Evidence map›Paper›PMID 26871471›Full record

ArticleOncotarget2016

Exploiting machine learning for predicting skeletal-related events in cancer patients with bone metastases.

Zhiyu Wang, Xiaoting Wen, Yaohong Lu, Yang Yao, Hui Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

  1. Pooled it
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  9. Fracture risk assessment and clinical decision making for patients with metastatic bone disease.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2020
    Review
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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

5 authors.

Zhiyu WangDepartment of Internal Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Xiaoting WenDepartment of Internal Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Yaohong LuDepartment of Internal Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Yang YaoDepartment of Internal Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Hui ZhaoDepartment of Internal Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of the bone metastases (BM) treatment is to prevent the occurrence of skeletal-related events (SREs). In clinical, physicians could only predict the occurrence of SREs by subjective experience. Machine learning (ML) could be used as predictive models in the medical field. But there is no published research using ML to predict SREs in cancer patients with BM. The purpose of this study was to assess the associations of clinical variables with the occurrence of SREs and to subsequently develop prediction models to help identify SREs risk groups.We analyzed 1143 cancer patients with BM. We used the statistical package of SPSS and SPSS Modeler for data analysis and the development of the prediction model. We compared the performance of logistic regression (LR), decision tree (DT) and support vector machine(SVM). The results suggested that Visual Analog Scale (VAS) scale was a key factor to SREs in LR, DT and SVM model. Modifiable factors such as Frankel classification, Mirels score, Ca, aminoterminal propeptide of type I collagen (PINP) and bone-specific alkaline phosphatase (BALP) were identified. We found that the result of applying LR, DT and SVM classification accuracy was 79.2%, 85.8% and 88.2%, with 9, 4 and 8 variables, respectively.In conclusion, DT and SVM achieved higher accuracies with smaller number of variables than the number of variables used in LR. ML techniques can be used to build model to predict SREs in cancer patients with BM.

Indexed as

Support Vector MachineAdultAgedBone DiseasesBone NeoplasmsDecision TreesFemaleHumansLogistic ModelsMaleMiddle Agedbone metastasesdecision treemachine learningskeletal-related eventssupport vector machine

Identifiers

PMID26871471
PMCPMC4914308

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.