Evidence map›Paper›PMID 31444549›Full record

ArticleJournal of cancer research and clinical oncology2019

Predictive factors for survival outcomes of oligometastatic prostate cancer patients treated with metastases-directed therapy: a recursive partitioning-based analysis.

Ciro Franzese, Lucia Di Brina, Giuseppe D'Agostino, Davide Franceschini, Tiziana Comito, Fiorenza De Rose, Angelo Tozzi, Pierina Navarria, Marta Scorsetti

Abstract read
In one paragraph

Article in Journal of cancer research and clinical oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. The oligometastatic paradigm and the role of radiotherapy.Clinical medicine (London, England) · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ciro FranzeseRadiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, IRCCS, Via Manzoni 56, Milan, Rozzano, Italy. ciro.franzese@humanitas.it.ORCID http://orcid.org/0000-0001-6893-6284
Lucia Di BrinaRadiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, IRCCS, Via Manzoni 56, Milan, Rozzano, Italy.
Giuseppe D'AgostinoRadiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, IRCCS, Via Manzoni 56, Milan, Rozzano, Italy.
Davide FranceschiniRadiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, IRCCS, Via Manzoni 56, Milan, Rozzano, Italy.
Tiziana ComitoRadiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, IRCCS, Via Manzoni 56, Milan, Rozzano, Italy.
Fiorenza De RoseRadiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, IRCCS, Via Manzoni 56, Milan, Rozzano, Italy.
Angelo TozziRadiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, IRCCS, Via Manzoni 56, Milan, Rozzano, Italy.
Pierina NavarriaRadiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, IRCCS, Via Manzoni 56, Milan, Rozzano, Italy.
Marta ScorsettiRadiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, IRCCS, Via Manzoni 56, Milan, Rozzano, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of the present study was to provide predictive factors for survival outcomes of oligometastatic prostate cancer (PC) patients treated with stereotactic body radiation therapy (SBRT) as a metastases-directed therapy (MDT).

methodsIn this cohort study, endpoints included overall survival (OS), progression-free survival (PFS), distant progression-free survival (DFS) and local control of treated metastases (LC). The binary classification tree approach with recursive partitioning analysis (RPA) was applied to stratify the patients into risk groups based on OS, PFS and DPFS; for each endpoint, disease-free interval (DFI) was calculated. We included patients with synchronous or metachronous metastases from prostate adenocarcinoma treated with SBRT.

results119 Metastases were treated with SBRT in 92 patients. Median follow-up was 22.2 months. Rates of OS at 1 and 3 years were 96.9% and 88.0%, while DPFS was 51.9% and 20.9%. Recursive partitioning analysis identified three prognostic classes for OS: Class 1: castration-sensitive patients (3 years OS 95%); Class 2: castration-resistant patients with low-intermediate risk NCCN disease (3 years OS 88.8%); Class 3: castration-resistant patients with high-risk NCCN disease (3 years OS 76.9%). Regarding DPFS, RPA divided patients into two classes, according to a cutoff value of DFI of 34 months (3 years PFS of 28.7% vs 5.8%). Three classes were identified for DPFS: Class 1: DFI < 34 months (3 years DPFS 9.1%); Class 2: DFI > 34 months and high-risk NCCN PC (3 years DPFS 21%); Class 3: DFI > 34 months and low-intermediate risk NCCN disease (3 years DPFS 60.2%).

conclusionOligometastatic PC represents nowadays a setting of particular interest in which local ablative therapies play a decisive role. In the present study, we recognized the importance of DFI, together with NCCN class risk, to predict the risk of new metastases after SBRT in oligometastatic PC.

Indexed as

AgedAged, 80 and overCombined Modality TherapyDiagnostic ImagingHumansKaplan-Meier EstimateMaleMiddle AgedNeoplasm GradingNeoplasm MetastasisNeoplasm StagingPrognosisProstatic NeoplasmsTreatment OutcomeOligometastasesOligometastaticProstate cancerPSA kineticsRadiotherapySBRTStereotactic body radiation therapy

Identifiers

PMID31444549
PMCPMC11810186

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.