Evidence map›Paper›PMID 35490227›Full record

ArticleBMC cancer2022

Machine learning algorithms to estimate 10-Year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool.

Ashley B Anderson, Clare Grazal, Rikard Wedin, Claire Kuo, Yongmei Chen, Bryce R Christensen, Jennifer Cullen, Jonathan A Forsberg

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

Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Review
  8. Review
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

8 authors at 6 institutions in 2 countries.

Ashley B AndersonDivision of Orthopaedics, Department of Surgery, Uniformed Services University, Walter Reed National Military Medical Center, 8901 Rockville Pike, Bethesda, MD, 20889, USA.
Clare GrazalThe Henry Jackson Foundation for the Advancement of Sciences, 6720A Rockledge Dr, Suite 100, Bethesda, MD, 20817, USA.
Rikard WedinDepartment of Molecular Medicine and Surgery (MMK), K1, Orthopaedics, Karolinska, Institutet, A2:07 171 76, Stockholm, Sweden.
Claire KuoCenter for Prostate Disease Research, Department of Surgery, Uniformed Services University, Walter Reed National Military Medical Center, 6720A Rockledge Dr, Suite 300, Bethesda, MD, 20817, USA.
Yongmei ChenCenter for Prostate Disease Research, Department of Surgery, Uniformed Services University, Walter Reed National Military Medical Center, 6720A Rockledge Dr, Suite 300, Bethesda, MD, 20817, USA.
Bryce R ChristensenDepartment of Internal Medicine, San Antonio Military Medical Center, 3551 Roger Brooke Dr, San Antonio, TX, 78219, USA.
Jennifer CullenDepartment of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Wolstein Research Building 2520, 2103 Cornell Road, Cleveland, OH, 44106, USA.
Jonathan A ForsbergDivision of Orthopaedics, Department of Surgery, Uniformed Services University, Walter Reed National Military Medical Center, 8901 Rockville Pike, Bethesda, MD, 20889, USA. jforsbe1@jh.edu.
Walter Reed National Military Medical Center · USCornell University · USHenry M. Jackson Foundation · USJohns Hopkins University · USKarolinska Institutet · SESan Antonio Military Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrognostic indicators, treatments, and survival estimates vary by cancer type. Therefore, disease-specific models are needed to estimate patient survival. Our primary aim was to develop models to estimate survival duration after treatment for skeletal-related events (SREs) (symptomatic bone metastasis, including impending or actual pathologic fractures) in men with metastatic bone disease due to prostate cancer. Such disease-specific models could be added to the PATHFx clinical-decision support tool, which is available worldwide, free of charge. Our secondary aim was to determine disease-specific factors that should be included in an international cancer registry.

methodsWe analyzed records of 438 men with metastatic prostate cancer who sustained SREs that required treatment with radiotherapy or surgery from 1989-2017. We developed and validated 6 models for 1-, 2-, 3-, 4-, 5-, and 10-year survival after treatment. Model performance was evaluated using calibration analysis, Brier scores, area under the receiver operator characteristic curve (AUC), and decision curve analysis to determine the models' clinical utility. We characterized the magnitude and direction of model features.

resultsThe models exhibited acceptable calibration, accuracy (Brier scores < 0.20), and classification ability (AUCs > 0.73). Decision curve analysis determined that all 6 models were suitable for clinical use. The order of feature importance was distinct for each model. In all models, 3 factors were positively associated with survival duration: younger age at metastasis diagnosis, proximal prostate-specific antigen (PSA) < 10 ng/mL, and slow-rising alkaline phosphatase velocity (APV).

conclusionsWe developed models that estimate survival duration in patients with metastatic bone disease due to prostate cancer. These models require external validation but should meanwhile be included in the PATHFx tool. PSA and APV data should be recorded in an international cancer registry.

Indexed as

Bone NeoplasmsProstatic NeoplasmsAlgorithmsAlkaline PhosphataseHumansMachine LearningMaleProstate-Specific AntigenAlkaline PhosphataseProstate-Specific AntigenBone metastasisMachine learningOncologyPATHFxProstate cancerSkeletal-related eventSurvival estimates

Identifiers

PMID35490227
PMCPMC9055684
OpenAlexW4225137614

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.