1
|
Chakrabarty N, Mahajan A. Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review. Clin Oncol (R Coll Radiol) 2024; 36:498-513. [PMID: 37806795 DOI: 10.1016/j.clon.2023.09.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/09/2023] [Accepted: 09/21/2023] [Indexed: 10/10/2023]
Abstract
The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.
Collapse
Affiliation(s)
- N Chakrabarty
- Department of Radiodiagnosis, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Parel, Mumbai, Maharashtra, India.
| | - A Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.
| |
Collapse
|
2
|
Zuo X, Kumar A, Shen S, Li J, Cong G, Jin E, Chen Q, Warner JL, Yang P, Xu H. Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition. JCO Clin Cancer Inform 2024; 8:e2300166. [PMID: 38885475 DOI: 10.1200/cci.23.00166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/27/2024] [Accepted: 03/11/2024] [Indexed: 06/20/2024] Open
Abstract
PURPOSE The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning-/deep learning-based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.
Collapse
Affiliation(s)
- Xu Zuo
- University of Texas Health Science Center, Houston, TX
| | | | | | - Jianfu Li
- University of Texas Health Science Center, Houston, TX
| | - Grace Cong
- University of Maryland, College Park, College Park, MD
| | - Edward Jin
- University of Southern California, Los Angeles, CA
| | - Qingxia Chen
- Vanderbilt University Medical Center, Nashville, TN
| | - Jeremy L Warner
- Vanderbilt University Medical Center, Nashville, TN
- Legorreta Cancer Center at Brown University, Providence, RI
- Lifespan Cancer Institute, Providence, RI
| | | | - Hua Xu
- Yale University, New Haven, CT
| |
Collapse
|
3
|
Bryant AK, Zamora‐Resendiz R, Dai X, Morrow D, Lin Y, Jungles KM, Rae JM, Tate A, Pearson AN, Jiang R, Fritsche L, Lawrence TS, Zou W, Schipper M, Ramnath N, Yoo S, Crivelli S, Green MD. Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances. Cancer Med 2024; 13:e7253. [PMID: 38899720 PMCID: PMC11187737 DOI: 10.1002/cam4.7253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/05/2024] [Accepted: 04/28/2024] [Indexed: 06/21/2024] Open
Abstract
PURPOSE Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. METHODS We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. RESULTS Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. CONCLUSIONS Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.
Collapse
Affiliation(s)
- Alex K. Bryant
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
| | - Rafael Zamora‐Resendiz
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Xin Dai
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Destinee Morrow
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Yuewei Lin
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Kassidy M. Jungles
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - James M. Rae
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Internal MedicineUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Akshay Tate
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Ashley N. Pearson
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Ralph Jiang
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Lars Fritsche
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Theodore S. Lawrence
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Weiping Zou
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
- Center of Excellence for Cancer Immunology and ImmunotherapyUniversity of Michigan Rogel Cancer CenterAnn ArborMichiganUSA
- Department of PathologyUniversity of MichiganAnn ArborMichiganUSA
- Graduate Program in ImmunologyUniversity of MichiganAnn ArborMichiganUSA
| | - Matthew Schipper
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Nithya Ramnath
- Division of Hematology Oncology, Department of MedicineUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Division of Hematology Oncology, Department of MedicineVeterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Silvia Crivelli
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Michael D. Green
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
- Graduate Program in ImmunologyUniversity of MichiganAnn ArborMichiganUSA
- Graduate Program in Cancer BiologyUniversity of MichiganAnn ArborMichiganUSA
- Department of Microbiology and ImmunologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| |
Collapse
|
4
|
Sinha T, Khan A, Awan M, Bokhari SFH, Ali K, Amir M, Jadhav AN, Bakht D, Puli ST, Burhanuddin M. Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review. Cureus 2024; 16:e61220. [PMID: 38939246 PMCID: PMC11210434 DOI: 10.7759/cureus.61220] [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] [Accepted: 05/27/2024] [Indexed: 06/29/2024] Open
Abstract
Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.
Collapse
Affiliation(s)
- Tanya Sinha
- Internal Medicine, Tribhuvan University, Kathmandu, NPL
| | - Aiman Khan
- Medicine, Liaquat College of Medicine and Dentistry, Karachi, PAK
| | - Manahil Awan
- General Practice, Liaquat National Hospital and Medical College, Karachi, PAK
| | | | - Khawar Ali
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | - Maaz Amir
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | - Aneesh N Jadhav
- Pediatrics, Bharat Ratna Dr. Babasaheb Ambedkar Memorial Hospital, Mumbai, IND
| | - Danyal Bakht
- Medicine and Surgery, Mayo Hospital, Lahore, PAK
| | - Sai Teja Puli
- Internal Medicine, Bhaskar Medical College, Hyderabad, IND
| | | |
Collapse
|
5
|
Qin Y, Huo M, Liu X, Li SC. Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy. Front Immunol 2024; 15:1368749. [PMID: 38524135 PMCID: PMC10957591 DOI: 10.3389/fimmu.2024.1368749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 02/27/2024] [Indexed: 03/26/2024] Open
Abstract
Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.
Collapse
Affiliation(s)
- Yurong Qin
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Miaozhe Huo
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Xingwu Liu
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| |
Collapse
|
6
|
Qian L, Wu T, Kong S, Lou X, Jiang Y, Tan Z, Wu L, Gao C. Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer? A systematic review. Eur J Radiol 2024; 171:111314. [PMID: 38244306 DOI: 10.1016/j.ejrad.2024.111314] [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: 10/27/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/22/2024]
Abstract
OBJECTIVES To summarize the underlying biological correlation of prognostic radiomics and deep learning signatures in patients with lung cancer and evaluate the quality of available studies. METHODS This study examined databases including the PubMed, Embase, Web of Science Core Collection, and Cochrane Library, for studies that elaborated on the underlying biological correlation with prognostic radiomics and deep learning signatures based on CT or PET/CT for predicting the prognosis in patients with lung cancer. Information about the patient and radiogenomic analyses was extracted for the included studies. The Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of these studies. RESULTS Twelve studies were included with 7,338 patients from 2014 to 2022. All studies except for one were retrospective. Supervised machine learning was adopted in six studies, and the remaining used unsupervised machine learning methods. Gene sequencing and histopathological data were analyzed by 83.33% and 16.67% of the included studies, respectively. Gene set enrichment analysis and correlation analysis were most used to explore the biological meaning of prognostic signatures. The median RQS for supervised learning articles was 13.5 (range 12-19) and 7.0 (range 5-14) for unsupervised learning articles. The studies included in this report were assessed to have high risk of bias overall. CONCLUSION The biological basis for the interpretability of data-driven models mainly focused on genomics and histopathological factors, and it may improve the prognosis of lung cancer with more proper biological interpretation in the future.
Collapse
Affiliation(s)
- Lujie Qian
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ting Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuaihang Kong
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinjing Lou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yixiao Jiang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhengxin Tan
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Linyu Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
| |
Collapse
|
7
|
Cobanaj M, Corti C, Dee EC, McCullum L, Boldrini L, Schlam I, Tolaney SM, Celi LA, Curigliano G, Criscitiello C. Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow. Eur J Cancer 2024; 198:113504. [PMID: 38141549 DOI: 10.1016/j.ejca.2023.113504] [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: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/25/2023]
Abstract
Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.
Collapse
Affiliation(s)
- Marisa Cobanaj
- National Center for Radiation Research in Oncology, OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Chiara Corti
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy.
| | - Edward C Dee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucas McCullum
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Laura Boldrini
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Ilana Schlam
- Department of Hematology and Oncology, Tufts Medical Center, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sara M Tolaney
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Leo A Celi
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| |
Collapse
|
8
|
Prelaj A, Miskovic V, Zanitti M, Trovo F, Genova C, Viscardi G, Rebuzzi SE, Mazzeo L, Provenzano L, Kosta S, Favali M, Spagnoletti A, Castelo-Branco L, Dolezal J, Pearson AT, Lo Russo G, Proto C, Ganzinelli M, Giani C, Ambrosini E, Turajlic S, Au L, Koopman M, Delaloge S, Kather JN, de Braud F, Garassino MC, Pentheroudakis G, Spencer C, Pedrocchi ALG. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol 2024; 35:29-65. [PMID: 37879443 DOI: 10.1016/j.annonc.2023.10.125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/31/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.
Collapse
Affiliation(s)
- A Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland.
| | - V Miskovic
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - M Zanitti
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - F Trovo
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - C Genova
- UO Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genoa; Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa
| | - G Viscardi
- Precision Medicine Department, Università degli Studi della Campania Luigi Vanvitelli, Naples
| | - S E Rebuzzi
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa; Medical Oncology Unit, Ospedale San Paolo, Savona, Italy
| | - L Mazzeo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - L Provenzano
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - S Kosta
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - M Favali
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - A Spagnoletti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - L Castelo-Branco
- ESMO European Society for Medical Oncology, Lugano, Switzerland; NOVA National School of Public Health, Lisboa, Portugal
| | - J Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - A T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - G Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Giani
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - E Ambrosini
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - S Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London
| | - L Au
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK; Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne; Sir Peter MacCallum Department of Medical Oncology, The University of Melbourne, Melbourne, Australia
| | - M Koopman
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - S Delaloge
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - J N Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - F de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M C Garassino
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | | | - C Spencer
- Cancer Dynamics Laboratory, The Francis Crick Institute, London.
| | - A L G Pedrocchi
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| |
Collapse
|
9
|
Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Front Oncol 2023; 13:1260374. [PMID: 38148837 PMCID: PMC10750392 DOI: 10.3389/fonc.2023.1260374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.
Collapse
Affiliation(s)
- Minal Çalışkan
- Translational Science Department, Precision Medicine Function, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States
| | - Koichi Tazaki
- Translational Science Department I, Precision Medicine Function, Daiichi Sankyo, Tokyo, Japan
| |
Collapse
|
10
|
Amorrortu R, Garcia M, Zhao Y, El Naqa I, Balagurunathan Y, Chen DT, Thieu T, Schabath MB, Rollison DE. Overview of approaches to estimate real-world disease progression in lung cancer. JNCI Cancer Spectr 2023; 7:pkad074. [PMID: 37738580 PMCID: PMC10637832 DOI: 10.1093/jncics/pkad074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/28/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023] Open
Abstract
BACKGROUND Randomized clinical trials of novel treatments for solid tumors normally measure disease progression using the Response Evaluation Criteria in Solid Tumors. However, novel, scalable approaches to estimate disease progression using real-world data are needed to advance cancer outcomes research. The purpose of this narrative review is to summarize examples from the existing literature on approaches to estimate real-world disease progression and their relative strengths and limitations, using lung cancer as a case study. METHODS A narrative literature review was conducted in PubMed to identify articles that used approaches to estimate real-world disease progression in lung cancer patients. Data abstracted included data source, approach used to estimate real-world progression, and comparison to a selected gold standard (if applicable). RESULTS A total of 40 articles were identified from 2008 to 2022. Five approaches to estimate real-world disease progression were identified including manual abstraction of medical records, natural language processing of clinical notes and/or radiology reports, treatment-based algorithms, changes in tumor volume, and delta radiomics-based approaches. The accuracy of these progression approaches were assessed using different methods, including correlations between real-world endpoints and overall survival for manual abstraction (Spearman rank ρ = 0.61-0.84) and area under the curve for natural language processing approaches (area under the curve = 0.86-0.96). CONCLUSIONS Real-world disease progression has been measured in several observational studies of lung cancer. However, comparing the accuracy of methods across studies is challenging, in part, because of the lack of a gold standard and the different methods used to evaluate accuracy. Concerted efforts are needed to define a gold standard and quality metrics for real-world data.
Collapse
Affiliation(s)
| | - Melany Garcia
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Yayi Zhao
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Dung-Tsa Chen
- Department of Biostatistics and Bionformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Thanh Thieu
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Dana E Rollison
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| |
Collapse
|
11
|
Lin B, Tan Z, Mo Y, Yang X, Liu Y, Xu B. Intelligent oncology: The convergence of artificial intelligence and oncology. JOURNAL OF THE NATIONAL CANCER CENTER 2023; 3:83-91. [PMID: 39036310 PMCID: PMC11256531 DOI: 10.1016/j.jncc.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 10/07/2022] [Accepted: 11/13/2022] [Indexed: 12/12/2022] Open
Abstract
With increasingly explored ideologies and technologies for potential applications of artificial intelligence (AI) in oncology, we here describe a holistic and structured concept termed intelligent oncology. Intelligent oncology is defined as a cross-disciplinary specialty which integrates oncology, radiology, pathology, molecular biology, multi-omics and computer sciences, aiming to promote cancer prevention, screening, early diagnosis and precision treatment. The development of intelligent oncology has been facilitated by fast AI technology development such as natural language processing, machine/deep learning, computer vision, and robotic process automation. While the concept and applications of intelligent oncology is still in its infancy, and there are still many hurdles and challenges, we are optimistic that it will play a pivotal role for the future of basic, translational and clinical oncology.
Collapse
Affiliation(s)
- Bo Lin
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital and Chongqing University School of Medicine, Institute of Intelligent Oncology, Chongqing University, China
| | - Zhibo Tan
- Department of Radiation Oncology, Peking University Shenzhen Hospital, Department of Radiation Oncology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Yaqi Mo
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital and Chongqing University School of Medicine, Institute of Intelligent Oncology, Chongqing University, China
| | - Xue Yang
- Department of Biochemistry and Molecular Biology, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yajie Liu
- Department of Radiation Oncology, Peking University Shenzhen Hospital, Department of Radiation Oncology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Bo Xu
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital and Chongqing University School of Medicine, Institute of Intelligent Oncology, Chongqing University, China
- Department of Biochemistry and Molecular Biology, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| |
Collapse
|
12
|
Araki K, Matsumoto N, Togo K, Yonemoto N, Ohki E, Xu L, Hasegawa Y, Satoh D, Takemoto R, Miyazaki T. Developing Artificial Intelligence Models for Extracting Oncologic Outcomes from Japanese Electronic Health Records. Adv Ther 2023; 40:934-950. [PMID: 36547809 PMCID: PMC9988800 DOI: 10.1007/s12325-022-02397-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION A framework that extracts oncological outcomes from large-scale databases using artificial intelligence (AI) is not well established. Thus, we aimed to develop AI models to extract outcomes in patients with lung cancer using unstructured text data from electronic health records of multiple hospitals. METHODS We constructed AI models (Bidirectional Encoder Representations from Transformers [BERT], Naïve Bayes, and Longformer) for tumor evaluation using the University of Miyazaki Hospital (UMH) database. This data included both structured and unstructured data from progress notes, radiology reports, and discharge summaries. The BERT model was applied to the Life Data Initiative (LDI) data set of six hospitals. Study outcomes included the performance of AI models and time to progression of disease (TTP) for each line of treatment based on the treatment response extracted by AI models. RESULTS For the UMH data set, the BERT model exhibited higher precision accuracy compared to the Naïve Bayes or the Longformer models, respectively (precision [0.42 vs. 0.47 or 0.22], recall [0.63 vs. 0.46 or 0.33] and F1 scores [0.50 vs. 0.46 or 0.27]). When this BERT model was applied to LDI data, prediction accuracy remained quite similar. The Kaplan-Meier plots of TTP (months) showed similar trends for the first (median 14.9 [95% confidence interval 11.5, 21.1] and 16.8 [12.6, 21.8]), the second (7.8 [6.7, 10.7] and 7.8 [6.7, 10.7]), and the later lines of treatment for the predicted data by the BERT model and the manually curated data. CONCLUSION We developed AI models to extract treatment responses in patients with lung cancer using a large EHR database; however, the model requires further improvement.
Collapse
Affiliation(s)
- Kenji Araki
- Patient Advocacy Center, University of Miyazaki Hospital, Miyazaki, Japan
| | - Nobuhiro Matsumoto
- Division of Respirology, Rheumatology, Infectious Diseases, and Neurology, Department of Internal Medicine, University of Miyazaki, Miyazaki, Japan
| | - Kanae Togo
- Health & Value, Pfizer Japan Inc., Tokyo, Japan.
| | | | - Emiko Ohki
- Oncology Medical Affairs, Pfizer Japan Inc, Tokyo, Japan
| | - Linghua Xu
- Health & Value, Pfizer Japan Inc., Tokyo, Japan
| | | | - Daisuke Satoh
- Research and Development Headquarters, NTT DATA Corporation, Tokyo, Japan
| | - Ryota Takemoto
- Manufacturing IT Innovation Sector, NTT DATA Corporation, Tokyo, Japan
| | - Taiga Miyazaki
- Division of Respirology, Rheumatology, Infectious Diseases, and Neurology, Department of Internal Medicine, University of Miyazaki, Miyazaki, Japan
| |
Collapse
|
13
|
Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin Cancer Biol 2023; 91:1-15. [PMID: 36801447 DOI: 10.1016/j.semcancer.2023.02.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/30/2023] [Accepted: 02/15/2023] [Indexed: 02/21/2023]
Abstract
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
Collapse
|
14
|
Prisciandaro E, Sedda G, Cara A, Diotti C, Spaggiari L, Bertolaccini L. Artificial Neural Networks in Lung Cancer Research: A Narrative Review. J Clin Med 2023; 12:jcm12030880. [PMID: 36769528 PMCID: PMC9918295 DOI: 10.3390/jcm12030880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/08/2023] [Accepted: 01/16/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Artificial neural networks are statistical methods that mimic complex neural connections, simulating the learning dynamics of the human brain. They play a fundamental role in clinical decision-making, although their success depends on good integration with clinical protocols. When applied to lung cancer research, artificial neural networks do not aim to be biologically realistic, but rather to provide efficient models for nonlinear regression or classification. METHODS We conducted a comprehensive search of EMBASE (via Ovid), MEDLINE (via PubMed), Cochrane CENTRAL, and Google Scholar from April 2018 to December 2022, using a combination of keywords and related terms for "artificial neural network", "lung cancer", "non-small cell lung cancer", "diagnosis", and "treatment". RESULTS Artificial neural networks have shown excellent aptitude in learning the relationships between the input/output mapping from a given dataset, without any prior information or assumptions about the statistical distribution of the data. They can simultaneously process numerous variables, managing complexity; hence, they have found broad application in tasks requiring attention. CONCLUSIONS Lung cancer is the most common and lethal form of tumor, with limited diagnostic and treatment methods. The advances in tailored medicine have led to the development of novel tools for diagnosis and treatment. Artificial neural networks can provide valuable support for both basic research and clinical decision-making. Therefore, tight cooperation among surgeons, oncologists, and biostatisticians appears mandatory.
Collapse
Affiliation(s)
- Elena Prisciandaro
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giulia Sedda
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Andrea Cara
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Cristina Diotti
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenzo Spaggiari
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy
- Correspondence: ; Tel.: +39-02-57489665; Fax: +39-02-56562994
| |
Collapse
|
15
|
Liu X, Zhu Z, Wang K, Zhang Y, Li J, Wang X, Zhang X, Wang X. Semiautomated pelvic lymph node treatment response evaluation for patients with advanced prostate cancer: based on MET-RADS-P guidelines. Cancer Imaging 2023; 23:7. [PMID: 36650584 PMCID: PMC9847043 DOI: 10.1186/s40644-023-00523-4] [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: 10/04/2022] [Accepted: 01/05/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The evaluation of treatment response according to METastasis Reporting and Data System for Prostate Cancer (MET-RADS-P) criteria is an important but time-consuming task for patients with advanced prostate cancer (APC). A deep learning-based algorithm has the potential to assist with this assessment. OBJECTIVE To develop and evaluate a deep learning-based algorithm for semiautomated treatment response assessment of pelvic lymph nodes. METHODS A total of 162 patients who had undergone at least two scans for follow-up assessment after APC metastasis treatment were enrolled. A previously reported deep learning model was used to perform automated segmentation of pelvic lymph nodes. The performance of the deep learning algorithm was evaluated using the Dice similarity coefficient (DSC) and volumetric similarity (VS). The consistency of the short diameter measurement with the radiologist was evaluated using Bland-Altman plotting. Based on the segmentation of lymph nodes, the treatment response was assessed automatically with a rule-based program according to the MET-RADS-P criteria. Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and two radiologists [attending radiologist (R1) and fellow radiologist (R2)]. RESULTS The mean DSC and VS of the pelvic lymph node segmentation were 0.82 ± 0.09 and 0.88 ± 0.12, respectively. Bland-Altman plotting showed that most of the lymph node measurements were within the upper and lower limits of agreement (LOA). The accuracies of automated segmentation-based assessment were 0.92 (95% CI: 0.85-0.96), 0.91 (95% CI: 0.86-0.95) and 75% (95% CI: 0.46-0.92) for target lesions, nontarget lesions and nonpathological lesions, respectively. The consistency of treatment response assessment based on automated segmentation and manual segmentation was excellent for target lesions [K value: 0.92 (0.86-0.98)], good for nontarget lesions [0.82 (0.74-0.90)] and moderate for nonpathological lesions [0.71 (0.50-0.92)]. CONCLUSION The deep learning-based semiautomated algorithm showed high accuracy for the treatment response assessment of pelvic lymph nodes and demonstrated comparable performance with radiologists.
Collapse
Affiliation(s)
- Xiang Liu
- grid.411472.50000 0004 1764 1621Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034 China
| | - Zemin Zhu
- grid.501248.aDepartment of Hepatobiliary and Pancreatic Surgery, Zhuzhou Central Hospital, Zhuzhou, 412000 China
| | - Kexin Wang
- grid.24696.3f0000 0004 0369 153XSchool of Basic Medical Sciences, Capital Medical University, Beijing, 100069 China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, 100011 China
| | - Jialun Li
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, 100011 China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, 100011 China
| | - Xiaodong Zhang
- grid.411472.50000 0004 1764 1621Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034 China
| | - Xiaoying Wang
- grid.411472.50000 0004 1764 1621Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034 China
| |
Collapse
|
16
|
Joo MS, Pyo KH, Chung JM, Cho BC. Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide. Front Bioeng Biotechnol 2023; 11:1081950. [PMID: 36873350 PMCID: PMC9975749 DOI: 10.3389/fbioe.2023.1081950] [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: 10/27/2022] [Accepted: 01/24/2023] [Indexed: 02/17/2023] Open
Abstract
The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connection with clinical cohort and ribonucleic acid (RNA) sequencing data, including single-cell ribonucleic acid (scRNA) sequencing data. This paper investigates statistical techniques and artificial intelligence (AI) based non-small cell lung cancer transcriptome data analysis methods divided into target and analysis technology groups. The methodologies of transcriptome data were schematically categorized so researchers can easily match analysis methods according to their goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. Transcriptome analysis methods are divided into three major categories: Statistical analysis, machine learning, and deep learning. Specific models and ensemble techniques typically used in NSCLC analysis are summarized in this paper, with the intent to lay a foundation for advanced research by converging and linking the various analysis methods available.
Collapse
Affiliation(s)
- Min Soo Joo
- School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea
| | - Kyoung-Ho Pyo
- Department of Oncology, Severance Hospital, College of Medicine, Yonsei University, Seoul, Republic of Korea.,Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.,Yonsei New Il Han Institute for Integrative Lung Cancer Research, Yonsei University College of Medicine, Seoul, Republic of Korea.,Division of Medical Oncology, Department of Internal Medicine and Yonsei Cancer Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong-Moon Chung
- School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea.,Department of Emergency Medicine, College of Medicine, Yonsei University, Seoul, Republic of Korea
| | - Byoung Chul Cho
- Division of Medical Oncology, Department of Internal Medicine and Yonsei Cancer Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
17
|
He X, Liu X, Zuo F, Shi H, Jing J. Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Semin Cancer Biol 2023; 88:187-200. [PMID: 36596352 DOI: 10.1016/j.semcancer.2022.12.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/16/2022] [Accepted: 12/29/2022] [Indexed: 01/02/2023]
Abstract
With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.
Collapse
Affiliation(s)
- Xiujing He
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Xiaowei Liu
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Fengli Zuo
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Hubing Shi
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Jing Jing
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China.
| |
Collapse
|
18
|
Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:2053-2061. [PMID: 35989154 DOI: 10.1016/j.jval.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/10/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.
Collapse
Affiliation(s)
- Woojung Lee
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
| | - Naomi Schwartz
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Aasthaa Bansal
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Sara Khor
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Noah Hammarlund
- Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA
| | - Anirban Basu
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Beth Devine
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| |
Collapse
|
19
|
Peng J, Zhang J, Zou D, Xiao L, Ma H, Zhang X, Li Y, Han L, Xie B. Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade. Front Immunol 2022; 13:960459. [PMID: 36420269 PMCID: PMC9677530 DOI: 10.3389/fimmu.2022.960459] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 10/10/2022] [Indexed: 11/09/2022] Open
Abstract
Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligand (PD-L1) blockade. We aimed to use deep-learning algorithm to estimate clinical benefit in patients with non-small-cell lung cancer (NSCLC) before immunotherapy. Peripheral blood samples or tumor tissues of 915 patients from three independent centers were profiled by whole-exome sequencing or next-generation sequencing. Based on convolutional neural network (CNN) and three conventional machine learning (cML) methods, we used multi-panels to train the models for predicting the durable clinical benefit (DCB) and combined them to develop a nomogram model for predicting prognosis. In the three cohorts, the CNN achieved the highest area under the curve of predicting DCB among cML, PD-L1 expression, and tumor mutational burden (area under the curve [AUC] = 0.965, 95% confidence interval [CI]: 0.949–0.978, P< 0.001; AUC =0.965, 95% CI: 0.940–0.989, P< 0.001; AUC = 0.959, 95% CI: 0.942–0.976, P< 0.001, respectively). Patients with CNN-high had longer progression-free survival (PFS) and overall survival (OS) than patients with CNN-low in the three cohorts. Subgroup analysis confirmed the efficient predictive ability of CNN. Combining three cML methods (CNN, SVM, and RF) yielded a robust comprehensive nomogram for predicting PFS and OS in the three cohorts (each P< 0.001). The proposed deep-learning method based on mutational genes revealed the potential value of clinical benefit prediction in patients with NSCLC and provides novel insights for combined machine learning in PD-1/PD-L1 blockade.
Collapse
Affiliation(s)
- Jie Peng
- Department of Medical Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
- *Correspondence: Jie Peng,
| | - Jing Zhang
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Dan Zou
- Department of Medical Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
| | - Lushan Xiao
- Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Honglian Ma
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Hanzhou, China
| | - Xudong Zhang
- The Second Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ya Li
- Department of Medical Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
| | - Lijie Han
- Department of Hematology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Baowen Xie
- Yino Research, Shenzhen Yino Intelligence Technology Development Co., Ltd., Shenzhen, China
| |
Collapse
|
20
|
Zhao W, Liu J. Artificial intelligence in lung cancer: Application and future thinking. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2022; 47:994-1000. [PMID: 36097766 PMCID: PMC10950116 DOI: 10.11817/j.issn.1672-7347.2022.210645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Indexed: 06/15/2023]
Abstract
The availability of medical big data and the rapid development of computer software and hardware have greatly promoted the advancement of intelligent medical healthcare. Artificial intelligence (AI) has been successfully applied in many fields of medicine, especially in lung cancer. The performance of AI in some specific tasks has surpassed that of humans. Several AI software has been deeply used in clinical practice to help decision-making, which is producing a profound influence on clinicians. At present, the application of AI in the field of lung cancer mainly includes detection, segmentation, classification, prognosis prediction, efficacy evaluation, and so on. AI faces certain challenges and opportunities in the era of big data in terms of data acquisition, annotation and interpretability. Researchers have conducted deep and extensive studies using AI in the field of lung cancer, and AI is expected to become a powerful assistant in the prevention and treatment of lung cancer. AI is bringing an unprecedented revolution to radiologists, but the role of radiologists is crucial in the development of AI.
Collapse
Affiliation(s)
- Wei Zhao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China.
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China.
| |
Collapse
|
21
|
Ren Q, Xiong F, Zhu P, Chang X, Wang G, He N, Jin Q. Assessing the robustness of radiomics/deep learning approach in the identification of efficacy of anti–PD-1 treatment in advanced or metastatic non-small cell lung carcinoma patients. Front Oncol 2022; 12:952749. [PMID: 35992867 PMCID: PMC9390967 DOI: 10.3389/fonc.2022.952749] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/04/2022] [Indexed: 12/11/2022] Open
Abstract
Administration of anti–PD-1 is now a standard therapy in advanced non-small cell lung carcinoma (NSCLC) patients. The clinical application of biomarkers reflecting tumor immune microenvironment is hurdled by the invasiveness of obtaining tissues despite its importance in immunotherapy. This study aimed to develop a robust and non-invasive radiomics/deep learning machine biomarker for predicting the response to immunotherapy in NSCLC patients. Radiomics/deep learning features were exacted from computed tomography (CT) images of NSCLC patients treated with Nivolumab or Pembrolizumab. The robustness of radiomics/deep learning features was assessed against various perturbations, then robust features were selected based on the Intraclass Correlation Coefficient (ICC). Radiomics/deep learning machine-learning classifiers were constructed by combining seven feature exactors, 13 feature selection methods, and 12 classifiers. The optimal model was selected using the mean area under the curve (AUC) and relative standard deviation (RSD). The consistency of image features against various perturbations was high (the range of median ICC: 0.78–0.97), but the consistency was poor in test–retest testing (the range of median ICC: 0.42–0.67). The optimal model, InceptionV3_RELF_Nearest Neighbors classifiers, had the highest prediction efficacy (AUC: 0.96 and RSD: 0.50) for anti–PD-1/PD-L1 treatment. Accuracy (ACC), sensitivity, specificity, precision, and F1 score were 95.24%, 95.00%, 95.50%, 91.67%, and 95.30%, respectively. For successful model robustification, tailoring perturbations for robustness testing to the target dataset is key. Robust radiomics/deep learning features, when paired with machine-learning methodologies, will work on the exactness and the repeatability of anticipating immunotherapy adequacy.
Collapse
Affiliation(s)
- Qianqian Ren
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fu Xiong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Zhu
- Department of Hepatobiliary Surgery, Wuhan No.1 Hospital, Wuhan, China
| | - Xiaona Chang
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guobin Wang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Nan He
- Cancer Center, Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Nan He, ; Qianna Jin,
| | - Qianna Jin
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Nan He, ; Qianna Jin,
| |
Collapse
|
22
|
Dong K, Liu J, Zhou W, Zhang G. Exploring the Relationship Between Senescence and Colorectal Cancer in Prognosis, Immunity, and Treatment. Front Genet 2022; 13:930248. [PMID: 35783270 PMCID: PMC9240351 DOI: 10.3389/fgene.2022.930248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/31/2022] [Indexed: 11/21/2022] Open
Abstract
Background: Senescence, as an effective barrier against tumorigenesis, plays a critical role in cancer therapy. However, the role of senescence in colorectal cancer (CRC) has not yet been reported. This study aimed to build a prognostic signature for the prognosis of patients with CRC based on senescence-related genes. Methods: A prognostic signature was built from TCGA based on differentially expressed senescence-related genes by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses, which were further validated using two Gene Expression Omnibus (GEO) cohorts. The CIBERSORT and ssGSEA algorithms were utilized to analyze the infiltrating abundance of immune cells. The relationship of signature with the immune therapy and the sensitivity of different therapies was explored. Results: We found 93 genes associated with senescence that were differentially expressed. Based on expression and clinical parameters, we developed a senescence-related prognostic signature and its effectiveness was verified using two external validation cohorts. Overall survival was predicted using a prognostic nomogram that incorporated the predictive values of the risk score and clinical traits. Additionally, the risk score was significantly correlated with immune cells infiltration, tumor immune microenvironment (TME) score, immune checkpoints, immunotherapeutic efficacy, and chemotherapy sensitivity. Conclusion: The senescence-related prognostic model can well predict the prognosis, immunotherapeutic response, and identify potential drug targets, which can help guide individualized treatment.
Collapse
Affiliation(s)
- Kechen Dong
- Department of Oncology of Head and Neck, Huangshi Central Hospital (Pu Ai Hospital), Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
| | - Jianping Liu
- Department of Abdominal and Pelvic Medical Oncology II Ward, Huangshi Central Hospital (Pu Ai Hospital), Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
| | - Wei Zhou
- Department of Urology, Huangshi Central Hospital (Pu Ai Hospital), Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
- *Correspondence: Wei Zhou, ; Guanglin Zhang,
| | - Guanglin Zhang
- Department of Abdominal and Pelvic Medical Oncology II Ward, Huangshi Central Hospital (Pu Ai Hospital), Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
- *Correspondence: Wei Zhou, ; Guanglin Zhang,
| |
Collapse
|
23
|
Peng J, Xiao L, Zou D, Han L. A Somatic Mutation Signature Predicts the Best Overall Response to Anti-programmed Cell Death Protein-1 Treatment in Epidermal Growth Factor Receptor/Anaplastic Lymphoma Kinase-Negative Non-squamous Non-small Cell Lung Cancer. Front Med (Lausanne) 2022; 9:808378. [PMID: 35592856 PMCID: PMC9112854 DOI: 10.3389/fmed.2022.808378] [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: 11/03/2021] [Accepted: 03/25/2022] [Indexed: 12/02/2022] Open
Abstract
Background We aimed to exploit a somatic mutation signature (SMS) to predict the best overall response to anti-programmed cell death protein-1 (PD-1) therapy in non-small cell lung cancer (NSCLC). Methods Tumor samples of 248 patients with epidermal growth factor receptor (EGFR)/anaplastic lymphoma kinase (ALK)-negative non-squamous NSCLC treated with anti-PD-1 were molecularly tested by targeted next-generation sequencing or whole exome sequencing. On the basis of machine learning, we developed and validated a predictive model named SMS using the training (n = 83) and validation (n = 165) cohorts. Results The SMS model comprising a panel of 15 genes (TP53, PTPRD, SMARCA4, FAT1, MGA, NOTCH1, NTRK3, INPP4B, KMT2A, PAK1, ATRX, BCOR, KDM5C, DDR2, and ARID1B) was built to predict best overall response in the training cohort. The areas under the curves of the training and validation cohorts were higher than those of tumor mutational burden and PD-L1 expression. Patients with SMS-high in the training and validation cohorts had poorer progression-free survival [hazard ratio (HR) = 6.01, P < 0.001; HR = 3.89, P < 0.001] and overall survival (HR = 7.60, P < 0.001; HR = 2.82, P < 0.001) than patients with SMS-low. SMS was an independent factor in multivariate analyses of progression-free survival and overall survival (HR = 4.32, P < 0.001; HR = 3.07, P < 0.001, respectively). Conclusion This study revealed the predictive value of SMS for immunotherapy best overall response and prognosis in EGFR/ALK-negative non-squamous NSCLC as a potential biomarker in anti-PD-1 therapy.
Collapse
Affiliation(s)
- Jie Peng
- Department of Medical Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili City, China
| | - Lushan Xiao
- Hepatology Unit, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Dan Zou
- Department of Medical Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili City, China
| | - Lijie Han
- Department of Hematology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| |
Collapse
|
24
|
Wang C, Ma J, Shao J, Zhang S, Liu Z, Yu Y, Li W. Predicting EGFR and PD-L1 Status in NSCLC Patients Using Multitask AI System Based on CT Images. Front Immunol 2022; 13:813072. [PMID: 35250988 PMCID: PMC8895233 DOI: 10.3389/fimmu.2022.813072] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 01/17/2022] [Indexed: 12/23/2022] Open
Abstract
Background Epidermal growth factor receptor (EGFR) genotyping and programmed death ligand-1 (PD-L1) expressions are of paramount importance for treatment guidelines such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional identification of EGFR or PD-L1 status requires surgical or biopsied tumor specimens, which are obtained through invasive procedures associated with risk of morbidities and may be unavailable to access tissue samples. Here, we developed an artificial intelligence (AI) system that can predict EGFR and PD-L1 status in using non-invasive computed tomography (CT) images. Methods A multitask AI system including deep learning (DL) module, radiomics (RA) module, and joint (JO) module combining the DL, RA, and clinical features was developed, trained, and optimized with CT images to predict the EGFR and PD-L1 status. We used feature selectors and feature fusion methods to find the best model among combinations of module types. The models were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results Our multitask AI system yielded promising performance for gene expression status, subtype classification, and joint prediction. The AUCs of DL module achieved 0.842 (95% CI, 0.825–0.855) in the EGFR mutated status and 0.805 (95% CI, 0.779–0.829) in the mutated-EGFR subtypes discrimination (19Del, L858R, other mutations). DL module also demonstrated the AUCs of 0.799 (95% CI, 0.762–0.854) in the PD-L1 expression status and 0.837 (95% CI, 0.775–0.911) in the positive-PD-L1 subtypes (PD-L1 tumor proportion score, 1%–49% and ≥50%). Furthermore, the JO module of our AI system performed well in the EGFR and PD-L1 joint cohort, with an AUC of 0.928 (95% CI, 0.909–0.946) for distinguishing EGFR mutated status and 0.905 (95% CI, 0.886–0.930) for discriminating PD-L1 expression status. Conclusion Our AI system has demonstrated the encouraging results for identifying gene status and further assessing the genotypes. Both clinical indicators and radiomics features showed a complementary role in prediction and provided accurate estimates to predict EGFR and PD-L1 status. Furthermore, this non-invasive, high-throughput, and interpretable AI system can be used as an assistive tool in conjunction with or in lieu of ancillary tests and extensive diagnostic workups to facilitate early intervention.
Collapse
Affiliation(s)
- Chengdi Wang
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Jiechao Ma
- AI Lab, Deepwise Healthcare, Beijing, China
| | - Jun Shao
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Shu Zhang
- AI Lab, Deepwise Healthcare, Beijing, China
| | | | - Yizhou Yu
- AI Lab, Deepwise Healthcare, Beijing, China
- Faculty of Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- *Correspondence: Weimin Li, ; Yizhou Yu,
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
- *Correspondence: Weimin Li, ; Yizhou Yu,
| |
Collapse
|
25
|
Caruso D, Polici M, Lauri C, Laghi A. Radiomics and artificial intelligence. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00072-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
|
26
|
Wang C, Xu X, Shao J, Zhou K, Zhao K, He Y, Li J, Guo J, Yi Z, Li W. Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images. JOURNAL OF ONCOLOGY 2021; 2021:5499385. [PMID: 35003258 PMCID: PMC8741343 DOI: 10.1155/2021/5499385] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 10/25/2021] [Accepted: 11/14/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The detection of epidermal growth factor receptor (EGFR) mutation and programmed death ligand-1 (PD-L1) expression status is crucial to determine the treatment strategies for patients with non-small-cell lung cancer (NSCLC). Recently, the rapid development of radiomics including but not limited to deep learning techniques has indicated the potential role of medical images in the diagnosis and treatment of diseases. METHODS Eligible patients diagnosed/treated at the West China Hospital of Sichuan University from January 2013 to April 2019 were identified retrospectively. The preoperative CT images were obtained, as well as the gene status regarding EGFR mutation and PD-L1 expression. Tumor region of interest (ROI) was delineated manually by experienced respiratory specialists. We used 3D convolutional neural network (CNN) with ROI information as input to construct a classification model and established a prognostic model combining deep learning features and clinical features to stratify survival risk of lung cancer patients. RESULTS The whole cohort (N = 1262) was divided into a training set (N = 882, 70%), validation set (N = 125, 10%), and test set (N = 255, 20%). We used a 3D convolutional neural network (CNN) to construct a prediction model, with AUCs of 0.96 (95% CI: 0.94-0.98), 0.80 (95% CI: 0.72-0.88), and 0.73 (95% CI: 0.63-0.83) in the training, validation, and test cohorts, respectively. The combined prognostic model showed a good performance on survival prediction in NSCLC patients (C-index: 0.71). CONCLUSION In this study, a noninvasive and effective model was proposed to predict EGFR mutation and PD-L1 expression status as a clinical decision support tool. Additionally, the combination of deep learning features with clinical features demonstrated great stratification capabilities in the prognostic model. Our team would continue to explore the application of imaging markers for treatment selection of lung cancer patients.
Collapse
Affiliation(s)
- Chengdi Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Xiuyuan Xu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Jun Shao
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Kai Zhou
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Kefu Zhao
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Yanqi He
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Jingwei Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Jixiang Guo
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| |
Collapse
|
27
|
Peng J, Zou D, Han L, Yin Z, Hu X. A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer. Front Immunol 2021; 12:778276. [PMID: 35095850 PMCID: PMC8797141 DOI: 10.3389/fimmu.2021.778276] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/30/2021] [Indexed: 01/08/2023] Open
Abstract
The biomarkers for the pathological response of neoadjuvant chemotherapy plus anti-programmed cell death protein-1/programmed cell death-ligand 1 (PD-1/PD-L1) (CAPD) are unclear in non-small cell lung cancer (NSCLC). Two hundred and eleven patients with stage Ib-IIIa NSCLC undergoing CAPD prior to surgical resection were enrolled, and 11 immune cell subsets in peripheral blood were prospectively analyzed using multicolor flow cytometry. Immune cell subtypes were selected by recursive feature elimination and least absolute shrinkage and selection operator methods. The support vector machine (SVM) was used to build a model. Multivariate analysis for major pathological response (MPR) was also performed. Finally, five immune cell subtypes were identified and an SVM based on liquid immune profiling (LIP-SVM) was developed. The LIP-SVM model achieved high accuracies in discovery and validation sets (AUC = 0.886, 95% CI: 0.823–0.949, P < 0.001; AUC = 0.874, 95% CI: 0.791–0.958, P < 0.001, respectively). Multivariate analysis revealed that age, radiological response, and LIP-SVM were independent factors for MPR in the two sets (each P < 0.05). The integration of LIP-SVM, clinical factors, and radiological response showed significantly high accuracies for predicting MPR in discovery and validation sets (AUC = 0.951, 95% CI: 0.916–0.986, P < 0.001; AUC = 0.943, 95% CI: 0.912–0.993, P < 0.001, respectively). Based on immune cell profiling of peripheral blood, our study developed a predictive model for the MPR of patients with NSCLC undergoing CAPD treatment that can potentially guide clinical therapy.
Collapse
Affiliation(s)
- Jie Peng
- Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Hanzhou, China
- *Correspondence: Jie Peng,
| | - Dan Zou
- Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
| | - Lijie Han
- Department of Hematology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zuomin Yin
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Hanzhou, China
| | - Xiao Hu
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Hanzhou, China
| |
Collapse
|
28
|
Chen Z, Lin L, Wu C, Li C, Xu R, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 2021; 41:1100-1115. [PMID: 34613667 PMCID: PMC8626610 DOI: 10.1002/cac2.12215] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/10/2021] [Accepted: 09/01/2021] [Indexed: 12/12/2022] Open
Abstract
Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti-cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI-powered cancer care.
Collapse
Affiliation(s)
- Zi‐Hang Chen
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
- Zhongshan School of MedicineSun Yat‐sen UniversityGuangzhouGuangdong510080P. R. China
| | - Li Lin
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Chen‐Fei Wu
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Chao‐Feng Li
- Artificial Intelligence LaboratoryState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Rui‐Hua Xu
- Department of Medical OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Ying Sun
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| |
Collapse
|
29
|
Kehl KL, Groha S, Lepisto EM, Elmarakeby H, Lindsay J, Gusev A, Van Allen EM, Hassett MJ, Schrag D. Clinical Inflection Point Detection on the Basis of EHR Data to Identify Clinical Trial-Ready Patients With Cancer. JCO Clin Cancer Inform 2021; 5:622-630. [PMID: 34097438 PMCID: PMC8240790 DOI: 10.1200/cci.20.00184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE To inform precision oncology, methods are needed to use electronic health records (EHRs) to identify patients with cancer who are experiencing clinical inflection points, consistent with worsening prognosis or a high propensity to change treatment, at specific time points. Such patients might benefit from real-time screening for clinical trials. METHODS Using serial unstructured imaging reports for patients with solid tumors or lymphoma participating in a single-institution precision medicine study, we trained a deep neural network natural language processing (NLP) model to dynamically predict patients' prognoses and propensity to start new palliative-intent systemic therapy within 30 days. Model performance was evaluated using Harrell's c-index (for prognosis) and the area under the receiver operating characteristic curve (AUC; for new treatment and new clinical trial enrollment). Associations between model outputs and manual annotations of cancer progression were also evaluated using the AUC. RESULTS A deep NLP model was trained and evaluated using 302,688 imaging reports for 16,780 patients. In a held-out test set of 34,770 reports for 1,952 additional patients, the model predicted survival with a c-index of 0.76 and initiation of new treatment with an AUC of 0.77. Model-generated prognostic scores were associated with annotation of cancer progression on the basis of manual EHR review (n = 1,488 reports for 110 patients with lung or colorectal cancer) with an AUC of 0.78, and predictions of new treatment were associated with annotation of cancer progression on the basis of manual EHR review with an AUC of 0.84. CONCLUSION Training a deep NLP model to identify clinical inflection points among patients with cancer is feasible. This approach could identify patients who may benefit from real-time targeted clinical trial screening interventions at health system scale.
Collapse
Affiliation(s)
- Kenneth L Kehl
- Division of Population Sciences, the Knowledge Systems Group, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Stefan Groha
- Division of Population Sciences, the Knowledge Systems Group, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Eva M Lepisto
- Division of Population Sciences, the Knowledge Systems Group, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Haitham Elmarakeby
- Division of Population Sciences, the Knowledge Systems Group, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - James Lindsay
- Division of Population Sciences, the Knowledge Systems Group, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Alexander Gusev
- Division of Population Sciences, the Knowledge Systems Group, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Eliezer M Van Allen
- Division of Population Sciences, the Knowledge Systems Group, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Michael J Hassett
- Division of Population Sciences, the Knowledge Systems Group, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Deborah Schrag
- Division of Population Sciences, the Knowledge Systems Group, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| |
Collapse
|
30
|
Alkaitis MS, Agrawal MN, Riely GJ, Razavi P, Sontag D. Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer. JCO Clin Cancer Inform 2021; 5:550-560. [PMID: 33989016 DOI: 10.1200/cci.20.00139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Key oncology end points are not routinely encoded into electronic medical records (EMRs). We assessed whether natural language processing (NLP) can abstract treatment discontinuation rationale from unstructured EMR notes to estimate toxicity incidence and progression-free survival (PFS). METHODS We constructed a retrospective cohort of 6,115 patients with early-stage and 701 patients with metastatic breast cancer initiating care at Memorial Sloan Kettering Cancer Center from 2008 to 2019. Each cohort was divided into training (70%), validation (15%), and test (15%) subsets. Human abstractors identified the clinical rationale associated with treatment discontinuation events. Concatenated EMR notes were used to train high-dimensional logistic regression and convolutional neural network models. Kaplan-Meier analyses were used to compare toxicity incidence and PFS estimated by our NLP models to estimates generated by manual labeling and time-to-treatment discontinuation (TTD). RESULTS Our best high-dimensional logistic regression models identified toxicity events in early-stage patients with an area under the curve of the receiver-operator characteristic of 0.857 ± 0.014 (standard deviation) and progression events in metastatic patients with an area under the curve of 0.752 ± 0.027 (standard deviation). NLP-extracted toxicity incidence and PFS curves were not significantly different from manually extracted curves (P = .95 and P = .67, respectively). By contrast, TTD overestimated toxicity in early-stage patients (P < .001) and underestimated PFS in metastatic patients (P < .001). Additionally, we tested an extrapolation approach in which 20% of the metastatic cohort were labeled manually, and NLP algorithms were used to abstract the remaining 80%. This extrapolated outcomes approach resolved PFS differences between receptor subtypes (P < .001 for hormone receptor+/human epidermal growth factor receptor 2- v human epidermal growth factor receptor 2+ v triple-negative) that could not be resolved with TTD. CONCLUSION NLP models are capable of abstracting treatment discontinuation rationale with minimal manual labeling.
Collapse
Affiliation(s)
- Matthew S Alkaitis
- CSAIL & IMES, Massachusetts Institute of Technology, Cambridge, MA.,Harvard Medical School, Boston, MA
| | - Monica N Agrawal
- CSAIL & IMES, Massachusetts Institute of Technology, Cambridge, MA
| | - Gregory J Riely
- Memorial Sloan Kettering Cancer Center, New York, NY.,Weill-Cornell Medical College, New York, NY
| | - Pedram Razavi
- Memorial Sloan Kettering Cancer Center, New York, NY.,Weill-Cornell Medical College, New York, NY
| | - David Sontag
- CSAIL & IMES, Massachusetts Institute of Technology, Cambridge, MA
| |
Collapse
|
31
|
Sun R, Lerousseau M, Henry T, Carré A, Leroy A, Estienne T, Niyoteka S, Bockel S, Rouyar A, Alvarez Andres É, Benzazon N, Battistella E, Classe M, Robert C, Scoazec JY, Deutsch É. [Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]. Cancer Radiother 2021; 25:630-637. [PMID: 34284970 DOI: 10.1016/j.canrad.2021.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 06/19/2021] [Indexed: 12/24/2022]
Abstract
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.
Collapse
Affiliation(s)
- R Sun
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France.
| | - M Lerousseau
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - T Henry
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de médecine nucléaire, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - A Carré
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - A Leroy
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - T Estienne
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Niyoteka
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Bockel
- Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - A Rouyar
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - É Alvarez Andres
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - N Benzazon
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - E Battistella
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | | | - C Robert
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - J Y Scoazec
- Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France; Département de biologie et pathologie médicales, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - É Deutsch
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| |
Collapse
|
32
|
Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell 2021; 39:916-927. [PMID: 33930310 PMCID: PMC8282694 DOI: 10.1016/j.ccell.2021.04.002] [Citation(s) in RCA: 114] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/25/2021] [Accepted: 04/03/2021] [Indexed: 12/22/2022]
Abstract
Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer care. With recent advances in the field of artificial intelligence (AI), there is now a computational basis to integrate and synthesize this growing body of multi-dimensional data, deduce patterns, and predict outcomes to improve shared patient and clinician decision making. While there is high potential, significant challenges remain. In this perspective, we propose a pathway of clinical cancer care touchpoints for narrow-task AI applications and review a selection of applications. We describe the challenges faced in the clinical translation of AI and propose solutions. We also suggest paths forward in weaving AI into individualized patient care, with an emphasis on clinical validity, utility, and usability. By illuminating these issues in the context of current AI applications for clinical oncology, we hope to help advance meaningful investigations that will ultimately translate to real-world clinical use.
Collapse
Affiliation(s)
- Benjamin H Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine - HIM 343, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Ahmed Hosny
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine - HIM 343, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine - HIM 343, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands.
| |
Collapse
|
33
|
Kehl KL, Riely GJ, Lepisto EM, Lavery JA, Warner JL, LeNoue-Newton ML, Sweeney SM, Rudolph JE, Brown S, Yu C, Bedard PL, Schrag D, Panageas KS. Correlation Between Surrogate End Points and Overall Survival in a Multi-institutional Clinicogenomic Cohort of Patients With Non-Small Cell Lung or Colorectal Cancer. JAMA Netw Open 2021; 4:e2117547. [PMID: 34309669 PMCID: PMC8314138 DOI: 10.1001/jamanetworkopen.2021.17547] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
IMPORTANCE Contemporary observational cancer research requires associating genomic biomarkers with reproducible end points; overall survival (OS) is a key end point, but interpretation can be challenging when multiple lines of therapy and prolonged survival are common. Progression-free survival (PFS), time to treatment discontinuation (TTD), and time to next treatment (TTNT) are alternative end points, but their utility as surrogates for OS in real-world clinicogenomic data sets has not been well characterized. OBJECTIVE To measure correlations between candidate surrogate end points and OS in a multi-institutional clinicogenomic data set. DESIGN, SETTING, AND PARTICIPANTS A retrospective cohort study was conducted of patients with non-small cell lung cancer (NSCLC) or colorectal cancer (CRC) whose tumors were genotyped at 4 academic centers from January 1, 2014, to December 31, 2017, and who initiated systemic therapy for advanced disease. Patients were followed up through August 31, 2020 (NSCLC), and October 31, 2020 (CRC). Statistical analyses were conducted on January 5, 2021. EXPOSURES Candidate surrogate end points included TTD; TTNT; PFS based on imaging reports only; PFS based on medical oncologist ascertainment only; PFS based on either imaging or medical oncologist ascertainment, whichever came first; and PFS defined by a requirement that both imaging and medical oncologist ascertainment have indicated progression. MAIN OUTCOMES AND MEASURES The primary outcome was the correlation between candidate surrogate end points and OS. RESULTS There were 1161 patients with NSCLC (648 women [55.8%]; mean [SD] age, 63 [11] years) and 1150 with CRC (647 men [56.3%]; mean [SD] age, 54 [12] years) identified for analysis. Progression-free survival based on both imaging and medical oncologist documentation was most correlated with OS (NSCLC: ρ = 0.76; 95% CI, 0.73-0.79; CRC: ρ = 0.73; 95% CI, 0.69-0.75). Time to treatment discontinuation was least associated with OS (NSCLC: ρ = 0.45; 95% CI, 0.40-0.50; CRC: ρ = 0.13; 95% CI, 0.06-0.19). Time to next treatment was modestly associated with OS (NSCLC: ρ = 0.60; 0.55-0.64; CRC: ρ = 0.39; 95% CI, 0.32-0.46). CONCLUSIONS AND RELEVANCE This cohort study suggests that PFS based on both a radiologist and a treating oncologist determining that a progression event has occurred was the surrogate end point most highly correlated with OS for analysis of observational clinicogenomic data.
Collapse
Affiliation(s)
- Kenneth L. Kehl
- Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Gregory J. Riely
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Eva M. Lepisto
- Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Jessica A. Lavery
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jeremy L. Warner
- Department of Medicine, Division of Hematology/Oncology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Shawn M. Sweeney
- American Association for Cancer Research, Philadelphia, Pennsylvania
| | - Julia E. Rudolph
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Samantha Brown
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Celeste Yu
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada
| | - Philippe L. Bedard
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Deborah Schrag
- Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
- Associate Editor, JAMA
| | - Katherine S. Panageas
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| |
Collapse
|
34
|
Saini KS, Twelves C. Determining lines of therapy in patients with solid cancers: a proposed new systematic and comprehensive framework. Br J Cancer 2021; 125:155-163. [PMID: 33850304 PMCID: PMC8292475 DOI: 10.1038/s41416-021-01319-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 01/25/2021] [Accepted: 02/10/2021] [Indexed: 12/18/2022] Open
Abstract
The complexity of neoplasia and its treatment are a challenge to the formulation of general criteria that are applicable across solid cancers. Determining the number of prior lines of therapy (LoT) is critically important for optimising future treatment, conducting medication audits, and assessing eligibility for clinical trial enrolment. Currently, however, no accepted set of criteria or definitions exists to enumerate LoT. In this article, we seek to open a dialogue to address this challenge by proposing a systematic and comprehensive framework to determine LoT uniformly across solid malignancies. First, key terms, including LoT and 'clinical progression of disease' are defined. Next, we clarify which therapies should be assigned a LoT, and why. Finally, we propose reporting LoT in a novel and standardised format as LoT N (CLoT + PLoT), where CLoT is the number of systemic anti-cancer therapies (SACT) administered with curative intent and/or in the early setting, PLoT is the number of SACT given with palliative intent and/or in the advanced setting, and N is the sum of CLoT and PLoT. As a next step, the cancer research community should develop and adopt standardised guidelines for enumerating LoT in a uniform manner.
Collapse
Affiliation(s)
- Kamal S Saini
- Covance Inc., Princeton, NJ, USA.
- East Suffolk and North Essex NHS Foundation Trust, Ipswich, UK.
| | - Chris Twelves
- University of Leeds and Leeds Teaching Hospitals Trust, Leeds, UK.
| |
Collapse
|
35
|
Ma X, Bellomo L, Magee K, Bennette CS, Tymejczyk O, Samant M, Tucker M, Nussbaum N, Bowser BE, Kraut JS, Bourla AB. Characterization of a Real-World Response Variable and Comparison with RECIST-Based Response Rates from Clinical Trials in Advanced NSCLC. Adv Ther 2021; 38:1843-1859. [PMID: 33674928 PMCID: PMC8004504 DOI: 10.1007/s12325-021-01659-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 02/08/2021] [Indexed: 01/29/2023]
Abstract
INTRODUCTION Effectiveness metrics for real-word research, analogous to clinical trial ones, are needed. This study aimed to develop a real-world response (rwR) variable applicable to solid tumors and to evaluate its clinical relevance and meaningfulness. METHODS This retrospective study used patient cohorts with advanced non-small cell lung cancer from a nationwide, de-identified electronic health record (EHR)-derived database. Disease burden information abstracted manually was classified into response categories anchored to discrete therapy lines (per patient-line). In part 1, we quantified the feasibility and reliability of data capture, and estimated the association between rwR status and real-world progression-free survival (rwPFS) and real-world overall survival (rwOS). In part 2, we investigated the correlation between published clinical trial overall response rates (ORRs) and real-world response rates (rwRRs) from corresponding real-world patient cohorts. RESULTS In part 1, 85.4% of patients (N = 3248) had at least one radiographic assessment documented. Median abstraction time per patient-line was 15.0 min (IQR 7.8-28.1). Inter-abstractor agreement on presence/absence of at least one assessment was 0.94 (95% CI 0.92-0.96; n = 503 patient-lines abstracted in duplicate); inter-abstractor agreement on best confirmed response category was 0.82 (95% CI 0.78-0.86; n = 384 with at least one captured assessment). Confirmed responders at a 3-month landmark showed significantly lower risk of death and progression in rwOS and rwPFS analyses across all line settings. In part 2, rwRRs (from 12 rw cohorts) showed a high correlation with trial ORRs (Spearman's ρ = 0.99). CONCLUSIONS We developed a rwR variable generated from clinician assessments documented in EHRs following radiographic evaluations. This variable provides clinically meaningful information and may provide a real-world measure of treatment effectiveness.
Collapse
Affiliation(s)
- Xinran Ma
- Flatiron Health, Inc, New York, NY, USA.
| | | | | | | | | | | | | | - Nathan Nussbaum
- Flatiron Health, Inc, New York, NY, USA
- New York University School of Medicine, New York, NY, USA
| | | | | | | |
Collapse
|
36
|
Bitterman DS, Miller TA, Mak RH, Savova GK. Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer. Int J Radiat Oncol Biol Phys 2021; 110:641-655. [PMID: 33545300 DOI: 10.1016/j.ijrobp.2021.01.044] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 12/22/2020] [Accepted: 01/23/2021] [Indexed: 02/07/2023]
Abstract
Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.
Collapse
Affiliation(s)
- Danielle S Bitterman
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, Massachusetts; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts; Artificial Intelligence in Medicine Program, Brigham and Women's Hospital, Boston, Massachusetts.
| | - Timothy A Miller
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
| | - Raymond H Mak
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, Massachusetts; Artificial Intelligence in Medicine Program, Brigham and Women's Hospital, Boston, Massachusetts
| | - Guergana K Savova
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
| |
Collapse
|
37
|
Han L, Yao Z, Xie L, Li D, Wang C, Yang Y, Yang J, Huang Z, Li K, Zhang Y, Ye L, Tan Z, Liu Y, Chen Q, Wang T, Yang Z. Transcriptome Sequencing reveals the expressed profiles of mRNA and ncRNAs and regulate network via ceRNA mediated molecular mechanism of lung adenocarcinoma bone metastasis in Xuanwei. Transl Cancer Res 2021; 10:73-87. [PMID: 35116241 PMCID: PMC8799022 DOI: 10.21037/tcr-20-2376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 11/20/2020] [Indexed: 12/25/2022]
Abstract
Background The most ordinary subtype of lung cancer is lung adenocarcinoma (LuAC), which is characterized by strong metastatic ability. And LuAC rates in Xuanwei leads to the poor prognosis and high death rate. In this study, we systematically explored the molecular mechanism of LuAC bone metastasis in Xuanwei by transcriptome sequencing. Methods RNA Sequencing was conducted to explore the noncoding RNAs (ncRNAs) expression profiles in primary LuAC and LuAC bone metastasis. We identified differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), lncRNAs (DElncRNAs) and circRNAs (DEcircRNAs). Bioinformatics analyses the possible relationships and functions of the LuAC bone metastasis-related competing endogenous RNA (ceRNA). And qRT-PCR was performed to evaluate the expression of these differently expressed genes in serum. Results A total of 2,141 DEmRNAs, 43 DEmiRNAs, 136 DElncRNAs and 706 DEcircRNAs were identified in the Xuanwei patients with primary LuAC vs. LuAC bone metastasis, respectively. The circRNA/miRNA/mRNA and lncRNA/miRNA/mRNA networks of LuAC in Xuanwei with bone metastasis were built, and the gene expression mechanisms regulated by ncRNAs were unveiled via the ceRNA regulatory networks. We observe that lncRNA (ADAMTS9-AS2, TEX41, DLEU2, LINC00152)-miR-223-3p-SCARB1 and hsa_circ_0000053-miR-196a-5p/miR-196b-5p-HOXA5 ceRNA networks might play an important role in bone metastasis of Xuanwei LuAC. Conclusions We comprehensively identified ceRNA regulatory networks of LuAC in Xuanwei with bone metastasis as well as revealed the contribution of different ncRNAs expression profiles. Our data demonstrate the association between mRNAs and ncRNAs in the metastasis mechanism of LuAC in Xuanwei with bone metastasis.
Collapse
Affiliation(s)
- Lei Han
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Zhihong Yao
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Lin Xie
- Department of Medical Oncology, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Dongqi Li
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Cao Wang
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Yihao Yang
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Jifei Yang
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Zeyong Huang
- Medical School, Kunming University of Science and Technology, Kunming, China
| | - Kecheng Li
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Ya Zhang
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Lijuan Ye
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Zunxian Tan
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Yan Liu
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Qiuyun Chen
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Tiying Wang
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| | - Zuozhang Yang
- Bone and Soft Tissue Tumors Research Center of Yunnan Province, Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Cancer Hospital of Yunnan Province), Kunming, China
| |
Collapse
|
38
|
Bourla AB, Herrmann K. Real-World Data as an Evidence Source in Nuclear Medicine. J Nucl Med 2020; 62:156-157. [PMID: 33246976 DOI: 10.2967/jnumed.120.258665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 11/02/2020] [Indexed: 11/16/2022] Open
Affiliation(s)
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany .,German Cancer Consortium, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; and.,West German Cancer Center, Essen, Germany
| |
Collapse
|