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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2020
Data sources: Datacite
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2020
Data sources: ZENODO
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2020
Data sources: Datacite
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Dataset related to article "Recursive partitioning model-based analysis for survival of colorectal cancer patients with lung and liver oligometastases treated with stereotactic body radiation therapy"

Authors: franzese, ciro; comito, tiziana; franceschini, davide; loi, mauro; clerici, elena; navarria, pierina; de rose, fiorenza; +5 Authors

Dataset related to article "Recursive partitioning model-based analysis for survival of colorectal cancer patients with lung and liver oligometastases treated with stereotactic body radiation therapy"

Abstract

This record contains raw data related to article “Recursive partitioning model-based analysis for survival of colorectal cancer patients with lung and liver oligometastases treated with stereotactic body radiation therapy" Introduction: Liver and lung are common sites of metastases from colorectal cancer (CRC). Stereotactic body radiation therapy (SBRT) represents a valid treatment, with high rates of local control (LC). In this study, we applied recursive partitioning model-based analysis (RPA) to define class risks for overall survival (OS) and progression free survival (PFS) in oligometastatic CRC patients. Materials and methods: In this monocentric analysis, we included patients with lung or liver metastases. Patients were candidate to SBRT if a maximum of 5 metastases. End points of the present analysis were LC, PFS, and OS. The binary classification tree approach with RPA was applied to stratify the patients into risk groups based on OS and PFS. Results: 218 patients were treated with SBRT on 371 metastases. Majority of patients (56%) was treated on single lesion, followed by 2 (26.1%) and 3 lesions (14.7%). Median follow-up was 22.7 months. Rates of LC were 84.2% at 1 year and 73.8% at 3 years. Rates of PFS at 1 and 3 years were 42.2% and 14.9%, respectively. RPA identified 3 classes for PFS, according to age and number of metastases with 3-year PFS of 30.6%, 13.5% and 8.4%. Overall survival was 87.2% at 1 year, 51.9% at 3 years, and 36.8% at 5 years. RPA identified 3 nodes. Class 1 included patients with liver metastases (3-year OS 35.2%). Class 2 included patients with lung metastases and DFI ≤ 48 months (3-year OS 65%). Class 3 included patients with lung metastases and DFI > 48 months (3-year OS 73.5%). Conclusions: Stereotactic body radiation therapy can be considered an effective treatment for the management of liver and lung metastases from CRC. With RPA, we identified prognostic risk class to define patients who could benefit the most from SBRT.

Keywords

SBRT, Radiotherapy, Stereotactic body radiation therapy, Recursive partitioning analysis, Colorectal cancer, Oligometastases, RPA

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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