Browsing by Subject "Postoperative"

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    Deep learning-based postoperative glioblastoma segmentation and extent of resection evaluation: development, external validation, and model comparison
    (Oxford University Press, 2024-11-16) Cepeda, Santiago; Romero, Roberto; Luque, Lidia; García Pérez, Daniel; Blasco, Guillermo; Tommaso Luppino, Luigi; Kuttner, Samuel; Estéban-Sinovas, Olga; Arrese, Ignacio; Solheim, Ole; Eikenes, Live; Karlberg, Anna; Pérez-Núñez, Ángel; Zanier, Olivier; Serra, Carlo; Staartjes, Victor E.; Bianconi, Andrea; Rossi, Luca Francesco; Garbossa, Diego; Escudero, Trinidad; Hornero, Roberto; Sarabia, Rosario; Farmacología; Farmacia
    Background: The pursuit of automated methods to assess the extent of resection (EOR) in glioblastomas is challenging, requiring precise measurement of residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was to develop a deep learning-based model for postoperative segmentation using magnetic resonance imaging (MRI). We also compared our model’s performance with other available algorithms. Methods: To develop the segmentation model, a training cohort from 3 research institutions and 3 public databases was used. Multiparametric MRI scans with ground truth labels for contrast-enhancing tumor (ET), edema, and surgical cavity, served as training data. The models were trained using MONAI and nnU-Net frameworks. Comparisons were made with currently available segmentation models using an external cohort from a research institution and a public database. Additionally, the model’s ability to classify EOR was evaluated using the RANO-Resect classification system. To further validate our best-trained model, an additional independent cohort was used. Results: The study included 586 scans: 395 for model training, 52 for model comparison, and 139 scans for independent validation. The nnU-Net framework produced the best model with median Dice scores of 0.81 for contrast ET, 0.77 for edema, and 0.81 for surgical cavities. Our best-trained model classified patients into maximal and submaximal resection categories with 96% accuracy in the model comparison dataset and 84% in the independent validation cohort. Conclusions: Our nnU-Net-based model outperformed other algorithms in both segmentation and EOR classification tasks, providing a freely accessible tool with promising clinical applicability.
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    Is Azithromycin the first-choice macrolide for treatment of community-acquired pneumonia?
    (Infectious Diseases Society of America, 2003-05-06) Sánchez, F.; Mensa, J.; Martínez, J.A.; García-Vázquez, Elisa; Marco, F.; González, J.; Marcos, F.A.; Soriano, A.; Torres, A.; Medicina
    Combination treatment with ab-lactam plus a macrolide may improve the outcome for elderly patients with community-acquired pneumonia (CAP). The prognoses and mortality rates for elderly patients with CAP who receive ceftriaxone combined with a 3-day course of azithromycin or a 10-day course of clarithromycin were compared in an open-label, prospective study. Of 896 assessable patients, 220 received clarithromycin and 383 received azithromycin. There were no significant differences between groups with regard to the severity score defined by the Pneumonia Patient Outcomes Research Team (PORT) study group; the incidence of bacteremia was also not significantly different. However, for patients treated with azithromycin, the length of hospital stay was shorter (mean+_ SD, 7.4+_5 vs 9.4+_7 days; P<.01) and the mortality rate was lower (3.6% vs. 7.2%; P<.05); compared with those treated with clarithromycin. There might be a difference in the outcome for patients with CAP depending on the macrolide used. A shorter treatment course with azithromycin may result in better compliance with therapy