Evidence map›Paper›PMID 35026041›Full record

ArticleMedical physics2022

Differentiation between immune checkpoint inhibitor-related and radiation pneumonitis in lung cancer by CT radiomics and machine learning.

Jun Cheng, Yi Pan, Wei Huang, Kun Huang, Yanhai Cui, Wenhui Hong, Lingling Wang, Dong Ni, Peixin Tan

Abstract read
In one paragraph

Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

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

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  8. Drug-Induced Interstitial Lung Disease in Lung Cancer Patients.Respiration; international review of thoracic diseases · 2026
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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.

Jun ChengNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Yi PanDepartment of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Wei HuangDepartment of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Kun HuangDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Yanhai CuiDepartment of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Wenhui HongNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Lingling WangNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Dong NiNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Peixin TanDepartment of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.

Funding

Guangzhou Science and Technology Plan Foundation 2021-02-01-04-1002-0017Medical Scientific Research Foundation of Guangdong Province, China B2018031Medical Scientific Research Foundation of Guangdong Province, China B2020024National Key R&D Program of China 2019YFC0118300National Natural Science Foundation of China 61901275National Natural Science Foundation of Guangdong Provincial People's Hospital 8210032051Shenzhen Peacock Plan KQJSCX20180328095606003Shenzhen Peacock Plan KQTD2016053112051497Shenzhen University Startup Fund 2019131
6 · The paper itself

Abstract

purposeConsolidation immunotherapy after completion of chemoradiotherapy has become the standard of care for unresectable locally advanced non-small cell lung cancer and can induce potentially severe and life-threatening adverse events, including both immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP), which are very challenging for radiologists to diagnose. Differentiating between CIP and RP has significant implications for clinical management such as the treatments for pneumonitis and the decision to continue or restart immunotherapy. The purpose of this study is to differentiate between CIP and RP by a CT radiomics approach.

methodsWe retrospectively collected the CT images and clinical information of patients with pneumonitis who received immune checkpoint inhibitor (ICI) only (n = 28), radiotherapy (RT) only (n = 31), and ICI+RT (n = 14). Three kinds of radiomic features (intensity histogram, gray-level co-occurrence matrix [GLCM] based, and bag-of-words [BoW] features) were extracted from CT images, which characterize tissue texture at different scales. Classification models, including logistic regression, random forest, and linear SVM, were first developed and tested in patients who received ICI or RT only with 10-fold cross-validation and further tested in patients who received ICI+RT using clinicians' diagnosis as a reference.

resultsUsing 10-fold cross-validation, the classification models built on the intensity histogram features, GLCM-based features, and BoW features achieved an area under curve (AUC) of 0.765, 0.848, and 0.937, respectively. The best model was then applied to the patients receiving combination treatment, achieving an AUC of 0.896.

conclusionsThis study demonstrates the promising potential of radiomic analysis of CT images for differentiating between CIP and RP in lung cancer, which could be a useful tool to attribute the cause of pneumonitis in patients who receive both ICI and RT.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsRadiation PneumonitisHumansImmune Checkpoint InhibitorsMachine LearningRetrospective StudiesTomography, X-Ray ComputedImmune Checkpoint InhibitorsCT radiomicsimmune checkpoint inhibitor-related pneumonitislung cancermachine learningradiation pneumonitis

Identifiers

PMID35026041
PMCPMC9306809

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.