Evidence map›Paper›PMID 35237892›Full record

ArticleJournal of digital imaging2022

Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma.

Gengbo Liu, Mini Poon, Matthew A Zapala, William C Temple, Kieuhoa T Vo, Kathrine K Matthay, Debasis Mitra, Youngho Seo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Radiomics-Based Machine Learning for DeterminingTechnology in cancer research & treatment
    Pooled it
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  11. Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024
    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

8 authors.

Gengbo LiuDepartment of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA.
Mini PoonDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
Matthew A ZapalaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
William C TempleDepartment of Pediatrics, University of California, San Francisco, CA, USA.
Kieuhoa T VoDepartment of Pediatrics, University of California, San Francisco, CA, USA.
Kathrine K MatthayDepartment of Pediatrics, University of California, San Francisco, CA, USA.
Debasis MitraDepartment of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA. dmitra@cs.fit.edu.ORCID http://orcid.org/0000-0002-4351-1252
Youngho SeoDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA. youngho.seo@ucsf.edu.

Funding

Research Training in Childhood CancerT32CA128583 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Eric Alejandro Sweet-Cordero · 2007 to 2026
$6.0M
Pretherapy 124I-MIBG Dosimetry for Planning 131I-MIBG Neuroblastoma TherapyR01CA154561 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SEO, YOUNGHO · 2011 to 2016
$1.9M
INCREASING CLINICAL ACCESS BY REDUCING SCAN TIME OF DYNAMIC NUCLEAR CARDIAC IMAGING WITH SUPERIOR DIAGNOSISR15EB030807 · NIBIB · FLORIDA INSTITUTE OF TECHNOLOGY · PI MITRA, DEBASIS · 2021 to 2021
$455k
NCI NIH HHS R01 CA154561NCI NIH HHS T32 CA128583NIBIB NIH HHS R15 EB030807
6 · The paper itself

Abstract

Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of multiple ML algorithms over retrospective CT images of 65 neuroblastoma patients are analyzed. An artificial neural network (ANN) is used on tumor radiomic features extracted from 3D CT images. A pre-trained 2D convolutional neural network (CNN) is used on slices of the same images. ML models are trained for various pathologically investigated outcomes of these patients. A subspecialty-trained pediatric radiologist independently reviewed the manually segmented primary tumors. Pyradiomics library is used to extract 105 radiomic features. Six ML algorithms are compared to predict the following outcomes: mortality, presence or absence of metastases, neuroblastoma differentiation, mitosis-karyorrhexis index (MKI), presence or absence of MYCN gene amplification, and presence of image-defined risk factors (IDRF). The prediction ranges over multiple experiments are measured using the area under the receiver operating characteristic (ROC-AUC) for comparison. Our results show that the radiomics-based ANN method slightly outperforms the other algorithms in predicting all outcomes except classification of the grade of neuroblastic differentiation, for which the elastic regression model performed the best. Contributions of the article are twofold: (1) noninvasive models for the prognosis from CT images of neuroblastoma, and (2) comparison of relevant ML models on this medical imaging problem.

Indexed as

Machine LearningNeuroblastomaAlgorithmsChildHumansRetrospective StudiesROC CurveCTMachine learningNeural networkNeuroblastomaRadiomics

Identifiers

PMID35237892
PMCPMC9156639

What OpenQuestion holds

Textmetadata
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.