Nuclear Medicine Communications最新文献

Objective: To establish nomograms for predicting preoperative lymph-vascular space invasion (LVSI) and survival outcomes of cervical squamous cell carcinoma (CSCC) based on PET/CT radiomics.

Methods: One hundred and twenty-three patients with CSCC and LVSI status were enrolled retrospectively. Independent predictors of LVSI were identified through clinicopathological factors and PET/CT metabolic parameters. We extracted 1316 features from PET and CT volume of interest, respectively. Additionally, four models (PET-RS: radiomic signature of PET only; CT-RS: radiomic signature of CT only; PET/CT-RS + clinical data; PET/CT-RS: radiomic signature of PET and CT) were established to predict LVSI status. Calculation of radiomics scores of PET/CT was executed for assessment of the survival outcomes, followed by development of nomograms with radiomics (NR) or without radiomics (NWR).

Results: One hundred and twenty-three patients with pathologically confirmed CSCC had been categorized into two sets (training and testing sets). It was found that only maximum standardized uptake value (SUV max ) and squamous cell carcinoma antigen were independent predictors of LVSI. Meanwhile, the PET/CT-RS + clinical data outperformed the other three models in the training set [area under the curve (AUC): 0.91 vs. 0.861 vs. 0.81 vs. 0.814] and the testing set (AUC: 0.885 vs. 0.857 vs. 0.783 vs. 0.798). Additionally, SUV max and LVSI had been demonstrated to be independent prognostic indicators for progression-free survival and overall survival. Decision curve analysis and calibration curve indicated that NRs were superior to NWRs. The survival outcomes were assessed.

Conclusion: PET/CT-based radiomic signature nomogram enables a new method for preoperative prediction of LVSI and survival prognosis for patients with CSCC.

Purpose: This study aimed to investigate a deep learning model to classify amyloid plaque deposition in the brain PET images of patients suspected of Alzheimer's disease.

Methods: A retrospective study was conducted on patients who were suspected of having a mild cognitive impairment or dementia, and brain amyloid 18 F florapronol PET/computed tomography images were obtained from 2019 to 2022. Brain PET images were visually assessed by two nuclear medicine specialists, who classified them as either positive or negative. Image rotation was applied for data augmentation. The dataset was split into training and testing sets at a ratio of 8 : 2. For the convolutional neural network (CNN) analysis, stratified k-fold ( k  = 5) cross-validation was applied using training set. Trained model was evaluated using testing set.

Results: A total of 175 patients were included in this study. The average age at the time of PET imaging was 70.4 ± 9.3 years and included 77 men and 98 women (44.0% and 56.0%, respectively). The visual assessment revealed positivity in 62 patients (35.4%) and negativity in 113 patients (64.6%). After stratified k-fold cross-validation, the CNN model showed an average accuracy of 0.917 ± 0.027. The model exhibited an accuracy of 0.914 and an area under the curve of 0.958 in the testing set. These findings affirm the model's high reliability in distinguishing between positive and negative cases.

Conclusion: The study verifies the potential of the CNN model to classify amyloid positive and negative cases using brain PET images. This model may serve as a supplementary tool to enhance the accuracy of clinical diagnoses.

Objective: Pancreatic cancer is an increasing cause of cancer-related mortality, with persistently low survival rates. We investigated the clinical diagnostic value of the combination of preoperative serum carbohydrate antigen 19-9 (CA19-9), albumin-bilirubin (ALBI) score, and 18 F-fluoro-2-deoxy- d -glucose PET integrated with computed tomography ( 18 F-FDG PET/CT) imaging in pancreatic cancer preoperative resectability.

Methods: This study included 143 pancreatic cancer patients, including 68 preoperative resectable and 75 preoperative unresectable pancreatic cancer patients. Meanwhile, 67 patients with non-pancreatic cancer were included as the control group. The clinical data were collected. Serum CA19-9 level was measured by ELISA. The levels of total bilirubin and albumin were determined using a biochemical analyzer, with the ALBI score calculated. All patients underwent 18 F-FDG PET/CT imaging. The consistency of the diagnosis was evaluated by the Kappa test. Logistic univariate and multivariate regression analyses were performed. The diagnostic efficacy of these parameters was evaluated using receiver operating characteristic (ROC) curves, and the optimal ROC curve thresholds were obtained using the Youden index.

Results: The preoperative serum CA19-9 and ALBI score of patients with preoperative resectable pancreatic cancer were increased, which helped diagnose preoperative resectable pancreatic cancer. 18 F-FDG PET/CT imaging had diagnostic value for preoperative resectable pancreatic cancer. Preoperative serum CA19-9, ALBI score, and 18 F-FDG PET/CT imaging were independent influencing factors for pancreatic cancer preoperative resectability, and their combination had higher diagnostic value for preoperative resectable pancreatic cancer than any single of these indexes.

Conclusion: The combination of preoperative serum CA19-9, ALBI score, and 18 F-FDG PET/CT imaging had high diagnostic value for pancreatic cancer preoperative resectability.

Purpose: The purposes was to build model incorporating PET +  computed tomography (CT) radiomics features from baseline PET/CT + clinical parameters to predict outcomes in patients with non-Hodgkin lymphomas.

Methods: Cohort of 138 patients with complete clinical parameters and follow up times of 25.3 months recorded. Textural analysis of PET and manual correlating contouring in CT images analyzed using LIFE X software. Defined outcomes were overall survival (OS), disease free-survival, radiotherapy, and unfavorable response (defined as disease progression) assessed by end of therapy PET/CT or contrast CT. Univariable and multivariable analysis performed to assess association between PET, CT, and clinical.

Results: Male ( P  = 0.030), abnormal lymphocytes ( P  = 0.030), lower value of PET entropy ( P  = 0.030), higher value of SHAPE sphericity ( P  = 0.002) were significantly associated with worse OS. Advanced stage (III or IV, P  = 0.013), abnormal lymphocytes ( P  = 0.032), higher value of CT gray-level run length matrix (GLRLM) LRLGE mean ( P  = 0.010), higher value of PET gray-level co-occurrence matrix energy angular second moment ( P  < 0.001), and neighborhood gray-level different matrix (NGLDM) busyness mean ( P  < 0.001) were significant predictors of shorter DFS. Abnormal lymphocyte ( P  = 0.033), lower value of CT NGLDM coarseness ( P  = 0.082), and higher value of PET GLRLM gray-level nonuniformity zone mean ( P  = 0.040) were significant predictors of unfavorable response to chemotherapy. Area under the curve for the three models (clinical alone, clinical + PET parameters, and clinical + PET + CT parameters) were 0.626, 0.716, and 0.759, respectively.

Objectives: Administration of sublingual glyceryl trinitrate (GTN) prior to resting radiotracer injection during myocardial perfusion imaging (MPI) has been advocated to aid detection of viable myocardium and increase the extent of reversible perfusion defects. However, GTN is also known to reduce resting left ventricular volume and could thus increase the transient ischaemic dilation (TID) ratio, independently of severe or extensive coronary artery disease. We aimed to determine if GTN administration causes an increase in the TID ratio.

Methods: Causal inference using propensity score matched analysis was used to assess the effect of GTN on TID ratios in subjects undergoing adenosine sestamibi MPI.

Results: From 597 consecutive patients undergoing MPI, we selected a treatment group of 51 who received 400 μg of sublingual GTN before resting sestamibi injection and 51 propensity score matched controls. Mean TID ratios were 1.24 in treated subjects and 1.10 in controls (mean difference 0.15; 95% CI, 0.05-0.25; P = 0.0018). The mean difference in TID ratio fell progressively in each quartile of time elapsed between GTN administration and image acquisition. The proportion with TID ratios equal and greater than an abnormal threshold of 1.39 was 17.6% among the treated and 0% in controls (P = 0.0010). The effect on TID ratio was not restricted to those with moderate-to-severe stress perfusion defects or accompanied by greater reversible perfusion defects.

Conclusions: There is evidence of a cause-and-effect relationship between administering GTN before resting sestamibi injection and increased TID ratio on MPI. This may be a source of misleading false positive TID findings.

Objective: This study aimed to assess the usefulness of thyroglobulin (Tg) after radioiodine (RAI) therapy in predicting excellent response (ER) to therapy in postoperative differentiated thyroid cancer (DTC).

Methods: A retrospective observational study was conducted on postoperative DTC patients who underwent RAI from August 2018 to December 2022. Various factors were analyzed to predict ER to treatment. This involved Tg under stimulation (sTg) before RAI, Tg immediately (imTg) 112 h post-RAI and imTg/sTg(rTg). Based on the efficacy of RAI, patients were categorized into two groups: ER and non-ER (NER). Univariate logistic analysis was utilized to compare parameters between the two groups, followed by binary logistic regression analysis on factors associated with ER. Receiver operating characteristic (ROC) curves were employed to evaluate the sensitivity, specificity, and optimal diagnostic cutoff points for parameters affecting ER.

Results: The analysis included 45 ER patients and 56 NER patients. Statistical significance was found in the binary logistic regression analysis for the number of lymph nodes in the lateral cervical region (P = 0.016), sTg (P = 0.021), and rTg (P ≤ 0.001) concerning ER. ROC curve analysis revealed that the rTg area under the curve was 0.845, with an optimal cutoff value of 11.78, sensitivity of 82.6%, and specificity of 74.5%.

Conclusion: Post-RAI therapy, significant value is demonstrated by rTg with high sensitivity and specificity. This provides a foundation for the evaluation and decisions about DTC treatment in advance.

Purpose: To develop a predictive model for identifying the higher-risk growth pattern of invasive lung adenocarcinoma using multiphase multidetector computed tomography (MDCT) and 18F-fluorodeoxyglucose (FDG) PET radiomics.

Methods: A total of 203 patients with confirmed invasive lung adenocarcinoma between January 2018 and December 2021 were enrolled and randomly divided into training (n = 143) and testing sets (n = 60). Patients were classified into two groups according to the predominant growth pattern (lower-risk group: lepidic/acinar; higher-risk group: papillary/solid/micropapillary). Preoperative multiphase MDCT and 18F-FDG PET images were evaluated. The Artificial Intelligence Kit software was used to extract radiomic features. Five predictive models [arterial phase, venous phase, and plain scan (AVP), PET, AVP-PET, clinical, and radiomic-clinical (Rad-Clin) combined model] were developed. The models' performance was assessed using receiver-operating characteristic (ROC) curves and compared using the DeLong test.

Results: Among the radiomics models (AVP, PET, and AVP-PET), the AVP-PET model [area under ROC curve (AUC) = 0.888] outperformed the PET model (AUC = 0.814; P = 0.015) in predicting the higher-risk growth patterns. The combined Rad-Clin model (AUC = 0.923), which integrates AVP-PET radiomics and five independent clinical predictors (gender, spiculation, long-axis diameter, maximum standardized uptake value, and average standardized uptake value), exhibited superior performance in predicting the higher-risk growth pattern compared with radiomic models (P = 0.043, vs. AVP-PET; P = 0.016, vs. AVP; P = 0.002, vs. PET) or the clinical model alone (constructing based on five clinical predictors; AUC = 0.793; P < 0.001).

Conclusion: The combined Rad-Clin model can predict the higher-risk growth patterns of invasive adenocarcinoma (IAC). This approach could help determine individual therapeutic strategies for IAC patients by distinguishing predominant growth patterns with high risk.

Purpose: Parathyroid imaging with dual-phase technetium-99m methoxyisobutrylizonitrile (Tc-99m MIBI) scintigraphy serves as an important prerequisite for the identification of hyperfunctioning parathyroid gland(s) in patients with primary hyperparathyroidism (PHPT) for a successful targeted parathyroidectomy. This study aimed to evaluate the clinical value of additional lateral imaging and single-photon emission computed tomography/computed tomography (SPECT/CT) versus conventional planar imaging for locating parathyroid pathologies in patients with PHPT.

Materials and methods: A retrospective review was performed on 105 patients who underwent dual-phase Tc-99m MIBI scintigraphy and were surgically treated by parathyroidectomy. Dual-phase Tc-99m-MIBI planar scintigraphy with additional lateral pinhole views and SPECT/CT imaging was performed on a routine basis, as per departmental protocol. Comparison study between imaging modalities was done by patient-based analysis and scintigraphy results were compared with the clinical findings, biochemical markers, and histopathological findings.

Results: Sensitivity and specificity for anterior planar dual-phase Tc-99m MIBI scintigraphy were 78.8 and 80%, respectively. In comparison, lateral pinhole scan and SPECT/CT alone were found to have sensitivities of 85.9 and 90.9%, respectively, with the same specificity. Sensitivity decreased in patients with normocalcaemia and multiglandular disease. The mean adenoma weight and size for true-positive studies were significantly higher than those for false-negative or false-positive studies.

Conclusion: SPECT/CT provided the highest diagnostic accuracy for preoperative identification of parathyroid lesions in PHPT patients. Lateral pinhole imaging offers comparable sensitivity and aids in adenoma localisation when SPECT/CT is unavailable.

Background: Differentiated thyroid carcinoma (DTC) is managed by surgery followed by radioiodine (RAI) therapy in most intermediate and high-risk patients. Most nonmetastatic patients have excellent treatment responses and have long-term disease-free status. A lack of comprehensive medical services in resource-limited nation leads to attrition of critical clinical prognostication information. This study aimed to identify readily available clinical, biochemical, and histopathological parameters to predict remnant ablation success and long-term outcomes.

Methods: The study included DTC patients who underwent RAI after surgery. Ablation success was determined by thyroglobulin (Tg) and whole-body radioiodine scan. Patients were followed for at least 5 years to assess biochemical incomplete response (BIR) and structural recurrence.

Results: The study included 383 patients (a mean age of 37.8 ± 12.9 years). Successful ablation was noted in 251 (65.5%). High preablative stimulated serum Tg (presTg), papillary variants, and central and lateral compartment lymph nodal metastases were associated with ablation failure. PresTg (P < 0.001) was the most significant predictor. After a 102.9 ± 34.5 months follow-up, 280 (73.1%) patients were disease-free. BIR and structural recurrence were noted in 103 and 32 patients. PresTg (8.1 ± 27.7 vs. 92.3 ± 99.9 ng/ml), ATg (112.9 ± 389.8 vs. 43.2 ± 89.8 IU/ml), papillary variant, central [109 (66.1%) vs. 56 (33.9%)], and lateral compartment [65 (63.7%) vs. 37 (36.3%) lymph nodal metastases were associated (P < 0.05) with BIR. PresTg >10.5 has a sensitivity and specificity of 86.6 and 86.0% for predicting BIR. Patients with successful remnant ablation and a presTg level <10.5 ng/ml had a low risk of long-term disease recurrence (less than 5%).

Conclusion: This ambispective study found that successful ablation and long-term disease-free survival were achievable in a significant proportion of DTC patients. BIR (26.9%) and structural recurrence (8.4%) were not uncommon. PresTg levels emerged as a crucial predictor of ablation success and subsequent outcomes. In resource-limited regions, presTg levels and ablation failure can aid in optimizing treatment strategies and improving patient care.

Background: The study aimed to assess the role of 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) radiomics combined with clinical features using machine learning (ML) in predicting sarcopenia and prognosis of patients with diffuse large B-cell lymphoma (DLBCL).

Methods: A total of 178 DLBCL patients (118 and 60 applied for training and test sets, respectively) who underwent pretreatment 18F-FDG PET/CT were retrospectively enrolled. Clinical characteristics and PET/CT radiomics features were analyzed, and feature selection was performed using univariate logistic regression and correlation analysis. Sarcopenia prediction models were built by ML algorithms and evaluated. Besides, prognostic models were also developed, and their associations with progression-free survival (PFS) and overall survival (OS) were identified.

Results: Fourteen features were finally selected to build sarcopenia prediction and prognosis models, including two clinical (maximum standard uptake value of muscle and BMI), nine PET (seven gray-level and two first-order), and three CT (three gray-level) radiomics features. Among sarcopenia prediction models, combined clinical-PET/CT radiomics features models outperformed other models; especially the support vector machine algorithm achieved the highest area under curve of 0.862, with the sensitivity, specificity, and accuracy of 79.2, 83.3, and 78.3% in the test set. Furthermore, the consistency index based on the prognostic models was 0.753 and 0.807 for PFS and OS, respectively. The enrolled patients were subsequently divided into high-risk and low-risk groups with significant differences, regardless of PFS or OS (P < 0.05).

Conclusion: ML models incorporating clinical and PET/CT radiomics features could effectively predict the presence of sarcopenia and assess the prognosis in patients with DLBCL.