rectal cancer (LARC). Methods: Eighty LARC patients admitted to the Tianjin Medical University Cancer Institute/Hospital and First Affiliated Hospital of Hebei North University from Jan 2015 to Jan 2016 were enrolled. The patients were followed up for 60 months through hospital visits. All patients received neoadjuvant chemoradiotherapy (long range radiotherapy + oral capecitabine) + total mesorecta excision (TME) surgery. The clinical efficacy of the treatments was evaluated through endoscopic, radiography, and tumor regression grade (TRG). In addition, expression level of RUNX3 and EZH2 was quantified via immunohistochemistry. The association of RUNX3 and EZH2 with clinicopathological characteristics of advanced tumors and efficacy of neoadjuvant therapy was explored. Logistic regression analysis was performed to identify predictors of efficacy of neoadjuvant chemoradiotherapy. Survival curve was used to evaluate the impact of RUNX3 and EZH2 on the prognosis of LARC patients. Results: A total of 80 patients diagnosed with LARC were enrolled in the study. Expression of RUNX3 was elevated in 25 (31.25%) patients, whereas expression of EZH2 was upregulated in 44 (55.00%) patients. Analysis of tumor regression identified 10 cases with TRG grade 0 (pathologic complete response, PCR), 24 cases with TRG grade 1, 35 cases with TRG grade 2, and 11 cases with TRG grade 3. Furthermore, 38 cases had significant down-staging, and 42 cases showed no significant down-staging as revealed by endoscopy and imaging. Patients with high expression of RUNX3 showed better tumor regression response and down-staging compared with those with low expression of RUNX3 (P < 0.001, P < 0.001). Moreover, patients with low EZH2 expression achieved TRG grade 0 and 1 response and down-staging effect compared with those with high expression of EZH2 (P < 0.001, P < 0.001). Logistic regression analysis showed that high expression of RUNX3, low expression of EZH2, and clinical N (cN) stage were good predictors of tumor regression response and down-staging. The 5-year disease free survival (DFS) and overall survival (OS) were 48.75 (39/80) and 58.75% (47/80), respectively. The 5-year DFS and OS of patients with high RUNX3 expression were significantly higher than low RUNX3 expression, whereas the 5-year DFS and OS of patients with high EZH2 expression were significantly lower than low EZH2 expression (P < 0.001). Univariate survival analysis showed that RUNX3 expression, EZH2 expression, cN, clinical T (cT), pathological T (pT) and pathological N (pN) were significantly correlated with the 5-year DFS and 5-year OS. Multivariate survival analysis showed that EZH2 expression and PN were good predictors of 5-year DFS and 5-year OS, whereas RUNX3 was a good predictor of 5-year DFS but not 5-year OS. Conclusions: Expression level of RUNX3 and EZH2 accurately predicts clinical efficacy of neoadjuvant chemoradiotherapy and the prognosis of LARC patients, suggesting that RUNX3 and EZH2 can be used as pivotal clinical predictors for LARC.

The role of 18F-FDG PET/CT metabolic parameters in pediatric lymphoblastic lymphoma.

OBJECTIVE: This retrospective study aimed to evaluate the role of metabolic parameters of 18F-FDG PET/CT in pediatric lymphoblastic lymphoma (LBL). METHODS: Thirty patients with LBL underwent 42 scans. Metabolic parameters including maximum standardized uptake value (SUVmax), total metabolic tumor volume (TMTV), and total lesion glycolysis (TLG) were measured at baseline PET/CT. Univariate and multivariate analysis for survival were performed to assess their prognostic value. Twelve patients underwent PET/CT after reinduction regime, and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PET/CT for predicting relapse were calculated. RESULTS: Patients with stage Ⅳ had a higher TMTV than those with stage III (p = 0.031). Besides, patients with T-LBL or mediastinal involvement had a high TMTV and TLG (p < 0.05). There was no significant difference in PET/CT metabolic parameters between patients with different outcomes (p > 0.05). Children with a low TMTV (<242.91 cm³) had a better 3-year EFS compared with those with a high TMTV (88.9% vs. 56.3%; p = 0.036). SUVmax and TLG were not predictive of EFS (p = 0.874; p = 0.152). However, none of the metabolic parameters of baseline PET/CT were independent prognostic factors for outcomes of pediatric LBL. PET/CT underwent after reinduction regime present with higher sensitivity (50% vs. 0%) and NPV (90% vs. 83.3%) for predicting relapse than CT alone. CONCLUSIONS: Metabolic parameters of baseline PET/CT were not predictive of outcomes in children with LBL. PET/CT done after the reinduction regime had better sensitivity and NPV than CT alone, and a negative scan could be a reliable indicator for sustained remission.