Evidence map›Paper›PMID 37932419›Full record

ArticleNPJ precision oncology2023

Biology-aware mutation-based deep learning for outcome prediction of cancer immunotherapy with immune checkpoint inhibitors.

Junyan Liu, Md Tauhidul Islam, Shengtian Sang, Liang Qiu, Lei Xing

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 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

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

2 citing papers in PubMed.

  1. Review
  2. Review
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.

Junyan LiuDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
Md Tauhidul IslamDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.ORCID http://orcid.org/0000-0001-6259-632X
Shengtian SangDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
Liang QiuDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
Lei XingDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. lei@stanford.edu.

Funding

Leveraging deep learning for markerless motion management in radiation therapyR01CA256890 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2021 to 2025
$2.1M
NCI NIH HHS R01 CA256890
6 · The paper itself

Abstract

The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59 ± 0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52 ± 0.10). The "black box" nature of deep learning is a major concern in healthcare field. This model's interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a 'black box' approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment.

Identifiers

PMID37932419
PMCPMC10628135

What OpenQuestion holds

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LicenceCC BY
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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.