Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma.

BACKGROUND: To introduce a three-dimensional convolutional neural network (3D CNN) leveraging transfer learning for fusing PET/CT images and clinical data to predict EGFR mutation status in lung adenocarcinoma (LADC). METHODS: Retrospective data from 516 LADC patients, encompassing preoperative PET/CT images, clinical information, and EGFR mutation status, were divided into training (n = 404) and test sets (n = 112). Several deep learning models were developed utilizing transfer learning, involving CT-only and PET-only models. A dual-stream model fusing PET and CT and a three-stream transfer learning model (TS_TL) integrating clinical data were also developed. Image preprocessing includes semi-automatic segmentation, resampling, and image cropping. Considering the impact of class imbalance, the performance of the model was evaluated using ROC curves and AUC values. RESULTS: TS_TL model demonstrated promising performance in predicting the EGFR mutation status, with an AUC of 0.883 (95%CI = 0.849-0.917) in the training set and 0.730 (95%CI = 0.629-0.830) in the independent test set. Particularly in advanced LADC, the model achieved an AUC of 0.871 (95%CI = 0.823-0.919) in the training set and 0.760 (95%CI = 0.638-0.881) in the test set. The model identified distinct activation areas in solid or subsolid lesions associated with wild and mutant types. Additionally, the patterns captured by the model were significantly altered by effective tyrosine kinase inhibitors treatment, leading to notable changes in predicted mutation probabilities. CONCLUSION: PET/CT deep learning model can act as a tool for predicting EGFR mutation in LADC. Additionally, it offers clinicians insights for treatment decisions through evaluations both before and after treatment.

The relationship between right atrial wall inflammation and poor prognosis of atrial fibrillation based on 18F-FDG positron emission tomography/computed tomography.

Background: Atrial fibrillation (AF) has been identified to increase stroke risk, even after oral anticoagulants (OACs), and the recurrence rate is high after radiofrequency catheter ablation (RFCA). Inflammation is an essential factor in the occurrence and persistence of AF. 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) is an established molecular imaging modality to detect local inflammation. We aimed to investigate the relationship between atrial inflammatory activity and poor prognosis of AF based on 18F-FDG PET/CT. Methods: A total of 204 AF patients including 75 with paroxysmal AF (ParAF) and 129 with persistent AF (PerAF) who underwent PET/CT before treatment were enrolled in this prospective cohort study. Clinical data, electrocardiograph (ECG), echocardiography, and cardiac 18F-FDG uptake were collected. Follow-up information was obtained from patient clinical case notes or telephone reviews, with the starting point being the time of PET/CT scan. The follow-up deadline was either the date of AF recurrence after RFCA, new-onset stroke, or May 2023. Cox proportional hazards regression models were used to identify predictors of poor prognosis and hazard ratios (HRs) with 95% confidence intervals (CIs) was calculated. Results: Median follow-up time was 29 months [interquartile range (IQR), 22-36 months]. Poor prognosis occurred in 52 patients (25.5%), including 34 new-onset stroke patients and 18 recrudescence after RFCA. The poor prognosis group had higher congestive heart failure, hypertension, age ≥75 years (doubled), diabetes mellitus, prior stroke or transient ischemic attack (TIA) or thromboembolism (doubled), vascular disease, age 65-74 years, sex category (female) (CHA2DS2-VASc) score [3.0 (IQR, 1.0-3.75) vs. 2.0 (IQR, 1.0-3.0), P=0.01], right atrial (RA) wall maximum standardized uptake value (SUVmax) (4.13±1.82 vs. 3.74±1.58, P=0.04), higher percentage of PerAF [39 (75.0%) vs. 90 (59.2%), P=0.04], left atrial (LA) enlargement [45 (86.5%) vs. 104 (68.4%), P=0.01], and RA wall positive FDG uptake [40 (76.9%) vs. 79 (52.0%), P=0.002] compared with the non-poor prognosis group. Univariate and multivariate Cox proportional hazard regression analysis concluded that only CHA2DS2-VASc score (HR, 1.29; 95% CI: 1.06-1.57; P=0.01) and RA wall positive FDG uptake (HR, 2.68; 95% CI: 1.10-6.50; P=0.03) were significantly associated with poor prognosis. Conclusions: RA wall FDG positive uptake based on PET/CT is tightly related to AF recurrence after RFCA or new-onset stroke after antiarrhythmic and anticoagulation treatment.

The impact of fear of childbirth on mode of delivery, postpartum mental health and breastfeeding: A prospective cohort study in Shanghai, China.

BACKGROUND: The prevalence and impact of fear of childbirth (FOC) has not been sufficiently understood. We aimed to investigate the prevalence of FOC among Chinese population and its impact on mode of delivery, postpartum mental health and breastfeeding. METHODS: We conducted a prospective cohort study, wherein pregnant women in their third trimester who underwent antenatal assessments at Shanghai Changning Maternity and Infant Health Hospital between September 2020 and March 2021 were recruited. Sociodemographic data of the participants were gathered by self-administered questionnaire, and their FOC was assessed using the Wijma Delivery Expectancy Questionnaire. Participants were followed up to 42 days postpartum. Information regarding their modes of delivery was retrieved from medical records, and data regarding postpartum mental health symptoms and one-month postpartum breastfeeding were obtained through self-administered questionnaires. RESULTS: Among 1287 participants, 461 (35.8 %) had high-level FOC (W-DEQ ≥ 66). Logistic regressions showed that women with high-level of FOC had higher rates of caesarean delivery on maternal request (CDMR) (aOR = 1.55, 95 % CI: 1.00-2.41, p = 0.049), a higher incidence of postpartum mental health symptoms (aOR = 1.68, 95 % CI: 1.09-2.59, p = 0.018), lower rates of one-month postpartum exclusive breastfeeding (aOR = 0.33, 95 % CI: 0.16-0.69, p = 0.003) and mixed feeding (aOR = 0.44, 95 % CI: 0.21-0.91, p = 0.028). LIMITATIONS: The long-term implications of FOC beyond the immediate postpartum period were not explored in the study. CONCLUSIONS: High-level FOC during the third trimester was associated with increased CDMR and postpartum mental health symptoms and reduced breastfeeding establishment. These results underscore the significance of FOC screening and tailored interventions for affected women.

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review.

Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of cancer-associated deaths. In recent years, significant progress has been achieved in basic and clinical research concerning the epidermal growth factor receptor (EGFR), and the treatment of lung adenocarcinoma has also entered a new era of individualized, targeted therapies. However, the detection of lung adenocarcinoma is usually invasive. 18F-FDG PET/CT can be used as a noninvasive molecular imaging approach, and radiomics can acquire high-throughput data from standard images. These methods play an increasingly prominent role in diagnosing and treating cancers. Herein, we reviewed the progress in applying 18F-FDG PET/CT and radiomics in lung adenocarcinoma clinical research and how these data are analyzed via traditional statistics, machine learning, and deep learning to predict EGFR mutation status, all of which achieved satisfactory results. Traditional statistics extract features effectively, machine learning achieves higher accuracy with complex algorithms, and deep learning obtains significant results through end-to-end methods. Future research should combine these methods to achieve more accurate predictions, providing reliable evidence for the precision treatment of lung adenocarcinoma. At the same time, facing challenges such as data insufficiency and high algorithm complexity, future researchers must continuously explore and optimize to better apply to clinical practice.

Right atrial wall inflammation detected by 18F-FDG PET/CT may be significantly associated with persistent atrial fibrillation: a prospective case-control study.

AIM: Atrial fibrillation (AF) is a progressive disease from paroxysmal to persistent, and persistent AF (PerAF) had worse prognosis. AF has potential link with inflammation, but it is not clear whether PerAF or paroxysmal AF (ParAF) is more closely related to inflammation. On the basis of inhibiting myocardial physiological uptake, 18F-fluorodeoxyglucosepositron emission tomography/computed tomography (18F-FDG PET/CT) is an established imaging modality to detect cardiac inflammation. We aimed to decipher the association between AF and atrial inflammatory activity by 18F-FDG PET/CT. METHODS: Thirty-five PerAF patients were compared to age and sex matched ParAF group with baseline 18F-FDG PET/CT scans prior to radiofrequency catheter ablation (RFCA) in the prospective case-control study. High-fat and low-carbohydrate diet and prolonged fast (HFLC+Fast) was applied to all AF patients before PET/CT. Then 22 AF patients with positive right atrial (RA) wall FDG uptake (HFLC+Fast) were randomly selected and underwent HFLC+Fast+heparin the next day. The CHA2DS2-VASc score was calculated to evaluate the risk of stroke. Clinical data, ECG, echocardiography, and atrial 18F-FDG uptake were compared. RESULTS: PerAF patients had significantly higher probability of RA wall positive FDG uptake and higher SUVmax than ParAF group [91.4% VS. 28.6%, P < 0.001; SUVmax: 4.10(3.20-4.90) VS. 2.60(2.40-3.10), P < 0.001]. Multivariate logistic regression analyses demonstrated that RA wall SUVmax was the independent influencing factor of PerAF (OR = 1.80, 95%CI 1.02-3.18, P = 0.04). In 22 AF patients with RA wall positive FDG uptake (HFLC+Fast), the "HFLC+Fast+Heparin" method did not significantly change RA wall FDG uptake evaluated by either quantitative analysis or visual analysis. High CHA2DS2-VASc score group had higher RA wall 18F-FDG uptake [3.35 (2.70, 4.50) vs, 2.8 (2.4, 3.1) P = 0.01]. CONCLUSIONS: RA wall FDG positive uptake was present mainly in PerAF. A higher RA wall 18F-FDG uptake was an independent influencing factor of PerAF. RA wall FDG uptake based on 18F-FDG PET/CT may indicate pathological inflammation. TRIAL REGISTRATION: http://www.chictr.org.cn , ChiCTR2000038288.

Machine learning to predict hemodynamically significant CAD based on traditional risk factors, coronary artery calcium and epicardial fat volume.

We sought to establish an explainable machine learning (ML) model to screen for hemodynamically significant coronary artery disease (CAD) based on traditional risk factors, coronary artery calcium (CAC) and epicardial fat volume (EFV) measured from non-contrast CT scans. 184 symptomatic inpatients who underwent Single Photon Emission Computed Tomography/Myocardial Perfusion Imaging (SPECT/MPI) and Invasive Coronary Angiography (ICA) were enrolled. Clinical and imaging features (CAC and EFV) were collected. Hemodynamically significant CAD was defined when coronary stenosis severity ≥ 50% with a matched reversible perfusion defect in SPECT/MPI. Data was randomly split into a training cohort (70%) on which five-fold cross-validation was done and a test cohort (30%). The normalized training phase was preceded by the selection of features using recursive feature elimination (RFE). Three ML classifiers (LR, SVM, and XGBoost) were used to construct and choose the best predictive model for hemodynamically significant CAD. An explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanation of the model's decision. In the training cohort, hemodynamically significant CAD patients had significantly higher age, BMI and EFV, higher proportions of hypertension and CAC comparing with controls (P all < .05). In the test cohorts, hemodynamically significant CAD had significantly higher EFV and higher proportion of CAC. EFV, CAC, diabetes mellitus (DM), hypertension, and hyperlipidemia were the highest ranking features by RFE. XGBoost produced better performance (AUC of 0.88) compared with traditional LR model (AUC of 0.82) and SVM (AUC of 0.82) in the training cohort. Decision Curve Analysis (DCA) demonstrated that XGBoost model had the highest Net Benefit index. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of 0.89, 68.0%, 96.8%, 94.4%, 79.0% and 83.9% in the XGBoost model. A XGBoost model based on EFV, CAC, hypertension, DM and hyperlipidemia to assess hemodynamically significant CAD was constructed and validated, which showed favorable predictive value. ML combined with SHAP can offer a transparent explanation of personalized risk prediction, enabling physicians to gain an intuitive understanding of the impact of key features in the model.

18F-FDG PET-CT for the prediction of mortality in patients with dermatomyositis and without malignant tumors: a pilot study.

Background: 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT) is typically used to screen malignancy in patients with dermatomyositis (DM). The aim of this study was to investigate the value of using PET-CT in assessing the prognosis of patients with DM and without malignant tumors. Methods: A total of 62 patients with DM who underwent 18F-FDG PET-CT were enrolled in the retrospective cohort study. Clinical data and laboratory indicators were obtained. The muscle max standardized uptake value (SUVmax), splenic SUVmax, target-to-background ratio (TBR) of the aorta, pulmonary highest value (hv)/SUVmax, epicardial fat volume (EFV), and coronary artery calcium (CAC) were measured using 18F-FDG PET-CT. The follow-up was conducted until March 2021, and the endpoint was death from any cause. Univariable and multivariable Cox regression analyses were used to analyze prognostic factors. The survival curves were produced with the Kaplan-Meier method. Results: The median duration of follow-up was 36 [interquartile range (IQR), 14-53] months. The survival rates were 85.2% and 73.4% for 1 and 5 years, respectively. A total of 13 (21.0%) patients died during a median follow-up of 7 (IQR, 4-15.5) months. Compared with the survival group, the death group had significantly higher levels of C-reactive protein [CRP; median (IQR), 4.2 (3.0, 6.0) vs. 6.30 (3.7, 22.8)], hypertension [7 (14.3%) vs. 6 (46.2%)], interstitial lung disease [ILD; 26 (53.1%) vs. 12 (92.3%)], positive anti-Ro52 antibody [19 (38.8%) vs. 10 (76.9%)], pulmonary FDG uptake [median (IQR), 1.8 (1.5, 2.9) vs. 3.5 (2.0, 5.8)], CAC [1 (2.0%) vs. 4 (30.8%)], and EFV [median (IQR), 74.1 (44.8, 92.1) vs. 106.5 (75.0, 128.5)] (all P values <0.001). Univariable and multivariable Cox analyses identified high pulmonary FDG uptake [hazard ratio (HR), 7.59; 95% confidence interval (CI), 2.08-27.76; P=0.002] and high EFV (HR, 5.86; 95% CI, 1.77-19.42; P=0.004) as independent risk factors for mortality. The survival rate was significantly lower in patients with the concurrent presence of high pulmonary FDG uptake and high EFV. Conclusions: Pulmonary FDG uptake and EFV detected with PET-CT were independent risk factors for death in patients with DM and without malignant tumors. Patients with the concurrent presence of high pulmonary FDG uptake and high EFV had a worse prognosis compared with patients with 1 or neither of these two risk factors. Early treatment should be applied in patients with concurrent presence of high pulmonary FDG uptake and high EFV to improve the survival rate.

Association of epicardial adipose tissue volume with increased risk of hemodynamically significant coronary artery disease.

Background: Epicardial adipose tissue (EAT) is closely related to coronary artery disease (CAD). Hemodynamically significant CAD has a worse prognosis and is more likely to benefit from revascularization. However, the specific relationship between EAT and hemodynamically significant CAD remains unclear. Methods: A total of 164 inpatients received single-photon emission computerized tomography-myocardial perfusion imaging (SPECT/MPI) and coronary angiography (CAG) between March 2018 and October 2019 at the Third Affiliated Hospital of Soochow University were enrolled in the retrospective cross-sectional study. Data on body mass index (BMI), hypertension, hyperlipidemia, diabetes mellitus (DM), active smoking, and symptoms were gathered. Epicardial fat volume (EFV) and coronary artery calcium (CAC) were quantified by noncontrast computed tomography (CT). Hemodynamically significant CAD was defined by coronary stenosis severity ≥50% with reversible perfusion defects in the corresponding areas of SPECT/MPI. Results: A total of 37.8% of patients had hemodynamically significant CAD. Age and BMI increased with tertiles of EFV (P for trend =0.009 and P<0.001). The ratios of hemodynamically significant CAD in EFV from low to high were 16.4%, 37.0%, and 60.0%, respectively (P for the trend <0.001). In univariate regression analysis, EFV was associated with hemodynamically significant CAD [odds ratio (OR) per 10 cm3 =1.36; 95% confidence interval (CI): 1.20-1.55; P<0.001]. After correcting for traditional risk factors and CAC, EFV was firmly linked to hemodynamically significant CAD (OR per 10 cm3 =1.53; 95% CI: 1.25-1.88; P<0.001). With an increasing trend in EFV for the tripartite groups, the likelihood of hemodynamically significant CAD increased significantly (P for trend <0.001). There was a saturation effect between EFV and hemodynamically significant CAD according to the generalized additive model (GAM). When EFV <134.43 cm3, EFV was linearly correlated with hemodynamically significant CAD (OR per 10 cm3 =2.06; 95% CI: 1.45-2.94; P<0.001). When EFV ≥134.43 cm3, the hemodynamically significant CAD risk was steeper and gradually reached saturation. Hypertension affected the relationship between EFV and hemodynamically significant CAD (P for the interaction =0.02) with an interaction effect. Conclusions: There was a robust relationship between EFV and hemodynamically significant CAD. After adjustment for confounders, we found that the risk of hemodynamically significant CAD onset increased nonlinearly for EFV above 134.4 cm3. This refined understanding of the relationship is helpful for the accurate clinical prediction of hemodynamically significant CAD.

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma.

BACKGROUND: This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. METHODS: A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built. RESULTS: Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I-II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III-IV lesions (training and testing sets AUC: 0.722 vs. 0.723). CONCLUSIONS: Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.

Association Analysis of Maximum Standardized Uptake Values Based on 18F-FDG PET/CT and EGFR Mutation Status in Lung Adenocarcinoma.

(1) Background: To investigate the association between maximum standardized uptake value (SUVmax) based on 18F-FDG PET/CT and EGFR mutation status in lung adenocarcinoma. (2) Methods: A total of 366 patients were retrospectively collected and divided into the EGFR mutation group (n = 228) and EGFR wild-type group (n = 138) according to their EGFR mutation status. The two groups' general information and PET/CT imaging parameters were compared. A hierarchical binary logistic regression model was used to assess the interaction effect on the relationship between SUVmax and EGFR mutation in different subgroups. Univariate and multivariate logistic regression was used to analyze the association between SUVmax and EGFR mutation. After adjusting for confounding factors, a generalized additive model and smooth curve fitting were applied to address possible non-linearities. (3) Results: Smoking status significantly affected the relationship between SUVmax and EGFR mutation (p for interaction = 0.012), with an interaction effect. After adjusting for age, gender, nodule type, bronchial sign, and CEA grouping, in the smoking subgroup, curve fitting results showed that the relationship between SUVmax and EGFR mutation was approximately linear (df = 1.000, c2 = 3.897, p = 0.048); with the increase in SUVmax, the probability of EGFR mutation gradually decreased, and the OR value was 0.952 (95%CI: 0.908-0.999; p = 0.045). (4) Conclusions: Smoking status can affect the relationship between SUVmax and EGFR mutation status in lung adenocarcinoma, especially in the positive smoking history subgroup. Fully understanding the effect of smoking status will help to improve the accuracy of SUVmax in predicting EGFR mutations.

Detecting obstructive coronary artery disease with machine learning: rest-only gated single photon emission computed tomography myocardial perfusion imaging combined with coronary artery calcium score and cardiovascular risk factors.

Background: The rest-only single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has low diagnostic performance for obstructive coronary artery disease (CAD). Coronary artery calcium score (CACS) is strongly associated with obstructive CAD. The aim of this study was to investigate the performance of rest-only gated SPECT MPI combined with CACS and cardiovascular risk factors in diagnosing obstructive CAD through machine learning (ML). Methods: We enrolled 253 suspected CAD patients who underwent the 1-stop rest-only SPECT MPI and computed tomography (CT) scan due to stress test-related contraindications. Myocardial perfusion and wall motion were assessed using quantitative perfusion SPECT + quantitative gated SPECT (QPS + QGS) automated quantification software. The Agatston algorithm was used to calculate CACS. The clinical data of patients, including cardiovascular risk factors, were collected. Based on feature selection and clinical experience, 8 factors were identified as modeling variables. Subsequently, patients were divided randomly into 2 groups: the training (70%) and test (30%) groups. The performance of 8 supervised ML algorithms was evaluated in the training and test groups. Results: Obstructive CAD was diagnosed by coronary angiography in 94 (37.2%, 94/253) patients. In the training group, the area under the receiver operator characteristic (ROC) curve (AUC) of the random forest was the highest, and the AUCs of Logistic, extreme gradient boosting (XGBoost), support vector machine (SVM), and adaptive boosting (AdaBoost) were all above 0.9. In the test group, the AUC of recursive partitioning and regression trees (Rpart) was the highest (0.911). Rpart and Naïve Bayes had the highest accuracy (0.840). Rpart had a sensitivity and specificity of 0.851 and 0.821, respectively; Naïve Bayes had a sensitivity and specificity of 0.809 and 0.893, respectively. Next was Logistic, with an accuracy of 0.827, a sensitivity of 0.872, and a specificity of 0.750. The random forest and XGBoost algorithms also had high accuracy, which was 0.813 for each algorithm. Conclusions: Rest-only SPECT MPI combined with CACS and cardiovascular risk factors using an ML algorithm to detect obstructive CAD is feasible. Among the algorithms validated in the test group, Rpart, Naïve Bayes, XGBoost, Logistic, and random forest are all highly accurate for diagnosing obstructive CAD. The application of ML in resting MPI and CACS may be used for screening obstructive CAD.

The value of regional and global CACS combined with SPECT MPI in detecting obstructive CAD: a retrospective real-world comparative study.

OBJECTIVE: Previous studies have shown that global coronary artery calcium score (CACS) can improve single photon emission computerized tomography (SPECT) myocardial perfusion imaging (MPI) to detect obstructive coronary artery disease (CAD). Whether regional CACS can improve SPECT MPI to detect obstructive CAD remains unclear. The aim of this study was to verify whether regional CACS has additional diagnostic value for obstructive CAD in suspected patients, compared to SPECT MPI and global CACS. METHODS: The study included 321 suspected CAD patients who underwent one-stop rest-stress SPECT MPI and low-dose computed tomography (CT) scan. All patients underwent coronary angiography within one month after examination. MPI images were visually analyzed by 2 experienced nuclear cardiologists. The regional CACS of left anterior descending coronary artery (LAD), left circumflex coronary artery (LCX), right coronary artery (RCA) and global CACS were calculated. Obstructive CAD was defined as ≥ 70% narrowing of the inner diameter of the LAD, LCX, RCA or their main branches and ≥ 50% narrowing of the left main coronary artery (LM). RESULTS: Among the 321 patients, 86 (26.8%, 86/321) had obstructive CAD. With the increased in global and regional CACS, there was an increasing trend of patients with obstructive CAD (P for trend < 0.001). Regional CACS had a better diagnostic performance in RCA territories (AUC 0.856, P < 0.001) compared with LAD, LCX territories (AUC 0.690, 0.674, respectively). The AUC of combined regional CACS and MPI was significantly higher than that of MPI alone (0.735 vs. 0.600, P < 0.001). However, based on MPI, the AUC of combined regional CACS was not significantly higher than that of global CACS (0.735 vs. 0.732, P = 0.898). The sensitivity and specificity of regional CACS combined with MPI for detecting obstructive CAD were 64.0% and 72.8%, respectively. CONCLUSIONS: Regional CACS was effective in detecting obstructive CAD in RCA territory. Based on SPECT MPI, regional CACS improved the detection of obstructive CAD, but was not superior to global CACS.

A model combining rest-only ECG-gated SPECT myocardial perfusion imaging and cardiovascular risk factors can effectively predict obstructive coronary artery disease.

OBJECTIVE: The rest-only single photon emission computerized tomography (SPECT) myocardial perfusion imaging (MPI) had low sensitivity in diagnosing obstructive coronary artery disease (CAD). Improving the efficacy of resting MPI in diagnosing CAD has important clinical significance for patients with contraindications to stress. The purpose of this study was to develop and validate a model predicting obstructive CAD in suspected CAD patients, based on rest-only MPI and cardiovascular risk factors. METHODS: A consecutive retrospective cohort of 260 suspected CAD patients who underwent rest-only gated SPECT MPI and coronary angiography was constructed. All enrolled patients had stress MPI contraindications. Clinical data such as age and gender were collected. Automated quantitative analysis software QPS and QGS were used to evaluate myocardial perfusion and function parameters. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the variables and build the prediction model. RESULTS: Among the enrolled 260 patients with suspected CAD, there were 95 (36.5%, 95/260) patients with obstructive CAD. The prediction model was presented in the form of a nomogram and developed based on selected predictors, including age, sex, SRS ≥ 4, SMS ≥ 2, STS ≥ 2, hypertension, diabetes, and hyperlipidemia. The AUC of the prediction model was 0.795 (95% CI: 0.741-0.843), which was better than the traditional models. The AUC calculated by enhanced bootstrapping validation (500 bootstrap resamples) was 0.785. Subsequently, the calibration curve (intercept = - 0.106; slope = 0.843) showed a good calibration of the model. The decision curve analysis (DCA) shows that the constructed clinical prediction model had good clinical applications. CONCLUSIONS: In patients with suspected CAD and contraindications to stress MPI, a prediction model based on rest-only ECG-gated SPECT MPI and cardiovascular risk factors have been developed and validated to predict obstructive CAD effectively.

Risk analysis in peripheral clinical T1 non-small cell lung cancer correlations between tumor-to-blood standardized uptake ratio on 18F-FDG PET-CT and primary tumor pathological invasiveness: a real-world observational study.

BACKGROUND: Sublobar resection is not suitable for patients with pathological invasiveness [including lymph node metastasis (LNM), visceral pleural invasion (VPI), and lymphovascular invasion (LVI)] of peripheral clinical T1 (cT1) non-small cell lung cancer (NSCLC), while primary tumor maximum standardized uptake value (SUVmax) on 18F-FDG PET-CT is related to pathological invasiveness, the significance differed among different institutions is still challenging. This study explored the relationship between the tumor-to-blood standardized uptake ratio (SUR) of 18F-FDG PET-CT and primary tumor pathological invasiveness in peripheral cT1 NSCLC patients. METHODS: This retrospective study included 174 patients with suspected lung neoplasms who underwent preoperative 18F-FDG PET-CT. We compared the differences of the clinicopathological variables, metabolic and morphological parameters in the pathological invasiveness and less-invasiveness group. We performed a trend test for these parameters based on the tertiles of SUR. The relationship between SUR and pathological invasiveness was evaluated by univariate and multivariate logistics regression models (included unadjusted, simple adjusted, and fully adjusted models), odds ratios (ORs), and 95% confidence intervals (95% CIs) were calculated. A smooth fitting curve between SUR and pathological invasiveness was produced by the generalized additive model (GAM). RESULTS: Thirty-eight point five percent of patients had pathological invasiveness and tended to have a higher SUR value than the less-invasiveness group [6.50 (4.82-11.16) vs. 4.12 (2.04-6.61), P<0.001]. The trend of SUVmax, mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), mean CT value (CTmean), size of the primary tumor, neuron-specific enolase (NSE), the incidence of LNM, adenocarcinoma (AC), and poor differentiation in the tertiles of SUR value were statistically significant (P were <0.001, <0.001, 0.010, <0.001, <0.001, 0.002, 0.033, <0.001, 0.002, and <0.001, respectively). Univariate analysis showed that the risk of pathological invasiveness increased significantly with increasing SUR [OR: 1.13 (95% CI: 1.06-1.21), P<0.001], and multivariate analysis demonstrated SUR, as a continuous variable, was still significantly related to pathological invasiveness [OR: 1.09 (95% CI: 1.01-1.18), P=0.032] after adjusting for confounding covariates. GAM revealed that SUR tended to be linearly and positively associated with pathological invasiveness and E-value analysis suggested robustness to unmeasured confounding. CONCLUSIONS: SUR is linearly and positively associated with primary tumor pathological invasiveness independent of confounding covariates in peripheral cT1 NSCLC patients and could be used as a supplementary risk maker to assess the risk of pathological invasiveness.

Longitudinal evaluation of diastolic dyssynchrony by SPECT gated myocardial perfusion imaging early after acute myocardial infarction and the relationship with left ventricular remodeling progression in a swine model.

BACKGROUND: Left ventricular diastolic dyssynchrony (LVDD), a dyssynchronous relaxation pattern, has been known to develop after myocardial damage. We aimed to evaluate the dynamic changes in LVDD in the early stage of acute myocardial infarction (AMI) by phase analysis of 99mtechnetium methoxyisobutylisonitrile (99mTc-MIBI) single-photon emission computed tomography (SPECT) gated myocardial perfusion imaging (GMPI) and explore its relationship with the progression of left ventricular remodeling (LVR). METHODS: The left anterior descending coronary arteries of 16 Bama miniature swine were occluded with a balloon to build AMI models. Animals were imaged by SPECT GMPI before AMI and at 1 day, 1 week and 4 weeks after AMI, and quantitative analysis was performed to determine the extent of left ventricle (LV) perfusion defects, left ventricular systolic dyssynchrony (LVSD) and the LVDD parameters: phase histogram bandwidth (PBW) and phase standard deviation (PSD). Echocardiography was simultaneously applied to evaluate left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), left ventricular ejection fraction (LVEF), and the LVDD parameters: Te-12-diff and Te-12-SD. Myocardial injury markers were measured, and 12-lead ECGs were performed. The degree of LVR progression was defined as ΔLVESV (%) = (LVESVAMI4weeks - LVESVAMI1day)/LVESVAMI1day. RESULTS: Thirteen swine completed the study. LVDD parameters changed dynamically at different time points after AMI. LVDD occurred as early as 1 day after AMI, peaked at 1 week, and trended toward a partial recovery at 4 weeks. Phase analysis on SPECT GMPI showed a significant correlation with tissue Doppler imaging for the assessment of LVDD during the longitudinal evaluation (r = 0.569 to 0.787, both P <0.05). During the univariate and multivariate regression analyses, the LVDD parameters PBW and PSD as of 1 day after AMI were significantly associated with the progression of LVR, respectively (PBW, ß = 0.004, 95% CI 0.001 to 0.007, P = 0.024; PSD, ß = 0.008, 95% CI 0.000 to 0.017, P = 0.049). Adjusted smooth curve fitting and threshold effect analysis indicated PBW and PSD break-point values of 142° and 60.4°, respectively, to predict the progression of LVR after AMI. CONCLUSIONS: Phase analysis of SPECT GMPI can accurately and reliably characterize LVDD. LVDD occurred on the first day after AMI, reached its peak at 1 week, and partially recovered at 4 weeks after AMI. LVDD as evaluated by phase analysis of SPECT GMPI early after AMI was significantly associated with the progression of LVR. The early assessment of LVDD after AMI may provide helpful information for predicting the progression of LVR in the future.

Incremental value of epicardial fat volume to coronary artery calcium score and traditional risk factors for predicting myocardial ischemia in patients with suspected coronary artery disease.

BACKGROUND: Epicardial fat volume (EFV) has been reported to be associated with coronary artery disease (CAD). CAD is the leading cause of myocardial ischemia and myocardial ischemia is closely related to major adverse cardiovascular events. We hypothesized that EFV could provide incremental value to traditional risk factors and coronary artery calcium score (CACS) in predicting myocardial ischemia in Chinese patients with suspected CAD. METHODS: We retrospectively studied 204 Chinese patients with suspected CAD who underwent single-photon emission computerized tomography-myocardial perfusion imaging (SPECT-MPI) combined with computed tomography (CT). Pericardial contours were manually defined, and EFV was automatically calculated. A reversible perfusion defect with summed difference score (SDS) ≥ 2 was defined as myocardial ischemia. RESULTS: The myocardial ischemia group had higher EFV than normal MPI group (137.80 ± 34.95cm3 vs. 106.63 ± 29.10 cm3, P < .001). In multivariable logistic regression analysis, high EFV was significantly associated with myocardial ischemia [odds ratio (OR): 8.30, 95% CI: 3.72-18.49, P < .001]. Addition of EFV to CACS and traditional risk factors could predict myocardial ischemia more effectively, with larger AUC .82 (P < .001), positive net reclassification index .14 (P = .04) and integrated discrimination improvement .14 (P < .001). The bootstrap resampling method (times = 500) was used to internally validation and calculate the 95% confidence interval (CI) of the AUC (95% CI .75-.87). The calibration curve for the probability of myocardial ischemia demonstrated good agreement between prediction and observation. CONCLUSIONS: In Chinese patients with suspected CAD, EFV was significantly associated with myocardial ischemia, and improved prediction of myocardial ischemia above traditional risk factors and CACS.

Relationship between total lesion glycolysis of primary lesions based on 18F-FDG PET/CT and lymph node metastasis in gastric adenocarcinoma: a cross-sectional preliminary study.

OBJECTIVES: We explored the relationship between lymph node metastasis (LNM) and total lesion glycolysis (TLG) of primary lesions determined by 18fluoro-2-deoxyglucose PET/computed tomography (18F-FDG PET/CT) in patients with gastric adenocarcinoma, and evaluated the independent effect of this association. METHODS: This retrospective study included 106 gastric adenocarcinoma patients who were examined by preoperative 18F-FDG PET/CT imaging between April 2016 and April 2020. We measured TLG of primary gastric lesions and evaluated its association with LNM. Multivariate logistic regression and a two-piece-wise linear regression were performed to evaluate the relationship between TLG of primary lesions and LNM. RESULTS: Of the 106 patients, 75 cases (71%) had LNM and 31 cases (29%) did not have LNM. Univariate analyses revealed that a per-SD increase in TLG was independently associated with LNM [odds ratio (OR) = 2.37; 95% confidence interval (CI), 1.42-3.98; P = 0.0010]. After full adjustment of confounding factors, multivariate analyses exhibited that TLG of primary lesions was still significantly associated with LNM (OR per-SD: 2.20; 95% CI, 1.16-4.19; P = 0.0164). Generalized additive model indicated a nonlinear relationship and saturation effect between TLG of primary lesions and LNM. When TLG of primary lesions was <23.2, TLG was significantly correlated with LNM (OR = 1.26; 95% CI, 1.07-1.48; P = 0.0053), whereas when TLG of primary lesions was ≥ 23.2, the probability of LNM was greater than 60%, gradually reached saturation effect, as high as 80% or more. CONCLUSIONS: In this preliminary study, there were saturation and segmentation effects between TLG of primary lesions determined by preoperative 18F-FDG PET/CT and LNM. When TLG of primary lesions was ≥ 23.2, the probability of LNM was greater than 60%, gradually reached saturation effect, as high as 80% or more. TLG of primary lesions is helpful in the preoperative diagnosis of LNM in patients with gastric adenocarcinoma.

Radiomics model based on preoperative 18F-fluorodeoxyglucose PET predicts N2-3b lymph node metastasis in gastric cancer patients.

OBJECTIVE: The aim of the study was to construct and validate 18F-fluorodeoxyglucose (18F-FDG) PET-based radiomics nomogram and use it to predict N2-3b lymph node metastasis in Chinese patients with gastric cancer (GC). METHODS: A total of 127 patients with pathologically confirmed GC who underwent preoperative 18F-FDG PET/CT imaging between January 2014 and September 2020 were enrolled as subjects in this study. We use the LIFEx software to extract PET radiomic features. A radiomics signature (Rad-score) was developed with the least absolute shrinkage and selection operator algorithm. Then a prediction model, which incorporated the Rad-score and independent clinical risk factors, was constructed and presented with a radiomics nomogram. Receiver operating characteristic (ROC) analysis was used to assess the performance of Rad-score and the nomogram. Finally, decision curve analysis (DCA) was applied to evaluate the clinical usefulness of the nomogram. RESULTS: The PET Rad-score, which includes four selected features, was significantly related to pN2-3b (all P < 0.05). The prediction model, which comprised the Rad-score and carcinoembryonic antigen (CEA) level, showed good calibration and discrimination [area under the ROC curve: 0.81(95% confidence interval: 0.74-0.89), P < 0.001)]. The DCA also indicated that the prediction model was clinically useful. CONCLUSION: This study presents a radiomics nomogram consisting of a radiomics signature based on PET images and CEA level that can be conveniently used for personalized prediction of high-risk N2-3b metastasis in Chinese GC patients.

WITHDRAWN: The clinical application of an innovative tracheotomy fixation belt in critically ill patients.

Ahead of Print article withdrawn by publisher.

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules.

PURPOSE: This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). METHODS: We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). RESULTS: A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372-1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. CONCLUSION: The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.