Background & Objective: So far, there is still no standard salvage regimen for relapsed or refractory non-Hodgkin's lymphoma (NHL). The response rates (RR) of NHL patients received common salvage regimens, such as DICE, ESHAP, MINE, and EPOCH, are only 30%-70%. This study was to evaluate the efficacy and safety of DICE regimen, as a salvage regimen, in treating patients with relapsed or refractory intermediate and high grade NHL.
Methods: Thirty-five patients with relapsed or refractory intermediate and high grade NHL, who had been pretreated with chemotherapy dominated by CHOP or CHOP-like regimen with a median of 6 cycles (ranged 2-12 cycles), were salvaged by DICE regimen from Jun. 1999 to Jan. 2004. Of the 35 patients, 14 were T-cell original, and 21 were B-cell original.
Results: The 35 patients received DICE regimen with a median of 4 cycles (ranged 2-7 cycles). All patients were assessable in the efficacy and adverse events. The total RR was 74.3% with complete response (CR) rate of 31.4%, median response time (MST) of 4 months (ranged 1-30 months), median time to failure (TTF) of 7 months (ranged 2-34 months),median survival time (MST) of 14 months (ranged 3-51 months), and 2-year survival rate of 33.3%. The RRs of T-cell and B-cell NHL were 85.7% and 66.7%. The CR rate was higher in T-cells NHL than in B-cell NHL (50.0% vs. 19.0%, P=0.073). Elevated serum lactate dehydrogenase (LDH) and bulky disease were high risk factors of the efficacy of DICE regimen (P < 0.05). The response to DICE reginmen was an independent prognostic factor of patients with relapsed or refractory NHL (P = 0.001). The major toxicity was myelosuppression. Incidences of neutropenia and thrombocytopenia of grade III-IV were 71.4% and 8.6%.
Conclusions: DICE regimen is a safe and effective salvage regimen for the patients with relapsed or refractory intermediate and high grade advanced NHL. Elevated serum LDH and bulky disease are the adverse prognostic factors. The response to DICE regimen may directly influence survival time of patients with relapsed or refractory NHL.
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J Magn Reson Imaging
January 2025
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.
Purpose: This work tests the viability of semi-supervision for brain metastases segmentation.
Diagnostics (Basel)
December 2024
Department of Radiology and Interventional Radiology, Lausanne University Hospital, Lausanne University, 1015 Lausanne, Switzerland.
Background/objectives: Recent advancements in artificial intelligence (AI) have spurred interest in developing computer-assisted analysis for imaging examinations. However, the lack of high-quality datasets remains a significant bottleneck. Labeling instructions are critical for improving dataset quality but are often lacking.
View Article and Find Full Text PDFHum Brain Mapp
December 2024
SEB Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada.
White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel disease (CSVD). WMH are associated with cognitive decline and increased risk of stroke and dementia, and are commonly observed in aging, vascular cognitive impairment, and neurodegenerative diseases. The reliable and rapid measurement of WMH in large-scale multisite clinical studies with heterogeneous patient populations remains challenging, where the diversity of imaging characteristics across studies adds additional complexity to this task.
View Article and Find Full Text PDFComput Med Imaging Graph
January 2025
ICMUB, Université de Bourgogne, Dijon, France. Electronic address:
In real-world scenarios, medical image segmentation models encounter input images that may deviate from the training images in various ways. These differences can arise from changes in image scanners and acquisition protocols, or even the images can come from a different modality or domain. When the model encounters these out-of-distribution (OOD) images, it can behave unpredictably.
View Article and Find Full Text PDFComput Biol Med
December 2024
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA. Electronic address:
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