7 results match your criteria: "Department of Radiation Oncology Stanford University School of Medicine[Affiliation]"
The safety and efficacy of CAR T-cell therapy are unknown in pediatric and adolescent patients with relapsed or refractory primary mediastinal large B-cell lymphoma (R/R PMBCL) which is associated with dismal prognosis. Here, we present a case report of a 16-year-old patient with R/R PMBCL treated with lisocabtagene maraleucel including correlative studies. Patient achieved complete response at 6 months without cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome.
View Article and Find Full Text PDFA novel recursive cascaded full-resolution residual network (RCFRR-Net) for abdominal four-dimensional computed tomography (4D-CT) image registration was proposed. The entire network was end-to-end and trained in the unsupervised approach, which meant that the deformation vector field, which presented the ground truth, was not needed during training. The network was designed by cascading three full-resolution residual subnetworks with different architectures.
View Article and Find Full Text PDFLangerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm arising from the proliferation of pathologic Langerhans cells. LCH has a spectrum of presentations predominantly affecting male pediatric patients. As LCH is a relatively uncommon diagnosis, there is no standard of care for treatment of the disease and treatment is based largely on clinical judgment, lesion characteristics, and symptoms at presentation.
View Article and Find Full Text PDFArticle Synopsis
- - Low-grade lymphomas have a low annual risk (1%-3%) of transforming into high-grade types, and the study aimed to better understand this process using PET imaging metrics.
- - Researchers analyzed PET parameters like SUV-max and total lesion glycolysis (TLG), creating a scoring model combining these metrics with serum lactate dehydrogenase (LDH) to predict transformation status.
- - The scoring model showed strong performance in identifying transformation in both discovery (AUC = 0.91) and validation cohorts (AUC = 0.90), potentially serving as a noninvasive tool for patient risk assessment.
Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning.
Eur J Radiol
December 2019
Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, PR China. Electronic address:
Purpose: This study sought to establish a robust and fully automated Type B aortic dissection (TBAD) segmentation method by leveraging the emerging deep learning techniques.
Methods: Preoperative CTA images of 276 patients with TBAD were retrospectively collected from January 2011 to December 2018. Using a reproducible manual segmentation protocol of three labels (whole aorta, true lumen (TL), and false lumen (FL)), a ground truth database (n = 276) was established and randomly divided into training and testing sets in a rough 8:1 ratio.