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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [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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Barcroft JF, Linton-Reid K, Landolfo C, Al-Memar M, Parker N, Kyriacou C, Munaretto M, Fantauzzi M, Cooper N, Yazbek J, Bharwani N, Lee SR, Kim JH, Timmerman D, Posma J, Savelli L, Saso S, Aboagye EO, Bourne T. Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound. NPJ Precis Oncol 2024; 8:41. [PMID: 38378773 PMCID: PMC10879532 DOI: 10.1038/s41698-024-00527-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 01/30/2024] [Indexed: 02/22/2024] Open
Abstract
Ultrasound-based models exist to support the classification of adnexal masses but are subjective and rely upon ultrasound expertise. We aimed to develop an end-to-end machine learning (ML) model capable of automating the classification of adnexal masses. In this retrospective study, transvaginal ultrasound scan images with linked diagnoses (ultrasound subjective assessment or histology) were extracted and segmented from Imperial College Healthcare, UK (ICH development dataset; n = 577 masses; 1444 images) and Morgagni-Pierantoni Hospital, Italy (MPH external dataset; n = 184 masses; 476 images). A segmentation and classification model was developed using convolutional neural networks and traditional radiomics features. Dice surface coefficient (DICE) was used to measure segmentation performance and area under the ROC curve (AUC), F1-score and recall for classification performance. The ICH and MPH datasets had a median age of 45 (IQR 35-60) and 48 (IQR 38-57) years old and consisted of 23.1% and 31.5% malignant cases, respectively. The best segmentation model achieved a DICE score of 0.85 ± 0.01, 0.88 ± 0.01 and 0.85 ± 0.01 in the ICH training, ICH validation and MPH test sets. The best classification model achieved a recall of 1.00 and F1-score of 0.88 (AUC:0.93), 0.94 (AUC:0.89) and 0.83 (AUC:0.90) in the ICH training, ICH validation and MPH test sets, respectively. We have developed an end-to-end radiomics-based model capable of adnexal mass segmentation and classification, with a comparable predictive performance (AUC 0.90) to the published performance of expert subjective assessment (gold standard), and current risk models. Further prospective evaluation of the classification performance of this ML model against existing methods is required.
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Affiliation(s)
- Jennifer F Barcroft
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | | | - Chiara Landolfo
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Maya Al-Memar
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Nina Parker
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Chris Kyriacou
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Maria Munaretto
- Department of Obstetrics and Gynaecology, Ospedale Morgagni-Pierantoni, Forli, Italy
| | - Martina Fantauzzi
- Department of Medicine and Surgery, University of Milan-Bicocca, Milan, Italy
| | - Nina Cooper
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Joseph Yazbek
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Nishat Bharwani
- Department of Radiology, Imperial College Healthcare NHS Trust, London, UK
| | - Sa Ra Lee
- Department of Obstetrics and Gynaecology, Asan Medical Center, Seoul, South Korea
| | - Ju Hee Kim
- Department of Obstetrics and Gynaecology, Asan Medical Center, Seoul, South Korea
| | - Dirk Timmerman
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Joram Posma
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Luca Savelli
- Department of Obstetrics and Gynaecology, Ospedale Morgagni-Pierantoni, Forli, Italy
| | - Srdjan Saso
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College London, London, UK.
| | - Tom Bourne
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
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3
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Boubnovski Martell M, Linton-Reid K, Hindocha S, Chen M, Moreno P, Álvarez-Benito M, Salvatierra Á, Lee R, Posma JM, Calzado MA, Aboagye EO. Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis. NPJ Precis Oncol 2024; 8:28. [PMID: 38310164 PMCID: PMC10838282 DOI: 10.1038/s41698-024-00502-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/04/2024] [Indexed: 02/05/2024] Open
Abstract
The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients' prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.
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Affiliation(s)
| | | | - Sumeet Hindocha
- Early Diagnosis and Detection Centre, National Institute for Health and Care Research Biomedical Research Centre at the Royal Marsden and Institute of Cancer Research, London, SW3 6JJ, UK
| | - Mitchell Chen
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK
| | - Paula Moreno
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Departamento de Cirugía Toráxica y Trasplante de Pulmón, Hospital Universitario Reina Sofía, Córdoba, 14014, Spain
| | - Marina Álvarez-Benito
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Unidad de Radiodiagnóstico y Cáncer de Mama, Hospital Universitario Reina Sofía, Córdoba, 14004, Spain
| | - Ángel Salvatierra
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Unidad de Radiodiagnóstico y Cáncer de Mama, Hospital Universitario Reina Sofía, Córdoba, 14004, Spain
| | - Richard Lee
- Early Diagnosis and Detection Centre, National Institute for Health and Care Research Biomedical Research Centre at the Royal Marsden and Institute of Cancer Research, London, SW3 6JJ, UK
- National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse Street, London, SW3 6LY, UK
| | - Joram M Posma
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK
| | - Marco A Calzado
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain.
- Departamento de Biología Celular, Fisiología e Inmunología, Universidad de Córdoba, Córdoba, 14014, Spain.
| | - Eric O Aboagye
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK.
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4
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Hunter B, Argyros C, Inglese M, Linton-Reid K, Pulzato I, Nicholson AG, Kemp SV, L Shah P, Molyneaux PL, McNamara C, Burn T, Guilhem E, Mestas Nuñez M, Hine J, Choraria A, Ratnakumar P, Bloch S, Jordan S, Padley S, Ridge CA, Robinson G, Robbie H, Barnett J, Silva M, Desai S, Lee RW, Aboagye EO, Devaraj A. Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis. Br J Cancer 2023; 129:1949-1955. [PMID: 37932513 PMCID: PMC10703918 DOI: 10.1038/s41416-023-02480-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 09/21/2023] [Accepted: 10/23/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Methods to improve stratification of small (≤15 mm) lung nodules are needed. We aimed to develop a radiomics model to assist lung cancer diagnosis. METHODS Patients were retrospectively identified using health records from January 2007 to December 2018. The external test set was obtained from the national LIBRA study and a prospective Lung Cancer Screening programme. Radiomics features were extracted from multi-region CT segmentations using TexLab2.0. LASSO regression generated the 5-feature small nodule radiomics-predictive-vector (SN-RPV). K-means clustering was used to split patients into risk groups according to SN-RPV. Model performance was compared to 6 thoracic radiologists. SN-RPV and radiologist risk groups were combined to generate "Safety-Net" and "Early Diagnosis" decision-support tools. RESULTS In total, 810 patients with 990 nodules were included. The AUC for malignancy prediction was 0.85 (95% CI: 0.82-0.87), 0.78 (95% CI: 0.70-0.85) and 0.78 (95% CI: 0.59-0.92) for the training, test and external test datasets, respectively. The test set accuracy was 73% (95% CI: 65-81%) and resulted in 66.67% improvements in potentially missed [8/12] or delayed [6/9] cancers, compared to the radiologist with performance closest to the mean of six readers. CONCLUSIONS SN-RPV may provide net-benefit in terms of earlier cancer diagnosis.
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Affiliation(s)
- Benjamin Hunter
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Christos Argyros
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Marianna Inglese
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
- Department of Biomedicine and Prevention, University of Rome, Tor Vergata, Italy
| | - Kristofer Linton-Reid
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Ilaria Pulzato
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Andrew G Nicholson
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Histopathology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Samuel V Kemp
- Nottingham University Hospitals NHS Trust, Department of Respiratory Medicine, Nottingham, UK
| | - Pallav L Shah
- Imperial College London, National Heart and Lung Institute, London, UK
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Respiratory Medicine, London, UK
| | - Philip L Molyneaux
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Respiratory Medicine, London, UK
| | - Cillian McNamara
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Toby Burn
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Emily Guilhem
- King's College Hospital, Department of Radiology, London, UK
| | | | - Julia Hine
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Anika Choraria
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Prashanthi Ratnakumar
- Imperial College London, National Heart and Lung Institute, London, UK
- St Mary's Hospital, Imperial College Healthcare Trust, Department of Respiratory Medicine, London, UK
| | - Susannah Bloch
- Imperial College London, National Heart and Lung Institute, London, UK
- St Mary's Hospital, Imperial College Healthcare Trust, Department of Respiratory Medicine, London, UK
| | - Simon Jordan
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Thoracic Surgery, London, UK
| | - Simon Padley
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Carole A Ridge
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Graham Robinson
- The Royal United Hospital, Bath, Department of Radiology, Bath, UK
| | - Hasti Robbie
- King's College Hospital, Department of Radiology, London, UK
| | - Joseph Barnett
- Department of Radiology, Royal Free Hospital, London, UK
| | - Mario Silva
- Section of "Scienze Radiologiche", Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Sujal Desai
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
- Imperial College London, Margaret Turner-Warwick Centre for Fibrosing Lung Disease, London, UK
| | - Richard W Lee
- Imperial College London, National Heart and Lung Institute, London, UK
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
- Early Diagnosis and Detection, Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
| | - Eric O Aboagye
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Anand Devaraj
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK.
- Imperial College London, National Heart and Lung Institute, London, UK.
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5
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Lu H, Lou H, Wengert G, Paudel R, Patel N, Desai S, Crum B, Linton-Reid K, Chen M, Li D, Ip J, Mauri F, Pinato DJ, Rockall A, Copley SJ, Ghaem-Maghami S, Aboagye EO. Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer. Cell Rep Med 2023:101092. [PMID: 37348499 PMCID: PMC10394173 DOI: 10.1016/j.xcrm.2023.101092] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 03/29/2023] [Accepted: 05/30/2023] [Indexed: 06/24/2023]
Abstract
Tertiary lymphoid structure (TLS) is associated with prognosis in copy-number-driven tumors, including high-grade serous ovarian cancer (HGSOC), although the function of TLS and its interaction with copy-number alterations in HGSOC are not fully understood. In the current study, we confirm that TLS-high HGSOC patients show significantly better progression-free survival (PFS). We show that the presence of TLS in HGSOC tumors is associated with B cell maturation and cytotoxic tumor-specific T cell activation and proliferation. In addition, the copy-number loss of IL15 and CXCL10 may limit TLS formation in HGSOC; a list of genes that may dysregulate TLS function is also proposed. Last, a radiomics-based signature is developed to predict the presence of TLS, which independently predicts PFS in both HGSOC patients and immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients. Overall, we reveal that TLS coordinates intratumoral B cell and T cell response to HGSOC tumor, while the cancer genome evolves to counteract TLS formation and function.
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Affiliation(s)
- Haonan Lu
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK
| | - Hantao Lou
- Ludwig Cancer Research, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
| | - Georg Wengert
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK
| | - Reema Paudel
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK
| | - Naina Patel
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK
| | - Saral Desai
- Imperial College Healthcare NHS Trust, Du Cane Road, W12 0HS London, UK
| | - Bill Crum
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK
| | - Kristofer Linton-Reid
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK
| | - Mitchell Chen
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK; Imperial College Healthcare NHS Trust, Du Cane Road, W12 0HS London, UK
| | - Dongyang Li
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK
| | - Jacey Ip
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK; Imperial College Healthcare NHS Trust, Du Cane Road, W12 0HS London, UK
| | - Francesco Mauri
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK
| | - David J Pinato
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK; Division of Oncology, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Andrea Rockall
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK
| | - Susan J Copley
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK; Imperial College Healthcare NHS Trust, Du Cane Road, W12 0HS London, UK
| | - Sadaf Ghaem-Maghami
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK; Imperial College Healthcare NHS Trust, Du Cane Road, W12 0HS London, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College, Hammersmith Campus, The Commonwealth Building, Du Cane Road, W12 0NN London, UK.
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6
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Hunter B, Chen M, Ratnakumar P, Alemu E, Logan A, Linton-Reid K, Tong D, Senthivel N, Bhamani A, Bloch S, Kemp SV, Boddy L, Jain S, Gareeboo S, Rawal B, Doran S, Navani N, Nair A, Bunce C, Kaye S, Blackledge M, Aboagye EO, Devaraj A, Lee RW. A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules. EBioMedicine 2022; 86:104344. [PMID: 36370635 PMCID: PMC9664396 DOI: 10.1016/j.ebiom.2022.104344] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/12/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk. METHODS 502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores. FINDINGS 499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77-0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70-0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80-0.93) compared to 0.67 (95% CI 0.55-0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75-0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63-0.85). 18 out of 22 (82%) malignant nodules in the Herder 10-70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention. INTERPRETATION The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models. FUNDING This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316).
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Affiliation(s)
- Benjamin Hunter
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK; Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Mitchell Chen
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Prashanthi Ratnakumar
- Department of Respiratory Medicine, Charing Cross Hospital, Imperial College Healthcare Trust, Fulham Palace Road, London, W6 8RF, UK
| | - Esubalew Alemu
- 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
| | - Kristofer Linton-Reid
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Daniel Tong
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Nishanthi Senthivel
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Amyn Bhamani
- Department of Respiratory Medicine, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK
| | - Susannah Bloch
- Department of Respiratory Medicine, Charing Cross Hospital, Imperial College Healthcare Trust, Fulham Palace Road, London, W6 8RF, UK
| | - Samuel V Kemp
- Department of Respiratory Medicine, Nottingham University Hospitals NHS Foundation Trust, Hucknall Road, Nottingham, NG5 1PB, UK
| | - Laura Boddy
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Sejal Jain
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Shafick Gareeboo
- Department of Respiratory Medicine, Queen Elizabeth Hospital, Stadium Road, Woolwich, London, SE18 4QH, UK
| | - Bhavin Rawal
- Department of Radiology, The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Sydney Street, London, SW3 6NP, UK
| | - Simon Doran
- CRUK Cancer Imaging Centre, The Institute of Cancer Research, Cotswold Road, Sutton, SM2 5NG, UK
| | - Neal Navani
- Department of Respiratory Medicine, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK
| | - Arjun Nair
- Department of Radiology, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK
| | - Catey Bunce
- Clinical Trials Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, SM2 5PT, UK
| | - Stan Kaye
- Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, SM2 5PT, UK
| | - Matthew Blackledge
- Computational Imaging Group, The Institute of Cancer Research, Cotswold Road, Sutton, SM2 5NG, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Anand Devaraj
- Department of Radiology, The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Sydney Street, London, SW3 6NP, UK; National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse Street, London, SW3 6LY, UK
| | - Richard W Lee
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK; Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK; National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse Street, London, SW3 6LY, UK.
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Boubnovski MM, Chen M, Linton-Reid K, Posma JM, Copley SJ, Aboagye EO. Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs. Clin Radiol 2022; 77:e620-e627. [PMID: 35636974 DOI: 10.1016/j.crad.2022.04.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 04/21/2022] [Indexed: 02/08/2023]
Abstract
AIM To develop a multi-task learning (MTL) V-Net for pulmonary lobar segmentation on computed tomography (CT) and application to diseased lungs. MATERIALS AND METHODS The described methodology utilises tracheobronchial tree information to enhance segmentation accuracy through the algorithm's spatial familiarity to define lobar extent more accurately. The method undertakes parallel segmentation of lobes and auxiliary tissues simultaneously by employing MTL in conjunction with V-Net-attention, a popular convolutional neural network in the imaging realm. Its performance was validated by an external dataset of patients with four distinct lung conditions: severe lung cancer, COVID-19 pneumonitis, collapsed lungs, and chronic obstructive pulmonary disease (COPD), even though the training data included none of these cases. RESULTS The following Dice scores were achieved on a per-segment basis: normal lungs 0.97, COPD 0.94, lung cancer 0.94, COVID-19 pneumonitis 0.94, and collapsed lung 0.92, all at p<0.05. CONCLUSION Despite severe abnormalities, the model provided good performance at segmenting lobes, demonstrating the benefit of tissue learning. The proposed model is poised for adoption in the clinical setting as a robust tool for radiologists and researchers to define the lobar distribution of lung diseases and aid in disease treatment planning.
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Affiliation(s)
- M M Boubnovski
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK
| | - M Chen
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK; Department of Radiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London W12 0HS, UK
| | - K Linton-Reid
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK
| | - J M Posma
- Department of Metabolism, Digestion and Reproduction, South Kensington, London SW7 2AZ, UK
| | - S J Copley
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK; Department of Radiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London W12 0HS, UK
| | - E O Aboagye
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK.
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Hindocha S, Charlton T, Linton-Reid K, Hunter B, Chan C, Ahmed M, Robinson E, Orton M, Lunn J, Ahmed S, McDonald F, Locke I, Power D, Doran S, Blackledge M, Lee R, Aboagye E. MO-0384 A CT-radiomics model to predict recurrence post curative-intent radiotherapy for stage I-III NSCLC. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)02350-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Hindocha S, Charlton TG, Linton-Reid K, Hunter B, Chan C, Ahmed M, Robinson EJ, Orton M, Ahmad S, McDonald F, Locke I, Power D, Blackledge M, Lee RW, Aboagye EO. A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models. EBioMedicine 2022; 77:103911. [PMID: 35248997 PMCID: PMC8897583 DOI: 10.1016/j.ebiom.2022.103911] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/16/2022] [Accepted: 02/16/2022] [Indexed: 11/01/2022] Open
Abstract
BACKGROUND Surveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform optimal surveillance strategies is lacking. Machine learning demonstrates promise in accurate outcome prediction for a variety of health conditions. The purpose of this study was to utilise readily available patient, tumour, and treatment data to develop, validate and externally test machine learning models for predicting recurrence, recurrence-free survival (RFS) and overall survival (OS) at 2 years from treatment. METHODS A retrospective, multicentre study of patients receiving curative-intent radiotherapy for NSCLC was undertaken. A total of 657 patients from 5 hospitals were eligible for inclusion. Data pre-processing derived 34 features for predictive modelling. Combinations of 8 feature reduction methods and 10 machine learning classification algorithms were compared, producing risk-stratification models for predicting recurrence, RFS and OS. Models were compared with 10-fold cross validation and an external test set and benchmarked against TNM-stage and performance status. Youden Index was derived from validation set ROC curves to distinguish high and low risk groups and Kaplan-Meier analyses performed. FINDINGS Median follow-up time was 852 days. Parameters were well matched across training-validation and external test sets: Mean age was 73 and 71 respectively, and recurrence, RFS and OS rates at 2 years were 43% vs 34%, 54% vs 47% and 54% vs 47% respectively. The respective validation and test set AUCs were as follows: 1) RFS: 0·682 (0·575-0·788) and 0·681 (0·597-0·766), 2) Recurrence: 0·687 (0·582-0·793) and 0·722 (0·635-0·81), and 3) OS: 0·759 (0·663-0·855) and 0·717 (0·634-0·8). Our models were superior to TNM stage and performance status in predicting recurrence and OS. INTERPRETATION This robust and ready to use machine learning method, validated and externally tested, sets the stage for future clinical trials entailing quantitative personalised risk-stratification and surveillance following curative-intent radiotherapy for NSCLC. FUNDING A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
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Affiliation(s)
- Sumeet Hindocha
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK; AI for Healthcare Centre for Doctoral Training, Imperial College London, Exhibition Road, London SW7 2BX, UK; Department of Clinical Oncology, Institute of Cancer Research NIHR Biomedical Research Centre, London, UK; Cancer Imaging Centre, Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK; Early Diagnosis and Detection Centre, National Institute for Health Research (NIHR) Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, London
| | - Thomas G Charlton
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London SE19RT UK
| | - Kristofer Linton-Reid
- Cancer Imaging Centre, Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Benjamin Hunter
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK; Department of Clinical Oncology, Institute of Cancer Research NIHR Biomedical Research Centre, London, UK; Cancer Imaging Centre, Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK; Early Diagnosis and Detection Centre, National Institute for Health Research (NIHR) Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, London
| | - Charleen Chan
- Department of Clinical Oncology, Institute of Cancer Research NIHR Biomedical Research Centre, London, UK
| | - Merina Ahmed
- Lung Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Emily J Robinson
- Clinical Trials Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Matthew Orton
- Artificial Intelligence Imaging Hub, Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Shahreen Ahmad
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London SE19RT UK
| | - Fiona McDonald
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK; Department of Clinical Oncology, Institute of Cancer Research NIHR Biomedical Research Centre, London, UK
| | - Imogen Locke
- Lung Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Danielle Power
- Department of Clinical Oncology, Charing Cross Hospital, Fulham Palace Road, London W6 8RF, UK
| | - Matthew Blackledge
- Radiotherapy and Imaging, Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Richard W Lee
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK; Early Diagnosis and Detection Centre, National Institute for Health Research (NIHR) Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, London; National Heart and Lung Institute, Imperial College, London, UK.
| | - Eric O Aboagye
- Department of Clinical Oncology, Institute of Cancer Research NIHR Biomedical Research Centre, London, UK.
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Hindocha S, Charlton T, Linton-Reid K, Hunter B, Chan C, Ahmed M, Robinson E, Orton M, Ahmad S, McDonald F, Locke I, Power D, Blackledge M, Lee R, Aboagye E. Combined CT radiomics of primary tumour and metastatic lymph nodes improves prediction of recurrence following radiotherapy for non-small cell lung cancer. Lung Cancer 2022. [DOI: 10.1016/s0169-5002(22)00175-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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