The status of nuclear medicine in China: the first official national survey.

AIM/INTRODUCTION: The National Nuclear Medicine Quality Control Center of China conducted the first official survey to investigate the nationwide situation of nuclear medicine in 2020. The survey aimed to unveil the current nuclear medicine situation and its quality control in China. MATERIALS AND METHODS: The web-based survey was conducted and the data was collected via the National Clinical Improvement System (NCIS) of China from 1st April to 31st May 2021. RESULTS: A total of 808 institutes across 30 provinces responded to the national survey. For human resources, there are 4460 physicians, 3077 technologists, 339 physicists, and 309 radiochemists. There are 887 single-photon imaging instruments, including 823 SPECT or SPECT/CT, and 365 PET instruments including 314 PET/CT. Six hundred twenty-four institutes perform SPECT examinations and 319 institutes perform PET examinations. 60% of SPECT scans are bone scintigraphy. A total of 97% of PET scans use an [18F]F-FDG tracer. Furthermore, 587 institutes provide radionuclide therapy services but only 280 institutes have admission rooms. The top three radionuclide therapies are [131I] therapy of hyperthyroidism with 546 institutes, [89Sr] therapy of bone metastasis with 400 institutes, and [131I] therapy of differentiated thyroid cancer with 286 institutes. Finally, for the frequency of equipment quality control per year, there are about 67 times self-test within the department for SPECT instruments and 111 times for PET instruments on average in each province. There are about three failures of SPECT and five failures of PET on average per year in each province. There are 408 institutes (of 624 SPECT institutes) performing quality control of SPECT radiopharmaceuticals, 216 (of 319) for PET radiopharmaceuticals, and 373 (of 587) for radionuclide therapy. CONCLUSION: These results of the first official survey towards current status of nuclear medicine in China are the foundation for the establishment of the quality control management system.

Synthesis and Preliminary Study of 99mTc-Labeled HYNIC-FAPi for Imaging of Fibroblast Activation Proteins in Tumors.

Fibroblast activation protein (FAP) is an emerging target for cancer diagnosis. Different types of FAP inhibitor (FAPI)-based radiotracers have been developed and applied for tumor imaging. However, few FAPI tracers for single photon emission computed tomography (SPECT) imaging have been reported. SPECT imaging is less expensive and more widely distributed than positron emission tomography (PET), and thus, 99mTc-labeled FAPIs would be more available to patients in developing regions. Herein, we developed a FAPI-04-derived radiotracer, HYNIC-FAPi-04 (HFAPi), for SPECT imaging. 99mTc-HFAPi, with a radiochemical purity of >98%, was prepared using a kit formula within 30 min. The specificity of 99mTc-HFAPi for FAP was validated by a cell binding assay in vitro and SPECT/CT imaging in vivo. The binding affinity (Kd value) of 99mTc-HFAPi for human FAP and murine FAP was 4.49 and 2.07 nmol/L, respectively. SPECT/CT imaging in HT1080-hFAP tumor-bearing mice showed the specific FAP targeting ability of 99mTc-HFAPi in vivo. In U87MG tumor-bearing mice, 99mTc-HFAPi had a higher tumor uptake compared with that of HT1080-hFAP and 4T1-mFAP tumor models. Interestingly, 99mTc-HFAPi showed a relatively high uptake in some murine joints. 99mTc-HFAPi accumulated in tumor lesions with a high tumor-to-background ratio. A preliminary clinical study was also performed in breast cancer patients. Additionally, 99mTc-HFAPi exhibited an advantage over 18F-FDG in the detection of lymph node metastatic lesions in breast cancer patients, which is helpful in improving treatment strategies. In short, 99mTc-HFAPi showed excellent affinity and specificity for FAP and is a promising SPECT radiotracer for (re)staging and treatment planning of breast cancers.

Differentiation of benign versus malignant indistinguishable vertebral compression fractures by different machine learning with MRI-based radiomic features.

OBJECTIVES: To explore an optimal machine learning (ML) model trained on MRI-based radiomic features to differentiate benign from malignant indistinguishable vertebral compression fractures (VCFs). METHODS: This retrospective study included patients within 6 weeks of back pain (non-traumatic) who underwent MRI and were diagnosed with benign and malignant indistinguishable VCFs. The two cohorts were retrospectively recruited from the Affiliated Hospital of Qingdao University (QUH) and Qinghai Red Cross Hospital (QRCH). Three hundred seventy-six participants from QUH were divided into the training (n = 263) and validation (n = 113) cohort based on the date of MRI examination. One hundred three participants from QRCH were used to evaluate the external generalizability of our prediction models. A total of 1045 radiomic features were extracted from each region of interest (ROI) and used to establish the models. The prediction models were established based on 7 different classifiers. RESULTS: These models showed favorable efficacy in differentiating benign from malignant indistinguishable VCFs. However, our Gaussian naïve Bayes (GNB) model attained higher AUC and accuracy (0.86, 87.61%) than the other classifiers in validation cohort. It also remains the high accuracy and sensitivity for the external test cohort. CONCLUSIONS: Our GNB model performed better than the other models in the present study, suggesting that it may be more useful for differentiating indistinguishable benign form malignant VCFs. KEY POINTS: • The differential diagnosis of benign and malignant indistinguishable VCFs based on MRI is rather difficult for spine surgeons or radiologists. • Our ML models facilitate the differential diagnosis of benign and malignant indistinguishable VCFs with improved diagnostic efficacy. • Our GNB model had the high accuracy and sensitivity for clinical application.

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.

OBJECTIVES: To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the University of California, Los Angeles, Integrated Staging System (UISS), the Memorial Sloan-Kettering Cancer Center (MSKCC), and the International Metastatic Renal Cell Database Consortium (IMDC). METHODS: A multicenter of 799 localized (training/ test cohort, 558/241) and 45 metastatic ccRCC patients were studied. A DLRN was developed for predicting recurrence-free survival (RFS) in localized ccRCC patients, and another DLRN was developed for predicting overall survival (OS) in metastatic ccRCC patients. The performance of the two DLRNs was compared with that of the SSIGN, UISS, MSKCC, and IMDC. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA). RESULTS: In the test cohort, the DLRN achieved higher time-AUCs (0.921, 0.911, and 0.900 for 1, 3, and 5 years, respectively), C-index (0.883), and net benefit than SSIGN and UISS in predicting RFS for localized ccRCC patients. The DLRN provided higher time-AUCs (0.594, 0.649, and 0.754 for 1, 3, and 5 years, respectively) than MSKCC and IMDC in predicting OS for metastatic ccRCC patients. CONCLUSIONS: The DLRN can accurately predict outcomes and outperformed the existing prognostic models in ccRCC patients. CLINICAL RELEVANCE STATEMENT: This deep learning radiomics nomogram may facilitate individualized treatment, surveillance, and adjuvant trial design for patients with clear cell renal cell carcinoma. KEY POINTS: • SSIGN, UISS, MSKCC, and IMDC may be insufficient for outcome prediction in ccRCC patients. • Radiomics and deep learning allow for the characterization of tumor heterogeneity. • The CT-based deep learning radiomics nomogram outperforms the existing prognostic models in ccRCC outcome prediction.

Incremental value of radiomics-based heterogeneity to the existing risk criteria in predicting recurrence of hepatocellular carcinoma after liver transplantation.

OBJECTIVES: The aim of the study was to evaluate the association between the radiomics-based intratumoral heterogeneity (ITH) and the recurrence risk in hepatocellular carcinoma (HCC) patients after liver transplantation (LT), and to assess its incremental to the Milan, University of California San Francisco (UCSF), Metro-Ticket 2.0, and Hangzhou criteria. METHODS: A multicenter cohort of 196 HCC patients were investigated. The endpoint was recurrence-free survival (RFS) after LT. A CT-based radiomics signature (RS) was constructed and assessed in the whole cohort and in the subgroups stratified by the Milan, UCSF, Metro-Ticket 2.0, and Hangzhou criteria. The R-Milan, R-UCSF, R-Metro-Ticket 2.0, and R-Hangzhou nomograms which combined RS and the four existing risk criteria were developed respectively. The incremental value of RS to the four existing risk criteria in RFS prediction was evaluated. RESULTS: RS was significantly associated with RFS in the training and test cohorts as well as in the subgroups stratified by the existing risk criteria. The four combined nomograms showed better predictive capability than the existing risk criteria did with higher C-indices (R-Milan [training/test] vs. Milan, 0.745/0.765 vs. 0.677; R-USCF vs. USCF, 0.748/0.767 vs. 0.675; R-Metro-Ticket 2.0 vs. Metro-Ticket 2.0, 0.756/0.783 vs. 0.670; R-Hangzhou vs. Hangzhou, 0.751/0.760 vs. 0.691) and higher clinical net benefit. CONCLUSIONS: The radiomics-based ITH can predict outcomes and provide incremental value to the existing risk criteria in HCC patients after LT. Incorporating radiomics-based ITH in HCC risk criteria may facilitate candidate selection, surveillance, and adjuvant trial design. KEY POINTS: • Milan, USCF, Metro-Ticket 2.0, and Hangzhou criteria may be insufficient for outcome prediction in HCC after LT. • Radiomics allows for the characterization of tumor heterogeneity. • Radiomics adds incremental value to the existing criteria in outcome prediction.

Computed Tomography-Based Radiomics Analysis for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Patients.

PURPOSE: Previous studies have pointed out that magnetic resonance- and fluorodeoxyglucose positron emission tomography-based radiomics had a high predictive value for the response of the neoadjuvant chemotherapy (NAC) in breast cancer by respectively characterizing tumor heterogeneity of the relaxation time and the glucose metabolism. However, it is unclear whether computed tomography (CT)-based radiomics based on density heterogeneity can predict the response of NAC. This study aimed to develop and validate a CT-based radiomics nomogram to predict the response of NAC in breast cancer. METHODS: A total of 162 breast cancer patients (110 in the training cohort and 52 in the validation cohort) who underwent CT scans before receiving NAC and had pathological response results were retrospectively enrolled. Grades 4 to 5 cases were classified as response to NAC. According to the Miller-Payne grading system, grades 1 to 3 cases were classified as nonresponse to NAC. Radiomics features were extracted, and the optimal radiomics features were obtained to construct a radiomics signature. Multivariate logistic regression was used to develop the clinical prediction model and the radiomics nomogram that incorporated clinical characteristics and radiomics score. We assessed the performance of different models, including calibration and clinical usefulness. RESULTS: Eight optimal radiomics features were obtained. Human epidermal growth factor receptor 2 status and molecular subtype showed statistical differences between the response group and the nonresponse group. The radiomics nomogram had more favorable predictive efficacy than the clinical prediction model (areas under the curve, 0.82 vs 0.70 in the training cohort; 0.79 vs 0.71 in the validation cohort). The Delong test showed that there are statistical differences between the clinical prediction model and the radiomics nomogram ( z = 2.811, P = 0.005 in the training cohort). The decision curve analysis showed that the radiomics nomogram had higher overall net benefit than the clinical prediction model. CONCLUSION: The radiomics nomogram based on CT radiomics signature and clinical characteristics has favorable predictive efficacy for the response of NAC in breast cancer.

Anlotinib Combined with Toripalimab as Second-Line Therapy for Advanced, Relapsed Gastric or Gastroesophageal Junction Carcinoma.

Our study aimed to explore the efficacy and safety of anlotinib-toripalimab combination therapy as a second-line treatment for advanced relapsed gastric or gastroesophageal junction carcinoma (GC/GEJC). In this single arm, single-center extension clinical trial, patients with advanced relapsed GC/GEJC received toripalimab (240 mg, intravenously over 60 minutes, once every 2 weeks) plus anlotinib (12 mg/day, orally, 2 weeks on and 1 week off, every 3 weeks) as second-line therapy. There were 29 patients who achieved partial response, and the ORR was 32.3% (95% CI, 26.6%-38.5%). Grade 3 treatment-related adverse events (TRAEs) were recorded in 7 participants (11.3%), all of which were manageable. The PFS and OS were 4.0 and 11.1 months, respectively. Patients with programmed death-ligand 1 (PD-L1) positive expression showed numerically longer OS than the negative ones although the difference was not significantly. The tumor mutational burden-high (TMB-H) group showed a significantly better OS (P = .05) than the TMB-Low (TMB-L) group. Next-generation sequencing (NGS) revealed that fibroblast growth factor receptor 2 (FGFR2) mutations positively correlated with target lesion reduction (odds ratio [OR] = 0.14; P = .02). The new regimen increased tumor-infiltration of CD8+ T and CD3+ T cells. Furthermore, a patient-derived organoid (PDO) study indicated that anlotinib could promote an immune-supportive tumor microenvironment. As conclusion, the anlotinib-toripalimab combination showed promising efficacy and favorable safety as a second-line treatment for advanced, relapsed GC/GEJC. The PD-L1 expression, TMB, and FGFR2 mutation are potential biomarkers for predicting the efficacy of this regimen (ClinicalTrials.gov registration number: NCT04713059).

The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study.

PURPOSE: Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN. METHODS: A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics. RESULTS: Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P<0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit. CONCLUSIONS: The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design.

An Experimental Study on [125I]I-pHLIP (Var7) for SPECT/CT Imaging of an MDA-MB-231 Triple-Negative Breast Cancer Mouse Model by Targeting the Tumor Microenvironment.

Objective: To evaluate the diagnostic efficacy of MDA-MB-231 triple-negative breast cancer with 125I-labeled pHLIP (Var7) by single-photon emission computed tomography/computed tomography (SPECT/CT) imaging. Methods: The binding fraction of [125I]I-pHLIP (Var7) and MDA-MB-231 cells was measured at pH 7.4 and pH 6.0, and tumor-bearing mice were subjected to small-animal SPECT/CT imaging studies. Results: At pH = 6.0, the binding fractions of [125I]I-pHLIP (Var7) and MDA-MB-231 cells at 10 min, 40 min, 1 h, and 2 h were 1.9 ± 0.1%, 3.5 ± 0.1%, 6.3 ± 0.8%, and 6.6 ± 0.3%, respectively. At pH = 7.4, there was no measured binding between [125I]I-pHLIP (Var7) and MDA-MB-231 cells. Small-animal SPECT/CT imaging showed clearly visible tumors at 1 and 2 h after injection. Conclusions: [125I]I-pHLIP (Var7) could bind to MDA-MB-231 cells in an acidic environment, and small-animal SPECT/CT imaging showed clear tumors at 1 and 2 h after probe injection.

An Integrin-αvß6/α5ß1-Bitargeted Probe for the SPECT Imaging of Pancreatic Adenocarcinoma in Preclinical and Primary Clinical Studies.

Pancreatic adenocarcinoma (PA) is one of the deadliest human malignancies. However, early detection, prediction of surgical resectability, and prognosis of PA are challenging with current conventional imaging technologies in the clinic. Molecular imaging technologies combined with novel imaging probes could be useful for early detection and accurate staging of PA. Integrin αvß6 and α5ß1 are found to be overexpressed in PA. In this study, integrin αvß6/α5ß1-bitargeted probes 99mTc-HYNIC-isoDGR (99mTc-isoDGR) and 99mTc-HYNIC-PEG4-PisoDGR2 (99mTc-3PisoDGR2) were prepared and evaluated in the BxPC-3 human pancreatic tumor model. Both subcutaneous and in situ BxPC-3 tumors could be clearly visualized by 99mTc-isoDGR nanoScan SPECT/CT imaging with a high ratio of tumor to background. The blocking study with excess nonradioactive peptide showed a significantly reduced tumor uptake, which confirmed the specificity of 99mTc-isoDGR. Biodistribution results confirmed the imaging results. The dimer tracer 99mTc-3PisoDGR2 significantly enhanced tumor uptake compared with 99mTc-isoDGR, and the spontaneous PA lesion in the mouse model could be clearly visualized by 99mTc-3PisoDGR2. The primary clinical study also verified the ability of 99mTc-3PisoDGR2 for detection of PA. Therefore, SPECT/CT imaging using the integrin αvß6/α5ß1-bitargeted 99mTc-3PisoDGR2 provided a potential approach for the noninvasive detection of PA.

An FDG PET/CT metabolic parameter-based nomogram for predicting the early recurrence of hepatocellular carcinoma after liver transplantation.

PURPOSE: To construct an FDG PET/CT metabolic parameter-based model to predict early recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT). METHODS: A total of 62 patients with HCC after LT were enrolled with a follow-up period of 1 year. Basic clinical, pathology, and laboratory data, CT features (CPLC), and PET metabolic parameters (CPLCP) were collected for model construction. A CPLC nomogram without metabolic parameters and a CPLCP nomogram with metabolic parameters were established. The net reclassification index (NRI) and integrated discrimination improvement (IDI) of the two models were calculated. The constructed model was compared with Milan criteria and University of California San Francisco (UCSF) criteria. The time-dependent area under the receiver operating characteristic curve (time-AUC) was used to compare the efficiency of the models, and the bootstrap method was used to for verification. Harrell's concordance index (C-index) was used to evaluate the performance of these models. Decision curve analysis (DCA) was used to evaluate the clinical practicability of each model. RESULTS: Thirty out of 62 patients experienced a recurrence during the 1-year follow-up. BCLC stage (P = 0.009), MVI (P = 0.032), AFP (P = 0.004), CTdmax (P = 0.033), and MTV (P = 0.039) were the independent predictors. The CPLC nomogram and the CPLCP nomogram were established. Compared with the CPLC nomogram, the NRI of the CPLCP nomogram increased by 38.98% (95% CI = -18.77-60.43%) and the IDI increased by 4.40% (95% CI = -1.00-16.62%). The AUC value of the CPLCP nomogram was higher than those of Milan criteria and UCSF criteria in the time-AUC curve. Moreover, the CPLCP nomogram had a higher C-index (0.774) than other models. Finally, the DCA curve showed that clinical practicability of the CPLCP nomogram outperformed the Milan criteria and UCSF criteria. CONCLUSIONS: The CPLCP nomogram combining basic clinical data, pathology data, laboratory data, CT features, and PET metabolic parameters showed good efficacy and high clinical practicability in predicting the early recurrence of HCC after LT.

Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma.

PURPOSE: Lymphovascular invasion (LVI) impairs surgical outcomes in lung adenocarcinoma (LAC) patients. Preoperative prediction of LVI is challenging by using traditional clinical and imaging parameters. The purpose of this study was to investigate the value of the radiomics nomogram integrating clinical factors, CT features, and maximum standardized uptake value (SUVmax) to predict LVI and outcome in LAC and to evaluate the additional value of the SUVmax to the PET/CT-based radiomics nomogram. METHODS: A total of 272 LAC patients (87 LVI-present LACs and 185 LVI-absent LACs) with PET/CT scans were retrospectively enrolled, and 160 patients with SUVmax ≥ 2.5 of them were used for PET radiomics analysis. Clinical data and CT features were analyzed to select independent LVI predictors. The performance of the independent LVI predictors and SUVmax was evaluated. Two-dimensional (2D) and three-dimensional (3D) CT radiomics signatures (RSs) and PET-RS were constructed with the least absolute shrinkage and selection operator algorithm and radiomics scores (Rad-scores) were calculated. The radiomics nomograms, incorporating Rad-score and independent clinical and CT factors, with SUVmax (RNWS) or without SUVmax (RNWOS) were built. The performance of the models was assessed with respect to calibration, discrimination, and clinical usefulness. All the clinical, PET/CT, pathologic, therapeutic, and radiomics parameters were assessed to identify independent predictors of progression-free survival (PFS). RESULTS: CT morphology was the independent LVI predictor. SUVmax provided better discrimination capability compared with CT morphology in the training set (P < 0.001) and test set (P = 0.042). A total of 1409 CT and PET radiomics features were extracted and reduced to 8, 8, and 10 features to build the 2D CT-RS, 3D CT-RS, and the PET-RS, respectively. There was no significant difference in AUC between the 2D-RS and 3D-RS (P > 0.05), and 2D CT-RS showed a relatively higher AUC than 3D CT-RS. The CT-RS, the CT-RNWOS, and the CT-RNWS showed good discrimination in the training set (AUC [area under the curve], 0.799, 0.796, and 0.851, respectively) and the test set (AUC, 0.818, 0.822, and 0.838, respectively). There was significant difference in AUC between the CT-RNWS and CT-RNWOS (P = 0.044) in the training set. Decision curve analysis (DCA) demonstrated the CT-RNWS outperformed the CT-RS and the CT-RNWOS in terms of clinical usefulness. Furthermore, DCA showed the PETCT-RNWS provided the highest net benefit compared with the PET-RNWS and CT-RNWS. PFS was significantly different between the pathologic and RNWS-predicted LVI-present and LVI-absent patients (P < 0.001). Carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), pathologic LVI, histologic subtype, and SUVmax were independent predictors of PFS in the 244 CT-RNWS-predicted cohort; and CA125, NSE, pathologic LVI, and SUVmax were the independent predictors of PFS in the 141 PETCT-RNWS-predicted cohort. CONCLUSIONS: The radiomics nomogram, incorporating Rad-score, clinical and PET/CT parameters, shows favorable predictive efficacy for LVI status in LAC. Pathologic LVI and SUVmax are associated with LAC prognosis.

Improved Cellular Delivery of Antisense Oligonucleotide for miRNA-21 Imaging In Vivo Using Cell-Penetrating Peptide-Based Nanoprobes.

Most oligonucleotides fail to enter a cell and cannot escape from endosomes after endocytosis because of their negative charge and large molecular weight. More efficient cellular delivery of oligonucleotides should be developed for the widespread implementation of antisense imaging. The purpose of this study was to construct a novel antisense nanoprobe, 99mTc-labeled anti-miRNA oligonucleotides/cell-penetrating peptide PepFect6 (99mTc-AMO/PF6), and to evaluate its efficacy for imaging the miRNA-21 expression in A549 lung adenocarcinoma xenografts. Naked AMO and commercial Lipofectamine 2000-based nanoparticles (AMO/LIP) were used for comparison. The cellular delivery efficiency of AMO/PF6 was first investigated by laser confocal scanning microscopy using Cy5.5-labeled probes and further validated by in vivo fluorescence imaging. Then, the probes were labeled with 99mTc via hydrazinonicotinamide (HYNIC). The cytotoxicity assay, cellular uptake, and retention kinetics of the probes were evaluated in vitro. The biodistribution of the probes was investigated in A549 lung cancer xenografts, and SPECT imaging was performed in vivo. AMO/PF6 showed lower cytotoxicity than AMO/LIP (P < 0.05) but showed no significant difference with naked AMO. Fluorescence microscopy demonstrated more extensive and scattered signal distribution inside the A549 cells by AMO/PF6 than AMO/LIP. The labeling efficiency of 99mTc-AMO/PF6 was 72.6 ± 1.42%, and the specific activity was 11.6 ± 0.13 MBq/ng. The cellular uptake of 99mTc-PF6/AMO peaked at 12 h, with the uptake of 11.24 ± 0.12 mol/cell × 10-16, and the cellular retention of 99mTc-AMO/PF6 was 3.92 ± 0.15 mol/cell × 10-16 at 12 h after interrupted incubation. AMO/PF6 showed higher cellular uptake and retention than naked AMO and AMO/LIP. The biodistribution study showed that the tumor had the highest radioactivity accumulation, with the uptake ratio of tumor/muscle (T/M) increasing from 14.59 ± 0.67 to 21.76 ± 0.98 between 1 and 6 h after injection, followed by the uptake in the kidneys and the liver. The results of in vivo fluorescence and SPECT imaging were consistent with the results of the biodistribution. The tumor was visualized at 6 h after injection of AMO/PF6 with the highest T/M ratio among these probes (P < 0.05). PF6 improves cellular delivery of antisense oligonucleotides via noncovalent nanoparticles. 99mTc-AMO/PF6 shows favorable imaging properties and is promising for miRNAs imaging in vivo.

Feasibility of Low Volume of High-Concentration Iodinated Contrast Medium With 70 kVp Tube Voltage on High-Pitch Dual-Source Computed Tomography Angiography in Children With Congenital Heart Disease.

OBJECTIVE: The objective of this study was to investigate the feasibility of high-concentration iodinated contrast medium (CM) with 70 kVp tube voltage on high-pitch dual-source computed tomography (DSCT) in children with congenital heart disease (CHD). METHODS: Fifty-eight CHD patients underwent high-pitch DSCT in 2 protocols: 70 kVp tube voltage, 1.0 mL/kg CM volume, 370 mg I/mL concentration (group A); 80 kVp tube voltage, 1.5 mL/kg CM volume, 350 mg I/mL concentration (group B). The diagnostic accuracy, image quality, iodine delivery rate, iodine dose, and radiation dose were compared. RESULTS: There was no significant difference in the diagnostic accuracy (P > 0.05), image quality (P > 0.05) and iodine delivery rate (P > 0.05) between the 2 groups. The iodine dose (P < 0.05) and radiation dose (P < 0.05) in group A were significantly lower than those in group B. CONCLUSIONS: Reduction in iodine dose and radiation exposure can be achieved with 70 kVp high-pitch DSCT by administering a smaller volume of high-concentration CM in children with CHD.

Developing PEGylated Reversed D-Peptide as a Novel HER2-Targeted SPECT Imaging Probe for Breast Cancer Detection.

Human epidermal growth factor receptor-2 (HER2)-enriched breast cancer is characterized by strong invasiveness, high recurrence rate, and poor prognosis. HER2-specific imaging can help screening right patients for appropriate HER2-targeted therapies. Previously, we have developed a 99mTc-labeled HER2-targeted H6 peptide for SPECT imaging of breast cancer. However, the poor metabolic stability and high gallbladder uptake hamper its clinical application. In this study, a retro-inverso D-peptide of H6 (RDH6) was designed to increase the metabolic stability. PEGylation was used to improve its water solubility and in vivo pharmacokinetics. The results showed that the D-amino acids in 99mTc-PEG4-RDH6 brought better metabolic stability than 99mTc-PEG4-H6, thus achieving higher tumor uptake. As the length of the PEG chain increases, the hydrophilicity of the probes gradually increased, which may also be the main cause for the decreased liver uptake. Compared with radiotracers modified by PEG4 and PEG12, 99mTc-PEG24-RDH6 had a comparable tumor uptake and the lowest liver radioactivity. The SPECT imaging demonstrated that 99mTc-PEG24-RDH6 could specifically distinguish HER2-positive tumors from HER2-negative tumors with better imaging contrast, which thus has the potential for clinical screening of HER2-positive breast patients.

An Integrin Alpha 6-Targeted Radiotracer with Improved Receptor Binding Affinity and Tumor Uptake.

In this study, we reported a 99mTc-labeled integrin α6-targeted peptide as the molecular imaging probe for tumor imaging by single-photon emission computed tomography (SPECT). We found that replacing Cys-Cys cyclized RWY peptide (sequence: cCRWYDENAC) with lactam-bridged cyclic cKiE peptide (sequence: cKRWYDENAisoE) did not sacrifice the integrin α6-binding affinity and specificity of cKiE radiotracer. To further improve the radiotracer's tumor targeting capability, the dimerized cKiE peptide (termed cKiE2) was designed, and the corresponding radiotracer 99mTc-cKiE2 was evaluated for tumor uptake and in vivo pharmacokinetics properties in tumor models. We found that cKiE2 showed higher binding affinity to integrin α6 than did monomeric RWY or cKiE peptide. The biodistribution results showed that the tumor uptake of 99mTc-cKiE2 was twice higher than that of 99mTc-RWY (3.20 ± 0.12 vs 1.26 ± 0.06 %ID/g, P < 0.001) at 0.5 h postinjection. The tumor to nontargeting tissue ratios were also enhanced in most normal organs. Specificity of 99mTc-cKiE2 for integrin α6 was demonstrated by competitive blocking of tumor uptake with excess cold peptide (3.20 ± 0.24 to 1.38 ± 0.23 %ID/g, P < 0.001). The integrin α6-positive tumors were clearly visualized by 99mTc-cKiE2/SPECT with low background except with a relatively high kidney uptake. The tumor uptake of 99mTc-cKiE2 correlates well with the tumor integrin α6 expression levels in a linear fashion (R2 = 0.9623). We also compared 99mTc-cKiE2 with an integrin αvß3-targeted radiotracer 99mTc-3PRGD2 in the orthotopic hepatocellular carcinoma tumor models. We found that the orthotopic tumor was clearly visualized with 99mTc-cKiE2. 99mTc-3PRGD2 imaging did not show tumor contours in situ as clearly as 99mTc-cKiE2. The tumor-to-liver ratios of 99mTc-cKiE2 and 99mTc-3PRGD2 were 2.20 ± 0.17 and 0.85 ± 0.20. In conclusion, 99mTc-cKiE2 is an improved SPECT radiotracer for imaging integrin α6-positive tumors and has great potential for further clinical application.

A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.

OBJECTIVES: To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC). METHODS: Ninety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness. RESULTS: Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793-0.966) and the validation set (AUC, 0.846; 95% CI, 0.643-1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810-0.983) and the validation set (AUC, 0.949; 95% CI, 0.856-1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683-0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness. CONCLUSIONS: The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy. KEY POINTS: • Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.

Diosmetin exhibits anti-proliferative and anti-inflammatory effects on TNF-α-stimulated human rheumatoid arthritis fibroblast-like synoviocytes through regulating the Akt and NF-κB signaling pathways.

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by inflammation and proliferation of synovial tissues. Diosmetin is a bioflavonoid possessing an anti-inflammatory property. Herein, we aimed to study the effects of diosmetin on the inflammation and proliferation of RA fibroblast-like synoviocytes MH7A cells. MH7A cell proliferation was measured using cell counting kit-8 assay. Cell apoptosis was examined using flow cytometry. The production of inflammatory cytokines including interleukin (IL)-1ß, IL-6, IL-8, and matrix metalloproteinase-1 (MMP-1) was measured using enzyme-linked immunosorbent assay (ELISA). Results showed that diosmetin inhibited tumor necrosis factor-α (TNF-α)-induced proliferation increase in MH7A cells in a dose-dependent manner. Diosmetin treatment resulted in an increase in apoptotic rates and a reduction in TNF-α-induced production of IL-1ß, IL-6, IL-8, and MMP-1 in MH7A cells. Furthermore, diosmetin inhibited TNF-α-induced activation of protein kinase B (Akt) and nuclear factor-κB (NF-κB) pathways in MH7A cells. Suppression of Akt or NF-κB promoted apoptosis and inhibited TNF-α-induced proliferation increase and production of IL-1ß, IL-6, IL-8, and MMP-1 in MH7A cells, and diosmetin treatment enhanced these effects. Taken together, these findings suggested that diosmetin exhibited anti-proliferative and anti-inflammatory effects via inhibiting the Akt and NF-κB pathways in MH7A cells.

A 64 Cu-porphyrin-based dual-modal molecular probe with integrin αv ß3 targeting function for tumour imaging.

Pyropheophorbide-a (Pyro) is a promising multifunctional molecule for multimodal tumour imaging and photodynamic therapy, but its clinical applications are seriously restricted by the limited tumour accumulation capability. Here, we designed and synthesized a small-molecule probe that achieved specific dual-modal tumour imaging based on Pyro. Briefly, a novel molecule combining Pyro, an RGD dimer peptide (3PRGD2 ) and 64 Cu, was designed and synthesized, and the obtained molecule, 64 Cu-Pyro-3PRGD2 , exhibited high tumour specificity in both positron emission tomography and optical imaging in vivo. c (RGDfk) peptide blocking significantly reduced the efficacy of the probe, which confirmed the integrin αV ß3 targeting of this molecular probe. 64 Cu-Pyro-3PRGD2 had very low accumulation in normal organs and could be rapidly cleared through kidney metabolism, which prevented the potential damage to adjacent normal tissues. Overall, combining tumour targeting, dual-modal imaging, and biosafety, 64 Cu-Pyro-3PRGD2 has the potential for clinical use as a molecular imaging probe for tumour diagnosis.

Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma.

OBJECTIVE: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). METHODS: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness. RESULTS: Of the 177 and 183 texture features extracted from 2D and 3D regions of interest, respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good discrimination and calibration (P > .05). There was no significant difference in AUC between the 2 models (P = .093). Decision curve analysis showed the 3D model outperformed the 2D model in terms of clinical usefulness. CONCLUSIONS: The CTTA models based on contrast-enhanced CT images had a high value in differentiating fpAML from chRCC.