Evidence map›Paper›PMID 33486339›Full record

ArticleComputer methods and programs in biomedicine2021

Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images.

Seyedehnafiseh Mirniaharikandehei, Morteza Heidari, Gopichandh Danala, Sivaramakrishnan Lakshmivarahan, Bin Zheng

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

Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 4 pooled it
7.3field-weighted citation impact, top 2% 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

23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 52 citations in OpenAlex.

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  16. Peritoneal Carcinosis: What the Radiologist Needs to Know.Diagnostics (Basel, Switzerland) · 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

5 authors at 1 institution in 1 country.

Seyedehnafiseh MirniaharikandeheiSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA. Electronic address: snmirnia@ou.edu.
Morteza HeidariSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
Gopichandh DanalaSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
Sivaramakrishnan LakshmivarahanSchool of Computer Sciences, University of Oklahoma, Norman, OK 73019, USA.
Bin ZhengSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
University of Oklahoma · US

Funding

Increasing Cancer Detection Yield Using Breast MRI Screening ModalityR01CA197150 · NCI · UNIVERSITY OF OKLAHOMA · PI HOLLINGSWORTH, ALAN B., LIU, HONG · 2015 to 2019
$2.5M
NCI NIH HHS R01 CA197150
6 · The paper itself

Abstract

BACKGROUND AND

objectiveNon-invasively predicting the risk of cancer metastasis before surgery can play an essential role in determining which patients can benefit from neoadjuvant chemotherapy. This study aims to investigate and test the advantages of applying a random projection algorithm to develop and optimize a radiomics-based machine learning model to predict peritoneal metastasis in gastric cancer patients using a small and imbalanced computed tomography (CT) image dataset.

methodsA retrospective dataset involving CT images acquired from 159 patients is assembled, including 121 and 38 cases with and without peritoneal metastasis, respectively. A computer-aided detection scheme is first applied to segment primary gastric tumor volumes and initially compute 315 image features. Then, five gradients boosting machine (GBM) models embedded with five feature selection methods (including random projection algorithm, principal component analysis, least absolute shrinkage, and selection operator, maximum relevance and minimum redundancy, and recursive feature elimination) along with a synthetic minority oversampling technique, are built to predict the risk of peritoneal metastasis. All GBM models are trained and tested using a leave-one-case-out cross-validation method.

resultsResults show that the GBM model embedded with a random projection algorithm yields a significantly higher prediction accuracy (71.2%) than the other four GBM models (p<0.05). The precision, sensitivity, and specificity of this optimal GBM model are 65.78%, 43.10%, and 87.12%, respectively.

conclusionsThis study demonstrates that CT images of the primary gastric tumors contain discriminatory information to predict the risk of peritoneal metastasis, and a random projection algorithm is a promising method to generate optimal feature vector, improving the performance of machine learning based prediction models.

Indexed as

Peritoneal NeoplasmsStomach NeoplasmsAlgorithmsHumansMachine LearningRetrospective StudiesTomography, X-Ray ComputedComputed tomographyFeature dimensionality reductionGastric cancerQuantitative featuresRandom projection

Identifiers

PMID33486339
PMCPMC7920928
OpenAlexW3118355326

What OpenQuestion holds

Textmetadata
LicenceTDM
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.