Revista espanola de medicina nuclear e imagen molecular最新文献

Introduction: In response to the increasing demand for radioguided procedures, the integration of the Nursing Care Assistants (NCA) profile was proposed as support professionals in the preoperative marking of breast cancer (BC) patients using I-125 seeds.

Objectives: To analyze the clinical impact and professional development resulting from the integration of NCA into these procedures, assessing the operational efficiency of the Radioguided Surgery Team (RGST) and the training process that enabled them to assume new competencies.

Materials and methods: A four-phase theoretical-practical training program was implemented (theoretical instruction, practical mentoring, clinical simulation, and supervised autonomous practice), aimed at training NCA to assist in the localization and marking of non-palpable lesions and metastatic axillary lymph nodes using I-125 seeds. After completing the training, their impact was evaluated through a comparative analysis of RGST activity in 2023 and 2024.

Results: During the study period, NCA autonomously performed 116 marking procedures with I-125 seeds without incidents, accounting for 11.8% of total procedures in 2023 (32/270) and 23% in 2024 (84/366). Operational support was required on 21 working days in 2023 (8.5%) and 49 days in 2024 (19.6%), indicating a significant increase in their involvement.

Conclusions: The incorporation of the NCA profile into radioguided marking procedures has proven to be an effective strategy for optimizing healthcare resources, enhancing the operational efficiency of the RGST, and promoting the professional development of this role within highly specialized clinical settings.

Purpose: The aim of this study was to evaluate the utility of radiomics based on [¹⁸F]FDG PET/CT imaging for the prediction of histological differentiation grade in patients with non-small cell lung cancer (NSCLC) of either adenocarcinoma or squamous cell carcinoma subtype.

Materials and methods: A single-center retrospective observational study was conducted, including 150 patients with histologically confirmed NSCLC who underwent [¹⁸F]FDG PET/CT imaging prior to complete surgical resection of the tumor. Patients were excluded if they showed no significant FDG uptake, lacked differentiation data, or had insufficient tumor volume. Image segmentation and feature extraction were performed using LIFEx software, obtaining both textural and morphological features. Predictive variables were selected using classical statistical techniques and LASSO regression, and model performance was evaluated using ROC curve analysis.

Results: Among the 150 patients included, 25 had well-differentiated tumors, 88 moderately differentiated, and 37 had poorly differentiated. Comparison between well-differentiated tumors and those with moderate or poor differentiation revealed statistically significant differences in several radiomic features. The LASSO model identified seven variables with high discriminative power. The model achieved a sensitivity of 76%, specificity of 99.2%, positive predictive value of 95%, negative predictive value of 95.4%, and an overall accuracy of 95.3%.

Conclusions: Radiomic features extracted from [¹⁸F]FDG PET/CT images can predict tumor differentiation grade in NSCLC with high specificity. This non-invasive approach may serve as a valuable adjunct to histopathological evaluation, potentially aiding clinical decision-making.