Evidence map›Paper›PMID 37568732›Full record

ArticleCancers2023

Predicting Overall Survival for Patients with Malignant Mesothelioma Following Radiotherapy via Interpretable Machine Learning.

Zitian Wang, Vincent R Li, Fang-I Chu, Victoria Yu, Alan Lee, Daniel Low, Drew Moghanaki, Percy Lee, X Sharon Qi

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
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 at 4 institutions in 1 country.

Zitian WangDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Vincent R LiDepartment of Biology, University of Southern California Dornsife School of Arts and Sciences, Los Angeles, CA 90089, USA.ORCID 0000-0002-5455-758X
Fang-I ChuDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Victoria YuDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Alan LeeDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Daniel LowDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Drew MoghanakiDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Percy LeeDepartment of Radiation Oncology, City of Hope Orange County Lennar Foundation Cancer Center, Irvine, CA 92618, USA.
X Sharon QiDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA.
University of California, Los Angeles · USCity Of Hope National Medical Center · USMemorial Sloan Kettering Cancer Center · USUniversity of Southern California · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

PURPOSE/

objectivesMalignant pleural mesothelioma (MPM) is a rare but aggressive cancer arising from the cells of the thoracic pleura with a poor prognosis. We aimed to develop a model, via interpretable machine learning (ML) methods, predicting overall survival for MPM following radiotherapy based on dosimetric metrics as well as patient characteristics. MATERIALS/

methodsSixty MPM (37 right, 23 left) patients treated on a Tomotherapy unit between 2013 and 2018 were retrospectively analyzed. All patients received 45 Gy (25 fractions). The multivariable Cox regression (Cox PH) model and Survival Support Vector Machine (sSVM) were applied to build predictive models of overall survival (OS) based on clinical, dosimetric, and combined variables.

resultsSignificant differences in dosimetric endpoints for critical structures, i.e., the lung, heart, liver, kidney, and stomach, were observed according to target laterality. The OS was found to be insignificantly different (

conclusionsClinical and dosimetric variables may predict the overall survival of mesothelioma patients, which could guide personalized treatment planning towards a better treatment response. The identified predictors and their impact on survival offered additional value for translational application in clinical practice.

Indexed as

interpretable machine learningmesotheliomaoutcome prediction and assessmentoverall survivalradiation therapy

Identifiers

PMID37568732
PMCPMC10416916
OpenAlexW4385454641

What OpenQuestion holds

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LicenceCC BY
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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.