Evidence map›Paper›PMID 33594323›Full record

ArticleAmerican journal of translational research2021

A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1/PD-L1 immunotherapy in advanced stage non-small-cell lung cancer.

Yi Yang, Jiancheng Yang, Lan Shen, Jiajun Chen, Liliang Xia, Bingbing Ni, Liang Ge, Ying Wang, Shun Lu

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PubMed Full text at PMC
In one paragraph

Article in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 4 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

60 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

9 authors.

Yi YangDepartment of Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University Shanghai, China.
Jiancheng YangDepartment of Electronic Engineering, Shanghai Jiao Tong University Shanghai, China.
Lan ShenDepartment of Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University Shanghai, China.
Jiajun ChenDianei Technology Shanghai, China.
Liliang XiaDepartment of Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University Shanghai, China.
Bingbing NiDepartment of Electronic Engineering, Shanghai Jiao Tong University Shanghai, China.
Liang GeDianei Technology Shanghai, China.
Ying WangShanghai Institute of Immunology, Department of Immunology and Microbiology, School of Medicine, Shanghai Jiao Tong University Shanghai, China.
Shun LuDepartment of Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Only 20% NSCLC patients benefit from immunotherapy with a durable response. Current biomarkers are limited by the availability of samples and do not accurately predict who will benefit from immunotherapy. To develop a unified deep learning model to integrate multimodal serial information from CT with laboratory and baseline clinical information. We retrospectively analyzed 1633 CT scans and 3414 blood samples from 200 advanced stage NSCLC patients who received single anti-PD-1/PD-L1 agent between April 2016 and December 2019. Multidimensional information, including serial radiomics, laboratory data and baseline clinical data, was used to develop and validate deep learning models to identify immunotherapy responders and nonresponders. A Simple Temporal Attention (SimTA) module was developed to process asynchronous time-series imaging and laboratory data. Using cross-validation, the 90-day deep learning-based predicting model showed a good performance in distinguishing responders from nonresponders, with an area under the curve (AUC) of 0.80 (95% CI: 0.74-0.86). Before immunotherapy, we stratified the patients into high- and low-risk nonresponders using the model. The low-risk group had significantly longer progression-free survival (PFS) (8.4 months, 95% CI: 5.49-11.31

Indexed as

multi-omics serial deep learningNSCLCSimTA

Identifiers

PMID33594323
PMCPMC7868825

What OpenQuestion holds

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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.