Revista espanola de medicina nuclear e imagen molecular最新文献

Non-invasive techniques for the evaluation of myocardial perfusion provide key functional information for the diagnosis of coronary disease and the stratification of risk in ischemic cardiopathy. Nonetheless, despite their adequate diagnostic performance, the myocardial perfusion images present limitations in the detection of balanced ischemia due to multivessel disease and in the assessment of microvascular pathology. Evaluation of the parameters of myocardial blood flow (MBF) and myocardial flow reserve may be useful in cases in which the technique is limited due to balanced coronary disease or in patients with healthy coronary arteries and suspicion of microvascular involvement. The greater spatial resolution and energy and greater sensitivity of the new generation cadmium-zinc-telluride gamma cameras compared to conventional equipment allow isotopic evaluation by single photon emission computed tomography of microvascular disease, which, previously, was mainly evaluated with the gold standard technique of positron emission tomography. The aim of this article of collaboration is to describe the protocol for the quantification of MBF developed by the Department of Nuclear Medicine of the Hospital Vall d'Hebron using a cardio-dedicated gamma camera (MyoSPECT™- GE HealthCare), highlighting the particularities of the acquisition of post-processing of the images as well as the possible artifacts.

Objective: To construct and validate a model for predicting lymph node metastasis (LNMs) of gastric cancer (GC) based on 18F-FDG PET/CT multi-parameter data and multiple machine learning (ML) algorithms.

Methods: We conducted a retrospective analysis of clinical records and 18F-FDG PET/CT images from 167 GC patients treated with radical gastrectomy. The dataset was randomly divided into two cohorts (training and validation cohort) with a ratio of 7:3. Seven machine learning algorithms were used to develop models. The model's performance was evaluated using metrics such as the receiver operating characteristic curve (ROC), sensitivity, specificity, accuracy, negative and positive predictive value, F1 score, calibration curve, and decision curve. Shapley Additive Explanations (SHAP) analysis was conducted to determine feature importance and enhance model interpretability. Additionally, Kaplan-Meier analysis was used to evaluate the prognosis of GC patients.

Results: The Artificial Neural Network algorithm(ANN)model outperformed other algorithms in the validation set, with an AUC of 0.814 (95% CI: 0.707-0.921), a sensitivity of 0.872, a specificity of 0.500 and an accuracy of 0.784. Both calibration and decision curve analyses demonstrated excellent calibrated ability and favorable clinical applicability. Through SHAP method, tumor invasion depth (clinical T stage), platelet count (PLT), and total lesion glycolysis (TLG) emerged as the most influential predictors in the ML model. Furthermore, the model exhibited robust risk stratification capability for overall survival (OS).

Conclusion: The ML model utilizing 18F-FDG PET/CT metabolic parameters showed good efficacy and reliability in predicting LNS preoperatively in GC patients.

Objetive: The detection of amyloid deposition has become a pivotal element in the diagnostic workup of Alzheimer's disease (AD). Among the available PET radiotracers, [¹⁸F]Florbetaben has proven to be a reliable tool for identifying amyloid-beta plaques, validated through both clinical assessments and histopathological correlation. This study aimed to assess the diagnostic accuracy and reliability of two key imaging methods: visual interpretation and semi-quantitative analysis using the Centiloid (CL) scale.

Methods: We included 208 patients who were attended at the outpatient Memory Clinic of a tertiary hospital, with mild cognitive impairment or mild dementia, suspected of AD. We performed receiver-operating-characteristic (ROC) analyses to compare the discriminative power of visual and quantification between AD and non-AD patients, characterized through the previously established standard of truth (SoT) as clinical follow-up.

Results: A total of 198 cases (95.0%) demonstrated concordance between both diagnostic methods (visual and CL-based quantification) and the SoT, with a weighted kappa coefficient k = 0.903 (p < 0.001), indicating almost perfect agreement. When comparing visual diagnosis to the SoT, the sensitivity was 96.8%, specificity was 96.5%, and diagnostic accuracy was 96.6%. Both approaches exhibited excellent diagnostic performance, effectively correlating with the final clinical diagnoses.

Conclusion: Our findings highlight that both visual assessment and semi quantitative analysis are reliable tools for the detection and monitoring of amyloid deposition in AD, particularly for early diagnosis and disease progression tracking.

Objective: To analyze the impact of [18F]F-Choline PET/CT findings on therapeutic management compared to conventional imaging techniques in the high-risk prostate cancer (PC) staging.

Materials and methods: Observational, longitudinal and retrospective study including patients with high-risk PC who underwent a [18F]F-choline PET/CT after initial staging with conventional imaging techniques (bone scan and abdomen-pelvis CT) between January 2018 and December 2023. Findings were classified as limited disease (lymph node involvement in pelvis and/or axial skeleton) or extended disease (extrapelvic lymph node involvement, involvement of extra-axial skeleton and/or distant sites). Finally, the impact of the results on therapeutic management and on disease volume reclassification (CHAARTED criteria) was analyzed.

Results: One hundred and nineteen patients were included (mean age: 68.43 ± 9.33 years, diagnostic prostate-specific antigen 64.22 ± 159.40 ng/mL). [18F]F-choline PET/CT was positive in 71.4% of patients (50.4% with limited disease and 21.0% with extended disease) versus 21.2% detected by conventional techniques (P < .001). Based on PET findings, 18% of patients had high-volume disease compared to 5% based on conventional imaging (P = .002). [18F]F-choline PET/CT findings led to a change in therapeutic management in 54 patients (45.4%). Among them, 11 (9.2%) received radiotherapy to the pelvic lymph nodes, 14 patients (11.8%) hormone therapy, 23 patients (19.3%) were candidates for therapy with new antiandrogens, and 6 cases (5%) received systemic chemotherapy.

Conclusion: [18F]F-choline PET/CT is superior to conventional imaging techniques in high-risk prostate cancer staging, detecting 18% of patients with high-volume disease and leading to a change in therapeutic management in 45% of cases. CRITICAL RELEVANCE STATEMENT: [18F]F-choline PET/CT is superior to conventional imaging techniques in high-risk prostate cancer staging.

Purpose: This study retrospectively evaluated the relationship between disease stage and metabolic parameters, along with asphericity, derived from ¹⁸F-FDG PET/CT images in patients with Small Cell Lung Cancer (SCLC).

Methods: We analyzed primary tumor metabolic parameters, including maximum and mean standardized uptake values (SUVmax, SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), as well as Asphericity (ASP). Disease staging was determined from ¹⁸F-FDG PET/CT images as either limited-stage (LS) or extensive-stage (ES). Statistical analyses assessed the correlation among these metabolic parameters and ASP, their relationship with disease stage, their ability to predict disease stage.

Results: The study included 134 SCLC patients (mean age: 62.3; 112 males, 22 females). Of these, 61.2% had ES and 38.8% had LS disease. MTV, TLG, and ASP values were significantly higher in the ES group than in the LS group (p < 0.001). In the multivariate binary logistic regression analysis, Age (p = 0.025), Gender (p = 0.033), and ASP (p = 0.001) were identified as independent predictors of disease stage. Notably, a one-unit increase in ASP was associated with a 6.35-fold increase in the likelihood of Extensive-Stage disease (OR = 6.350). In ROC analysis, MTV, TLG, and ASP significantly distinguished disease stages (p < 0.001) with optimal cut-off values of 45.05, 382.81, and 0.536, respectively.

Conclusion: Relying solely on SUVmax for SCLC patient evaluation may be insufficient. Incorporating other metabolic parameters like MTV and TLG, in addition to ASP, which was identified as a strong independent predictor, may enhance the understanding of tumor behavior during staging. These additional parameters can significantly aid in comprehensive patient assessment.