Related Experiment Video
Updated: Jul 19, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Predictive model for treatment outcomes of peripheral T-cell lymphoma, not otherwise specified, in Taiwanese patients
Ning-Chun Chen1, Hung Chang2, Ming-Chung Kuo2
1Division of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Taiwan.
Purpose:
We aimed to explore the clinical outcomes and prognostic factors for PTCL-NOS patients in the real world.
Methods:
Clinical data were retrospectively collected from adult patients with PTCL-NOS treated at a single center in Taiwan.
Results:
104 PTCL-NOS patients with a median age of 53.0 years were enrolled. Patients with the International Prognostic Index (IPI) or prognostic index for peripheral T-cell lymphoma (PIT) scores of zero had a longer overall survival (OS) and progression free survival (PFS), while patients with IPI or PIT scores ≥1 did poorly. For patients who are eligible for transplantation, the use of pralatrexate as salvage chemotherapy has shown better OS (2-year OS 83.3% vs. 24.4%, P = 0.011) compared to patients who did not. By multivariate analysis, age >60 years, male, B symptoms, ECOG >1, lung involvement, and thrombocytopenia were independent adverse factors for OS. Incorporating factors in multivariate analysis, we established a novel predictive index for PTCL-NOS which efficiently stratifies patients into low (0-1 factor), intermediate-1 (2 factors), intermediate-2 (3 factors), and high risk (4-6 factors) groups with 2-year OS rates of 81.5%, 32.9%, 8.8%, and 0%, respectively (P < 0.001).
Conclusion:
PTCL-NOS patients have a dismal prognosis in Taiwan. Novel agents may improve the outcomes of PTCL-NOS patients. The usefulness of the novel prognostic index for PTCL-NOS needs further validation.
More Related Videos
06:08Establishment and Characterization of Patient-Derived Xenograft Models of Anaplastic Thyroid Carcinoma and Head and Neck Squamous Cell Carcinoma
Published on: June 2, 2023
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018