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Holder AM, Dedeilia A, Sierra-Davidson K, Cohen S, Liu D, Parikh A, Boland GM. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer 2024:10.1038/s41568-024-00705-7. [PMID: 38867074 DOI: 10.1038/s41568-024-00705-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/14/2024]
Abstract
Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.
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Affiliation(s)
- Ashley M Holder
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sonia Cohen
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - David Liu
- Dana Farber Cancer Institute, Boston, MA, USA
| | - Aparna Parikh
- Cancer Center, Massachusetts General Hospital, Boston, MA, USA
| | - Genevieve M Boland
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
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Hindocha S, Hunter B, Linton-Reid K, George Charlton T, Chen M, Logan A, Ahmed M, Locke I, Sharma B, Doran S, Orton M, Bunce C, Power D, Ahmad S, Chan K, Ng P, Toshner R, Yasar B, Conibear J, Murphy R, Newsom-Davis T, Goodley P, Evison M, Yousaf N, Bitar G, McDonald F, Blackledge M, Aboagye E, Lee R. Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis. Radiother Oncol 2024; 195:110266. [PMID: 38582181 DOI: 10.1016/j.radonc.2024.110266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 03/27/2024] [Accepted: 03/31/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns. METHODS In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists. RESULTS Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6). CONCLUSION Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy.
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Affiliation(s)
- Sumeet Hindocha
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK; Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK.
| | - Benjamin Hunter
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Kristofer Linton-Reid
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Thomas George Charlton
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Mitchell Chen
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Andrew Logan
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Merina Ahmed
- Lung Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Imogen Locke
- Lung Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Bhupinder Sharma
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Simon Doran
- Institute of Cancer Research NIHR Biomedical Research Centre, London, UK
| | - Matthew Orton
- Artificial Intelligence Imaging Hub, Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Catey Bunce
- Institute of Cancer Research NIHR Biomedical Research Centre, London, UK
| | - Danielle Power
- Department of Clinical Oncology, Imperial College Healthcare NHS Trust, Fulham Palace Road, London W6 8RF, UK
| | - Shahreen Ahmad
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Karen Chan
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Peng Ng
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Richard Toshner
- Interstitial lung disease unit, St Bartholomews' Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Binnaz Yasar
- Department of Clinical Oncology, St Batholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - John Conibear
- Department of Clinical Oncology, St Batholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Ravindhi Murphy
- Chelsea and Westminster Hospital, Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London SW10 9NH, UK
| | - Tom Newsom-Davis
- Chelsea and Westminster Hospital, Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London SW10 9NH, UK
| | - Patrick Goodley
- Lung Cancer & Thoracic Surgery Directorate, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Greater Manchester, UK; Division of Immunology, Immunity to Infection & Respiratory Medicine, University of Manchester, Manchester, UK
| | - Matthew Evison
- Lung Cancer & Thoracic Surgery Directorate, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Greater Manchester, UK
| | - Nadia Yousaf
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - George Bitar
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Fiona McDonald
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Matthew Blackledge
- Radiotherapy and Imaging, Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Eric Aboagye
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Richard Lee
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
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Zhang GY, Du XZ, Xu R, Chen T, Wu Y, Wu XJ, Liu S. Development and Validation of a Machine Learning-Based Model Using CT Radiomics for Predicting Immune Checkpoint Inhibitor-related Pneumonitis in Patients With NSCLC Receiving Anti-PD1 Immunotherapy: A Multicenter Retrospective CaseControl Study. Acad Radiol 2024; 31:2128-2143. [PMID: 37977890 DOI: 10.1016/j.acra.2023.10.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/19/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to develop and evaluate a radiomics-based model combined with clinical and qualitative radiological (semantic feature [SF]) features to predict immune checkpoint inhibitor-related pneumonitis (CIP) in patients with non-small cell lung cancer (NSCLC) treated with programmed cell death protein 1 inhibitors. MATERIALS AND METHODS This was a multicenter retrospective casecontrol study conducted from January 1, 2018, to December 31, 2022, at three centers. Patients with NSCLC treated with anti-PD1 were enrolled and randomly divided into two groups (7:3): training (n = 95) and validation (n = 39). Logistic regression (LR) and support vector machine (SVM) algorithms were used to transform features into the models. RESULTS The study comprised 134 participants from three independent centers (male, 114/134, 85%; mean [±standard deviation] age, 63.92 [±7.9] years). The radiomics score (RS) models built based on the LR and SVM algorithms could accurately predict CIP (area under the receiver operating characteristics curve [AUC], 0.860 [0.780, 0.939] and 0.861 [0.781, 0.941], respectively). The AUCs for the RS-clinic-SF combined model were 0.903 (0.839, 0.967) and 0.826 (0.688, 0.964) in the training and validation cohorts, respectively. Decision curve analysis showed that the combined models achieved high clinical net benefit across the majority of the range of reasonable threshold probabilities. CONCLUSION This study demonstrated that the combined model constructed by the identified features of RS, clinical features, and SF has the potential to precisely predict CIP. The RS-clinic-SF combined model has the potential to be used more widely as a practical tool for the noninvasive prediction of CIP to support individualized treatment planning.
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Affiliation(s)
- Guo-Yue Zhang
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.).
| | - Xian-Zhi Du
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.).
| | - Rui Xu
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.).
| | - Ting Chen
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (T.C.).
| | - Yue Wu
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.).
| | - Xiao-Juan Wu
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.); Department of Respiratory and Critical Care Medicine, Suining Central Hospital, Suining, 629000, Sichuan, P.R. China (X.-j.W.).
| | - Shui Liu
- Department of Respiratory and Critical Care Medicine, People's Hospital of Fengjie, Fengjie, Chongqing, 404600, P.R. China (S.L.).
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Peiliang Wang MD, Yikun Li MM, Mengyu Zhao MM, Jinming Yu MD, Feifei Teng MD. Distinguishing immune checkpoint inhibitor-related pneumonitis from radiation pneumonitis by CT radiomics features in non-small cell lung cancer. Int Immunopharmacol 2024; 128:111489. [PMID: 38266450 DOI: 10.1016/j.intimp.2024.111489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/26/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024]
Abstract
PURPOSE To develop a CT-based model to classify pneumonitis etiology in patients with non-small cell lung cancer(NSCLC) after radiotherapy(RT) and Immune checkpoint inhibitors(ICIs). METHODS We retrospectively identified 130 NSCLC patients who developed pneumonitis after receipt of ICIs only (n = 50), thoracic RT only (n = 50) (ICIs only + thoracic RT only, the training cohort, n = 100), and RT + ICIs (the test cohort, n = 30). Clinical and CT radiomics features were described and compared between different groups. We constructed a random forest (RF) classifier and a linear discriminant analysis (LDA) classifier by CT radiomics to discern pneumonitis etiology. RESULTS The patients in RT + ICIs group have more high grade (grade 3-4) pneumonitis compared to patients in ICIs only or RT only group (p < 0.05). Pneumonitis after the combined therapy was not a simple superposition mode of RT-related pneumonitis(RP) and ICI-related pneumonitis(CIP), resulting in the distinct characteristics of both RT and ICIs-related pneumonitis. The RF classifier showed favorable discrimination between RP and CIP with an area under the receiver operating curve (AUC) of 0.859 (95 %CI: 0.788-0.929) in the training cohort and 0.851 (95 % CI: 0.700-1) in the test cohort. The LDA classifier achieved an AUC of 0.881 (95 %CI: 0.815-0.947) in the training cohort and 0.842 (95 %CI: 0.686-0.997) in the test cohort. Our analysis revealed four principal CT-based features shared across both models:original_glrlm_LongRunLowGrayLevelEmphasis, wavelet-HLL_firstorder_Median, wavelet-LLL_ngtdm_Busyness, and wavelet-LLL_glcm_JointAverage. CONCLUSION CT radiomics-based classifiers could provide a noninvasive method to identify the predominant etiology in NSCLC patients who developed pneumonitis after RT alone, ICIs alone or RT + ICIs.
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Affiliation(s)
- M D Peiliang Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan 250117, China
| | - M M Yikun Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - M M Mengyu Zhao
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - M D Jinming Yu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan 250117, China
| | - M D Feifei Teng
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan 250117, China.
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5
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Roisman LC, Kian W, Anoze A, Fuchs V, Spector M, Steiner R, Kassel L, Rechnitzer G, Fried I, Peled N, Bogot NR. Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer. NPJ Precis Oncol 2023; 7:125. [PMID: 37990050 PMCID: PMC10663598 DOI: 10.1038/s41698-023-00473-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/24/2023] [Indexed: 11/23/2023] Open
Abstract
Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.
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Affiliation(s)
- Laila C Roisman
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.
- Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Waleed Kian
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
- The Institute of Oncology, Assuta Ashdod, Ashdod, Israel
| | - Alaa Anoze
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Vered Fuchs
- Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Maria Spector
- The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Roee Steiner
- The Institute for Nuclear Medicine, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Levi Kassel
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Gilad Rechnitzer
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Iris Fried
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Nir Peled
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.
| | - Naama R Bogot
- The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel
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Aminu M, Daver N, Godoy MCB, Shroff G, Wu C, Torre-Sada LF, Goizueta A, Shannon VR, Faiz SA, Altan M, Garcia-Manero G, Kantarjian H, Ravandi-Kashani F, Kadia T, Konopleva M, DiNardo C, Pierce S, Naing A, Kim ST, Kontoyiannis DP, Khawaja F, Chung C, Wu J, Sheshadri A. Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept. Front Immunol 2023; 14:1249511. [PMID: 37841255 PMCID: PMC10570510 DOI: 10.3389/fimmu.2023.1249511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
Background Immune checkpoint inhibitors (ICI) may cause pneumonitis, resulting in potentially fatal lung inflammation. However, distinguishing pneumonitis from pneumonia is time-consuming and challenging. To fill this gap, we build an image-based tool, and further evaluate it clinically alongside relevant blood biomarkers. Materials and methods We studied CT images from 97 patients with pneumonia and 29 patients with pneumonitis from acute myeloid leukemia treated with ICIs. We developed a CT-derived signature using a habitat imaging algorithm, whereby infected lungs are segregated into clusters ("habitats"). We validated the model and compared it with a clinical-blood model to determine whether imaging can add diagnostic value. Results Habitat imaging revealed intrinsic lung inflammation patterns by identifying 5 distinct subregions, correlating to lung parenchyma, consolidation, heterogenous ground-glass opacity (GGO), and GGO-consolidation transition. Consequently, our proposed habitat model (accuracy of 79%, sensitivity of 48%, and specificity of 88%) outperformed the clinical-blood model (accuracy of 68%, sensitivity of 14%, and specificity of 85%) for classifying pneumonia versus pneumonitis. Integrating imaging and blood achieved the optimal performance (accuracy of 81%, sensitivity of 52% and specificity of 90%). Using this imaging-blood composite model, the post-test probability for detecting pneumonitis increased from 23% to 61%, significantly (p = 1.5E - 9) higher than the clinical and blood model (post-test probability of 22%). Conclusion Habitat imaging represents a step forward in the image-based detection of pneumonia and pneumonitis, which can complement known blood biomarkers. Further work is needed to validate and fine tune this imaging-blood composite model and further improve its sensitivity to detect pneumonitis.
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Affiliation(s)
- Muhammad Aminu
- Departments of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Naval Daver
- Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Myrna C. B. Godoy
- Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Girish Shroff
- Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Carol Wu
- Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Luis F. Torre-Sada
- Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Alberto Goizueta
- Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Vickie R. Shannon
- Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Saadia A. Faiz
- Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mehmet Altan
- Departments of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Guillermo Garcia-Manero
- Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Hagop Kantarjian
- Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Farhad Ravandi-Kashani
- Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Tapan Kadia
- Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Marina Konopleva
- Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Courtney DiNardo
- Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sherry Pierce
- Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Aung Naing
- Departments of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sang T. Kim
- Departments of Rheumatology and Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dimitrios P. Kontoyiannis
- Departments of Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Fareed Khawaja
- Departments of Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Caroline Chung
- Departments of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jia Wu
- Departments of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ajay Sheshadri
- Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Shu Y, Xu W, Su R, Ran P, Liu L, Zhang Z, Zhao J, Chao Z, Fu G. Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis. Front Immunol 2023; 14:1251645. [PMID: 37799725 PMCID: PMC10547882 DOI: 10.3389/fimmu.2023.1251645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 08/24/2023] [Indexed: 10/07/2023] Open
Abstract
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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Affiliation(s)
- Yang Shu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Wei Xu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Rui Su
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Pancen Ran
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Lei Liu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhizhao Zhang
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Jing Zhao
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhen Chao
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Guobin Fu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Department of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Department of Oncology, The Third Affiliated Hospital of Shandong First Medical University, Jinan, Shandong, China
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Chen M, Lu H, Copley SJ, Han Y, Logan A, Viola P, Cortellini A, Pinato DJ, Power D, Aboagye EO. A Novel Radiogenomics Biomarker for Predicting Treatment Response and Pneumotoxicity From Programmed Cell Death Protein or Ligand-1 Inhibition Immunotherapy in NSCLC. J Thorac Oncol 2023; 18:718-730. [PMID: 36773776 DOI: 10.1016/j.jtho.2023.01.089] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 11/23/2022] [Accepted: 01/17/2023] [Indexed: 02/11/2023]
Abstract
INTRODUCTION Patient selection for checkpoint inhibitor immunotherapy is currently guided by programmed death-ligand 1 (PD-L1) expression obtained from immunohistochemical staining of tumor tissue samples. This approach is susceptible to limitations resulting from the dynamic and heterogeneous nature of cancer cells and the invasiveness of the tissue sampling procedure. To address these challenges, we developed a novel computed tomography (CT) radiomic-based signature for predicting disease response in patients with NSCLC undergoing programmed cell death protein 1 (PD-1) or PD-L1 checkpoint inhibitor immunotherapy. METHODS This retrospective study comprises a total of 194 patients with suitable CT scans out of 340. Using the radiomic features computed from segmented tumors on a discovery set of 85 contrast-enhanced chest CTs of patients diagnosed with having NSCLC and their CD274 count, RNA expression of the protein-encoding gene for PD-L1, as the response vector, we developed a composite radiomic signature, lung cancer immunotherapy-radiomics prediction vector (LCI-RPV). This was validated in two independent testing cohorts of 66 and 43 patients with NSCLC treated with PD-1 or PD-L1 inhibition immunotherapy, respectively. RESULTS LCI-RPV predicted PD-L1 positivity in both NSCLC testing cohorts (area under the curve [AUC] = 0.70, 95% confidence interval [CI]: 0.57-0.84 and AUC = 0.70, 95% CI: 0.46-0.94). In one cohort, it also demonstrated good prediction of cases with high PD-L1 expression exceeding key treatment thresholds (>50%: AUC = 0.72, 95% CI: 0.59-0.85 and >90%: AUC = 0.66, 95% CI: 0.45-0.88), the tumor's objective response to treatment at 3 months (AUC = 0.68, 95% CI: 0.52-0.85), and pneumonitis occurrence (AUC = 0.64, 95% CI: 0.48-0.80). LCI-RPV achieved statistically significant stratification of the patients into a high- and low-risk survival group (hazard ratio = 2.26, 95% CI: 1.21-4.24, p = 0.011 and hazard ratio = 2.45, 95% CI: 1.07-5.65, p = 0.035). CONCLUSIONS A CT radiomics-based signature developed from response vector CD274 can aid in evaluating patients' suitability for PD-1 or PD-L1 checkpoint inhibitor immunotherapy in NSCLC.
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Affiliation(s)
- Mitchell Chen
- Department of Surgery and Cancer, Imperial College, London, United Kingdom; Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Haonan Lu
- Department of Surgery and Cancer, Imperial College, London, United Kingdom
| | - Susan J Copley
- Department of Surgery and Cancer, Imperial College, London, United Kingdom; Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Yidong Han
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Andrew Logan
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Patrizia Viola
- North West London Pathology, Charing Cross Hospital, London, United Kingdom
| | - Alessio Cortellini
- Department of Surgery and Cancer, Imperial College, London, United Kingdom; Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - David J Pinato
- Department of Surgery and Cancer, Imperial College, London, United Kingdom; Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom; Division of Oncology, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Danielle Power
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College, London, United Kingdom.
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9
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Les I, Martínez M, Pérez-Francisco I, Cabero M, Teijeira L, Arrazubi V, Torrego N, Campillo-Calatayud A, Elejalde I, Kochan G, Escors D. Predictive Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Events. Cancers (Basel) 2023; 15:cancers15051629. [PMID: 36900420 PMCID: PMC10000735 DOI: 10.3390/cancers15051629] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 03/09/2023] Open
Abstract
Immune-checkpoint inhibitors (ICIs) are antagonists of inhibitory receptors in the immune system, such as the cytotoxic T-lymphocyte-associated antigen-4, the programmed cell death protein-1 and its ligand PD-L1, and they are increasingly used in cancer treatment. By blocking certain suppressive pathways, ICIs promote T-cell activation and antitumor activity but may induce so-called immune-related adverse events (irAEs), which mimic traditional autoimmune disorders. With the approval of more ICIs, irAE prediction has become a key factor in improving patient survival and quality of life. Several biomarkers have been described as potential irAE predictors, some of them are already available for clinical use and others are under development; examples include circulating blood cell counts and ratios, T-cell expansion and diversification, cytokines, autoantibodies and autoantigens, serum and other biological fluid proteins, human leucocyte antigen genotypes, genetic variations and gene profiles, microRNAs, and the gastrointestinal microbiome. Nevertheless, it is difficult to generalize the application of irAE biomarkers based on the current evidence because most studies have been retrospective, time-limited and restricted to a specific type of cancer, irAE or ICI. Long-term prospective cohorts and real-life studies are needed to assess the predictive capacity of different potential irAE biomarkers, regardless of the ICI type, organ involved or cancer site.
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Affiliation(s)
- Iñigo Les
- Internal Medicine Department, Navarre University Hospital, 31008 Pamplona, Spain
- Autoimmune Diseases Unit, Internal Medicine Department, Navarre University Hospital, 31008 Pamplona, Spain
- Inflammatory and Immune-Mediated Diseases Group, Instituto de Investigación Sanitaria de Navarra (IdISNA), Navarrabiomed-Public University of Navarre, 31008 Pamplona, Spain
- Correspondence: (I.L.); (D.E.); Tel.: +34-84-842-9516 (I.L.)
| | - Mireia Martínez
- Osakidetza Basque Health Service, Department of Medical Oncology, Araba University Hospital, 01009 Vitoria-Gasteiz, Spain
- Lung Cancer Research Group, Bioaraba Health Research Institute, 01006 Vitoria-Gasteiz, Spain
| | - Inés Pérez-Francisco
- Breast Cancer Research Group, Bioaraba Health Research Institute, 01006 Vitoria-Gasteiz, Spain
| | - María Cabero
- Clinical Trials Platform, Bioaraba Health Research Institute, 01006 Vitoria-Gasteiz, Spain
| | - Lucía Teijeira
- Medical Oncology Department, Navarre University Hospital, 31008 Pamplona, Spain
| | - Virginia Arrazubi
- Medical Oncology Department, Navarre University Hospital, 31008 Pamplona, Spain
| | - Nuria Torrego
- Osakidetza Basque Health Service, Department of Medical Oncology, Araba University Hospital, 01009 Vitoria-Gasteiz, Spain
- Lung Cancer Research Group, Bioaraba Health Research Institute, 01006 Vitoria-Gasteiz, Spain
| | - Ana Campillo-Calatayud
- Inflammatory and Immune-Mediated Diseases Group, Instituto de Investigación Sanitaria de Navarra (IdISNA), Navarrabiomed-Public University of Navarre, 31008 Pamplona, Spain
| | - Iñaki Elejalde
- Internal Medicine Department, Navarre University Hospital, 31008 Pamplona, Spain
- Autoimmune Diseases Unit, Internal Medicine Department, Navarre University Hospital, 31008 Pamplona, Spain
- Inflammatory and Immune-Mediated Diseases Group, Instituto de Investigación Sanitaria de Navarra (IdISNA), Navarrabiomed-Public University of Navarre, 31008 Pamplona, Spain
| | - Grazyna Kochan
- Oncoimmunology Group, Instituto de Investigación Sanitaria de Navarra (IdISNA), Navarrabiomed-Public University of Navarre, 31008 Pamplona, Spain
| | - David Escors
- Oncoimmunology Group, Instituto de Investigación Sanitaria de Navarra (IdISNA), Navarrabiomed-Public University of Navarre, 31008 Pamplona, Spain
- Correspondence: (I.L.); (D.E.); Tel.: +34-84-842-9516 (I.L.)
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10
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Radiation Recall Pneumonitis Anticipates Bilateral Immune-Induced Pneumonitis in Non-Small Cell Lung Cancer. J Clin Med 2023; 12:jcm12041266. [PMID: 36835802 PMCID: PMC9961042 DOI: 10.3390/jcm12041266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/27/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023] Open
Abstract
Radiation recall pneumonitis (RRP) is a rare inflammatory reaction that occurs in previously irradiated fields, and it may be caused by various triggering agents. Immunotherapy has been reported to potentially be one of these triggers. However, precise mechanisms and specific treatments have not been explored yet due to a lack of data in this setting. Here, we report a case of a patient who received radiation therapy and immune checkpoint inhibitor therapy for non-small cell lung cancer. He developed first radiation recall pneumonitis and subsequently immune-checkpoint inhibitor-induced pneumonitis (IIP). After presenting the case, we discuss the currently available literature on RRP and the challenges of differential diagnosis between RRP, IIP, and other forms of pneumonitis. We believe that this case is of particular clinical value since it highlights the importance of including RRP in a differential diagnosis of lung consolidation during immunotherapy. Furthermore, it suggests that RRP might anticipate more extensive ICI-induced pneumonitis.
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11
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Chung YW, Choi IY. Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder. Sci Rep 2023; 13:1765. [PMID: 36720904 PMCID: PMC9889739 DOI: 10.1038/s41598-023-28082-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 01/12/2023] [Indexed: 02/02/2023] Open
Abstract
We sought to establish an unsupervised algorithm with a three-dimensional (3D) variational autoencoder model (VAE) for the detection of abnormal extraocular muscles in small datasets of orbital computed tomography (CT) images. 334 CT images of normal orbits and 96 of abnormal orbits diagnosed as thyroid eye disease were used for training and validation; 24 normal and 11 abnormal orbits were used for the test. A 3D VAE was developed and trained. All images were preprocessed to emphasize extraocular muscles and to suppress background noise (e.g., high signal intensity from bones). The optimal cut-off value was identified through receiver operating characteristic (ROC) curve analysis. The ability of the model to detect muscles of abnormal size was assessed by visualization. The model achieved a sensitivity of 79.2%, specificity of 72.7%, accuracy of 77.1%, F1-score of 0.667, and AUROC of 0.801. Abnormal CT images correctly identified by the model showed differences in the reconstruction of extraocular muscles. The proposed model showed potential to detect abnormalities in extraocular muscles using a small dataset, similar to the diagnostic approach used by physicians. Unsupervised learning could serve as an alternative detection method for medical imaging studies in which annotation is difficult or impossible to perform.
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Affiliation(s)
- Yeon Woong Chung
- Department of Ophthalmology and Visual Science, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.,Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Banpo Dae-Ro 222, Seoul, 06591, Republic of Korea
| | - In Young Choi
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Banpo Dae-Ro 222, Seoul, 06591, Republic of Korea.
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12
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Cheng M, Lin R, Bai N, Zhang Y, Wang H, Guo M, Duan X, Zheng J, Qiu Z, Zhao Y. Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer. Clin Radiol 2023; 78:e377-e385. [PMID: 36914457 DOI: 10.1016/j.crad.2022.12.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/14/2022] [Accepted: 12/20/2022] [Indexed: 01/15/2023]
Abstract
AIM To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P). MATERIALS AND METHODS Forty ICI-P patients and 101 patients without ICI-P were divided randomly into the training (n=113) and test (n=28) sets. The convolution neural network (CNN) algorithm was used to extract the CT-based radiological features of predictable ICI-P and calculated the CT score of each patient. A nomogram model to predict the risk of ICI-P was developed by logistic regression. RESULTS CT score was calculated from five radiological features extracted by the residual neural network-50-V2 with feature pyramid networks. Four predictors of ICI-P in the nomogram model included a clinical feature (pre-existing lung diseases), two serum markers (absolute lymphocyte count and lactate dehydrogenase), and a CT score. The area under curve of the nomogram model in the training (0.910 versus 0.871 versus 0.778) and test (0.900 versus 0.856 versus 0.869) sets was better than the radiological and clinical models. The nomogram model showed good consistency and better clinical practicability. CONCLUSION The nomogram model that combined CT-based radiological factors and clinical factors can be used as a new non-invasive tool for the early prediction of ICI-P in lung cancer patients after immunotherapy with low cost and low manual input.
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Affiliation(s)
- M Cheng
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - R Lin
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China
| | - N Bai
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China
| | - Y Zhang
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - H Wang
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - M Guo
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - X Duan
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - J Zheng
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Z Qiu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China
| | - Y Zhao
- Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China.
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13
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Zheng LP, Yang J, Chen XW, Li LC, Sun JG. Correlation of preclinical and clinical biomarkers with efficacy and toxicity of cancer immunotherapy. Ther Adv Med Oncol 2023; 15:17588359231163807. [PMID: 37113734 PMCID: PMC10126660 DOI: 10.1177/17588359231163807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 02/27/2023] [Indexed: 04/29/2023] Open
Abstract
Immune checkpoint inhibitors (ICIs) have revealed significant clinical values in different solid tumors and hematological malignancy, changing the landscape for the treatment of multiple types of cancer. However, only a subpopulation of patients has obvious tumor response and long-term survival after ICIs treatment, and many patients may experience other undesirable clinical features. Therefore, biomarkers are critical for patients to choose exact optimum therapy. Here, we reviewed existing preclinical and clinical biomarkers of immunotherapeutic efficacy and immune-related adverse events (irAEs). Based on efficacy prediction, pseudoprogression, hyperprogressive disease, or irAEs, these biomarkers were divided into cancer cell-derived biomarkers, tumor microenvironment-derived biomarkers, host-derived biomarkers, peripheral blood biomarkers, and multi-modal model and artificial intelligence assessment-based biomarkers. Furthermore, we describe the relation between ICIs efficacy and irAEs. This review provides the overall perspective of biomarkers of immunotherapeutic outcome and irAEs prediction during ICIs treatment.
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Affiliation(s)
| | | | - Xie-Wan Chen
- Department of Basic Medicine, Army Medical University, Chongqing, China
| | - Ling-Chen Li
- Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, China
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14
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Berz AM, Boughdad S, Vietti-Violi N, Digklia A, Dromain C, Dunet V, Duran R. Imaging assessment of toxicity related to immune checkpoint inhibitors. Front Immunol 2023; 14:1133207. [PMID: 36911692 PMCID: PMC9995973 DOI: 10.3389/fimmu.2023.1133207] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/10/2023] [Indexed: 02/25/2023] Open
Abstract
In recent years, a wide range of cancer immunotherapies have been developed and have become increasingly important in cancer treatment across multiple oncologic diseases. In particular, immune checkpoint inhibitors (ICIs) offer promising options to improve patient outcomes. However, a major limitation of these treatments consists in the development of immune-related adverse events (irAEs) occurring in potentially any organ system and affecting up to 76% of the patients. The most frequent toxicities involve the skin, gastrointestinal tract, and endocrine system. Although mostly manageable, potentially life-threatening events, particularly due to neuro-, cardiac, and pulmonary toxicity, occur in up to 30% and 55% of the patients treated with ICI-monotherapy or -combination therapy, respectively. Imaging, in particular computed tomography (CT), magnetic resonance imaging (MRI), and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG-PET/CT), plays an important role in the detection and characterization of these irAEs. In some patients, irAEs can even be detected on imaging before the onset of clinical symptoms. In this context, it is particularly important to distinguish irAEs from true disease progression and specific immunotherapy related response patterns, such as pseudoprogression. In addition, there are irAEs which might be easily confused with other pathologies such as infection or metastasis. However, many imaging findings, such as in immune-related pneumonitis, are nonspecific. Thus, accurate diagnosis may be delayed underling the importance for adequate imaging features characterization in the appropriate clinical setting in order to provide timely and efficient patient management. 18F-FDG-PET/CT and radiomics have demonstrated to reliably detect these toxicities and potentially have predictive value for identifying patients at risk of developing irAEs. The purpose of this article is to provide a review of the main immunotherapy-related toxicities and discuss their characteristics on imaging.
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Affiliation(s)
- Antonia M Berz
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Berlin, Germany
| | - Sarah Boughdad
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Naïk Vietti-Violi
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antonia Digklia
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Clarisse Dromain
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Vincent Dunet
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Rafael Duran
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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15
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Zhou H, Luo Q, Wu W, Li N, Yang C, Zou L. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front Immunol 2023; 14:1088874. [PMID: 36936913 PMCID: PMC10014595 DOI: 10.3389/fimmu.2023.1088874] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.
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Affiliation(s)
- Huijie Zhou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Qian Luo
- Department of Hematology, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China
| | - Wanchun Wu
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Na Li
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Chunli Yang
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Liqun Zou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
- *Correspondence: Liqun Zou,
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16
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Guo X, Chen S, Wang X, Liu X. Immune-related pulmonary toxicities of checkpoint inhibitors in non-small cell lung cancer: Diagnosis, mechanism, and treatment strategies. Front Immunol 2023; 14:1138483. [PMID: 37081866 PMCID: PMC10110908 DOI: 10.3389/fimmu.2023.1138483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 03/23/2023] [Indexed: 04/22/2023] Open
Abstract
Immune checkpoint inhibitors (ICI) therapy based on programmed cell death-1 (PD-1) and programmed cell death ligand 1 (PD-L1) has changed the treatment paradigm of advanced non-small cell lung cancer (NSCLC) and improved the survival expectancy of patients. However, it also leads to immune-related adverse events (iRAEs), which result in multiple organ damage. Among them, the most common one with the highest mortality in NSCLC patients treated with ICI is checkpoint inhibitor pneumonitis (CIP). The respiratory signs of CIP are highly coincident and overlap with those in primary lung cancer, which causes difficulties in detecting, diagnosing, managing, and treating. In clinical management, patients with serious CIP should receive immunosuppressive treatment and even discontinue immunotherapy, which impairs the clinical benefits of ICIs and potentially results in tumor recrudesce. Therefore, accurate diagnosis, detailedly dissecting the pathogenesis, and developing reasonable treatment strategies for CIP are essential to prolong patient survival and expand the application of ICI. Herein, we first summarized the diagnosis strategies of CIP in NSCLC, including the classical radiology examination and the rising serological test, pathology test, and artificial intelligence aids. Then, we dissected the potential pathogenic mechanisms of CIP, including disordered T cell subsets, the increase of autoantibodies, cross-antigens reactivity, and the potential role of other immune cells. Moreover, we explored therapeutic approaches beyond first-line steroid therapy and future direction based on targeted signaling pathways. Finally, we discussed the current impediments, future trends, and challenges in fighting ICI-related pneumonitis.
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17
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Huang J, Chen X, Xia B, Ma S. Advances in CT features and radiomics of checkpoint inhibitor-related pneumonitis: A short review. Front Immunol 2023; 14:1082980. [PMID: 36756121 PMCID: PMC9899831 DOI: 10.3389/fimmu.2023.1082980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/09/2023] [Indexed: 01/24/2023] Open
Abstract
Checkpoint inhibitor-related pneumonitis (CIP) is a complication of immunotherapy for malignant tumors that severely limits the treatment cycles as well as endangers patients' health. The chest CT imaging features or typing of CIP and the application of radiomics will contribute to the precise prevention, early diagnosis and instant treatment of CIP. This article reviews the advances in the CT features and the application of radiomics in CIP.
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Affiliation(s)
- Jie Huang
- Department of Thoracic Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Chen
- Department of Thoracic Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bing Xia
- Department of Thoracic Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shenglin Ma
- Department of Thoracic Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China
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18
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Deep Learning Chest CT for Clinically Precise Prediction of Sepsis-Induced Acute Respiratory Distress Syndrome: A Protocol for an Observational Ambispective Cohort Study. Healthcare (Basel) 2022; 10:healthcare10112150. [DOI: 10.3390/healthcare10112150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/16/2022] [Accepted: 10/24/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Sepsis commonly causes acute respiratory distress syndrome (ARDS), and ARDS contributes to poor prognosis in sepsis patients. Early prediction of ARDS for sepsis patients remains a clinical challenge. This study aims to develop and validate chest computed tomography (CT) radiomic-based signatures for early prediction of ARDS and assessment of individual severity in sepsis patients. Methods: In this ambispective observational cohort study, a deep learning model, a sepsis-induced acute respiratory distress syndrome (SI-ARDS) prediction neural network, will be developed to extract radiomics features of chest CT from sepsis patients. The datasets will be collected from these retrospective and prospective cohorts, including 400 patients diagnosed with sepsis-3 definition during a period from 1 May 2015 to 30 May 2022. 160 patients of the retrospective cohort will be selected as a discovering group to reconstruct the model and 40 patients of the retrospective cohort will be selected as a testing group for internal validation. Additionally, 200 patients of the prospective cohort from two hospitals will be selected as a validating group for external validation. Data pertaining to chest CT, clinical information, immune-associated inflammatory indicators and follow-up will be collected. The primary outcome is to develop and validate the model, predicting in-hospital incidence of SI-ARDS. Finally, model performance will be evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC), sensitivity and specificity, using internal and external validations. Discussion: Present studies reveal that early identification and classification of the SI-ARDS is essential to improve prognosis and disease management. Chest CT has been sought as a useful diagnostic tool to identify ARDS. However, when characteristic imaging findings were clearly presented, delays in diagnosis and treatment were impossible to avoid. In this ambispective cohort study, we hope to develop a novel model incorporating radiomic signatures and clinical signatures to provide an easy-to-use and individualized prediction of SI-ARDS occurrence and severe degree in patients at early stage.
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19
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Ter Maat LS, van Duin IAJ, Elias SG, van Diest PJ, Pluim JPW, Verhoeff JJC, de Jong PA, Leiner T, Veta M, Suijkerbuijk KPM. Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review. Eur J Cancer 2022; 175:60-76. [PMID: 36096039 DOI: 10.1016/j.ejca.2022.07.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/17/2022] [Accepted: 07/21/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types. METHODS PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted. RESULTS In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist. CONCLUSIONS There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.
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Affiliation(s)
- Laurens S Ter Maat
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Isabella A J van Duin
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Sjoerd G Elias
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Josien P W Pluim
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Tim Leiner
- Utrecht University, Utrecht, the Netherlands; Department of Radiology, Mayo Clinical, Rochester, MN, USA
| | - Mitko Veta
- Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Karijn P M Suijkerbuijk
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands.
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20
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Tan P, Huang W, Wang L, Deng G, Yuan Y, Qiu S, Ni D, Du S, Cheng J. Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images. Front Physiol 2022; 13:978222. [PMID: 35957985 PMCID: PMC9358138 DOI: 10.3389/fphys.2022.978222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Peixin Tan
- Department of Radiation Oncology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wei Huang
- Department of Radiation Oncology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Lingling Wang
- National-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
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Guanhua Deng
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, China
| | - Ye Yuan
- National-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
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Shili Qiu
- National-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
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Dong Ni
- National-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
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Shasha Du
- Department of Radiation Oncology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Shasha Du, ; Jun Cheng,
| | - Jun Cheng
- National-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
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
- *Correspondence: Shasha Du, ; Jun Cheng,
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21
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Chen X, Cheng G, Yang X, Liao Y, Zhou Z. Exploring the Value of Features of Lung Texture in Distinguishing Between Usual and Nonspecific Interstitial Pneumonia. Acad Radiol 2022; 30:1066-1072. [PMID: 35843833 DOI: 10.1016/j.acra.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/09/2022] [Accepted: 06/15/2022] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES This article aims to explore the potential use of lung texture assessed in CT images in distinguishing between the usual interstitial pneumonia and the nonspecific interstitial pneumonia. MATERIALS AND METHODS A retrospective analysis of 96 cases of interstitial pneumonia was performed. Among these cases, there were 40 cases of usual interstitial pneumonia (UIP) and 56 cases of the nonspecific interstitial pneumonia (NSIP) . All of the patients underwent computed tomography (CT) scans. A lung intelligence kit (LK) was utilized to perform lung segmentation and texture feature extraction. The significant variables were determined by variance analysis, least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Finally, a multivariate logistic regression model was established to distinguish between the two types of interstitial pneumonia. Receiver operating characteristic (ROC) curves, area under the curve (AUC) values, sensitivity, and specificity were used to evaluate the performance of the established model. RESULTS A total of 100 texture features were extracted from the whole lung that was segmented by LK, and 8 features remained after feature reduction. The AUC, sensitivity, and specificity of the multivariate logistic regression model in the training group and the test group were 0.952 and 0.838, 0.821 and 0.667, and 0.949 and 0.824, respectively. CONCLUSION It is possible to distinguish between UIP and NSIP using lung texture features obtained from CT images.
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Affiliation(s)
- Xinhui Chen
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin 541001, China; Department of Radiology, Zhanjiang Central People's Hospital, Zhanjiang 524037, China
| | - Ge Cheng
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin 541001, China
| | - Xinguan Yang
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin 541001, China
| | | | - Zhipeng Zhou
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin 541001, China.
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22
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Maniar A, Wei AZ, Dercle L, Bien HH, Fojo T, Bates SE, Schwartz LH. Novel biomarkers in NSCLC: Radiomic analysis, kinetic analysis, and circulating tumor DNA. Semin Oncol 2022; 49:S0093-7754(22)00042-2. [PMID: 35914982 DOI: 10.1053/j.seminoncol.2022.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/06/2022] [Indexed: 11/11/2022]
Abstract
Current radiographic methods of measuring treatment response for patients with nonsmall cell lung cancer have significant limitations. Recently, new modalities using standard of care images or minimally invasive blood-based DNA tests have gained interest as methods of evaluating treatment response. This article highlights three emerging modalities: radiomic analysis, kinetic analysis and serum-based measurement of circulating tumor DNA, with a focus on the clinical evidence supporting these methods. Additionally, we discuss the possibility of combining these modalities to develop a robust biomarker with strong correlation to clinically meaningful outcomes that could impact clinical trial design and patient care. At Last, we focus on how these methods specifically apply to a Veteran population.
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Affiliation(s)
- Ashray Maniar
- Columbia University Irving Medical Center, Division of Hematology and Oncology, New York, NY
| | - Alexander Z Wei
- Columbia University Irving Medical Center, Division of Hematology and Oncology, New York, NY
| | - Laurent Dercle
- Columbia University Irving Medical Center, Division of Radiology, New York, NY
| | - Harold H Bien
- Northport VA Medical Center, Division of Hematology and Oncology, Northport, NY
| | - Tito Fojo
- Columbia University Irving Medical Center, Division of Hematology and Oncology, New York, NY; James J. Peters Bronx VA Medical Center, Division of Hematology and Oncology, Bronx, NY
| | - Susan E Bates
- Columbia University Irving Medical Center, Division of Hematology and Oncology, New York, NY; Northport VA Medical Center, Division of Hematology and Oncology, Northport, NY.
| | - Lawrence H Schwartz
- Columbia University Irving Medical Center, Division of Radiology, New York, NY
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23
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Cortiula F, Reymen B, Peters S, Van Mol P, Wauters E, Vansteenkiste J, De Ruysscher D, Hendriks LEL. Immunotherapy in unresectable stage III non-small-cell lung cancer: state of the art and novel therapeutic approaches. Ann Oncol 2022; 33:893-908. [PMID: 35777706 DOI: 10.1016/j.annonc.2022.06.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022] Open
Abstract
The standard of care for patients with stage III non-small-cell lung cancer (NSCLC) is concurrent chemoradiotherapy (CCRT) followed by 1 year of adjuvant durvalumab. Despite the survival benefit granted by immunotherapy in this setting, only 1/3 of patients are alive and disease free at 5 years. Novel treatment strategies are under development to improve patient outcomes in this setting: different anti-programmed cell death protein 1/programmed death-ligand 1 [anti-PD-(L)1] antibodies after CCRT, consolidation immunotherapy after sequential chemoradiotherapy, induction immunotherapy before CCRT and immunotherapy concurrent with CCRT and/or sequential chemoradiotherapy. Cross-trial comparison is particularly challenging in this setting due to the different timing of immunotherapy delivery and different patients' inclusion and exclusion criteria. In this review, we present the results of clinical trials investigating immune therapy in unresectable stage III NSCLC and discuss in-depth their biological rationale, their pitfalls and potential benefits. Particular emphasis is placed on the potential mechanisms of synergism between chemotherapy, radiation therapy and different monoclonal antibodies, and how this affects the tumor immune microenvironment. The designs and questions tackled by ongoing clinical trials are also discussed. Last, we address open questions and unmet clinical needs, such as the necessity for predictive biomarkers (e.g. radiomics and circulating tumor DNA). Identifying distinct subsets of patients to tailor anticancer treatment is a priority, especially in a heterogeneous disease such as stage III NSCLC.
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Affiliation(s)
- F Cortiula
- Department of Radiation Oncology (Maastro), Maastricht University Medical Centre(+), GROW School for Oncology and Reproduction, Maastricht, the Netherlands; Department of Medical Oncology, Udine University Hospital, Udine, Italy
| | - B Reymen
- Department of Radiation Oncology (Maastro), Maastricht University Medical Centre(+), GROW School for Oncology and Reproduction, Maastricht, the Netherlands
| | - S Peters
- Oncology Department, Lausanne University Hospital, Lausanne, Switzerland
| | - P Van Mol
- Department of Respiratory Diseases KU Leuven, Respiratory Oncology Unit, University Hospitals KU Leuven, Leuven, Belgium
| | - E Wauters
- Department of Respiratory Diseases KU Leuven, Respiratory Oncology Unit, University Hospitals KU Leuven, Leuven, Belgium
| | - J Vansteenkiste
- Department of Respiratory Diseases KU Leuven, Respiratory Oncology Unit, University Hospitals KU Leuven, Leuven, Belgium.
| | - D De Ruysscher
- Department of Radiation Oncology (Maastro), Maastricht University Medical Centre(+), GROW School for Oncology and Reproduction, Maastricht, the Netherlands
| | - L E L Hendriks
- Department of Pulmonary Diseases, Maastricht University Medical Centre(+), GROW School for Oncology and Reproduction, Maastricht, the Netherlands
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24
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Kothari G. Role of radiomics in predicting immunotherapy response. J Med Imaging Radiat Oncol 2022; 66:575-591. [PMID: 35581928 PMCID: PMC9323544 DOI: 10.1111/1754-9485.13426] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022]
Abstract
Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue-based biomarkers. Quantitative image analysis or radiomics, which involves the high-throughput extraction of imaging features, has the potential to non-invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune-related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high-quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well-defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune-related adverse effects and less well-studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation OncologyPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of Oncology, University of MelbournePeter MacCallum Cancer CentreMelbourneVictoriaAustralia
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25
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Lung Inflammation Predictors in Combined Immune Checkpoint-Inhibitor and Radiation Therapy—Proof-of-Concept Animal Study. Biomedicines 2022; 10:biomedicines10051173. [PMID: 35625911 PMCID: PMC9138533 DOI: 10.3390/biomedicines10051173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 04/28/2022] [Accepted: 05/06/2022] [Indexed: 12/10/2022] Open
Abstract
Purpose: Combined radiotherapy (RT) and immune checkpoint-inhibitor (ICI) therapy can act synergistically to enhance tumor response beyond what either treatment can achieve alone. Alongside the revolutionary impact of ICIs on cancer therapy, life-threatening potential side effects, such as checkpoint-inhibitor-induced (CIP) pneumonitis, remain underreported and unpredictable. In this preclinical study, we hypothesized that routinely collected data such as imaging, blood counts, and blood cytokine levels can be utilized to build a model that predicts lung inflammation associated with combined RT/ICI therapy. Materials and Methods: This proof-of-concept investigational work was performed on Lewis lung carcinoma in a syngeneic murine model. Nineteen mice were used, four as untreated controls and the rest subjected to RT/ICI therapy. Tumors were implanted subcutaneously in both flanks and upon reaching volumes of ~200 mm3 the animals were imaged with both CT and MRI and blood was collected. Quantitative radiomics features were extracted from imaging of both lungs. The animals then received RT to the right flank tumor only with a regimen of three 8 Gy fractions (one fraction per day over 3 days) with PD-1 inhibitor administration delivered intraperitoneally after each daily RT fraction. Tumor volume evolution was followed until tumors reached the maximum size allowed by the Institutional Animal Care and Use Committee (IACUC). The animals were sacrificed, and lung tissues harvested for immunohistochemistry evaluation. Tissue biomarkers of lung inflammation (CD45) were tallied, and binary logistic regression analyses were performed to create models predictive of lung inflammation, incorporating pretreatment CT/MRI radiomics, blood counts, and blood cytokines. Results: The treated animal cohort was dichotomized by the median value of CD45 infiltration in the lungs. Four pretreatment radiomics features (3 CT features and 1 MRI feature) together with pre-treatment neutrophil-to-lymphocyte (NLR) ratio and pre-treatment granulocyte-macrophage colony-stimulating factor (GM-CSF) level correlated with dichotomized CD45 infiltration. Predictive models were created by combining radiomics with NLR and GM-CSF. Receiver operating characteristic (ROC) analyses of two-fold internal cross-validation indicated that the predictive model incorporating MR radiomics had an average area under the curve (AUC) of 0.834, while the model incorporating CT radiomics had an AUC of 0.787. Conclusions: Model building using quantitative imaging data, blood counts, and blood cytokines resulted in lung inflammation prediction models justifying the study hypothesis. The models yielded very-good-to-excellent AUCs of more than 0.78 on internal cross-validation analyses.
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26
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Qiu Q, Xing L, Wang Y, Feng A, Wen Q. 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. Front Immunol 2022; 13:870842. [PMID: 35558076 PMCID: PMC9088878 DOI: 10.3389/fimmu.2022.870842] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/24/2022] [Indexed: 01/27/2023] Open
Abstract
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.
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Affiliation(s)
- Qingtao Qiu
- Department of Radiation Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yu Wang
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, China
| | - Alei Feng
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, China
| | - Qiang Wen
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, China
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27
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Avery E, Sanelli PC, Aboian M, Payabvash S. Radiomics: A Primer on Processing Workflow and Analysis. Semin Ultrasound CT MR 2022; 43:142-146. [PMID: 35339254 PMCID: PMC8961004 DOI: 10.1053/j.sult.2022.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Quantitative analysis of medical images can provide objective tools for diagnosis, prognostication, and disease monitoring. Radiomics refers to automated extraction of a large number of quantitative features from medical images for characterization of underlying pathologies. In this review, we will discuss the principles of radiomics, image preprocessing, feature extraction workflow, and statistical analysis. We will also address the limitations and future directions of radiomics.
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Affiliation(s)
- Emily Avery
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Pina C Sanelli
- Northwell Health, and Feinstein Institute for Medical Research, Manhasset, NY
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
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28
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Viswanathan VS, Gupta A, Madabhushi A. Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes. Am Soc Clin Oncol Educ Book 2022; 42:1-13. [PMID: 35671432 DOI: 10.1200/edbk_350931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.
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Affiliation(s)
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH
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29
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Zhang Y, Zhang X, Li W, Du Y, Hu W, Zhao J. Biomarkers and risk factors for the early prediction of immune-related adverse events: a review. Hum Vaccin Immunother 2022; 18:2018894. [PMID: 35108160 PMCID: PMC8986173 DOI: 10.1080/21645515.2021.2018894] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
In recent years, immunotherapy has been widely used to treat patients with malignant tumors. While immune checkpoint inhibitors (ICIs) significantly improve the prognosis of cancer patients, the incidence of immune-related adverse events (irAEs) is increasing. Not only can irAEs accumulate in multiple organ systems throughout the body, but rare adverse reactions may also occur continuously. In severe cases, irAEs can be life-threatening or even lead to death. Therefore, the early identification, diagnosis and treatment of irAEs are very important. Early identification of patients with high-risk irAEs as well as the reduction or avoidance of severe irAEs have important clinical significance. This article will review the research progress of early predictive biomarkers and risk factors for the occurrence of irAEs and propose potential future directions for follow-up research and clinical applications.
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Affiliation(s)
- Ying Zhang
- Department of Oncology, Changzhi People's Hospital, The Affiliated Hospital of Changzhi Medical College, Changzhi, Shanxi, China
| | - Xiaoling Zhang
- Department of Oncology, Changzhi People's Hospital, The Affiliated Hospital of Changzhi Medical College, Changzhi, Shanxi, China
| | - Weiling Li
- Department of Oncology, Changzhi People's Hospital, The Affiliated Hospital of Changzhi Medical College, Changzhi, Shanxi, China
| | - Yunyi Du
- Department of Oncology, Changzhi People's Hospital, The Affiliated Hospital of Changzhi Medical College, Changzhi, Shanxi, China
| | - Wenqing Hu
- Department of Gastrointestinal Surgery, Changzhi People's Hospital, The Affiliated Hospital of Changzhi Medical College, Changzhi, Shanxi, China
| | - Jun Zhao
- Department of Oncology, Changzhi People's Hospital, The Affiliated Hospital of Changzhi Medical College, Changzhi, Shanxi, China
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Harnessing big data to characterize immune-related adverse events. Nat Rev Clin Oncol 2022; 19:269-280. [PMID: 35039679 DOI: 10.1038/s41571-021-00597-8] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2021] [Indexed: 12/17/2022]
Abstract
Immune-checkpoint inhibitors (ICIs) have transformed patient care in oncology but are associated with a unique spectrum of organ-specific inflammatory toxicities known as immune-related adverse events (irAEs). Given the expanding use of ICIs, an increasing number of patients with cancer experience irAEs, including severe irAEs. Proper diagnosis and management of irAEs are important to optimize the quality of life and long-term outcomes of patients receiving ICIs; however, owing to the substantial heterogeneity within irAEs, and despite multicentre initiatives, performing clinical studies of these toxicities with a sufficient cohort size is challenging. Pioneering studies from the past few years have demonstrated that aggregate clinical data, real-world data (such as data on pharmacovigilance or from electronic health records) and multi-omics data are alternative tools well suited to investigating the underlying mechanisms and clinical presentations of irAEs. In this Perspective, we summarize the advantages and shortcomings of different sources of 'big data' for the study of irAEs and highlight progress made using such data to identify biomarkers of irAE risk, evaluate associations between irAEs and therapeutic efficacy, and characterize the effects of demographic and anthropometric factors on irAE risk. Harnessing big data will accelerate research on irAEs and provide key insights that will improve the clinical management of patients receiving ICIs.
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Pulmonary Toxicities of Immunotherapy. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1342:357-375. [PMID: 34972974 DOI: 10.1007/978-3-030-79308-1_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Immune checkpoint inhibitors are a form of immunotherapy that are increasingly being used in a wide variety of cancers. Immune-related adverse events (irAEs) pose a major challenge in the treatment of cancer patients. Pneumonitis, the most common lung irAE, can cause significant disruptions in the treatment of cancer and may be life-threatening. The goal of this chapter is to instruct readers on the incidence and clinical manifestations of pneumonitis and to offer guidance in the evaluation and treatment of patients with pneumonitis.
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Ak M, Eleneen Y, Ayoub M, Colen RR. Cancer Imaging in Immunotherapy. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1342:431-447. [PMID: 34972979 DOI: 10.1007/978-3-030-79308-1_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Immune therapeutics are revolutionizing cancer treatments. In tandem, new and confounding imaging characteristics have appeared that are distinct from those typically seen with conventional cytotoxic therapies. In fact, only 10% of patients on immunotherapy may show tumor shrinkage, typical of positive responses on conventional therapy. Conversely, those on immune therapies may initially demonstrate a delayed response, transient enlargement followed by tumor shrinkage, stable size, or the appearance of new lesions. Response Evaluation Criteria in Solid Tumors (RECIST) or WHO criteria, developed to identify early effects of cytotoxic agents, may not provide a complete evaluation of new emerging treatment response pattern of immunotherapeutic agents. Therefore, new imaging response criteria, such as the immune-related Response Evaluation Criteria in Solid Tumors (irRECIST), immune Response Evaluation Criteria in Solid Tumors (iRECIST), and immune-related Response Criteria (irRC), are proposed. However, FDA approval of emerging therapies including immunotherapies still relies on the current RECIST criteria. In this chapter, we review the traditional and new imaging response criteria for evaluation of solid tumors and briefly touch on some of the more commonly associated immunotherapy-induced adverse events.
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Affiliation(s)
- Murat Ak
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Yousra Eleneen
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Mira Ayoub
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Rivka R Colen
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA. .,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
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Arimura H, Kodama T, Urakami A, Kamezawa H, Hirose TA, Ninomiya K. [6. Imaging Biopsy for Assisting Cancer Precision Therapy -Information Extracted from Radiomics]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:219-224. [PMID: 35185102 DOI: 10.6009/jjrt.780213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Hidetaka Arimura
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University
| | - Takumi Kodama
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
| | - Akimasa Urakami
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
| | - Hidemi Kamezawa
- Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University
| | - Taka-Aki Hirose
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital
| | - Kenta Ninomiya
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
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Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD, Kim Y, Kim L, Cho S, Oh Y, Gim G, Park I, Lee D, Abazeed M, Velichko YS, Chae YK. OUP accepted manuscript. Oncologist 2022; 27:e471-e483. [PMID: 35348765 PMCID: PMC9177100 DOI: 10.1093/oncolo/oyac036] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
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Affiliation(s)
| | | | - Hye Sung Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eugene Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Alice Daeun Lee
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yeseul Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital, Evanston, IL, USA
| | - Sukjoo Cho
- Department of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Yoojin Oh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gahyun Gim
- Department of Hematology and Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Inae Park
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dongyup Lee
- Department of Physical Medicine and Rehabilitation, Geisinger Health System, Danville, PA, USA
| | - Mohamed Abazeed
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Yury S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Young Kwang Chae
- Corresponding author: Young Kwang Chae, Department of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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35
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Morad G, Helmink BA, Sharma P, Wargo JA. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell 2021; 184:5309-5337. [PMID: 34624224 DOI: 10.1016/j.cell.2021.09.020] [Citation(s) in RCA: 589] [Impact Index Per Article: 196.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 07/21/2021] [Accepted: 09/13/2021] [Indexed: 12/16/2022]
Abstract
Unprecedented advances have been made in cancer treatment with the use of immune checkpoint blockade (ICB). However, responses are limited to a subset of patients, and immune-related adverse events (irAEs) can be problematic, requiring treatment discontinuation. Iterative insights into factors intrinsic and extrinsic to the host that impact ICB response and toxicity are critically needed. Our understanding of the impact of host-intrinsic factors (such as the host genome, epigenome, and immunity) has evolved substantially over the past decade, with greater insights on these factors and on tumor and immune co-evolution. Additionally, we are beginning to understand the impact of acute and cumulative exposures-both internal and external to the host (i.e., the exposome)-on host physiology and response to treatment. Together these represent the current day hallmarks of response, resistance, and toxicity to ICB. Opportunities built on these hallmarks are duly warranted.
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Affiliation(s)
- Golnaz Morad
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Beth A Helmink
- Department of Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Padmanee Sharma
- Department of Genitourinary Medical Oncology and Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jennifer A Wargo
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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36
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Manohar S, Jhaveri KD, Perazella MA. Immunotherapy-Related Acute Kidney Injury. Adv Chronic Kidney Dis 2021; 28:429-437.e1. [PMID: 35190109 DOI: 10.1053/j.ackd.2021.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/28/2021] [Accepted: 07/08/2021] [Indexed: 11/11/2022]
Abstract
Nephrotoxicity associated with immunotherapy is increasingly being encountered in clinical practice. Drugs that augment the immune system to eradicate cancer are revolutionary in the field of oncology. Older generation immunotherapies such as high-dose interleukin and interferon-alpha are now being replaced with more effective immune checkpoint inhibitors and chimeric antigen receptor T-cell therapies, which have shown promising results in numerous clinical trials. Unfortunately, these treatments come with a unique baggage of adverse effects including nephrotoxicity. This onconephrology review summarizes the immunotherapies currently in use and their kidney-related toxicities, pathophysiology, and their management.
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Russano M, Cortellini A, Giusti R, Russo A, Zoratto F, Rastelli F, Gelibter A, Chiari R, Nigro O, De Tursi M, Bracarda S, Gori S, Grossi F, Bersanelli M, Calvetti L, Di Noia V, Scartozzi M, Di Maio M, Bossi P, Falcone A, Citarella F, Pantano F, Ficorella C, Filetti M, Adamo V, Veltri E, Pergolesi F, Occhipinti MA, Nicolardi L, Tuzi A, Di Marino P, Macrini S, Inno A, Ghidini M, Buti S, Aprile G, Lai E, Audisio M, Intagliata S, Marconcini R, Brocco D, Porzio G, Piras M, Rijavec E, Simionato F, Natoli C, Tiseo M, Vincenzi B, Tonini G, Santini D. Clinical outcomes of NSCLC patients experiencing early immune-related adverse events to PD-1/PD-L1 checkpoint inhibitors leading to treatment discontinuation. Cancer Immunol Immunother 2021; 71:865-874. [PMID: 34462870 DOI: 10.1007/s00262-021-03045-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 08/23/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND The prognostic relevance of early immune-related adverse events (irAEs) in patients affected by non-small cell lung cancer (NSCLC) upon immunotherapy is not fully understood. METHODS The Leading to Treatment Discontinuation cohort included 24 patients experiencing severe irAEs after one of two administrations of single anti-PD-1/PD-L1 in any line setting for metastatic NSCLC between November 2015 and June 2019. The control cohort was composed of 526 patients treated with single anti-PD-1/PD-L1 in any line setting with no severe irAE reported. The primary end points were median progression-free survival, overall survival, objective response rate, risk of progression of disease and risk of death. The correlation of clinic pathological features with early severe irAEs represented the secondary end point. RESULTS Median PFS was 9.3 and 8.4 months, median OS was 12.0 months and 14.2 months at a median follow-up of 18.1 and 22.6 months in the LTD cohort and in the control cohort, respectively. The ORR was 40% (95% CI 17.2-78.8) in the LTD cohort and 32.7% (95% CI 27.8-38.2) in the control cohort. The risk of disease progression was higher in the LTD cohort (HR 2.52 [95% 1.10-5.78], P = .0288). CONCLUSIONS We found no survival benefit in LTD cohort compared to the control cohort. However, early and severe irAEs might underly an immune anti-tumor activation. We identified a significant association with first-line immune checkpoints inhibitors treatment and good PS. Further studies on risk prediction and management of serious and early irAEs in NSCLC patients are needed.
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Affiliation(s)
- Marco Russano
- Department of Medical Oncology, Campus Bio-Medico University, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Alessio Cortellini
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | | | - Alessandro Russo
- Medical Oncology, A.O. Papardo and Department of Human Pathology, University of Messina, Messina, Italy
| | | | | | - Alain Gelibter
- Medical Oncology (B), Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy
| | - Rita Chiari
- Medical Oncology, Ospedali Riuniti Padova Sud "Madre Teresa Di Calcutta", Monselice, Italy
| | - Olga Nigro
- Medical Oncology, ASST-Sette Laghi, Varese, Italy
| | | | - Sergio Bracarda
- Medical and Translational Oncology Unit, Department of Oncology, Azienda Ospedaliera Santa Maria, 05100, Terni, Italy
| | - Stefania Gori
- Oncology Department, IRCCS Sacro Cuore Don Calabria Hospital, 37024, Negrar, Verona, Italy
| | - Francesco Grossi
- Medical Oncology Unit, IRCCS Foundation Ca' Granda Maggiore Hospital Policlinic, Milan, Italy
| | - Melissa Bersanelli
- Department of Medicine and Surgery, University of Parma, Parma, Italy.,Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Lorenzo Calvetti
- Department of Oncology, San Bortolo General Hospital, Vicenza, Italy
| | | | - Mario Scartozzi
- Department of Medical Oncology, University Hospital of Cagliari, Cagliari, Italy
| | - Massimo Di Maio
- Department of Oncology, Medical Oncology Unit, Ordine Mauriziano Hospital, University of Turin, Turin, Italy
| | - Paolo Bossi
- Medical Oncology, ASST-Spedali Civili, University of Brescia, Brescia, Italy
| | - Alfredo Falcone
- Medical Oncology, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Fabrizio Citarella
- Department of Medical Oncology, Campus Bio-Medico University, Via Alvaro del Portillo 200, 00128, Rome, Italy.
| | - Francesco Pantano
- Department of Medical Oncology, Campus Bio-Medico University, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Corrado Ficorella
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | | | - Vincenzo Adamo
- Medical Oncology, A.O. Papardo and Department of Human Pathology, University of Messina, Messina, Italy
| | - Enzo Veltri
- Medical Oncology, Santa Maria Goretti Hospital, Latina, Italy
| | | | | | - Linda Nicolardi
- Medical Oncology, Ospedali Riuniti Padova Sud "Madre Teresa Di Calcutta", Monselice, Italy
| | | | | | - Serena Macrini
- Medical and Translational Oncology Unit, Department of Oncology, Azienda Ospedaliera Santa Maria, 05100, Terni, Italy
| | - Alessandro Inno
- Oncology Department, IRCCS Sacro Cuore Don Calabria Hospital, 37024, Negrar, Verona, Italy
| | - Michele Ghidini
- Medical Oncology Unit, IRCCS Foundation Ca' Granda Maggiore Hospital Policlinic, Milan, Italy
| | - Sebastiano Buti
- Department of Medicine and Surgery, University of Parma, Parma, Italy.,Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Giuseppe Aprile
- Department of Oncology, San Bortolo General Hospital, Vicenza, Italy
| | - Eleonora Lai
- Department of Medical Oncology, University Hospital of Cagliari, Cagliari, Italy
| | - Marco Audisio
- Department of Oncology, Medical Oncology Unit, Ordine Mauriziano Hospital, University of Turin, Turin, Italy
| | | | | | - Davide Brocco
- Department of Pharmacy, G. d'Annunzio" University of Chieti-Pescara, Via Dei Vestini 31, 66100, Chieti, Italy
| | - Giampiero Porzio
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Marta Piras
- Medical Oncology, St. Andrea Hospital, Rome, Italy
| | - Erika Rijavec
- Medical Oncology Unit, IRCCS Foundation Ca' Granda Maggiore Hospital Policlinic, Milan, Italy
| | | | - Clara Natoli
- Clinical Oncology Unit, S.S. Annunziata Hospital, Chieti, Italy
| | - Marcello Tiseo
- Department of Medicine and Surgery, University of Parma, Parma, Italy.,Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Bruno Vincenzi
- Department of Medical Oncology, Campus Bio-Medico University, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Giuseppe Tonini
- Department of Medical Oncology, Campus Bio-Medico University, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Daniele Santini
- Department of Medical Oncology, Campus Bio-Medico University, Via Alvaro del Portillo 200, 00128, Rome, Italy
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Chen X, Sheikh K, Nakajima E, Lin CT, Lee J, Hu C, Hales RK, Forde PM, Naidoo J, Voong KR. Radiation Versus Immune Checkpoint Inhibitor Associated Pneumonitis: Distinct Radiologic Morphologies. Oncologist 2021; 26:e1822-e1832. [PMID: 34251728 PMCID: PMC8488797 DOI: 10.1002/onco.13900] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 07/07/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Patients with non-small cell lung cancer may develop pneumonitis after thoracic radiotherapy (RT) and immune checkpoint inhibitors (ICIs). We hypothesized that distinct morphologic features are associated with different pneumonitis etiologies. MATERIALS AND METHODS We systematically compared computed tomography (CT) features of RT- versus ICI-pneumonitis. Clinical and imaging features were tested for association with pneumonitis severity. Lastly, we constructed an exploratory radiomics-based machine learning (ML) model to discern pneumonitis etiology. RESULTS Between 2009 and 2019, 82 patients developed pneumonitis: 29 after thoracic RT, 23 after ICI, and 30 after RT + ICI. Fifty patients had grade 2 pneumonitis, 22 grade 3, and 7 grade 4. ICI-pneumonitis was more likely bilateral (65% vs. 28%; p = .01) and involved more lobes (66% vs. 45% involving at least three lobes) and was less likely to have sharp border (17% vs. 59%; p = .004) compared with RT-pneumonitis. Pneumonitis morphology after RT + ICI was heterogeneous, with 47% bilateral, 37% involving at least three lobes, and 40% sharp borders. Among all patients, risk factors for severe pneumonitis included poor performance status, smoking history, worse lung function, and bilateral and multifocal involvement on CT. An ML model based on seven radiomic features alone could distinguish ICI- from RT-pneumonitis with an area under the receiver-operating curve of 0.76 and identified the predominant etiology after RT + ICI concordant with multidisciplinary consensus. CONCLUSION RT- and ICI-pneumonitis exhibit distinct spatial features on CT. Bilateral and multifocal lung involvement is associated with severe pneumonitis. Integrating these morphologic features in the clinical management of patients who develop pneumonitis after RT and ICIs may improve treatment decision-making. IMPLICATIONS FOR PRACTICE Patients with non-small cell lung cancer often receive thoracic radiation and immune checkpoint inhibitors (ICIs), both of which can cause pneumonitis. This study identified similarities and differences in pneumonitis morphology on computed tomography (CT) scans among pneumonitis due to radiotherapy (RT) alone, ICI alone, and the combination of both. Patients who have bilateral CT changes involving at least three lobes are more likely to have ICI-pneumonitis, whereas those with unilateral CT changes with sharp borders are more likely to have radiation pneumonitis. After RT and/or ICI, severe pneumonitis is associated with bilateral and multifocal CT changes. These results can help guide clinicians in triaging patients who develop pneumonitis after radiation and during ICI treatment.
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Affiliation(s)
- Xuguang Chen
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Khadija Sheikh
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Erica Nakajima
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Cheng Ting Lin
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chen Hu
- Division of Biostatistics, Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Russell K Hales
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Patrick M Forde
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jarushka Naidoo
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Khinh Ranh Voong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Wang D, Huang C, Bao S, Fan T, Sun Z, Wang Y, Jiang H, Wang S. Study on the prognosis predictive model of COVID-19 patients based on CT radiomics. Sci Rep 2021; 11:11591. [PMID: 34078950 PMCID: PMC8172890 DOI: 10.1038/s41598-021-90991-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 04/30/2021] [Indexed: 01/15/2023] Open
Abstract
Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong’s test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19.
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Affiliation(s)
- Dandan Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Harbin, Heilongjiang Province, China
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Siyu Bao
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Tingting Fan
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Harbin, Heilongjiang Province, China
| | - Zhongqi Sun
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Harbin, Heilongjiang Province, China
| | - Yiqiao Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Harbin, Heilongjiang Province, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Harbin, Heilongjiang Province, China.
| | - Song Wang
- Department of Radiology, Longhua Hospital,, Shanghai University of Traditional Chinese Medicine, No.725, South Wanping Road, Shanghai, 200032, China.
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40
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Miller AR, Manser R. The knowns & unknowns of pulmonary toxicity following immune checkpoint inhibitor therapies: a narrative review. Transl Lung Cancer Res 2021; 10:2752-2765. [PMID: 34295675 PMCID: PMC8264318 DOI: 10.21037/tlcr-20-806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 11/05/2020] [Indexed: 11/29/2022]
Abstract
Since their discovery immune checkpoint inhibitors (ICI) have dramatically changed the treatment landscape for many cancers. In addition to their efficacy they are generally well tolerated, however, they have led to a new range of immune-related adverse events (irAEs) including pneumonitis. While not the most frequently reported immune-related adverse event in the clinical trial setting, recent real-world data suggests a significantly higher rate of pneumonitis leading to treatment suspension or cessation. It also appears to disproportionately contribute to immune-related mortality, particularly with anti-PD-1/PD-L1 treatment. While indicators have emerged regarding risk factors, incomplete prospective recording of patient characteristics hampers strong conclusions. Presenting symptoms are non-specific and the differential diagnosis is broad, made more complex by concomitant treatment with traditional chemotherapy or radiotherapy. Radiological findings are diverse and inconsistent terminology makes comparison and more complete characterization difficult. Further, little is known about the role of baseline testing or surveillance for early detection of pneumonitis, or the real-world role of bronchoscopy or biopsy in assessment. Scant literature exists to direct these complex decisions, so treatment guidelines have been published based on expert consensus. Here we provide a narrative review of what is known about ICI pneumonitis and propose key questions to enhance our understanding into the future.
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Affiliation(s)
- Alistair R Miller
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Victoria, Australia.,Department of Internal Medicine, Peter MacCallum Cancer Centre, Victoria, Australia.,Department of Medicine, Monash Health, Monash University, Victoria, Australia
| | - Renee Manser
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Victoria, Australia.,Department of Internal Medicine, Peter MacCallum Cancer Centre, Victoria, Australia.,Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Victoria, Australia
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41
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van Dijk LV, Fuller CD. Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges. Am Soc Clin Oncol Educ Book 2021; 41:1-11. [PMID: 33929877 DOI: 10.1200/edbk_320951] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and artificial intelligence (which uses deep-learning techniques for "learning algorithms"); however, clinical implementation has yet to be realized at scale. To improve implementation, opportunities, mechanics, and challenges, models of imaging-enabled artificial intelligence approaches need to be understood by clinicians who make the treatment decisions. This article aims to convey the basic conceptual premises of radiomics and artificial intelligence using head and neck cancer as a use case. This educational overview focuses on approaches for head and neck oncology imaging, detailing current research efforts and challenges to implementation.
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Affiliation(s)
- Lisanne V van Dijk
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX.,Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Clifton D Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX
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Zhang Q, Tang L, Zhou Y, He W, Li W. Immune Checkpoint Inhibitor-Associated Pneumonitis in Non-Small Cell Lung Cancer: Current Understanding in Characteristics, Diagnosis, and Management. Front Immunol 2021; 12:663986. [PMID: 34122422 PMCID: PMC8195248 DOI: 10.3389/fimmu.2021.663986] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 05/04/2021] [Indexed: 02/05/2023] Open
Abstract
Immunotherapy that includes programmed cell death-1 (PD-1), programmed cell death- ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) inhibitors has revolutionized the therapeutic strategy in multiple malignancies. Although it has achieved significant breakthrough in advanced non-small cell lung cancer patients, immune-related adverse events (irAEs) including checkpoint inhibitor pneumonitis (CIP), are widely reported. As the particularly worrisome and potentially lethal form of irAEs, CIP should be attached more importance. Especially in non-small cell lung cancer (NSCLC) patients, the features of CIP may be more complicated on account of the overlapping respiratory signs compromised by primary tumor following immunotherapy. Herein, we included the previous relevant reports and comprehensively summarized the characteristics, diagnosis, and management of CIP. We also discussed the future direction of optimal steroid therapeutic schedule for patients with CIP in NSCLC based on the current evidence.
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Affiliation(s)
- Qin Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.,Department of Postgraduate Student, West China Hospital, Sichuan University, Chengdu, China
| | - Liansha Tang
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yuwen Zhou
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Wenbo He
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
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43
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CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias. Radiol Med 2021; 126:1037-1043. [PMID: 34043146 PMCID: PMC8155795 DOI: 10.1007/s11547-021-01370-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 05/05/2021] [Indexed: 01/08/2023]
Abstract
Purpose To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. Material and Methods CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C−), respectively. C− patients, however, presented with interstitial lung involvement. A subgroup of C−, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. Results The first model classified C + and C− pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C− (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). Conclusion Whole lung ML models based on radiomics can classify C + and C− interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.
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El Ayachy R, Giraud N, Giraud P, Durdux C, Giraud P, Burgun A, Bibault JE. The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up. Front Oncol 2021; 11:603595. [PMID: 34026602 PMCID: PMC8131863 DOI: 10.3389/fonc.2021.603595] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/06/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients’ care. Methods A literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020 Results We identified several studies at each point of patient’s care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications. Conclusion Promising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.
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Affiliation(s)
- Radouane El Ayachy
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Nicolas Giraud
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France.,Radiation Oncology Department, Haut-Lévêque Hospital, CHU de Bordeaux, Pessac, France
| | - Paul Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Catherine Durdux
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Philippe Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Anita Burgun
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Jean Emmanuel Bibault
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
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Wang H, Wang L, Lee EH, Zheng J, Zhang W, Halabi S, Liu C, Deng K, Song J, Yeom KW. Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures. Eur J Nucl Med Mol Imaging 2021; 48:1478-1486. [PMID: 33094432 PMCID: PMC7581467 DOI: 10.1007/s00259-020-05075-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/12/2020] [Indexed: 12/23/2022]
Abstract
PURPOSE High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework. METHODS A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA. All the pneumonia lesions on CT images were manually delineated by four radiologists. One hundred eighty-four patients (n = 93 COVID-19 positive; n = 91 COVID-19 negative; 24,216 pneumonia lesions from 12,001 CT image slices) from two hospitals from China served as discovery cohort for model development. Thirty-two patients (17 COVID-19 positive, 15 COVID-19 negative; 7883 pneumonia lesions from 3799 CT image slices) from a US hospital served as external validation cohort. A bi-directional adversarial network-based framework and PyRadiomics package were used to extract deep learning and radiomics features, respectively. Linear and Lasso classifiers were used to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia. RESULTS 120-dimensional deep learning image features and 120-dimensional radiomics features were extracted. Linear and Lasso classifiers identified 32 high-dimensional deep learning image features and 4 radiomics features associated with COVID-19 pneumonia diagnosis (P < 0.0001). Both models achieved sensitivity > 73% and specificity > 75% on external validation cohort with slight superior performance for radiomics Lasso classifier. Human expert diagnostic performance improved (increase by 16.5% and 11.6% in sensitivity and specificity, respectively) when using a combined deep learning-radiomics model. CONCLUSIONS We uncover specific deep learning and radiomics features to add insight into interpretability of machine learning algorithms and compare deep learning and radiomics models for COVID-19 pneumonia that might serve to augment human diagnostic performance.
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Affiliation(s)
- Hongmei Wang
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, 230036, Anhui, China
| | - Lu Wang
- School of Medical Informatics, China Medical University, Shenyang, 110122, Liaoning, China
| | - Edward H Lee
- Department of Radiology, School of Medicine Stanford University, 725 Welch Rd MC 5654, Palo Alto, CA, 94305, USA
| | - Jimmy Zheng
- Department of Radiology, School of Medicine Stanford University, 725 Welch Rd MC 5654, Palo Alto, CA, 94305, USA
| | - Wei Zhang
- Department of Radiology, The Lu'an Affiliated Hospital, Anhui Medical University, Luan, 237000, Anhui, China
| | - Safwan Halabi
- Department of Radiology, School of Medicine Stanford University, 725 Welch Rd MC 5654, Palo Alto, CA, 94305, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, 94720, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, USA
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, 230036, Anhui, China
| | - Jiangdian Song
- College of Medical Informatics, China Medical University, Shenyang, 110122, Liaoning, China.
- Department of Radiology, School of Medicine Stanford University, 1201 Welch Rd Lucas Center PS055, Stanford, CA, 94305, USA.
| | - Kristen W Yeom
- Department of Radiology, School of Medicine Stanford University, 725 Welch Rd MC 5654, Palo Alto, CA, 94305, USA
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Thouvenin L, Olivier T, Banna G, Addeo A, Friedlaender A. Immune checkpoint inhibitor-induced aseptic meningitis and encephalitis: a case-series and narrative review. Ther Adv Drug Saf 2021; 12:20420986211004745. [PMID: 33854755 PMCID: PMC8010823 DOI: 10.1177/20420986211004745] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 02/24/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Along with the increasing use of immune checkpoint inhibitors comes a surge in immune-related toxicity. Here, we review the currently available data regarding neurological immune adverse events, and more specifically aseptic meningitis and encephalitis, and present treatment and diagnostic recommendations. Furthermore, we present five cases of immunotherapy-induced aseptic meningitis and encephalitis treated at our institution. RECENT FINDINGS Neurological immune-related adverse events, including aseptic meningitis and encephalitis, secondary to checkpoint inhibitors are a rare but complex and clinically relevant entity, comprising a wide range of diseases, most often presenting with symptoms with a wide range of differential diagnoses. Our case-series highlights the challenges of such entities and the importance of properly identifying and managing aseptic meningitis and encephalitis. SUMMARY Checkpoint inhibitor-induced meningoencephalitis warrants prompt investigations and treatment. Properly diagnosing aseptic meningitis, encephalitis, or mixed presentations may guide the treatment decision, as highlighted by our case-series. After rapid exclusion of alternative diagnoses, urgent corticosteroids are the therapeutic backbone but this could change in favour of highly specific cytokine-directed treatment options. PLAIN LANGUAGE SUMMARY Aseptic meningitis and encephalitis with immune checkpoint inhibitors: a single centre case-series and review of the literature Over the course of the past decade, checkpoint inhibitors have revolutionized cancer care. With their favourable toxicity profile and potential for durable and deep responses, they have become ubiquitous across the field of oncology. Furthermore, combination checkpoint inhibitors are also gaining ground, with increased efficacy and, unfortunately, immune-related toxicity. While there are guidelines based on extensive clinical experience for frequent adverse events, uncommon entities are less readily identified and treated. Neurological immune-related adverse events secondary to checkpoint inhibitors are a rare but complex entity, comprising a wide range of diseases, most often presenting with aspecific symptoms. In this paper, we discuss a single institution case-series of patients with autoimmune aseptic meningitis and encephalitis, and we perform a narrative literature review on this subject. We conclude with our treatment recommendations based on available evidence.
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Affiliation(s)
- Laure Thouvenin
- Oncology Department, Geneva University Hospital, Geneva, Switzerland
| | - Timothée Olivier
- Oncology Department, Geneva University Hospital, Geneva, Switzerland
| | - Giuseppe Banna
- Oncology Department, Portsmouth Hospitals NHS Trust, Portsmouth, UK
| | - Alfredo Addeo
- Oncology Department, Geneva University Hospital, Geneva, Switzerland
| | - Alex Friedlaender
- Oncology Department, Geneva University Hospital, 4 Rue Gabrielle-Perret-Gentil, Geneva, 1205, Switzerland
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Song LL, Chen SJ, Chen W, Shi Z, Wang XD, Song LN, Chen DS. Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images. BMC Med Imaging 2021; 21:54. [PMID: 33743615 PMCID: PMC7981906 DOI: 10.1186/s12880-021-00581-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 03/07/2021] [Indexed: 01/04/2023] Open
Abstract
Background Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared. Results The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts. Conclusions The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00581-9.
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Affiliation(s)
- Le-le Song
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Shun-Jun Chen
- The Department of Ultrasound, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Wang Chen
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Zhan Shi
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Xiao-Dong Wang
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Li-Na Song
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dian-Sen Chen
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China.
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Wang L, Kelly B, Lee EH, Wang H, Zheng J, Zhang W, Halabi S, Liu J, Tian Y, Han B, Huang C, Yeom KW, Deng K, Song J. Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features. Eur J Radiol 2021; 136:109552. [PMID: 33497881 PMCID: PMC7810032 DOI: 10.1016/j.ejrad.2021.109552] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 12/09/2020] [Accepted: 01/12/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE To investigate the efficacy of radiomics in diagnosing patients with coronavirus disease (COVID-19) and other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19. METHODS Between 18 January 2020 and 20 May 2020, 110 SARS-CoV-2 positive and 108 SARS-CoV-2 negative patients were retrospectively recruited from three hospitals based on the inclusion criteria. Manual segmentation of pneumonia lesions on CT scans was performed by four radiologists. The latest version of Pyradiomics was used for feature extraction. Four classifiers (linear classifier, k-nearest neighbour, least absolute shrinkage and selection operator [LASSO], and random forest) were used to differentiate SARS-CoV-2 positive and SARS-CoV-2 negative patients. Comparison of the performance of the classifiers and radiologists was evaluated by ROC curve and Kappa score. RESULTS We manually segmented 16,053 CT slices, comprising 32,625 pneumonia lesions, from the CT scans of all patients. Using Pyradiomics, 120 radiomic features were extracted from each image. The key radiomic features screened by different classifiers varied and lead to significant differences in classification accuracy. The LASSO achieved the best performance (sensitivity: 72.2%, specificity: 75.1%, and AUC: 0.81) on the external validation dataset and attained excellent agreement (Kappa score: 0.89) with radiologists (average sensitivity: 75.6%, specificity: 78.2%, and AUC: 0.81). All classifiers indicated that "Original_Firstorder_RootMeanSquared" and "Original_Firstorder_Uniformity" were significant features for this task. CONCLUSIONS We identified radiomic features that were significantly associated with the classification of COVID-19 pneumonia using multiple classifiers. The quantifiable interpretation of the differences in features between the two groups extends our understanding of CT imaging characteristics of COVID-19 pneumonia.
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Affiliation(s)
- Lu Wang
- School of Medical Informatics, China Medical University Puhe Rd, Shenbei New District, Shenyang, Liaoning, 110122, China
| | - Brendan Kelly
- Department of Radiology, School of Medicine, Stanford University 725 Welch Rd MC 5654, Palo Alto, CA, 94305, United States
| | - Edward H. Lee
- Department of Radiology, School of Medicine, Stanford University 725 Welch Rd MC 5654, Palo Alto, CA, 94305, United States
| | - Hongmei Wang
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, No. 1 Swan Lake Road Hefei, Anhui, 230036, China
| | - Jimmy Zheng
- Department of Radiology, School of Medicine, Stanford University 725 Welch Rd MC 5654, Palo Alto, CA, 94305, United States
| | - Wei Zhang
- Department of Radiology, the Lu’an Affiliated Hospital, Anhui Medical University, No. 21 Wanxi Rd, Lu’an, Anhui, 237005, China
| | - Safwan Halabi
- Department of Radiology, School of Medicine, Stanford University 725 Welch Rd MC 5654, Palo Alto, CA, 94305, United States
| | - Jining Liu
- Bengbu Medical College, Department of Imaging Medicine, 2600 Donghai Avenue, Bengbu, Anhui, 233030, China
| | - Yulong Tian
- Wannan Medical College, Department of Imaging Medicine and Nuclear Medicine, 22 Wenchang West Rd, Higher Education Park, Wuhu, Anhui, 241002, China
| | - Baoqin Han
- Wannan Medical College, Department of Imaging Medicine and Nuclear Medicine, 22 Wenchang West Rd, Higher Education Park, Wuhu, Anhui, 241002, China
| | - Chuanbin Huang
- Wannan Medical College, Department of Imaging Medicine and Nuclear Medicine, 22 Wenchang West Rd, Higher Education Park, Wuhu, Anhui, 241002, China
| | - Kristen W. Yeom
- Department of Radiology, School of Medicine, Stanford University 725 Welch Rd MC 5654, Palo Alto, CA, 94305, United States
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, No. 1 Swan Lake Road Hefei, Anhui, 230036, China,Corresponding author
| | - Jiangdian Song
- School of Medical Informatics, China Medical University Puhe Rd, Shenbei New District, Shenyang, Liaoning, 110122, China; Department of Radiology, School of Medicine, Stanford University 1201 Welch Rd, Lucas Center, Palo Alto, CA, 94305, United States.
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Abstract
With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.
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Affiliation(s)
- Z. Bodalal
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - I. Wamelink
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Technical Medicine, University of Twente, Enschede, The Netherlands
| | - S. Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - R.G.H. Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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Sun Z, Wang S, Du H, Shen H, Zhu J, Li Y. Immunotherapy-induced pneumonitis in non-small cell lung cancer patients: current concern in treatment with immune-check-point inhibitors. Invest New Drugs 2021; 39:891-898. [PMID: 33428078 DOI: 10.1007/s10637-020-01051-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 12/10/2020] [Indexed: 12/22/2022]
Abstract
Purpose Immune-related adverse events (IrAEs) are auto-immune reactions associated with immune checkpoint inhibitor-based therapy (ICI). To date, little is known about immunotherapy-induced pneumonitis (IIP). In this study, we investigated the clinical and CT features of IIP in non-small cell lung cancer (NSCLC) patients treated with ICI. Methods CT images and clinical data of 98 NSCLC patients in our hospital were retrospectively analyzed after ICI therapy, and the incidence, onset time, CT findings, grade, treatment and prognosis of IIP were recorded. Results Nineteen patients developed IIP, which occurred 42∼210 days after ICI therapy, and the median time was 97 days. The CT findings for IIP showed multifocal ground-glass opacity (GGO) in 5 cases, patchy shadows in 6 cases, mixed distribution of patchy and strip-like shadows in 4 cases, and patchy shadows with honeycomb lung in 4 cases. The mean age and proportions of smokers, CD3+ and CD4+ of T lymphocyte subset in patients with IIP were significantly higher than those in patients without IIP (all p < 0.05). Among 19 patients with IIP, there were 10 patients with grade 1 ~ 2 and 9 patients with grade 3 ~ 4; 13 patients received hormone therapy, 12 of them were improved or stable, and 1 patient got worse after hormone therapy. No deaths from IIP were found. Conclusion IIP is a relatively rare but serious adverse event, and it is sensitive to hormone therapy. Its CT manifestations are diverse, and timely detection and treatment are the keys to reduce IIP.
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Affiliation(s)
- Zongqiong Sun
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, 215006, China
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, 214000, Jiangsu Province, China
| | - Sheng Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, 215006, China
| | - Hongdi Du
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou City, Jiangsu Province, 215028, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou City, Jiangsu Province, 215028, China.
| | - Jingfen Zhu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, 215006, China.
| | - Yonggang Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, 215006, China.
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