Evidence map›Paper›PMID 31858078›Full record

ArticleRadiology. Artificial intelligence2019

A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization.

Ali Morshid, Khaled M Elsayes, Ahmed M Khalaf, Mohab M Elmohr, Justin Yu, Ahmed O Kaseb, Manal Hassan, Armeen Mahvash, Zhihui Wang, John D Hazle and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 2 pooled it
7.7field-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

73 citing papers in PubMed, 2 syntheses or guidelines pooled it, 139 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

13 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Ali MorshidDepartments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Khaled M ElsayesDepartments of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Ahmed M KhalafDepartments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Mohab M ElmohrDepartments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Justin YuDepartments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Ahmed O KasebDepartments of Gastrointestinal Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Manal HassanDepartments of Gastrointestinal Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Armeen MahvashDepartments of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Zhihui WangDepartments of Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
John D HazleDepartments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
David FuentesDepartments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
The University of Texas MD Anderson Cancer Center · USBrown Foundation · US

Funding

Genome-Wide Association Study (GWAS) in Hepatocellular Carcinoma (HCC)R01CA186566 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI AMOS, CHRISTOPHER I., HASSAN, MANAL METWALLY · 2015 to 2019
$3.6M
NCI NIH HHS R01 CA186566
6 · The paper itself

Abstract

purposeSome patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE. MATERIALS AND

methodsOutcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP <14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input as well as the BCLC stage alone as a control.

resultsThe model's response prediction accuracy rate was 74.2% (95% CI=64%-82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI= 52%-72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome.

conclusionThis preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding HCC patient selection for TACE.

Identifiers

PMID31858078
PMCPMC6920060
OpenAlexW2975603936

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.