Objectives: Novel histopathologic prognostic factors are needed to identify patients with follicular lymphoma (FL) at risk of inferior outcomes. Our primary objective was to evaluate the Ki-67 proliferative index in follicular and interfollicular areas in tissue biopsy specimens from patients with newly diagnosed FL and correlate with clinical outcomes. Our secondary objective was to correlate PD-L1 and LAG-3 with clinical outcomes.
Methods: Seventy cases of low-grade FL from the University of Minnesota were evaluated with Ki-67 immunohistochemical stain. Ki-67 expression as a continuous variable was interpreted digitally and manually in follicular and interfollicular areas. Progression-free survival (PFS) and overall survival (OS) were analyzed by Cox regression, and hazard ratios (HRs) per 10-point increase in Ki-67 were calculated.
Results: Progression-free survival at 4 years was 28% (95% CI, 19%-41%). Interfollicular, but not follicular, Ki-67 was associated with PFS by manual (HR, 1.33; P = .01) and digital (HR, 1.38; P = .02) analysis. Digital and manual Ki-67 were only moderately correlated but demonstrated similar effects on PFS. At 4 years, OS was 90% with no association with follicular or interfollicular Ki-67 proliferation.
Conclusions: Higher interfollicular Ki-67 by either digital or manual analysis is associated with a poorer PFS in patients with low-grade FL. These results suggest further validation of this marker is warranted to improve pathologic risk stratification at FL diagnosis. PD-L1 and LAG-3 were not associated with PFS or OS.
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http://dx.doi.org/10.1093/ajcp/aqad161 | DOI Listing |
J Clin Med
January 2025
Department of Reconstructive Dentistry, UZB University Center for Dental Medicine Basel, University of Basel, 4058 Basel, Switzerland.
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December 2024
School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
BMC Med Inform Decis Mak
January 2025
AZTI, Food Research, Basque Research and Technology Alliance, Derio, Spain.
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View Article and Find Full Text PDFNeural Netw
January 2025
College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China. Electronic address:
Digital image watermarking is a prevalent method for image copyright protection. As watermark embedding techniques evolve, research in copyright protection has increasingly extended into watermark removal. Recent advancements in deep learning and generative technologies have led to the development of public watermark removal solutions, addressing issues such as plagiarized, illegal, or outdated watermarks while driving significant improvements in robust watermark embedding.
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