Evidence map›Paper›PMID 37768965›Full record

ArticlePloS one2023

A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study.

Yang Liu, Lang Xie, Dingxue Wang, Kaide Xia

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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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

2 citing papers in PubMed.

  1. Article
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4 · The record

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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

4 authors.

Yang LiuDepartment of Orthopedics, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China.
Lang XieHospital Infection Management Department, Bijie First People's Hospital, Bijie, China.
Dingxue WangDepartment of Oncology, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China.
Kaide XiaClinical College of Maternal and Child Health Care, Guizhou Medical University, Guiyang, China.ORCID 0009-0004-8324-3442

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveSuccessful prognosis is crucial for the management and treatment of osteosarcoma (OSC). This study aimed to predict the cancer-specific survival rate in patients with OSC using deep learning algorithms and classical Cox proportional hazard models to provide data to support individualized treatment of patients with OSC.

methodsData on patients diagnosed with OSC from 2004 to 2017 were obtained from the Surveillance, Epidemiology, and End Results database. The study sample was then divided randomly into a training cohort and a validation cohort in the proportion of 7:3. The DeepSurv algorithm and the Cox proportional hazard model were chosen to construct prognostic models for patients with OSC. The prediction efficacy of the model was estimated using the concordance index (C-index), the integrated Brier score (IBS), the root mean square error (RMSE), and the mean absolute error (SME).

resultsA total of 3218 patients were randomized into training and validation groups (n = 2252 and 966, respectively). Both DeepSurv and Cox models had better efficacy in predicting cancer-specific survival (CSS) in OSC patients (C-index >0.74). In the validation of other metrics, DeepSurv did not have superiority over the Cox model in predicting survival in OSC patients.

conclusionsAfter validation, our CSS prediction model for patients with OSC based on the DeepSurv algorithm demonstrated satisfactory prediction efficacy and provided a convenient webpage calculator.

Indexed as

Bone NeoplasmsDeep LearningOsteosarcomaAdolescentAdultAlgorithmsChildFemaleHumansMaleMiddle AgedPrognosisProportional Hazards ModelsRetrospective StudiesSEER ProgramSurvival Rate

Identifiers

PMID37768965
PMCPMC10538762

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