Quantification of preexisting lung ground glass opacities on CT for predicting checkpoint inhibitor pneumonitis in advanced non-small cell lung cancer patients
© 2024. The Author(s)..
BACKGROUND: Immune checkpoint inhibitors (ICIs) can lead to life-threatening pneumonitis, and pre-existing interstitial lung abnormalities (ILAs) are a risk factor for checkpoint inhibitor pneumonitis (CIP). However, the subjective assessment of ILA and the lack of standardized methods restrict its clinical utility as a predictive factor. This study aims to identify non-small cell lung cancer (NSCLC) patients at high risk of CIP using quantitative imaging.
METHODS: This cohort study involved 206 cases in the training set and 111 cases in the validation set. It included locally advanced or metastatic NSCLC patients who underwent ICI therapy. A deep learning algorithm labeled the interstitial lesions and computed their volume. Two predictive models were developed to predict the probability of grade ≥ 2 CIP or severe CIP (grade ≥ 3). Cox proportional hazard models were employed to analyze predictors of progression-free survival (PFS).
RESULTS: In a training cohort of 206 patients, 21.4% experienced CIP. Two models were developed to predict the probability of CIP based on different predictors. Model 1 utilized age, histology, and preexisting ground glass opacity (GGO) percentage of the whole lung to predict grade ≥ 2 CIP, while Model 2 used histology and GGO percentage in the right lower lung to predict grade ≥ 3 CIP. These models were validated, and their accuracy was assessed. In another exploratory analysis, the presence of GGOs involving more than one lobe on pretreatment CT scans was identified as a risk factor for progression-free survival.
CONCLUSIONS: The assessment of GGO volume and distribution on pre-treatment CT scans could assist in monitoring and manage the risk of CIP in NSCLC patients receiving ICI therapy.
CLINICAL RELEVANCE STATEMENT: This study's quantitative imaging and computational analysis can help identify NSCLC patients at high risk of CIP, allowing for better risk management and potentially improved outcomes in those receivingICI treatment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
BMC cancer - 24(2024), 1 vom: 26. Feb., Seite 269 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Xinyue [VerfasserIn] |
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| Links: |
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| Themen: |
Checkpoint inhibitor pneumonitis |
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| Anmerkungen: |
Date Completed 28.02.2024 Date Revised 29.02.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12885-024-12008-z |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368988899 |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a BACKGROUND: Immune checkpoint inhibitors (ICIs) can lead to life-threatening pneumonitis, and pre-existing interstitial lung abnormalities (ILAs) are a risk factor for checkpoint inhibitor pneumonitis (CIP). However, the subjective assessment of ILA and the lack of standardized methods restrict its clinical utility as a predictive factor. This study aims to identify non-small cell lung cancer (NSCLC) patients at high risk of CIP using quantitative imaging | ||
| 520 | |a METHODS: This cohort study involved 206 cases in the training set and 111 cases in the validation set. It included locally advanced or metastatic NSCLC patients who underwent ICI therapy. A deep learning algorithm labeled the interstitial lesions and computed their volume. Two predictive models were developed to predict the probability of grade ≥ 2 CIP or severe CIP (grade ≥ 3). Cox proportional hazard models were employed to analyze predictors of progression-free survival (PFS) | ||
| 520 | |a RESULTS: In a training cohort of 206 patients, 21.4% experienced CIP. Two models were developed to predict the probability of CIP based on different predictors. Model 1 utilized age, histology, and preexisting ground glass opacity (GGO) percentage of the whole lung to predict grade ≥ 2 CIP, while Model 2 used histology and GGO percentage in the right lower lung to predict grade ≥ 3 CIP. These models were validated, and their accuracy was assessed. In another exploratory analysis, the presence of GGOs involving more than one lobe on pretreatment CT scans was identified as a risk factor for progression-free survival | ||
| 520 | |a CONCLUSIONS: The assessment of GGO volume and distribution on pre-treatment CT scans could assist in monitoring and manage the risk of CIP in NSCLC patients receiving ICI therapy | ||
| 520 | |a CLINICAL RELEVANCE STATEMENT: This study's quantitative imaging and computational analysis can help identify NSCLC patients at high risk of CIP, allowing for better risk management and potentially improved outcomes in those receivingICI treatment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Checkpoint inhibitor pneumonitis | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Ground glass opacity | |
| 650 | 4 | |a Immune checkpoint inhibitor | |
| 650 | 4 | |a Non-small cell lung cancer | |
| 700 | 1 | |a Zhao, Jinkun |e verfasserin |4 aut | |
| 700 | 1 | |a Mei, Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Wenting |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xiuqiong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jingya |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Richeng |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Zhaoxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Dingzhi |e verfasserin |4 aut | |
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