Evidence map›Paper›PMID 36077686›Full record

ArticleCancers2022

Lung Cancer Recurrence Risk Prediction through Integrated Deep Learning Evaluation.

Peng Huang, Peter B Illei, Wilbur Franklin, Pei-Hsun Wu, Patrick M Forde, Saeed Ashrafinia, Chen Hu, Hamza Khan, Harshna V Vadvala, Ie-Ming Shih and 15 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection.Proceedings of SPIE--the International Society for Optical Engineering · 2024
    Article
  9. Article
  10. Article
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

25 authors.

Peng HuangDepartment of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0002-8678-8999
Peter B IlleiThe Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21205, USA.
Wilbur FranklinDepartment of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Pei-Hsun WuJohns Hopkins Physical Sciences Oncology Center, Baltimore, MD 21218, USA.
Patrick M FordeDepartment of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.
Saeed AshrafiniaDepartment of Radiology, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-9592-0437
Chen HuDepartment of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.
Hamza KhanDepartment of Surgery, Johns Hopkins University, Baltimore, MD 21218, USA.
Harshna V VadvalaDepartment of Radiology, Johns Hopkins University, Baltimore, MD 21218, USA.
Ie-Ming ShihThe Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21205, USA.
Richard J BattafaranoThe Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21205, USA.
Michael A JacobsThe Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21205, USA.ORCID 0000-0002-1125-1644
Xiangrong KongDepartment of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.
Justine LewisIntensive Care Unit, Howard University College of Medicine, Washington, DC 20059, USA.
Rongkai YanDepartment of Radiology, Johns Hopkins University, Baltimore, MD 21218, USA.
Yun ChenDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0001-6917-7814
Franck HousseauDepartment of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.
Arman RahmimDepartment of Radiology, Johns Hopkins University, Baltimore, MD 21218, USA.
Elliot K FishmanDepartment of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0002-2567-1658
David S EttingerDepartment of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.
Kenneth J PientaDepartment of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.ORCID 0000-0002-4138-2186
Denis WirtzDepartment of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.
Malcolm V BrockThe Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21205, USA.
Stephen LamBC Cancer Research Institute, University of British Columbia, Vancouver, BC V5Z 1L3, Canada.
Edward GabrielsonThe Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21205, USA.

Funding

SPORE in Ovarian CancerP50CA228991 · NCI · JOHNS HOPKINS UNIVERSITY · PI Ronny I DRAPKIN, IE-MING SHIH · 2018 to 2026
$20.5M
Tech Core 2U54CA268083 · NCI · JOHNS HOPKINS UNIVERSITY · PI Denis Wirtz, Laura DeLong Wood · 2022 to 2026
$10.2M
The Role of Physical Cues in Collective Cell InvasionU54CA210173 · NCI · JOHNS HOPKINS UNIVERSITY · PI PIENTA, KENNETH J., WIRTZ, DENIS · 2016 to 2020
$9.9M
NCI NIH HHS P50 CA228991NCI NIH HHS P50CA 228991NCI NIH HHS U54 CA210173NCI NIH HHS U54 CA268083
6 · The paper itself

Abstract

Background: Prognostic risk factors for completely resected stage IA non-small-cell lung cancers (NSCLCs) have advanced minimally over recent decades. Although several biomarkers have been found to be associated with cancer recurrence, their added value to TNM staging and tumor grade are unclear. Methods: Features of preoperative low-dose CT image and histologic findings of hematoxylin- and eosin-stained tissue sections of resected lung tumor specimens were extracted from 182 stage IA NSCLC patients in the National Lung Screening Trial. These features were combined to predict the risk of tumor recurrence or progression through integrated deep learning evaluation (IDLE). Added values of IDLE to TNM staging and tumor grade in progression risk prediction and risk stratification were evaluated. Results: The 5-year AUC of IDLE was 0.817 ± 0.037 as compared to the AUC = 0.561 ± 0.042 and 0.573 ± 0.044 from the TNM stage and tumor grade, respectively. The IDLE score was significantly associated with cancer recurrence (p < 0.0001) even after adjusting for TNM staging and tumor grade. Synergy between chest CT image markers and histological markers was the driving force of the deep learning algorithm to produce a stronger prognostic predictor. Conclusions: Integrating markers from preoperative CT images and pathologist’s readings of resected lung specimens through deep learning can improve risk stratification of stage 1A NSCLC patients over TNM staging and tumor grade alone. Our study suggests that combining markers from nonoverlapping platforms can increase the cancer risk prediction accuracy.

Indexed as

artificial intelligentbiomarkercomputer-aided diagnosispostoperative-stage IA NSCLCtumor grade

Identifiers

PMID36077686
PMCPMC9454871

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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.