Optimizing Inpatient Care for Lung Cancer Patients with Immune Checkpoint Inhibitor- Related Pneumonitis Using a Clinical Care Pathway Algorithm.

Purpose: Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a condition associated with high mortality, necessitating prompt recognition and treatment initiation. This study aimed to assess the impact of implementing a clinical care pathway algorithm on reducing the time to treatment for ICI-P. Methods: Patients with lung cancer and suspected ICI-P were enrolled, and a multi-modal intervention promoting algorithm use was implemented in two phases. Pre- and post-intervention analyses were conducted to evaluate the primary outcome of time from ICI-P diagnosis to treatment initiation. Results: Of the 82 patients admitted with suspected ICI-P, 73.17% were confirmed to have ICI-P, predominantly associated with non-small cell lung cancer (91.67%) and stage IV disease (95%). Pembrolizumab was the most commonly used immune checkpoint inhibitor (55%). The mean times to treatment were 2.37 days in the pre-intervention phase and, 3.07 days (p=0.46), and 1.27 days (p=0.40) in the post-intervention phases 1 and 2, respectively. Utilization of the immunotoxicity order set significantly increased from 0% to 27.27% (p = 0.04) after phase 2. While there were no significant changes in ICU admissions or inpatient mortality, outpatient pulmonology follow-ups increased statistically significantly, demonstrating enhanced continuity of care. The overall mortality for patients with ICI-P was 22%, underscoring the urgency of optimizing management strategies. Notably, all patients discharged on high-dose corticosteroids received appropriate gastrointestinal prophylaxis and prophylaxis against Pneumocystis jirovecii pneumonia infections at the end of phase 2. Conclusion: Implementing a clinical care pathway algorithm for ICI-P management standardizes care practices and enhances patient outcomes, underscoring the importance of structured approaches.

Immune checkpoint inhibitor-related pneumonitis: research advances in prediction and management.

The advent of immune-checkpoint inhibitors (ICIs) has revolutionized the treatment of malignant solid tumors in the last decade, producing lasting benefits in a subset of patients. However, unattended excessive immune responses may lead to immune-related adverse events (irAEs). IrAEs can manifest in different organs within the body, with pulmonary toxicity commonly referred to as immune checkpoint inhibitor-related pneumonitis (CIP). The CIP incidence remains high and is anticipated to rise further as the therapeutic indications for ICIs expand to encompass a wider range of malignancies. The diagnosis and treatment of CIP is difficult due to the large individual differences in its pathogenesis and severity, and severe CIP often leads to a poor prognosis for patients. This review summarizes the current state of clinical research on the incidence, risk factors, predictive biomarkers, diagnosis, and treatment for CIP, and we address future directions for the prevention and accurate prediction of CIP.

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

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.

Predictive factors and prognosis of immune checkpoint inhibitor-related pneumonitis in non-small cell lung cancer patients.

Objective: To investigate the influencing factors and prognosis of immune checkpoint inhibitor-related pneumonitis (CIP) in advanced non-small cell lung cancer (NSCLC) patients during or after receiving immune checkpoint inhibitors(ICIs). Methods: The clinical and laboratory indicator data of 222 advanced NSCLC patients treated with PD-1/PD-L1 inhibitors at the First Affiliated Hospital of Zhengzhou University between December 2017 and November 2021 were collected retrospectively. The patients were divided into a CIP group (n=41) and a non-CIP group (n=181) according to whether they developed CIP or not before the end of follow-up. Logistic regression was used to evaluate risk factors of CIP, and Kaplan‒Meier curves were used to describe the overall survival (OS) of different groups. The log-rank test was used to compare the survival of different groups. Results: There were 41 patients who developed CIP, and the incidence rate of CIP was 18.5%. Univariate and multivariate logistic regression analyses showed that low pretreatment hemoglobin (HB) and albumin (ALB) levels were independent risk factors for CIP. Univariate analysis suggested that history of chest radiotherapy was related to the incidence of CIP. The median OS of the CIP group and non-CIP were 15.63 months and 30.50 months (HR:2.167; 95%CI: 1.355-3.463, P<0.05), respectively. Univariate and multivariate COX analyses suggested that a high neutrophil-to-lymphocyte ratio (NLR) level, a low ALB level and the development of CIP were independent prognostic factors for worse OS of advanced NSCLC patients treated with ICIs. Additionally, the early-onset and high-grade CIP were related to shorter OS in the subgroup. Conclusion: Lower pretreatment HB and ALB levels were independent risk factors for CIP. A high NLR level, a low ALB level and the development of CIP were independent risk factors for the prognosis of advanced NSCLC patients treated with ICIs.

Application of CT-based radiomics analysis in predicting and identifying of treatment-associated pneumonitis / 国际肿瘤学杂志

As a non-invasive image analysis method, radiomics can deeply explore the clinical information hidden behind medical images, and has been widely used in medicine in recent years. Consolidation immunotherapy after concurrent chemoradiotherapy has become the standard treatment for locally advanced non-small cell lung cancer. The prediction and identification of treatment-associated adverse events radiation pneumonitis (RP) and immune checkpoint inhibitor-related pneumonitis (CIP) are of vital importance for the formulation of treatment plan and the selection of subsequent treatment. CT-based radiomics analysis shows great potential in predicting and identifying RP and CIP.

Immune checkpoint inhibitor-related pneumonitis in non-small cell lung cancer: A review.

Immune checkpoint inhibitors (ICIs) have shown definite therapeutic effects in various types of cancers, especially non-small cell lung cancer (NSCLC). However, ICIs have unique side effects, called immune-related adverse events (irAEs), which can occur in various systems throughout the body. Among such irAEs, immune checkpoint inhibitor-related pneumonitis (ICI-P) is a fatal adverse reaction. In this review, we discussed the risk factors, pathogenesis, clinical characteristics, radiological manifestations, pathological features, diagnosis, grading, and management of ICI-P in NSCLC and the relationship between ICI-P and the efficacy of ICI therapy. In addition, we discussed the predictive factors for ICI-P. This review will play a crucial role in the prediction, evaluation, and management of ICI-P for widespread application of immunotherapy.

Imaging Mass Cytometry Analysis of immune Checkpoint Inhibitor-Related Pneumonitis: A Case Report.

Immune checkpoint inhibitor-related pneumonitis (CIP) is a rare but well-recognized immune-related adverse event (irAE), causes 35% of irAE related deaths. However, the mechanism of CIP remains unclear and no evidence-based treatment except for glucocorticoids is available. Herein, we report the case of a patient with metastatic bladder cancer who received tislelizumab and was diagnosed with CIP. The patient underwent transbronchial cryobiopsy. The patient was treated with glucocorticoid, but CIP recurred when the glucocorticoid tapering. The paraffine-embedded lung tissue was sectioned, stained with 31 heavy-metal tagged antibodies, and analyzed using imaging mass cytometry (IMC) technology. We identified multiple immune cell subsets in the lung tissue and observed the infiltration of memory T cells and the CD4+ DC subset. The data indicated the great potential of IMC technology in the identification and characterization of irAEs. Further investigation is warranted to identify the mechanism of action of CIP.

Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images.

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detection of patients at risk for developing ICIP before the initiation of immunotherapy is critical for alleviating future complications with early interventions and improving treatment outcomes. In this study, we present the first reported work that explores the potential of deep learning to predict patients who are at risk for developing ICIP. To this end, we collected the pretreatment baseline CT images and clinical information of 24 patients who developed ICIP after immunotherapy and 24 control patients who did not. A multimodal deep learning model was constructed based on 3D CT images and clinical data. To enhance performance, we employed two-stage transfer learning by pre-training the model sequentially on a large natural image dataset and a large CT image dataset, as well as transfer learning. Extensive experiments were conducted to verify the effectiveness of the key components used in our method. Using five-fold cross-validation, our method accurately distinguished ICIP patients from non-ICIP patients, with area under the receiver operating characteristic curve of 0.918 and accuracy of 0.920. This study demonstrates the promising potential of deep learning to identify patients at risk for developing ICIP. The proposed deep learning model enables efficient risk stratification, close monitoring, and prompt management of ICIP, ultimately leading to better treatment outcomes.

Development and Validation of a Radiomics Nomogram Using Computed Tomography for Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From Radiation Pneumonitis for Patients With Non-Small Cell Lung Cancer.

Background: The combination of immunotherapy and chemoradiotherapy has become the standard therapeutic strategy for patients with unresected locally advance-stage non-small cell lung cancer (NSCLC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by pretreatment CT radiomics and clinical or radiological parameters. Methods: A total of 126 advance-stage NSCLC patients with pneumonitis were enrolled in this retrospective study and divided into the training dataset (n =88) and the validation dataset (n = 38). A total of 837 radiomics features were extracted from regions of interest based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator. A logistic regression was applied to develop a radiomics nomogram. Receiver operating characteristics curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification. Results: There was no significant difference between the training and the validation datasets for any clinicopathological parameters in this study. The radiomics signature, named Rad-score, consisting of 11 selected radiomics features, has potential ability to differentiate between CIP and RP with the empirical and α-binormal-based AUCs of 0.891 and 0.896. These results were verified in the validation dataset with AUC = 0.901 and 0.874, respectively. The clinical and radiological parameters of bilateral changes (p < 0.001) and sharp border (p = 0.001) were associated with the identification of CIP and RP. The nomogram model showed good performance on discrimination in the training dataset (AUC = 0.953 and 0.950) and in the validation dataset (AUC = 0.947 and 0.936). Conclusions: CT-based radiomics features have potential values for differentiating between patients with CIP and patients with RP. The addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.

Risk factors for immune checkpoint inhibitor-related pneumonitis in non-small cell lung cancer.

Background: Immune checkpoint inhibitors (ICIs) have led to dramatic improvements in survival a subset of patients with non-small cell lung cancer (NSCLC); however, they have been shown to cause life-threatening toxicity such as immune checkpoint inhibitor-related pneumonitis (CIP). Our previous studies have shown that chronic obstructive pulmonary disease (COPD) and circulating cytokines are associated with clinical outcomes in NSCLC patients receiving ICIs. However, the relationship between these factors and the development of CIP is unclear. In this study, we retrospectively assessed NSCLC patients receiving ICIs to identify CIP risk factors. Methods: This retrospective cohort study reviewed medical records of NSCLC patients receiving ICIs targeting programmed cell death 1 (PD-1) or its ligand PD-L1 between March 2017 and December 2020 at Zhongshan Hospital Fudan University. CIP was diagnosed by the treating investigator. Clinical characteristics and baseline plasma cytokines were collected. Logistic regression was used to compare clinical characteristics and circulating cytokine levels between patients with and without CIP to identify CIP risk factors. Results: Of 164 NSCLC patients who received ICIs, CIP developed in 20 cases (12.2%). The presence of COPD [odds ratio (OR), 7.194; 95% confidence interval (CI): 1.130 to 45.798; P=0.037] and PD-L1 expression of ≥50% (OR, 7.184; 95% CI: 1.154 to 44.721; P=0.035) were independently associated with a higher incidence of CIP, whereas a higher baseline level of interleukin-8 (IL-8) was associated with a lower incidence of CIP (OR, 0.758; 95% CI: 0.587 to 0.978; P=0.033). The independent risk factors from final multivariate analysis were incorporated into a nomogram to predict the incidence of CIP. The nomogram model receiver operating characteristic (ROC) curve had a good predictive accuracy of 0.883 (95% CI: 0.806 to 0.959). Conclusions: Increased risk of CIP independently associated with history of COPD, tumor PD-L1 expression ≥50%, and low baseline IL-8 level. The nomogram may hold promise for CIP risk assessment in the administration of ICIs.

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

PURPOSE: Consolidation 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. METHODS: We 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. RESULTS: Using 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. CONCLUSIONS: This 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.

Insights into Potential Pathogenesis and Treatment Options for Immune-Checkpoint Inhibitor-Related Pneumonitis.

Immune-checkpoint inhibitors (ICIs) targeting cytotoxic T-lymphocyte antigen 4 (CTLA-4), programmed cell death-1 (PD-1), and programmed cell death-1-ligand 1 (PD-L1) have become new treatment options for various malignancies. ICIs bind to immune-checkpoint inhibitory receptors or to the foregoing ligands and block inhibitory signals to release the brakes on the immune system, thereby enhancing immune anti-tumor responses. On the other hand, unlike conventional chemotherapies, ICIs can cause specific side effects, called immune-related adverse events (irAEs). These toxicities may affect various organs, including the lungs. ICI-related pneumonitis (ICI-pneumonitis) is not the most frequent adverse event, but it is serious and can be fatal. In this review, we summarize recent findings regarding ICI-pneumonitis, with a focus on potential pathogenesis and treatment.

Imaging diagnosis of bronchogenic carcinoma (the forgotten disease) during times of COVID-19 pandemic: Current and future perspectives.

Patients with bronchogenic carcinoma comprise a high-risk group for coronavirus disease 2019 (COVID-19), pneumonia and related complications. Symptoms of COVID-19 related pulmonary syndrome may be similar to deteriorating symptoms encountered during bronchogenic carcinoma progression. These resemblances add further complexity for imaging assessment of bronchogenic carcinoma. Similarities between clinical and imaging findings can pose a major challenge to clinicians in distinguishing COVID-19 super-infection from evolving bronchogenic carcinoma, as the above-mentioned entities require very different therapeutic approaches. However, the goal of bronchogenic carcinoma management during the pandemic is to minimize the risk of exposing patients to COVID-19, whilst still managing all life-threatening events related to bronchogenic carcinoma. The current pandemic has forced all healthcare stakeholders to prioritize per value resources and reorganize therapeutic strategies for timely management of patients with COVID-19 related pulmonary syndrome. Processing of radiographic and computed tomography images by means of artificial intelligence techniques can facilitate triage of patients. Modified and newer therapeutic strategies for patients with bronchogenic carcinoma have been adopted by oncologists around the world for providing uncompromised care within the accepted standards and new guidelines.

A Nasopharyngeal Carcinoma Patient With COVID-19 Infection After Immunotherapy: A Case Report and Literature Review.

BACKGROUND/AIM: Novel coronavirus infection in a cancer patient treated with immunotherapy, requires high attention. CASE REPORT: Clinical and radiological data were obtained from the electronic medical record. Pharynx swab was tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA by reverse transcription-polymerase chain reaction (RT-PCR). The nasopharyngeal carcinoma patient developed fever on the third day after chemotherapy and immunotherapy. Laboratory examination showed lymphocytopenia. On the sixth day, chest computed tomography (CT) images showed bilateral scattered ground-glass opacities and reticulation. Pharynx swab was positive for SARS-CoV-2 nucleic acid and the patient was confirmed as having Coronavirus Disease 2019 (COVID-19). Unfortunately, despite aggressive treatment after the diagnosis of COVID-19, the patient died quickly. CONCLUSION: The patient with nasopharyngeal carcinoma in this case developed severe COVID-19 after receiving immunotherapy. For patients treated with immune checkpoint inhibitors (ICIs) in epidemic areas, the safety of ICIs in cancer patients infected with SARS-CoV-2 should be considered.

Association of baseline peripheral-blood eosinophil count with immune checkpoint inhibitor-related pneumonitis and clinical outcomes in patients with non-small cell lung cancer receiving immune checkpoint inhibitors.

OBJECTIVES: Immune checkpoint inhibitors (ICIs) have revolutionized the oncologic treatment landscape, but have been accompanied by immune-related adverse events (irAEs). ICI-related pneumonitis (ICI-pneumonitis) is a potentially fatal irAE. However, the risk factors associated with ICI-pneumonitis remain unclear. There is an urgent need to identify risk factors for ICI-pneumonitis using reliable and accessible parameters. Here, we aimed to identify baseline peripheral-blood biomarkers correlated with ICI-pneumonitis and clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC) who were treated with ICIs. MATERIALS AND METHODS: We conducted a retrospective analysis of eligible patients with advanced NSCLC who were treated with ICIs at our center. Receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value for analyzing risk of ICI-pneumonitis. Multivariate logistic analysis was performed to identify risk factors of ICI-pneumonitis. Clinical characteristics and treatment outcomes were collected and compared according to the optimal cutoff value. RESULTS: A total of 300 patients were included, in which 54 patients (18 %) experienced ICI-pneumonitis. Patients with ICI-pneumonitis had a high level of baseline peripheral-blood absolute eosinophil count (AEC) than those without ICI-pneumonitis (P = 0.013). The optimal threshold of baseline peripheral-blood AEC to predict ICI-pneumonitis was 0.125 × 109 cells/L. The incidence of ICI-pneumonitis was higher in the high-AEC group (AEC ≥ 0.125 × 109 cells/L; 27.7 %) than in the low-AEC group (AEC < 0.125 × 109 cells/L; 9.8 %, P < 0.001). Moreover, patients with high AEC (compared with those with low AEC) had a higher objective response rate (ORR) (40.9 % versus 28.8 %, P = 0.029) and longer median progression-free survival (PFS) (8.93 months versus 5.87 months, P = 0.038). CONCLUSIONS: Among patients treated with ICIs, a baseline feature of high AEC (≥0.125 × 109 cells/L) was associated with an increasing risk of ICI-pneumonitis, and with a better clinical outcome.