1201P Evaluation of Efficacy TEMCAP Regiment As First-Line or Further Line Therapy in Patients with Advanced, Unresectable, Progressive GEP-NET. Real-world Data
Annals of Oncology(2023)
Maria Sklodowska Curie Natl Res Inst Oncol
Abstract
Retrospective, single-institution study accessing the efficacy of TEMCAP regiment in pts. with advanced, unresectable progressive GEP-NET as a first or further lines after initial disease progression. Primary endpoint: progression-free survival (PFS), as initial with/without SST analogues previous therapy, PFS as second or further lines therapy using locally evaluation according to RECIST 1.1. Secondary goals: PFS in different subgroups and overall survival (OS). 54 pts with advanced unresectable, progressive GEP-NET. Mean age 58.0 (SD+/-12.4). All histological confirmed NETs: G1 n=10, G2 n=31, G3 n=13. Standard therapy approach using TEMCAP in all subjects. Disease status and treatment efficiency were evaluated according to localization of primary, initial vs. second or further line systemic therapy, bulky (>25% of liver volume) vs. not bulky liver disease, male vs. female, BMI ≤24 or BM I>24. Standard KM method used to assess PFS and OS for all subjects and in different subgroups. Cox regression model to assess any significant covariates of PFS and OS. Pancreatic (panNET) n=32, midgut n=8; hindgut n=7 and 7 pts with CUP. PFS for all group 8.0 months (IQR 5.0-15.0), OS from initial diagnosis of GEP-NET 56.8 months (IQR 25.3-96.5); panNET n=32 -PFS=8.0 months (IQR 5.0-16.1), non-pancreas (n=22) PFS=6.2 (IQR 4.0-10.0), n.s. Initial therapy with TEMCAP n=24- PFS=9.2 (6.0-15.0) vs. second or further therapy n=30, PFS=6.0 (IQR 4.1-9.9) n.s. Bulky liver disease (n=31) PFS=7.9 (IQR 5.0-10.0) v.s. non-bulky liver disease (n=23) PFS=7.4 (IQR 4.4-17.0) n.s. Male (n=20) PFS=9.0 (IQR 5.7-15.0), vs. female (n=34) PFS=6.5 (IQR 5.0-11.5), n.s. Median BMI >24.0 PFS=7.0 (IQR 5.0-10.9), vs BMI≤24 PFS=9.0 (IQR 9.0-15.0), n.s. Cox Regression model did not find any significant predictor of improvement in PFS. In Cox regression model of OS indicated that high grade NET (G3) had HR=2.92 (CI 1.17-17.2) of death. TEMCAP in real world data seems to be a good option in advanced, progressive GEP-NET with PFS=8 months, independently from the primary site, sex, BMI, liver involvement and previous therapy. Overall potential benefits in OS is seen in NETG1 or G2, but not in G3 with HR=2,92.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined