Outcomes of Radiotherapy in Oligoprogressive Breast Cancer
JOURNAL OF PERSONALIZED MEDICINE（2024）
Fondazione Policlinico Universitario “A. Gemelli” IRCCS | Univ Perugia
Abstract
Introduction: Radiotherapy (RT) shows potential for improving local control in cases of oligoprogressive metastatic breast cancer (mBC). This retrospective analysis aims to evaluate the advantages of RT in such a clinical scenario. Methods: We conducted a retrospective analysis including patients with mBC who received radiation therapy (RT) for up to three sites of oligoprogression while continuing systemic therapy. The study took place between January 2014 and December 2021. Our endpoints were progression-free survival after radiotherapy (PFS-AR), the rate of discontinuation of systemic therapy (RDT) at three months post-RT, and overall survival (OS). We used Cox regression analysis to perform multivariate analysis for PFS-AR. Results: Fifty-nine patients met the inclusion criteria. The PFS-AR was 13 months (95% CI 8.5–18.8 months). At three months, the RDT was 3% (two patients). A significant difference in median PFS-AR was observed between patients in the first + second-line group and those in the subsequent line group (p = 0.03). In the multivariate analysis conducted for PFS-AR, the biologically effective dose (BED) with α/β = 4 > 100 Gy emerged as the sole significant variable (p = 0.0017). The median overall survival (OS) was 24.4 months (95% CI 17–24.4 months). Conclusions: This study is the first report on the outcomes of radiotherapy in a cohort of over 50 patients with oligoprogressive metastatic breast cancer (mBC). Our findings emphasize the significant relationship between PFS-AR, the number of ongoing lines of systemic therapy, and the BED of radiotherapy.
MoreTranslated text
Key words
oligoprogression,breast cancer,radiotherapy,target therapies
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