Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer

Cheng, Michael L.; Lin, R.; Bai, Na; Zhang, Y.; Wang, H.; Guo, madi; Duan, Xiaojie; Zheng, Jianghua; Qiu, Zhixin; Zhao, Yongliang

doi:10.1016/j.crad.2022.12.013

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…In addition, DL has been successfully developed to predict optimal radiotherapy for patients with brain metastases using CT images and non-image clinical information ( Cao et al, 2023 ). It has also been developed to predict pneumonitis risk in lung cancer patients treated with immune checkpoint inhibitors and to identify morphologic features that predict ERBB2 status and trastuzumab efficacy in breast cancer patients ( Bychkov et al, 2021 ; Cheng et al, 2023 ). In a retrospective multicenter study, a DL algorithm was developed to help radiologists diagnose breast cancer lesions and differentiate axillary lymph node metastases based on radiological features ( Zhou et al, 2023 ).…”

Section: Machine Learning In Cancer Researchmentioning

confidence: 99%

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Mondello,

Dal Bo,

Toffoli

et al. 2024

Front. Pharmacol.

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Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

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Section: Machine Learning In Cancer Researchmentioning

confidence: 99%

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Mondello,

Dal Bo,

Toffoli

et al. 2024

Front. Pharmacol.

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“…M. Cheng et al. ( 86 ) also developed a multi-modal nomogram model, based on clinical and deep radiomics features, to predict the risk of ICIP in 141 lung cancer patients (Including 128 cases of NSCLC). The model’s predictions for ICIP were presented using a nomogram.…”

Section: Radiomics Application In Patients With Nsclc Who Have Icipmentioning

confidence: 99%

Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis

Shu,

Xu,

et al. 2023

Front. Immunol.

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Immune checkpoint inhibitors (ICIs) modulate the body’s immune function to treat tumors but may also induce pneumonitis. Immune checkpoint inhibitor-related pneumonitis (ICIP) is a serious immune-related adverse event (irAE). Immunotherapy is currently approved as a first-line treatment for non-small cell lung cancer (NSCLC), and the incidence of ICIP in NSCLC patients can be as high as 5%-19% in clinical practice. ICIP can be severe enough to lead to the death of NSCLC patients, but there is a lack of a gold standard for the diagnosis of ICIP. Radiomics is a method that uses computational techniques to analyze medical images (e.g., CT, MRI, PET) and extract important features from them, which can be used to solve classification and regression problems in the clinic. Radiomics has been applied to predict and identify ICIP in NSCLC patients in the hope of transforming clinical qualitative problems into quantitative ones, thus improving the diagnosis and treatment of ICIP. In this review, we summarize the pathogenesis of ICIP and the process of radiomics feature extraction, review the clinical application of radiomics in ICIP of NSCLC patients, and discuss its future application prospects.

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“…More often, it is difficult to distinguish AE-IP from anticancer drug-induced pneumonia based on clinical findings, computed tomography (CT) imaging and laboratory findings [ 4 ]. Thus, immune checkpoint inhibitor (ICI)-induced adverse events have been reported as immune checkpoint inhibitor pneumonitis (ICIP) [ 7 ]. Additionally, checkpoint inhibitor pneumonitis and immune checkpoint inhibitor-related pneumonitis have been reported to have the same meaning.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, immune checkpoint inhibitor (ICI)-induced AE has been reported as immune checkpoint inhibitor pneumonitis (ICIP) [7]. Additionally, 'checkpoint inhibitor pneumonitis (CIP)' and 'immune checkpoint inhibitor related pneumonitis (ICIRP)' have been reported to be of same meaning.…”

Section: Introductionmentioning

confidence: 99%

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Immune checkpoint inhibitors in patients with lung cancer having chronic interstitial pneumonia

Isobe,

Nakamura,

Sakamoto

et al. 2024

ERJ Open Res

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BackgroundIn interstitial pneumonia (IP)-associated lung cancer, immune checkpoint inhibitor pneumonitis (ICIP) is common with immune checkpoint inhibitor (ICI) treatment.PurposeTo clarify the safety and efficacy of ICI for lung cancer with IP.MethodsThis multicentre retrospective observational study was conducted from June 2016 to December 2020 on patients with primary lung cancer with IP who received ICI.ResultsA total of 200 patients (median age 70 years; male/female, 176/24) were enrolled from 27 institutions. ICIP occurred in 61 patients (30.5%), grades 3–5 pneumonitis in 32 patients (15.5%) and death in nine patients (4.5%). The common computed tomography (CT) pattern of ICIP was organising pneumonia in 29 patients (47.5%). Subsequently, diffuse alveolar damage (DAD) pattern was observed in 19 patients (31.1%) who had a significantly worse prognosis than those with a non-DAD pattern (median progression-free survival [PFS]: 115 daysversus226 days p=0.042; median overall survival [OS]: 334 daysversus1316 days, p<0.001). Immune-related adverse events (irAE) occurred in approximately 50% of patients. Patients with irAEs (n=100) had a better prognosis than those without irAEs (n=100) (median PFS: 200 daysversus77 days, p<0.001; median OS: 597 daysversus390 days p=0.0074). The objective response rate and disease control rate were 41.3% and 68.5%, respectively.ConclusionsAlthough ICI treatment was effective for lung cancer with IP, ICIP developed in approximately 30%. Patients with irAE had a significantly better PFS and OS than those without irAE.

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Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer

Cited by 9 publications

References 34 publications

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis

Immune checkpoint inhibitors in patients with lung cancer having chronic interstitial pneumonia

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