1
|
Yeghaian M, Bodalal Z, Tareco Bucho T, Kurilova I, Blank C, Smit E, van der Heijden M, Nguyen-Kim T, van den Broek D, Beets-Tan R, Trebeschi S. Integrated noninvasive diagnostics for prediction of survival in immunotherapy. IMMUNO-ONCOLOGY TECHNOLOGY 2024; 24:100723. [PMID: 39185322 PMCID: PMC11342748 DOI: 10.1016/j.iotech.2024.100723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Background Integrating complementary diagnostic data sources promises enhanced robustness in the predictive performance of artificial intelligence (AI) models, a crucial requirement for future clinical validation/implementation. In this study, we investigate the potential value of integrating data from noninvasive diagnostic modalities, including chest computed tomography (CT) imaging, routine laboratory blood tests, and clinical parameters, to retrospectively predict 1-year survival in a cohort of patients with advanced non-small-cell lung cancer, melanoma, and urothelial cancer treated with immunotherapy. Patients and methods The study included 475 patients, of whom 444 had longitudinal CT scans and 475 had longitudinal laboratory data. An ensemble of AI models was trained on data from each diagnostic modality, and subsequently, a model-agnostic integration approach was adopted for combining the prediction probabilities of each modality and producing an integrated decision. Results Integrating different diagnostic data demonstrated a modest increase in predictive performance. The highest area under the curve (AUC) was achieved by CT and laboratory data integration (AUC of 0.83, 95% confidence interval 0.81-0.85, P < 0.001), whereas the performance of individual models trained on laboratory and CT data independently yielded AUCs of 0.81 and 0.73, respectively. Conclusions In our retrospective cohort, integrating different noninvasive data modalities improved performance.
Collapse
Affiliation(s)
- M. Yeghaian
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Z. Bodalal
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - T.M. Tareco Bucho
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - I. Kurilova
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - C.U. Blank
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - E.F. Smit
- Pulmonology Department, Leiden University Medical Center, Leiden, The Netherlands
| | - M.S. van der Heijden
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - T.D.L. Nguyen-Kim
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
| | - D. van den Broek
- Department of Laboratory Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - R.G.H. Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - S. Trebeschi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
2
|
Fiste O, Gkiozos I, Charpidou A, Syrigos NK. Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC. Cancers (Basel) 2024; 16:831. [PMID: 38398222 PMCID: PMC10887017 DOI: 10.3390/cancers16040831] [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: 01/31/2024] [Revised: 02/12/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.
Collapse
Affiliation(s)
- Oraianthi Fiste
- Oncology Unit, Third Department of Internal Medicine and Laboratory, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.G.); (A.C.); (N.K.S.)
| | | | | | | |
Collapse
|
3
|
Sathish G, Monavarshini LK, Sundaram K, Subramanian S, Kannayiram G. Immunotherapy for lung cancer. Pathol Res Pract 2024; 254:155104. [PMID: 38244436 DOI: 10.1016/j.prp.2024.155104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/22/2024]
Abstract
Immune checkpoint blockers have transformed non-small-cell lung cancer treatment, but they can lead to autoimmune and inflammatory side effects, leading to the concurrent use of immunosuppressive treatments. In this analysis, we delve into the potential of antibodies checkpoint blockade, focusing on CTLA-4 inhibition using ipilimumab, as a groundbreaking cancer immunotherapy. We also concentrate on the role of biomarkers, particularly PD-L1 activity and mutation significance, in predicting the response to programmed cell death protein 1 blockage and the prevalence of side effects associated with immune-related side effects. In describing the patterns of cancer response to immunotherapy, we underline the limitations of response assessment criteria like RECIST and World Health Organization. We also stress the necessity of ongoing studies and clinical trials, standardized guidelines, and additional research to improve response assessment in the era of immunotherapy.
Collapse
Affiliation(s)
- Girshani Sathish
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - L K Monavarshini
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - Keerthi Sundaram
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - Sendilvelan Subramanian
- Deparment of Mechanical Engineering, Dr.MGR Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - Gomathi Kannayiram
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India.
| |
Collapse
|
4
|
Lu J, Ji X, Liu X, Jiang Y, Li G, Fang P, Li W, Zuo A, Guo Z, Yang S, Ji Y, Lu D. Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC. Sci Rep 2024; 14:446. [PMID: 38172228 PMCID: PMC10764785 DOI: 10.1038/s41598-023-50984-7] [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: 10/15/2023] [Accepted: 12/28/2023] [Indexed: 01/05/2024] Open
Abstract
The epidermal growth factor receptor (EGFR) Thr790 Met (T790M) mutation is responsible for approximately half of the acquired resistance to EGFR-tyrosine kinase inhibitor (TKI) in non-small-cell lung cancer (NSCLC) patients. Identifying patients at diagnosis who are likely to develop this mutation after first- or second-generation EGFR-TKI treatment is crucial for better treatment outcomes. This study aims to develop and validate a radiomics-based machine learning (ML) approach to predict the T790M mutation in NSCLC patients at diagnosis. We collected retrospective data from 210 positive EGFR mutation NSCLC patients, extracting 1316 radiomics features from CT images. Using the LASSO algorithm, we selected 10 radiomics features and 2 clinical features most relevant to the mutations. We built models with 7 ML approaches and assessed their performance through the receiver operating characteristic (ROC) curve. The radiomics model and combined model, which integrated radiomics features and relevant clinical factors, achieved an area under the curve (AUC) of 0.80 (95% confidence interval [CI] 0.79-0.81) and 0.86 (0.87-0.88), respectively, in predicting the T790M mutation. Our study presents a convenient and noninvasive radiomics-based ML model for predicting this mutation at the time of diagnosis, aiding in targeted treatment planning for NSCLC patients with EGFR mutations.
Collapse
Affiliation(s)
- Jiameng Lu
- Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical Medicine, 16766 Jingshilu, Lixia, Jinan, 250014, Shandong, People's Republic of China
- School of Microelectronics, Shandong University, Jinan, 250100, Shandong, People's Republic of China
| | - Xiaoqing Ji
- Department of Nursing, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, 250014, Shandong, People's Republic of China
| | - Xinyi Liu
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Yunxiu Jiang
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Gang Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medicine Imaging, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, 250000, Shandong, China
| | - Ping Fang
- Department of Blood Transfusion, The First Affiliated Hospital of Shandong First Medical University and Shandong Province Qianfoshan Hospital, Jinan, 250014, Shandong, China
| | - Wei Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medicine Imaging, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, 250000, Shandong, China
| | - Anli Zuo
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Zihan Guo
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Shuran Yang
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Yanbo Ji
- Department of Nursing, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, 250014, Shandong, People's Republic of China
| | - Degan Lu
- Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical Medicine, 16766 Jingshilu, Lixia, Jinan, 250014, Shandong, People's Republic of China.
| |
Collapse
|
5
|
Tang X, Li Y, Shen LT, Yan WF, Qian WL, Yang ZG. CT Radiomics Predict EGFR-T790M Resistance Mutation in Advanced Non-Small Cell Lung Cancer Patients After Progression on First-line EGFR-TKI. Acad Radiol 2023; 30:2574-2587. [PMID: 36941156 DOI: 10.1016/j.acra.2023.01.040] [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] [Revised: 01/25/2023] [Accepted: 01/31/2023] [Indexed: 03/23/2023]
Abstract
RATIONALE AND OBJECTIVES We aim to explore the value of chest CT radiomics in predicting the epidermal growth factor receptor (EGFR)-T790M resistance mutation of advanced non-small cell lung cancer (NSCLC) patients after the failure of first-line EGFR-tyrosine kinase inhibitor (EGFR-TKI). MATERIALS AND METHODS A total of 211 and 135 advanced NSCLC patients with tumor tissue-based (Cohort-1) or circulating tumor DNA (ctDNA)-based (Cohort-2) EGFR-T790M testing were included, respectively. Cohort-1 was used for modeling and Cohort-2 was for models' validation. Radiomic features were extracted from tumor lesions on chest nonenhanced CT (NECT) and/or contrast-enhanced CT (CECT). We used eight feature selectors and eight classifier algorithms to establish radiomic models. Models were evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS CT morphological manifestations of peripheral location and pleural indentation sign were associated with EGFR-T790M. For NECT, CECT, and NECT+CECT radiomic features, the feature selector and classifier algorithms of LASSO and Stepwise logistic regression, Boruta and SVM, and LASSO and SVM were chosen to develop the optimal model, respectively (AUC: 0.844, 0.811, and 0.897). All models performed well in calibration curves and DCA. Independent validation of models in Cohort-2 revealed that both NECT and CECT models individually had limited power for predicting EGFR-T790M mutation detected by ctDNA (AUC: 0.649, 0.675), while the NECT+CECT radiomic model had a satisfactory AUC (0.760). CONCLUSION This study proved the feasibility of using CT radiomic features to predict the EGFR-T790M resistance mutation, which could be helpful in guiding personalized therapeutic strategies.
Collapse
Affiliation(s)
- Xin Tang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuan Li
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li-Ting Shen
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei-Feng Yan
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wen-Lei Qian
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhi-Gang Yang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| |
Collapse
|
6
|
Noortman WA, Aide N, Vriens D, Arkes LS, Slump CH, Boellaard R, Goeman JJ, Deroose CM, Machiels JP, Licitra LF, Lhommel R, Alessi A, Woff E, Goffin K, Le Tourneau C, Gal J, Temam S, Delord JP, van Velden FHP, de Geus-Oei LF. Development and External Validation of a PET Radiomic Model for Prognostication of Head and Neck Cancer. Cancers (Basel) 2023; 15:2681. [PMID: 37345017 DOI: 10.3390/cancers15102681] [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: 01/31/2023] [Revised: 04/30/2023] [Accepted: 05/03/2023] [Indexed: 06/23/2023] Open
Abstract
AIM To build and externally validate an [18F]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC). METHODS Two multicentre datasets of patients with operable HNSCC treated with preoperative afatinib who underwent a baseline and evaluation [18F]FDG PET/CT scan were included (EORTC: n = 20, Unicancer: n = 34). Tumours were delineated, and radiomic features were extracted. Each cohort served once as a training and once as an external validation set for the prediction of overall survival. Supervised feature selection was performed using variable hunting with variable importance, selecting the top two features. A Cox proportional hazards regression model using selected radiomic features and clinical characteristics was fitted on the training dataset and validated in the external validation set. Model performances are expressed by the concordance index (C-index). RESULTS In both models, the radiomic model surpassed the clinical model with validation C-indices of 0.69 and 0.79 vs. 0.60 and 0.67, respectively. The model that combined the radiomic features and clinical variables performed best, with validation C-indices of 0.71 and 0.82. CONCLUSION Although assessed in two small but independent cohorts, an [18F]FDG-PET radiomic signature based on the evaluation scan seems promising for the prediction of overall survival for HNSSC treated with preoperative afatinib. The robustness and clinical applicability of this radiomic signature should be assessed in a larger cohort.
Collapse
Affiliation(s)
- Wyanne A Noortman
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Nicolas Aide
- Nuclear Medicine Department, Centre Hospitalier Universitaire de Caen, 14000 Caen, France
| | - Dennis Vriens
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Lisa S Arkes
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Technical Medicine, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Cornelis H Slump
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Ronald Boellaard
- Amsterdam University Medical Center, 1081 HV Amsterdam, The Netherlands
| | - Jelle J Goeman
- Department of Biomedical Data Sciences, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Christophe M Deroose
- Nuclear Medicine and Molecular Imaging, Department of Imaging & Pathology, University Hospitals Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Jean-Pascal Machiels
- Department of Medical Oncology, Institut Roi Albert II, Cliniques Universitaires Saint-Luc, 1200 Brussels, Belgium
- Institute for Experimental and Clinical Research (IREC, pôle MIRO), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Lisa F Licitra
- Department of Head and Neck Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, University of Milan, 20133 Milan, Italy
| | - Renaud Lhommel
- Division of Nuclear Medicine, Institut de Recherche Clinique, Cliniques Universitaires Saint Luc, 1200 Brussels, Belgium
| | - Alessandra Alessi
- Department of Nuclear Medicine-PET Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Erwin Woff
- Nuclear Medicine Department, Institut Jules Bordet, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), 1070 Bruxelles, Belgium
| | - Karolien Goffin
- Nuclear Medicine and Molecular Imaging, Department of Imaging & Pathology, University Hospitals Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation, Institut Curie, Paris-Saclay University, 75005 Paris, France
| | - Jocelyn Gal
- Epidemiology and Biostatistics Department, Centre Antoine Lacassagne, University Côte d'Azur, 06100 Nice, France
| | - Stéphane Temam
- Department of Head and Neck Surgery Gustave Roussy, 94805 Villejuif, France
| | | | - Floris H P van Velden
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Lioe-Fee de Geus-Oei
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
- Department of Radiation Science & Technology, Delft University of Technology, 2628 CD Delft, The Netherlands
| |
Collapse
|
7
|
Lu CF, Liao CY, Chao HS, Chiu HY, Wang TW, Lee Y, Chen JR, Shiao TH, Chen YM, Wu YT. A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer. Cancer Imaging 2023; 23:9. [PMID: 36670497 PMCID: PMC9854198 DOI: 10.1186/s40644-023-00522-5] [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: 09/21/2022] [Accepted: 01/05/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with EGFR-TKIs and develop disease progression within 1 year. Therefore, the early prediction of tumor progression in patients who receive EGFR-TKIs can facilitate patient management and development of treatment strategies. We proposed a deep learning approach based on both quantitative computed tomography (CT) characteristics and clinical data to predict progression-free survival (PFS) in patients with advanced NSCLC after EGFR-TKI treatment. METHODS A total of 593 radiomic features were extracted from pretreatment chest CT images. The DeepSurv models for the progression risk stratification of EGFR-TKI treatment were proposed based on CT radiomic and clinical features from 270 stage IIIB-IV EGFR-mutant NSCLC patients. Time-dependent PFS predictions at 3, 12, 18, and 24 months and estimated personalized PFS curves were calculated using the DeepSurv models. RESULTS The model combining clinical and radiomic features demonstrated better prediction performance than the clinical model. The model achieving areas under the curve of 0.76, 0.77, 0.76, and 0.86 can predict PFS at 3, 12, 18, and 24 months, respectively. The personalized PFS curves showed significant differences (p < 0.003) between groups with good (PFS > median) and poor (PFS < median) tumor control. CONCLUSIONS The DeepSurv models provided reliable multi-time-point PFS predictions for EGFR-TKI treatment. The personalized PFS curves can help make accurate and individualized predictions of tumor progression. The proposed deep learning approach holds promise for improving the pre-TKI personalized management of patients with EGFR-mutated NSCLC.
Collapse
Affiliation(s)
- Chia-Feng Lu
- grid.260539.b0000 0001 2059 7017Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chien-Yi Liao
- grid.260539.b0000 0001 2059 7017Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Heng-Sheng Chao
- grid.278247.c0000 0004 0604 5314Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Hwa-Yen Chiu
- grid.278247.c0000 0004 0604 5314Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ting-Wei Wang
- grid.260539.b0000 0001 2059 7017Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen Lee
- grid.260539.b0000 0001 2059 7017Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jyun-Ru Chen
- grid.260539.b0000 0001 2059 7017Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tsu-Hui Shiao
- grid.278247.c0000 0004 0604 5314Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- grid.278247.c0000 0004 0604 5314Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Te Wu
- grid.260539.b0000 0001 2059 7017Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| |
Collapse
|
8
|
Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
Collapse
Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| |
Collapse
|
9
|
Han X, Tang X, Zhu H, Zhu D, Zhang X, Meng X, Hua Y, Wang Z, Zhang Y, Huang W, Wang L, Yuan S, Zhang P, Gong H, Sun Y, Zhang Y, Liu Z, Dong X, Gai F, Huang Z, Zhu C, Guo J, Wang Z. Short-term dynamics of circulating tumor DNA predicting efficacy of sintilimab plus docetaxel in second-line treatment of advanced NSCLC: biomarker analysis from a single-arm, phase 2 trial. J Immunother Cancer 2022; 10:jitc-2022-004952. [PMID: 36600554 PMCID: PMC9730395 DOI: 10.1136/jitc-2022-004952] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/10/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Robust biomarker predicting efficacy of immunotherapy is limited. Circulating tumor DNA (ctDNA) sought to effectively monitor therapeutic response as well as disease progression. This study aims to investigate predictive role of ctDNA short-term dynamic change (6 weeks postimmunotherapy) in a single-arm, phase 2 trial of sintilimab plus docetaxel for previously treated advanced non-small cell lung cancer (NSCLC) patients. METHODS A total of 33 patients with advanced NSCLC with disease progression during or after any first-line treatment were prospectively enrolled between 2019 and 2020. Patients received sintilimab (200 mg, day 1, every 3 weeks) plus docetaxel (75 mg/m2, day 3, every 3 weeks) for 4-6 cycles, followed by maintenance therapy with sintilimab (200 mg, day 1, every 3 weeks) until disease progression or unacceptable toxic effects. Blood samples were prospectively collected at baseline, and after 2 cycles of treatment (6 weeks post-treatment). All samples were subjected to targeted next-generation sequencing with a panel of 448 cancer-related genes. The landscape of high-frequency genomic profile of baseline and 6th week was described. Major molecular characteristics in preselected genes of interest associated with response to second-line chemoimmunotherapy were analyzed. The curative effects and prognosis of patients were evaluated. RESULTS Patients with ctDNA clearance at 6th week had decreased tumor volume, while most patients with positive ctDNA at 6th-week experienced an increase in tumor volume. Positive 6th-week ctDNA was associated with significantly shorter progression-free survival (PFS) (91 vs NR days; p<0.0001) and overall survival (47 vs 467 days; p =0.0039). Clearance of clonal mutations and none new clonal formation at 6th week were associated with longer PFS (mPFS 89 vs 266 days, p =0.003). ctDNA clearance at 6th week was an independent risk factor for progression or death (HR=100 (95% CI 4.10 to 2503.00), p=0.005). CONCLUSION ctDNA status and ctDNA mutation clearance putatively serve as predictive biomarkers for sintilimab combined with docetaxel chemotherapy in pretreated advanced NSCLC patients.
Collapse
Affiliation(s)
- Xiao Han
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Xiaoyong Tang
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Hui Zhu
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Dongyuan Zhu
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Xiqin Zhang
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Xiangjiao Meng
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ying Hua
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Zhongtang Wang
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Yan Zhang
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Wei Huang
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Linlin Wang
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Shuanghu Yuan
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Pinliang Zhang
- Internal Medicine Department, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Heyi Gong
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Yulan Sun
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Yingjie Zhang
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Zengjun Liu
- Internal Medicine Department, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Xiaomeng Dong
- Medical Department, Amoy Diagnostics Co Ltd, Xiamen, Fujian, China
| | - Fei Gai
- Medical Department, Amoy Diagnostics Co Ltd, Xiamen, Fujian, China
| | - Zhan Huang
- Medical Department, Amoy Diagnostics Co Ltd, Xiamen, Fujian, China
| | - Changbin Zhu
- Medical Department, Amoy Diagnostics Co Ltd, Xiamen, Fujian, China
| | - Jun Guo
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| | - Zhehai Wang
- Department of Internal Medicine Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China
| |
Collapse
|
10
|
Volpe S, Isaksson LJ, Zaffaroni M, Pepa M, Raimondi S, Botta F, Lo Presti G, Vincini MG, Rampinelli C, Cremonesi M, de Marinis F, Spaggiari L, Gandini S, Guckenberger M, Orecchia R, Jereczek-Fossa BA. Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer. Transl Lung Cancer Res 2022; 11:2452-2463. [PMID: 36636424 PMCID: PMC9830263 DOI: 10.21037/tlcr-22-248] [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] [Received: 03/30/2022] [Accepted: 08/31/2022] [Indexed: 11/24/2022]
Abstract
Background No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. Methods Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. Results Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). Conclusions Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models.
Collapse
Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy;,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Mattia Zaffaroni
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Giuliana Lo Presti
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Maria Giulia Vincini
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Cristiano Rampinelli
- Department of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Filippo de Marinis
- Division of Thoracic Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy;,Division of Thoracic Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Gandini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roberto Orecchia
- Scientific Direction, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy;,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| |
Collapse
|
11
|
Russo GI, Musso N, Lo Giudice A, Asmundo MG, Di Mauro M, Bonacci PG, Massimino M, Bivona D, Stefani S, Pricoco E, Ferro M, Camarda M, Cimino S, Morgia G, Caltabiano R, Broggi G. PD-1, PD-L1 and cAMP immunohistochemical expressions are associated with worse oncological outcome in patients with bladder cancer. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04262-0. [PMID: 35972693 DOI: 10.1007/s00432-022-04262-0] [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: 06/03/2022] [Accepted: 08/02/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE In this study, we aimed to identify prognostic factors of cancer mortality in patients who received radical cystectomy and to identify genomic alterations in a sub-cohort of patients with locally advanced (pT3-4) and/or positive lymph nodes bladder cancer (BC). METHODS We collected 101 BC samples from 2010 to 2018 who previously received radical cystectomy. Immunohistochemical slides were evaluated for PPAR, cAMP, IMP3, Ki67, CDK4, POU5F1, Cyclin E and MDM2, p65, CD3, CD4, CD8, CD20, CD68, CD163, FOXP3, PD-1 and PD-L1 expression. We calculated a prognostic score (PS) based on the positivity to PD-1, PD-L1 and of cAMP (final score ranging from 0 to 3). DNA of each sample have been used for sequencing by NGS in a sub-cohort of 6 patients with locally advanced (pT3-4) and/or positive lymph nodes BC. RESULTS PD-1 + (HR [hazard ratio] 2.59; p = 0.04), PD-L1+ (HR = 6.46; p < 0.01) and cAMP+ (HR 3.04; p = 0.02) were independent predictors of cancer-specific mortality (CSM). Increase of PS (score = 0 as reference) was associated with CSM, 0.81 (p = 0.80), 4.72 (p = 0.01) and 10.51 (p < 0.0) for PS 1, 2 and 3, respectively. ERBB2 was the gene most frequently mutated. CONCLUSION BC exhibited heterogenous protein expression and variable genomic features. Identification of expression of PD-1, PD-L1 and cAMP could help in predicting oncological outcomes.
Collapse
Affiliation(s)
- Giorgio Ivan Russo
- Urology Section, Department of Surgery, University of Catania, 95123, Catania, Italy.
| | - Nicolò Musso
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), University of Catania, 95123, Catania, Italy.,STLab S.R.L., Catania, Italy
| | - Arturo Lo Giudice
- Urology Section, Department of Surgery, University of Catania, 95123, Catania, Italy.,Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
| | | | - Marina Di Mauro
- Urology Section, Department of Surgery, University of Catania, 95123, Catania, Italy
| | - Paolo G Bonacci
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), University of Catania, 95123, Catania, Italy
| | - Mariacristina Massimino
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), University of Catania, 95123, Catania, Italy
| | - Dalida Bivona
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), University of Catania, 95123, Catania, Italy
| | - Stefania Stefani
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), University of Catania, 95123, Catania, Italy
| | - Elisabetta Pricoco
- Department of Medical and Surgical Sciences and Advanced Technologies "G. F. Ingrassia", Anatomic Pathology, University of Catania, 95123, Catania, Italy
| | - Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
| | | | - Sebastiano Cimino
- Urology Section, Department of Surgery, University of Catania, 95123, Catania, Italy
| | - Giuseppe Morgia
- Urology Section, Department of Surgery, University of Catania, 95123, Catania, Italy.,Department of Experimental Oncology, Mediterranean Institute of Oncology (IOM), 95029, Catania, Italy
| | - Rosario Caltabiano
- Department of Medical and Surgical Sciences and Advanced Technologies "G. F. Ingrassia", Anatomic Pathology, University of Catania, 95123, Catania, Italy
| | - Giuseppe Broggi
- Department of Medical and Surgical Sciences and Advanced Technologies "G. F. Ingrassia", Anatomic Pathology, University of Catania, 95123, Catania, Italy
| |
Collapse
|
12
|
Yin X, Liao H, Yun H, Lin N, Li S, Xiang Y, Ma X. Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer. Semin Cancer Biol 2022; 86:146-159. [PMID: 35963564 DOI: 10.1016/j.semcancer.2022.08.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 11/26/2022]
Abstract
Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.
Collapse
Affiliation(s)
- Xiaomeng Yin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hu Liao
- Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hong Yun
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Nan Lin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Shen Li
- West China School of Medicine, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Yu Xiang
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
| |
Collapse
|
13
|
The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers (Basel) 2022; 14:cancers14143349. [PMID: 35884409 PMCID: PMC9321521 DOI: 10.3390/cancers14143349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications. Abstract Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.
Collapse
|
14
|
Cucchiara F, Luci G, Giannini N, Giorgi FS, Orlandi P, Banchi M, Di Paolo A, Pasqualetti F, Danesi R, Bocci G. Association of plasma levetiracetam concentration, MGMT methylation and sex with survival of chemoradiotherapy-treated glioblastoma patients. Pharmacol Res 2022; 181:106290. [DOI: 10.1016/j.phrs.2022.106290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/18/2022] [Accepted: 06/02/2022] [Indexed: 12/27/2022]
|
15
|
Liquid biopsy: early and accurate diagnosis of brain tumor. J Cancer Res Clin Oncol 2022; 148:2347-2373. [PMID: 35451698 DOI: 10.1007/s00432-022-04011-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/01/2022] [Indexed: 12/15/2022]
Abstract
Noninvasive examination is an emerging area in the field of neuro-oncology. Liquid biopsy captures the landscape of genomic alterations of brain tumors and revolutionizes the traditional diagnosis approaches. Rapidly changing sequencing technologies and more affordable prices put the screws on more application of liquid biopsy in clinical settings. In the past few years, extensive application of liquid biopsy has been seen throughout the whole diagnosis and treatment process of brain tumors, including early and accurate detection, characterization and dynamic monitoring. Here, we summarized and compared the most advanced techniques and target molecules or macrostructures related to brain tumor liquid biopsy. We further reviewed and emphasized recent progression in different clinical settings for brain tumors in blood and CSF. The preferred protocol, potential novel biomarkers and future development are discussed in the last part.
Collapse
|
16
|
EGFR signaling pathway as therapeutic target in human cancers. Semin Cancer Biol 2022; 85:253-275. [PMID: 35427766 DOI: 10.1016/j.semcancer.2022.04.002] [Citation(s) in RCA: 76] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/12/2022] [Accepted: 04/04/2022] [Indexed: 02/08/2023]
Abstract
Epidermal Growth Factor Receptor (EGFR) enacts major roles in the maintenance of epithelial tissues. However, when EGFR signaling is altered, it becomes the grand orchestrator of epithelial transformation, and hence one of the most world-wide studied tyrosine kinase receptors involved in neoplasia, in several tissues. In the last decades, EGFR-targeted therapies shaped the new era of precision-oncology. Despite major advances, the dream of converting solid tumors into a chronic disease is still unfulfilled, and long-term remission eludes us. Studies investigating the function of this protein in solid malignancies have revealed numerous ways how tumor cells dysregulate EGFR function. Starting from preclinical models (cell lines, organoids, murine models) and validating in clinical specimens, EGFR-related oncogenic pathways, mechanisms of resistance, and novel avenues to inhibit tumor growth and metastatic spread enriching the therapeutic portfolios, were identified. Focusing on non-small cell lung cancer (NSCLC), where EGFR mutations are major players in the adenocarcinoma subtype, we will go over the most relevant discoveries that led us to understand EGFR and beyond, and highlight how they revolutionized cancer treatment by expanding the therapeutic arsenal at our disposal.
Collapse
|
17
|
The Role of Artificial Intelligence in Early Cancer Diagnosis. Cancers (Basel) 2022; 14:cancers14061524. [PMID: 35326674 PMCID: PMC8946688 DOI: 10.3390/cancers14061524] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 02/01/2023] Open
Abstract
Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards.
Collapse
|
18
|
Morrison G, Buckley J, Ostrow D, Varghese B, Cen SY, Werbin J, Ericson N, Cunha A, Lu YT, George T, Smith J, Quinn D, Duddalwar V, Triche T, Goldkorn A. Non-Invasive Profiling of Advanced Prostate Cancer via Multi-Parametric Liquid Biopsy and Radiomic Analysis. Int J Mol Sci 2022; 23:2571. [PMID: 35269713 PMCID: PMC8910093 DOI: 10.3390/ijms23052571] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/16/2022] [Accepted: 02/18/2022] [Indexed: 11/16/2022] Open
Abstract
Integrating liquid biopsies of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) with other minimally invasive measures may yield more comprehensive disease profiles. We evaluated the feasibility of concurrent cellular and molecular analysis of CTCs and cfDNA combined with radiomic analysis of CT scans from patients with metastatic castration-resistant PC (mCRPC). CTCs from 22 patients were enumerated, stained for PC-relevant markers, and clustered based on morphometric and immunofluorescent features using machine learning. DNA from single CTCs, matched cfDNA, and buffy coats was sequenced using a targeted amplicon cancer hotspot panel. Radiomic analysis was performed on bone metastases identified on CT scans from the same patients. CTCs were detected in 77% of patients and clustered reproducibly. cfDNA sequencing had high sensitivity (98.8%) for germline variants compared to WBC. Shared and unique somatic variants in PC-related genes were detected in cfDNA in 45% of patients (MAF > 0.1%) and in CTCs in 92% of patients (MAF > 10%). Radiomic analysis identified a signature that strongly correlated with CTC count and plasma cfDNA level. Integration of cellular, molecular, and radiomic data in a multi-parametric approach is feasible, yielding complementary profiles that may enable more comprehensive non-invasive disease modeling and prediction.
Collapse
Affiliation(s)
- Gareth Morrison
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
| | - Jonathan Buckley
- Department of Population and Public Health Sciences, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA;
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA; (D.O.); (T.T.)
| | - Dejerianne Ostrow
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA; (D.O.); (T.T.)
| | - Bino Varghese
- Department of Radiology, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA;
| | - Steven Y. Cen
- Departments of Radiology and Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA;
| | - Jeffrey Werbin
- RareCyte, Inc., Seattle, WA 98121, USA; (J.W.); (N.E.); (T.G.)
| | - Nolan Ericson
- RareCyte, Inc., Seattle, WA 98121, USA; (J.W.); (N.E.); (T.G.)
| | - Alexander Cunha
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
| | - Yi-Tsung Lu
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
| | - Thaddeus George
- RareCyte, Inc., Seattle, WA 98121, USA; (J.W.); (N.E.); (T.G.)
| | - Jeffrey Smith
- Clinical Sequencing Division, Thermo Fisher Scientific, San Francisco, CA 94080, USA;
| | - David Quinn
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
| | - Vinay Duddalwar
- Departments of Radiology and Urology, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA;
| | - Timothy Triche
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA; (D.O.); (T.T.)
| | - Amir Goldkorn
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
| |
Collapse
|
19
|
Kroschke J, von Stackelberg O, Heußel CP, Wielpütz MO, Kauczor HU. Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring. ROFO-FORTSCHR RONTG 2022; 194:720-727. [PMID: 35211928 DOI: 10.1055/a-1729-1516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths. The development of therapies targeting molecular alterations has significantly improved the treatment of NSCLC patients. To identify these targets, tumor phenotyping is required, with tissue biopsies and molecular pathology being the gold standard. Some patients do not respond to targeted therapies and many patients suffer from tumor recurrence, which can in part be explained by tumor heterogeneity. This points out the need for new biomarkers allowing for better tumor phenotyping and monitoring during treatment to assess patient outcome. METHOD The contents of this review are based on a literature search conducted using the PubMed database in March 2021 and the authors' experience. RESULTS AND CONCLUSION The use of radiomics and artificial intelligence-based approaches allows for the identification of imaging biomarkers in NSCLC patients for tumor phenotyping. Several studies show promising results for models predicting molecular alterations, with the best results being achieved by combining structural and functional imaging. Radiomics could help solve the pressing clinical need for assessing and predicting therapy response. To reach this goal, advanced tumor phenotyping, considering tumor heterogeneity, is required. This could be achieved by integrating structural and functional imaging biomarkers with clinical data sources, such as liquid biopsy results. However, to allow for radiomics-based approaches to be introduced into clinical practice, further standardization using large, multi-center datasets is required. KEY POINTS · Some NSCLC patients do not benefit from targeted therapies, and many patients suffer from tumor recurrence, pointing out the need for new biomarkers allowing for better tumor phenotyping and monitoring during treatment.. · The use of radiomics-based approaches allows for the identification of imaging biomarkers in NSCLC patients for tumor phenotyping.. · A multi-omics approach integrating not only structural and functional imaging biomarkers but also clinical data sources, such as liquid biopsy results, could further enhance the prediction and assessment of therapy response.. CITATION FORMAT · Kroschke J, von Stackelberg O, Heußel CP et al. Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1729-1516.
Collapse
Affiliation(s)
- Jonas Kroschke
- Department for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Germany.,Translational Lung Research Center (TLRC), German Center for Lung Research, Giessen, Germany.,Department for Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik am Universitätsklinikum Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Department for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Germany.,Translational Lung Research Center (TLRC), German Center for Lung Research, Giessen, Germany.,Department for Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik am Universitätsklinikum Heidelberg, Germany
| | - Claus Peter Heußel
- Department for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Germany.,Translational Lung Research Center (TLRC), German Center for Lung Research, Giessen, Germany.,Department for Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik am Universitätsklinikum Heidelberg, Germany
| | - Mark Oliver Wielpütz
- Department for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Germany.,Translational Lung Research Center (TLRC), German Center for Lung Research, Giessen, Germany.,Department for Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik am Universitätsklinikum Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Germany.,Translational Lung Research Center (TLRC), German Center for Lung Research, Giessen, Germany.,Department for Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik am Universitätsklinikum Heidelberg, Germany
| |
Collapse
|
20
|
Ma JW, Li M. Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects. Transl Cancer Res 2022; 10:4217-4231. [PMID: 35116717 PMCID: PMC8797562 DOI: 10.21037/tcr-21-1037] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 09/03/2021] [Indexed: 12/21/2022]
Abstract
Objective The purpose of this paper was to perform a narrative review of current research evidence on conventional computed tomography (CT) imaging features and CT image-based radiomic features for predicting gene mutations in lung adenocarcinoma and discuss how to translate the research findings to guide future practice. Background Lung cancer, especially lung adenocarcinoma, is the leading cause of cancer-related deaths. With advances in the diagnosis and treatment of lung adenocarcinoma with the emergence of molecular testing, the prediction of oncogenes and even drug resistance gene mutations have become key to individualized and precise clinical treatment in order to prolong survival and improve quality of life. The progress of imageological examination includes the development of CT and radiomics are promising quantitative methods for predicting different gene mutations in lung adenocarcinoma, especially common mutations, such as epidermal growth factor receptor (EGFR) mutation, anaplastic lymphoma kinase (ALK) mutation and Kirsten rat sarcoma viral oncogene (KRAS) mutation. Methods The PubMed electronic database was searched along with a set of terms specific to lung adenocarcinoma, radiomics (including texture analysis), CT, computed tomography, EGFR, ALK, KRAS, rearranging transfection (RET) rearrangement and c-ros oncogene 1 (ROS-1), v-raf murine sarcoma viral oncogene homolog B1 (BRAF), and human epidermal growth factor receptor 2 (HER2) mutations et al. This review has been reported in compliance with the Narrative Review checklist guidelines. From each full-text article, information was extracted regarding a set of terms above. Conclusions Research on the application of conventional CT features and CT image-based radiomic features for predicting the gene mutation status of lung adenocarcinoma is still in a preliminary stage. Noninvasively determination of mutation status in lung adenocarcinoma before targeted therapy with conventional CT features and CT image-based radiomic features remains both hopes and challenges. Before radiomics could be applied in clinical practice, more work needs to be done.
Collapse
Affiliation(s)
- Jing-Wen Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
21
|
Araujo-Filho JAB, Mayoral M, Horvat N, Santini F, Gibbs P, Ginsberg MS. Radiogenomics in personalized management of lung cancer patients: Where are we? Clin Imaging 2022; 84:54-60. [DOI: 10.1016/j.clinimag.2022.01.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 01/03/2022] [Accepted: 01/24/2022] [Indexed: 11/03/2022]
|
22
|
Treatment-driven tumour heterogeneity and drug resistance: lessons from solid tumours. Cancer Treat Rev 2022; 104:102340. [DOI: 10.1016/j.ctrv.2022.102340] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/16/2022] [Accepted: 01/17/2022] [Indexed: 02/07/2023]
|
23
|
Sardarabadi P, Kojabad AA, Jafari D, Liu CH. Liquid Biopsy-Based Biosensors for MRD Detection and Treatment Monitoring in Non-Small Cell Lung Cancer (NSCLC). BIOSENSORS 2021; 11:394. [PMID: 34677350 PMCID: PMC8533977 DOI: 10.3390/bios11100394] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/12/2021] [Accepted: 10/12/2021] [Indexed: 12/12/2022]
Abstract
Globally, non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths. Despite advancements in chemotherapy and targeted therapies, the 5-year survival rate has remained at 16% for the past forty years. Minimal residual disease (MRD) is described as the existence of either isolated tumour cells or circulating tumour cells in biological liquid of patients after removal of the primary tumour without any clinical signs of cancer. Recently, liquid biopsy has been promising as a non-invasive method of disease monitoring and treatment guidelines as an MRD marker. Liquid biopsy could be used to detect and assess earlier stages of NSCLC, post-treatment MRD, resistance to targeted therapies, immune checkpoint inhibitors (ICIs) and tumour mutational burden. MRD surveillance has been proposed as a potential marker for lung cancer relapse. Principally, biosensors provide the quantitative analysis of various materials by converting biological functions into quantifiable signals. Biosensors are usually operated to detect antibodies, enzymes, DNA, RNA, extracellular vesicles (EVs) and whole cells. Here, we present a category of biosensors based on the signal transduction method for identifying biosensor-based biomarkers in liquid biopsy specimens to monitor lung cancer treatment.
Collapse
Affiliation(s)
- Parvaneh Sardarabadi
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 30044, Taiwan;
| | - Amir Asri Kojabad
- Department of Hematology, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran 14535, Iran;
| | - Davod Jafari
- Department of Medical Biotechnology, School of Allied Medicine, Iran University of Medical Sciences, Tehran 14535, Iran;
| | - Cheng-Hsien Liu
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 30044, Taiwan;
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30044, Taiwan
| |
Collapse
|
24
|
Liu L, Chen X, Petinrin OO, Zhang W, Rahaman S, Tang ZR, Wong KC. Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey. Life (Basel) 2021; 11:638. [PMID: 34209249 PMCID: PMC8308091 DOI: 10.3390/life11070638] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 12/24/2022] Open
Abstract
With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.
Collapse
Affiliation(s)
- Linjing Liu
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Xingjian Chen
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Olutomilayo Olayemi Petinrin
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Weitong Zhang
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Saifur Rahaman
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Zhi-Ri Tang
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
- Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong, China
| |
Collapse
|