Objective:To assess the presence of chemotherapy-induced abnormal myocardial perfusion using SPECT myocardial perfusion imaging (MPI) in patients with malignant hematologic diseases before hematopoietic stem cell transplantation (HSCT), and to explore its predictive value for mid-to-long-term mortality risk after transplantation.Methods:From March 2016 to August 2022, 139 patients with malignant hematologic diseases (80 males, 59 females; age (45.7±13.0) years) who underwent resting MPI to assess the presence of chemotherapy-induced abnormal myocardial perfusion before HSCT at the First People′s Hospital of Changzhou were prospectively included. Baseline-data were collected and patients were followed up for mid-to-long-term (≥100d) adverse outcomes after transplantation. Overall survival (OS) of each patient was recorded. The χ2 test and independent-sample t test were used for data analysis. Cox regression analysis was utilized to identify independent risk factors affecting OS. Kaplan-Meier method and log-rank test were used for survival analysis. Results:The median follow-up time of 139 patients was 41.6(19.5, 65.6) months, with all-cause mortality of 28.8%(40/139), and the cardiovascular mortality was 42.5%(17/40). The prior cardiotoxic therapies rate (anthracycline dose ≥250mg/m 2) was higher in the death group compared to that in the survival group (15.0% (6/40) vs 5.1% (5/99); χ2=3.87, P=0.049). Pre-transplant abnormal myocardial perfusion rate was also higher in the death group compared to that in the survival group (55.0%(22/40) vs 22.2%(22/99); χ2=15.19, P<0.001). But pre-transplant left ventricular ejection fraction (LVEF) was lower in the death group compared to that in the survival group ((60.4±5.2)% vs (62.9±3.9)%; t=-3.07, P=0.003). Cox multivariate regression analysis showed that the abnormal myocardial perfusion indicated by MPI before transplantation was an independent risk factor affecting OS after HSCT in patients with malignant hematologic diseases (hazard rate ( HR)=2.70, 95% CI: 1.33-5.46, P=0.006). Kaplan-Meier analysis showed the 1-, 2-, 5-year OS rates of patients with the abnormal myocardial perfusion and the normal myocardial perfusion were 73.5%, 69.1%, 49.2% and 94.6%, 89.9%, 81.6%, respectively, with significant difference ( χ2=17.01, P<0.001). Conclusions:Patients with abnormal myocardial perfusion detected by MPI before HSCT for malignant hematologic diseases have a poorer prognosis, characterized by lower post-transplantation OS rates. The utilization of MPI for assessing abnormal myocardial perfusion before transplantation in patients with malignant hematologic diseases can aid in predicting the mid-to-long-term mortality risk after transplantation.

Objective:To investigate the relationship between collateral circulation and viable myocardium (VM) in patients with coronary chronic total occlusion (CTO).Methods:A total of 88 patients (76 males, 12 females, age (61.0±9.8) years) with coronary CTO were retrospectively analyzed. All patients underwent both 99Tc m-methoxyisobutylisonitrile (MIBI) SPECT myocardial perfusion imaging and 18F-FDG PET myocardial metabolism imaging for evaluation of VM at the First People′s Hospital of Changzhou between September 2012 and June 2023, and they were scheduled to receive coronary revascularization. The perfusion/metabolism mismatch myocardium was regarded as VM. The VM index within the CTO region was calculated, reflected the quantities of VM: VM index=(summed rest score within the CTO region-summed 18F-FDG uptake score within the CTO region)/reduced perfusion myocardial segments×4×100%. Rentrop grading of collateral circulation was performed based on coronary angiography. The differences of VM index within the CTO region between poor-developed (PD, Rentrop grade 0-1) and well-developed (WD, Rentrop grade 2-3) collateral circulation, and among different Rentrop grades were analyzed by the independent-sample t test or Kruskal-Wallis rank sum test. The linear regression analysis was used to evaluate the relationship between Rentrop grading and VM index within the CTO region. The ROC curve was constructed to analyze the predictive value of Rentrop grading for VM within the CTO region. Results:The VM index within the CTO region was significantly higher in WD patients ( n=54) compared to those in PD patients ( n=34): (45.8±16.3)% vs (21.3±16.7)% ( t=-6.79, P<0.001). Moreover, the VM index within the CTO region increased with increased Rentrop grade, and there was a significant difference among 4 groups ( H=30.22, P<0.001). Multiple linear regression analysis showed that only the Rentrop grading was an independent influencing factor for the VM index within the CTO region ( β=9.29, 95% CI: 5.91-12.67, P<0.001). ROC curve showed that the sensitivity and specificity of Rentrop grading score≥2 for predicting the presence or absence of VM within the CTO region were 65.8%(52/79) and 7/9, with the AUC of 0.724(95% CI: 0.619-0.814). Conclusions:In CTO patients who are scheduled for revascularization and evaluation of VM, as the Rentrop grading increases, the VM index within the CTO region also increases. The presence of VM within the CTO region can be predicted with Rentrop grading score ≥2.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

With the rapid development of generative adversarial networks (GAN), learning the mapping between CT and PET images enables cross-modality generation. This not only integrates anatomical and functional information to improve image quality, but also helps reduce the radiation exposure to some extent. Based on a review of representative GAN architectures such as conditional GAN and CycleGAN, this paper discusses their research progress and limitations in various application scenarios, including initial tumor diagnosis and staging, treatment evaluation and follow-up, as well as methods for reducing PET/CT radiation dose. Additionally, challenges related to small-sample learning, model interpretability, and cross-institutional standardization are highlighted, and the clinical application prospects of GAN-based cross-modality generation technology are explored.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

Objective:To assess the presence of chemotherapy-induced abnormal myocardial perfusion using SPECT myocardial perfusion imaging (MPI) in patients with malignant hematologic diseases before hematopoietic stem cell transplantation (HSCT), and to explore its predictive value for mid-to-long-term mortality risk after transplantation.Methods:From March 2016 to August 2022, 139 patients with malignant hematologic diseases (80 males, 59 females; age (45.7±13.0) years) who underwent resting MPI to assess the presence of chemotherapy-induced abnormal myocardial perfusion before HSCT at the First People′s Hospital of Changzhou were prospectively included. Baseline-data were collected and patients were followed up for mid-to-long-term (≥100d) adverse outcomes after transplantation. Overall survival (OS) of each patient was recorded. The χ2 test and independent-sample t test were used for data analysis. Cox regression analysis was utilized to identify independent risk factors affecting OS. Kaplan-Meier method and log-rank test were used for survival analysis. Results:The median follow-up time of 139 patients was 41.6(19.5, 65.6) months, with all-cause mortality of 28.8%(40/139), and the cardiovascular mortality was 42.5%(17/40). The prior cardiotoxic therapies rate (anthracycline dose ≥250mg/m 2) was higher in the death group compared to that in the survival group (15.0% (6/40) vs 5.1% (5/99); χ2=3.87, P=0.049). Pre-transplant abnormal myocardial perfusion rate was also higher in the death group compared to that in the survival group (55.0%(22/40) vs 22.2%(22/99); χ2=15.19, P<0.001). But pre-transplant left ventricular ejection fraction (LVEF) was lower in the death group compared to that in the survival group ((60.4±5.2)% vs (62.9±3.9)%; t=-3.07, P=0.003). Cox multivariate regression analysis showed that the abnormal myocardial perfusion indicated by MPI before transplantation was an independent risk factor affecting OS after HSCT in patients with malignant hematologic diseases (hazard rate ( HR)=2.70, 95% CI: 1.33-5.46, P=0.006). Kaplan-Meier analysis showed the 1-, 2-, 5-year OS rates of patients with the abnormal myocardial perfusion and the normal myocardial perfusion were 73.5%, 69.1%, 49.2% and 94.6%, 89.9%, 81.6%, respectively, with significant difference ( χ2=17.01, P<0.001). Conclusions:Patients with abnormal myocardial perfusion detected by MPI before HSCT for malignant hematologic diseases have a poorer prognosis, characterized by lower post-transplantation OS rates. The utilization of MPI for assessing abnormal myocardial perfusion before transplantation in patients with malignant hematologic diseases can aid in predicting the mid-to-long-term mortality risk after transplantation.

Objective:To investigate the relationship between collateral circulation and viable myocardium (VM) in patients with coronary chronic total occlusion (CTO).Methods:A total of 88 patients (76 males, 12 females, age (61.0±9.8) years) with coronary CTO were retrospectively analyzed. All patients underwent both 99Tc m-methoxyisobutylisonitrile (MIBI) SPECT myocardial perfusion imaging and 18F-FDG PET myocardial metabolism imaging for evaluation of VM at the First People′s Hospital of Changzhou between September 2012 and June 2023, and they were scheduled to receive coronary revascularization. The perfusion/metabolism mismatch myocardium was regarded as VM. The VM index within the CTO region was calculated, reflected the quantities of VM: VM index=(summed rest score within the CTO region-summed 18F-FDG uptake score within the CTO region)/reduced perfusion myocardial segments×4×100%. Rentrop grading of collateral circulation was performed based on coronary angiography. The differences of VM index within the CTO region between poor-developed (PD, Rentrop grade 0-1) and well-developed (WD, Rentrop grade 2-3) collateral circulation, and among different Rentrop grades were analyzed by the independent-sample t test or Kruskal-Wallis rank sum test. The linear regression analysis was used to evaluate the relationship between Rentrop grading and VM index within the CTO region. The ROC curve was constructed to analyze the predictive value of Rentrop grading for VM within the CTO region. Results:The VM index within the CTO region was significantly higher in WD patients ( n=54) compared to those in PD patients ( n=34): (45.8±16.3)% vs (21.3±16.7)% ( t=-6.79, P<0.001). Moreover, the VM index within the CTO region increased with increased Rentrop grade, and there was a significant difference among 4 groups ( H=30.22, P<0.001). Multiple linear regression analysis showed that only the Rentrop grading was an independent influencing factor for the VM index within the CTO region ( β=9.29, 95% CI: 5.91-12.67, P<0.001). ROC curve showed that the sensitivity and specificity of Rentrop grading score≥2 for predicting the presence or absence of VM within the CTO region were 65.8%(52/79) and 7/9, with the AUC of 0.724(95% CI: 0.619-0.814). Conclusions:In CTO patients who are scheduled for revascularization and evaluation of VM, as the Rentrop grading increases, the VM index within the CTO region also increases. The presence of VM within the CTO region can be predicted with Rentrop grading score ≥2.

With the rapid development of generative adversarial networks (GAN), learning the mapping between CT and PET images enables cross-modality generation. This not only integrates anatomical and functional information to improve image quality, but also helps reduce the radiation exposure to some extent. Based on a review of representative GAN architectures such as conditional GAN and CycleGAN, this paper discusses their research progress and limitations in various application scenarios, including initial tumor diagnosis and staging, treatment evaluation and follow-up, as well as methods for reducing PET/CT radiation dose. Additionally, challenges related to small-sample learning, model interpretability, and cross-institutional standardization are highlighted, and the clinical application prospects of GAN-based cross-modality generation technology are explored.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.