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.

Small animal PET imaging with the 68Ga-labeled pH (low) insertion peptide-like peptide YJL-4 in a triple-negative breast cancer mouse model.

BACKGROUND: The aim of this study was to prepare a novel 68Ga-labeled pH (low) insertion peptide (pHLIP)-like peptide, YJL-4, and determine its value for the early diagnosis of triple-negative breast cancer (TNBC) via in vivo imaging of tumor-bearing nude mice. The novel peptide YJL-4 was designed using a template-assisted method and synthesized by solid-phase peptide synthesis. After modification with the chelator 1,4,7­triazacyclononane-N,N',N″-triacetic acid (NOTA), the peptide was labeled with 68Ga. Then, the biodistribution of 68Ga-YJL-4 in tumor-bearing nude mice was investigated, and the mice were imaged by small animal positron emission tomography (PET). RESULTS: The radiochemical yield and radiochemical purity of 68Ga-YJL-4 were 89.5 ± 0.16% and 97.95 ± 0.06%, respectively. The biodistribution of 68Ga-YJL-4 in tumors (5.94 ± 1.27% ID/g, 6.72 ± 1.69% ID/g and 4.54 ± 0.58% ID/g at 1, 2 and 4 h after injection, respectively) was significantly greater than that of the control peptide in tumors at the corresponding time points (P < 0.01). Of the measured off-target organs, 68Ga-YJL-4 was highly distributed in the liver and blood. The small animal PET imaging results were consistent with the biodistribution results. The tumors were visualized by PET at 2 and 4 h after the injection of 68Ga-YJL-4. No tumors were observed in the control group. CONCLUSIONS: The novel pHLIP family peptide YJL-4 can adopt an α-helical structure for easy insertion into the cell membrane in an acidic environment. 68Ga-YJL-4 was produced in high radiochemical yield with good stability and can target TNBC tissue. Moreover, the strong concentration of radioactive 68Ga-YJL-4 in the abdomen does not hinder the imaging of early TNBC.

Sex Differences in Coronary Inflammation and Atherosclerosis Phenotypes in Response to Imaging Marker of Stress-Related Neural Activity.

BACKGROUND: Sex-specific differences in coronary phenotypes in response to stress have not been elucidated. This study investigated the sex-specific differences in the coronary computed tomography angiography-assessed coronary response to mental stress. METHODS: This retrospective study included patients with coronary artery disease and without cancer who underwent resting 18F-fluorodexoyglucose positron emission tomography/computed tomography and coronary computed tomography angiography within 3 months. 18F-flourodeoxyglucose resting amygdalar uptake, an imaging biomarker of stress-related neural activity, coronary inflammation (fat attenuation index), and high-risk plaque characteristics were assessed by coronary computed tomography angiography. Their correlation and prognostic values were assessed according to sex. RESULTS: A total of 364 participants (27.7% women and 72.3% men) were enrolled. Among those with heightened stress-related neural activity, women were more likely to have a higher fat attenuation index (43.0% versus 24.0%; P=0.004), while men had a higher frequency of high-risk plaques (53.7% versus 39.3%; P=0.036). High amygdalar 18F-flourodeoxyglucose uptake (B-coefficient [SE], 3.62 [0.21]; P<0.001) was selected as the strongest predictor of fat attenuation index in a fully adjusted linear regression model in women, and the first-order interaction term consisting of sex and stress-related neural activity was significant (P<0.001). Those with enhanced imaging biomarkers of stress-related neural activity showed increased risk of major adverse cardiovascular event both in women (24.5% versus 5.1%; adjusted hazard ratio, 3.62 [95% CI, 1.14-17.14]; P=0.039) and men (17.2% versus 6.9%; adjusted hazard ratio, 2.72 [95% CI, 1.10-6.69]; P=0.030). CONCLUSIONS: Imaging-assessed stress-related neural activity carried prognostic values irrespective of sex; however, a sex-specific mechanism linking psychological stress to coronary plaque phenotypes existed in the current hypothesis-generating study. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT05545618.

Clinical value of [18F]AlF-NOTA-FAPI-04 PET/CT for assessing early-stage liver fibrosis in adult liver transplantation recipients compared with chronic HBV patients.

AIMS: To investigate the clinical value and performance of [18F]AlF-NOTA-FAPI-04 PET/CT in assessing early-stage liver fibrosis in liver transplantation (LT) recipients. METHODS: A prospective study including 17 LT recipients and 12 chronic Hepatitis B (CHB) patients was conducted. All patients received liver biopsy, transient elastography (TE), and [18F]AlF-NOTA-FAPI-04 PET/CT. On [18F]AlF-NOTA-FAPI-04 PET/CT scans, the liver parenchyma's maximum standardized uptake values (SUVmax) were measured. The receiver operating characteristic (ROC) curve analysis was applied to determine the diagnostic efficacy of [18F]AlF-NOTA-FAPI-04 PET/CT in early-stage liver fibrosis (S1-S2) compared with the diagnostic performance of TE. RESULTS: Among those 29 patients enrolled in this study, 15(51.7%) had fibrosis S0, 10(34.5%) had S1, and 4(13.8%) had S2, respectively. The SUVmax of patients with early-stage liver fibrosis was significantly higher than those without liver fibrosis in LT recipients and CHB patients (P = 0.004, P = 0.02). In LT recipients, a SUVmax cut-off value of 2.0 detected early-stage liver fibrosis with an AUROC of 0.92 (P = 0.006), and a liver stiffness measurements (LSM) score cut-off value of 8.2 kPa diagnosed early-stage liver fibrosis with an AUROC of 0.80 (P = 0.012). In CHB patients, a SUVmax cut-off value of 2.7 detected early-stage liver fibrosis with an AUROC of 0.94 (P < 0.001) and an LSM scores cut-off value of 8.4 kPa diagnosed early-stage liver fibrosis with an AUROC of 0.91 (P < 0.001). CONCLUSION: [18F]AlF-NOTA-FAPI-04 PET/CT could be applied to evaluate early-stage liver fibrosis in LT recipients and CHB patients properly, with the potential additional advantages in monitoring and predicting complications after LT.

Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma.

OBJECTIVES: Tumor spread through air spaces (STAS) is associated with poor prognosis and impacts surgical options. We aimed to develop a user-friendly model based on 2-[18F] FDG PET/CT to predict STAS in stage I lung adenocarcinoma (LAC). MATERIALS AND METHODS: A total of 466 stage I LAC patients who underwent 2-[18F] FDG PET/CT examination and resection surgery were retrospectively enrolled. They were split into a training cohort (n = 232, 20.3% STAS-positive), a validation cohort (n = 122, 27.0% STAS-positive), and a test cohort (n = 112, 29.5% STAS-positive) according to chronological order. Some commonly used clinical data, visualized CT features, and SUVmax were analyzed to identify independent predictors of STAS. A prediction model was built using the independent predictors and validated using the three chronologically separated cohorts. Model performance was assessed using ROC curves and calculations of AUC. RESULTS: The differences in age (P = 0.009), lesion density subtype (P < 0.001), spiculation sign (P < 0.001), bronchus truncation sign (P = 0.001), and SUVmax (P < 0.001) between the positive and negative groups were statistically significant. Age ≥ 56 years [OR(95%CI):3.310(1.150-9.530), P = 0.027], lesion density subtype (P = 0.004) and SUVmax ≥ 2.5 g/ml [OR(95%CI):3.268(1.021-1.356), P = 0.005] were the independent factors predicting STAS. Logistic regression was used to build the A-D-S (Age-Density-SUVmax) prediction model, and the AUCs were 0.808, 0.786 and 0.806 in the training, validation, and test cohorts, respectively. CONCLUSIONS: STAS was more likely to occur in older patients, in solid lesions and higher SUVmax in stage I LAC. The PET/CT-based A-D-S prediction model is easy to use and has a high level of reliability in diagnosing.

Ct-based intratumoral and peritumoral radiomics for predicting prognosis in osteosarcoma: A multicenter study.

PURPOSE: To evaluate the performance of CT-based intratumoral, peritumoral and combined radiomics signatures in predicting prognosis in patients with osteosarcoma. METHODS: The data of 202 patients (training cohort:102, testing cohort:100) with osteosarcoma admitted to the two hospitals from August 2008 to February 2022 were retrospectively analyzed. Progression free survival (PFS) and overall survival (OS) were used as the end points. The radiomics features were extracted from CT images, three radiomics signatures（RSintratumoral, RSperitumoral, RScombined）were constructed based on intratumoral, peritumoral and combined radiomics features, respectively, and the radiomics score (Rad-score) were calculated. Kaplan-Meier survival analysis was used to evaluate the relationship between the Rad-score with PFS and OS, the Harrell's concordance index (C-index) was used to evaluate the predictive performance of the radiomics signatures. RESULTS: Finally, 8, 6, and 21 features were selected for the establishment of RSintratumoral, RSperitumoral, and RScombined, respectively. Kaplan-Meier survival analysis confirmed that the Rad-scores of the three RSs were significantly correlated with the PFS and OS of patients with osteosarcoma. Among the three radiomics signatures, RScombined had better predictive performance, the C-index of PSF prediction was 0.833 in the training cohort and 0.814 in the testing cohort, the C-index of OS prediction was 0.796 in the training cohort and 0.764 in the testing cohort. CONCLUSIONS: CT-based intratumoral, peritumoral and combined radiomics signatures can predict the prognosis of patients with osteosarcoma, which may assist in individualized treatment and improving the prognosis of osteosarcoma patients.

Intratumoral and peritumoral CT radiomics in predicting prognosis in patients with chondrosarcoma: a multicenter study.

OBJECTIVE: To evaluate the efficacy of the CT-based intratumoral, peritumoral, and combined radiomics signatures in predicting progression-free survival (PFS) of patients with chondrosarcoma (CS). METHODS: In this study, patients diagnosed with CS between January 2009 and January 2022 were retrospectively screened, and 214 patients with CS from two centers were respectively enrolled into the training cohorts (institution 1, n = 113) and test cohorts (institution 2, n = 101). The intratumoral and peritumoral radiomics features were extracted from CT images. The intratumoral, peritumoral, and combined radiomics signatures were constructed respectively, and their radiomics scores (Rad-score) were calculated. The performance of intratumoral, peritumoral, and combined radiomics signatures in PFS prediction in patients with CS was evaluated by C-index, time-dependent area under the receiver operating characteristics curve (time-AUC), and time-dependent C-index (time C-index). RESULTS: Eleven, 7, and 16 features were used to construct the intratumoral, peritumoral, and combined radiomics signatures, respectively. The combined radiomics signature showed the best prediction ability in the training cohort (C-index, 0.835; 95%; confidence interval [CI], 0.764-0.905) and the test cohort (C-index, 0.800; 95% CI, 0.681-0.920). Time-AUC and time C-index showed that the combined signature outperformed the intratumoral and peritumoral radiomics signatures in the prediction of PFS. CONCLUSION: The CT-based combined signature incorporating intratumoral and peritumoral radiomics features can predict PFS in patients with CS, which might assist clinicians in selecting individualized surveillance and treatment plans for CS patients. CRITICAL RELEVANCE STATEMENT: Develop and validate CT-based intratumoral, peritumoral, and combined radiomics signatures to evaluate the efficacy in predicting prognosis of patients with CS. KEY POINTS: • Reliable prognostic models for preoperative chondrosarcoma are lacking. • Combined radiomics signature incorporating intratumoral and peritumoral features can predict progression-free survival in patients with chondrosarcoma. • Combined radiomics signature may facilitate individualized stratification and management of patients with chondrosarcoma.

Low 18F-fluorodeoxyglucose dose positron emission tomography assisted by a deep-learning image-denoising technique in patients with lymphoma.

Background: Patients with lymphoma receive multiple positron emission tomography/computed tomography (PET/CT) exams for monitoring of the therapeutic response. With PET imaging, a reduced level of injected fluorine-18 fluorodeoxyglucose ([18F]FDG) activity can be administered while maintaining the image quality. In this study, we investigated the efficacy of applying a deep learning (DL) denoising-technique on image quality and the quantification of metabolic parameters and Deauville score (DS) of a low [18F]FDG dose PET in patients with lymphoma. Methods: This study retrospectively enrolled 62 patients who underwent [18F]FDG PET scans. The low-dose (LD) data were simulated by taking a 50% duration of routine-dose (RD) PET list-mode data in the reconstruction, and a U-Net-based denoising neural network was applied to improve the images of LD PET. The visual image quality score (1 = undiagnostic, 5 = excellent) and DS were assessed in all patients by nuclear radiologists. The maximum, mean, and standard deviation (SD) of the standardized uptake value (SUV) in the liver and mediastinum were measured. In addition, lesions in some patients were segmented using a fixed threshold of 2.5, and their SUV, metabolic tumor volume (MTV), and tumor lesion glycolysis (TLG) were measured. The correlation coefficient and limits of agreement between the RD and LD group were analyzed. Results: The visual image quality of the LD group was improved compared with the RD group. The DS was similar between the RD and LD group, and the negative (DS 1-3) and positive (DS 4-5) results remained unchanged. The correlation coefficients of SUV in the liver, mediastinum, and lesions were all >0.85. The mean differences of SUVmax and SUVmean between the RD and LD groups, respectively, were 0.22 [95% confidence interval (CI): -0.19 to 0.64] and 0.02 (95% CI: -0.17 to 0.20) in the liver, 0.13 (95% CI: -0.17 to 0.42) and 0.02 (95% CI: -0.12 to 0.16) in the mediastinum, and -0.75 (95% CI: -3.42 to 1.91), and -0.13 (95% CI: -0.57 to 0.31) in lesions. The mean differences in MTV and TLG were 0.85 (95% CI: -2.27 to 3.98) and 4.06 (95% CI: -20.53 to 28.64) between the RD and LD groups. Conclusions: The DL denoising technique enables accurate tumor assessment and quantification with LD [18F]FDG PET imaging in patients with lymphoma.

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.

99mTc-Labeled FAPI SPECT Imaging in Idiopathic Pulmonary Fibrosis: Preliminary Results.

AIM: Idiopathic pulmonary fibrosis (IPF) is associated with a poor prognosis, presenting the most aggressive form of interstitial lung diseases (ILDs). Activated fibroblasts are crucial for pathological processes. Fibroblast activation protein (FAP) inhibitor (FAPI) tracers would be promising imaging agents for these diseases. The purpose of this study was to evaluate a 99mTc-labeled FAPI tracer, 99mTc-HFAPI imaging in IPF patients. METHODS: Eleven IPF patients (nine males and two females; age range 55-75 year) were included in this pilot study. 99mTc-HFAPI serial whole-body scintigraphy at 5 min, 20 min, 40 min, 1 h, 2 h, 3 h, 4 h, and 6 h was acquired for dynamic biodistribution and dosimetry estimation in seven representative patients. SPECT/CT tomography fusion imaging of the chest region was performed in all patients at 4 h post-injection, which was considered as the optimal acquisition time. Dosimetry was calculated using OLINDA/EXM software (version 2.0; HERMES Medical Solutions). The quantified or semi-quantified standardized uptake values (SUVs) and lesion-to-background ratios (LBRs) of affected lung parenchyma were also calculated. The high-resolution CT (HRCT) stage was determined with visual evaluation, and the total HRCT score of each patient was measured using a weighting factor formula. Pulmonary function tests (PFTs) were recorded as well. Then, the relationships between the 99mTc-HFAPI results, disease extent on HRCT, and PFT results were investigated. RESULTS: Normal physiological uptake of 99mTc-HFAPI was observed mainly in the liver, intestinal tract, pancreas, gallbladder, and to a lesser extent in the spleen, kidneys, and thyroid, with no apparent retention in the blood circulation at the late time point. The mean injected activity of 99mTc-HFAPI was 813.4 MBq (range 695.6-888.0 MBq). No subjective side effects were noticed. The average whole-body effective dose was 0.0041 mSv/MBq per patient. IPF patients exhibited elevated pulmonary 99mTc-HFAPI uptake in abnormal lung regions, which was correlated with fibrotic regions on HRCT. Among different HRCT stage groups, both SUVmax and LBR showed significant differences (p < 0.001). The higher HRCT stage demonstrated significantly higher SUVmax and LBR. A linear correlation between 99mTc-HFAPI uptake and total HRCT score was observed for SUVmax (r = 0.7839, F = 54.41, p = 0.0094) and LBR (r = 0.7402, F = 56.33, p = 0.0092). 99mTc-HFAPI uptake also had moderate correlations with PFT results. CONCLUSIONS: Our preliminary data show that the 99mTc-HFAPI SPECT imaging is a promising new imaging modality in IPF patients. Investigations of its clinical value in monitoring disease progression and treatment response are needed in the future.

A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study.

BACKGROUND AND PURPOSE: The Stage, Size, Grade and Necrosis (SSIGN) score is the most commonly used prognostic model in clear cell renal cell carcinoma (ccRCC) patients. It is a great challenge to preoperatively predict SSIGN score and outcome of ccRCC patients. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting SSIGN score and outcome in localized ccRCC. METHODS: A multicenter 784 (training cohort/ test 1 cohort / test 2 cohort, 475/204/105) localized ccRCC patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting SSIGN score. Model performance was evaluated with area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis was used to assess the association of the model-predicted SSIGN with cancer-specific survival (CSS). Harrell's concordance index (C-index) was calculated to assess the CSS predictive accuracy of these models. RESULTS: The DLRM achieved higher micro-average/macro-average AUCs (0.913/0.850, and 0.969/0.942, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did for the prediction of SSIGN score. The CSS showed significant differences among the DLRM-predicted risk groups. The DLRM achieved higher C-indices (0.827 and 0.824, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did in predicting CSS for localized ccRCC patients. CONCLUSION: The DLRM can accurately predict the SSIGN score and outcome in localized ccRCC.

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.

Stress-Related Neural Activity Associates With Coronary Plaque Vulnerability and Subsequent Cardiovascular Events.

BACKGROUND: Stress-related neural activity (SNA) assessed by amygdalar activity can predict cardiovascular events. However, its mechanistic linkage with plaque vulnerability is not fully elucidated. OBJECTIVES: The authors aimed to investigate the association of SNA with coronary plaque morphologic and inflammatory features as well as their ability in predicting major adverse cardiovascular events (MACE). METHODS: A total of 299 patients with coronary artery disease (CAD) and without cancer underwent 18F-fluorodexoyglucose positron emission tomography/computed tomography (PET/CT) and available coronary computed tomographic angiography (CCTA) between January 1, 2013, and December 31, 2020. SNA and bone-marrow activity (BMA) were assessed with validated methods. Coronary inflammation (fat attenuation index [FAI]) and high-risk plaque (HRP) characteristics were assessed by CCTA. Relations between these features were analyzed. Relations between SNA and MACE were assessed with Cox models, log-rank tests, and mediation (path) analyses. RESULTS: SNA was significant correlated with BMA (r = 0.39; P < 0.001) and FAI (r = 0.49; P < 0.001). Patients with heightened SNA are more likely to have HRP (40.7% vs 23.5%; P = 0.002) and increase risk of MACE (17.2% vs 5.1%, adjusted HR 3.22; 95% CI: 1.31-7.93; P = 0.011). Mediation analysis suggested that higher SNA associates with MACE via a serial mechanism involving BMA, FAI, and HRP. CONCLUSIONS: SNA is significantly correlated with FAI and HRP in patients with CAD. Furthermore, such neural activity was associated with MACE, which was mediated in part by leukopoietic activity in the bone marrow, coronary inflammation, and plaque vulnerability.

Prognostic value of metabolic tumor volume and lesion dissemination from baseline PET/CT in patients with diffuse large B-cell lymphoma: Further risk stratification of the group with low-risk and high-risk NCCN-IPI.

PURPOSE: The purpose of this study was to determine the prognostic value of metabolic tumor volume and lesion dissemination from baseline PET/CT in patients with diffuse large B-cell lymphoma (DLBCL) and the prognostic value of them in the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) subgroups. METHODS: A total of 113 patients who underwent 18F-FDG PET/CT examination in our institution were retrospectively collected. The MTV was measured by iterative adaptive algorithm. The location of the lesion was obtained according to its three-dimensional coordinates, and Dmax was obtained. SDmax is derived from Dmax standardized by body surface area (BSA). The X-tile method was used to determine the optimal cut-off values for MTV, Dmax and SDmax. Cox regression analysis was used to perform univariate and multivariate analyses. Patient survival rates were derived from Kaplan-Meier curves and compared using the log-rank test. RESULTS: The median follow-up time was 24 months. The median of MTV was 196.86 cm3 (range 2.54-2925.37 cm3), and the optimal cut-off value was 489 cm3. The median of SDmax was 0.25 m-1 (range 0.12-0.51 m-1), and the best cut-off value was 0.31 m-1. MTV and SDmax were independent prognostic factors of PFS (all P < 0.001). Combined with MTV and SDmax, the patients were divided into three groups, and the difference of PFS among the groups was statistically significant (P < 0.001), and was able to stratify the risk of NCCN-IPI patients in the low-risk (NCCN-IPI < 4) and high-risk (NCCN-IPI ≥ 4) groups (P = 0.001 and P = 0.031). CONCLUSION: MTV and SDmax are independent prognostic factors for PFS in DCBCL patients, which describe tumor burden and tumor dissemination characteristics, respectively. The combination of the two could facilitate risk stratification between the low-risk and high-risk NCCN-IPI groups.

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.

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 combined model based on CT radiomics and clinical variables to predict uric acid calculi which have a good accuracy.

The aim of this study was to develop a CT-based radiomics and clinical variable diagnostic model for the preoperative prediction of uric acid calculi. In this retrospective study, 370 patients with urolithiasis who underwent preoperative urinary CT scans were enrolled. The CT images of each patient were manually segmented, and radiomics features were extracted. Sixteen radiomics features were selected by one-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO). Logistic regression (LR), random forest (RF) and support vector machine (SVM) were used to model the selected features, and the model with the best performance was selected. Multivariate logistic regression was used to screen out significant clinical variables, and the radiomics features and clinical variables were combined to construct a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), etc., were used to evaluate the diagnostic performance of the model. Among the three machine learning models, the LR model had the best performance and good robustness of the dataset. Therefore, the LR model was used to construct the nomogram. The AUCs of the nomogram model in the training set and validation set were 0.878 and 0.867, respectively, which were significantly higher than those of the radiomics model and the clinical feature model. The CT-based radiomics model based has good performance in distinguishing uric acid stones from nonuric acid stones, and the nomogram model has the best diagnostic performance among the three models. This model can provide an effective reference for clinical decision-making.

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.

Prognostic value of baseline 18F-FDG PET/CT in patients with esophageal squamous cell carcinoma treated with definitive (chemo)radiotherapy.

PURPOSE: To investigate the prognostic value of baseline 18F-FDG PET/CT in patients with esophageal squamous cell carcinoma (ESCC) treated with definitive (chemo)radiotherapy. METHODS: A total of 98 ESCC patients with cTNM stage T1-4, N1-3, M0 who received definitive (chemo)radiotherapy after 18F-FDG PET/CT examination from December 2013 to December 2020 were retrospectively analyzed. Clinical factors included age, sex, histologic differentiation grade, tumor location, clinical stage, and treatment strategies. Parameters obtained by 18F-FDG PET/CT included SUVmax of primary tumor (SUVTumor), metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmax of lymph node (SUVLN), PET positive lymph nodes (PLNS) number, the shortest distance between the farthest PET positive lymph node and the primary tumor in three-dimensional space after the standardization of the patient BSA (SDmax(LN-T)). Univariate and multivariate analysis was conducted by Cox proportional hazard model to explore the significant factors affecting overall survival (OS) and progression-free survival (PFS) in ESCC patients. RESULTS: Univariate analysis showed that tumor location, SUVTumor, MTV, TLG, PLNS number, SDmax (LN-T) were significant predictors of OS and tumor location, and clinical T stage, SUVTumor, MTV, TLG, SDmax (LN-T) were significant predictors of PFS (all p < 0.1). Multivariate analysis showed that MTV and SDmax (LN-T) were independent prognostic factors for OS (HR = 1.018, 95% CI 1.006-1.031; p = 0.005; HR = 6.988, 95% CI 2.119-23.042; p = 0.001) and PFS (HR = 1.019, 95% CI 1.005-1.034; p = 0.009; HR = 5.819, 95% CI 1.921-17.628; p = 0.002). Combined with independent prognostic factors MTV and SDmax (LN-T), we can further stratify patient risk. CONCLUSIONS: Before treatment, 18F-FDG PET/CT has important prognostic value for patients with ESCC treated with definitive (chemo)radiotherapy. The lower the value of MTV and SDmax (LN-T), the better the prognosis of patients.