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Yuan L, An L, Zhu Y, Duan C, Kong W, Jiang P, Yu QQ. Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT. Cancer Manag Res 2024; 16:361-375. [PMID: 38699652 PMCID: PMC11063459 DOI: 10.2147/cmar.s451871] [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: 11/29/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.
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Affiliation(s)
- Lili Yuan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Lin An
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Yandong Zhu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Chongling Duan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Weixiang Kong
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Pei Jiang
- Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Qing-Qing Yu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
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Schueler J, Sjöman H, Kriesi C. Sensor extended imaging workflow for creating fit for purpose models in basic and applied cell biology. Commun Biol 2024; 7:170. [PMID: 38341479 PMCID: PMC10858951 DOI: 10.1038/s42003-024-05843-0] [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: 03/21/2023] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
While various engineering disciplines spent years on developing methods and workflows to increase their R&D efficiency, the field of cell biology has seen limited evolution in the fundamental approaches to interact with living cells. Perturbations are mostly of chemical nature, and physiologically relevant contexts and stimuli are left with limited attention, resulting in a solution space constrained within the boundaries of presently manageable perturbations. To predict in the laboratory how a drug will work in a human patient, cell biology must have a closer look at life and strive to mimic the human being in all his complexity. By implementing an iterative process from perturbation to measurement and vice versa, the authors suggest using a sensor-extended imaging workflow to implement product development practices to cell biology, opening a physiologically relevant solution space for the development of truly translational and predictive fit for purpose in vitro cell models.
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Affiliation(s)
- Julia Schueler
- Charles River Germany GmbH, Am Flughafen 12-14, 79111, Freiburg, Germany.
| | - Heikki Sjöman
- Vitroscope AS, Leirfossvegen 5d, 7037, Trondheim, Norway
| | - Carlo Kriesi
- Vitroscope AS, Leirfossvegen 5d, 7037, Trondheim, Norway
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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Oswald E, Bug D, Grote A, Lashuk K, Bouteldja N, Lenhard D, Löhr A, Behnke A, Knauff V, Edinger A, Klingner K, Gaedicke S, Niedermann G, Merhof D, Feuerhake F, Schueler J. Immune cell infiltration pattern in non-small cell lung cancer PDX models is a model immanent feature and correlates with a distinct molecular and phenotypic make-up. J Immunother Cancer 2022; 10:jitc-2021-004412. [PMID: 35483746 PMCID: PMC9052060 DOI: 10.1136/jitc-2021-004412] [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] [Accepted: 04/04/2022] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND The field of cancer immunology is rapidly moving towards innovative therapeutic strategies, resulting in the need for robust and predictive preclinical platforms reflecting the immunological response to cancer. Well characterized preclinical models are essential for the development of predictive biomarkers in the oncology as well as the immune-oncology space. In the current study, gold standard preclinical models are being refined and combined with novel image analysis tools to meet those requirements. METHODS A panel of 14 non-small cell lung cancer patient-derived xenograft models (NSCLC PDX) was propagated in humanized NOD/Shi-scid/IL-2Rnull mice. The models were comprehensively characterized for relevant phenotypic and molecular features, including flow cytometry, immunohistochemistry, histology, whole exome sequencing and cytokine secretion. RESULTS Models reflecting hot (>5% tumor-infiltrating lymphocytes/TILs) as opposed to cold tumors (<5% TILs) significantly differed regarding their cytokine profiles, molecular genetic aberrations, stroma content, and programmed cell death ligand-1 status. Treatment experiments including anti cytotoxic T-lymphocyte-associated protein 4, anti-programmed cell death 1 or the combination thereof across all 14 models in the single mouse trial format showed distinctive tumor growth response and spatial immune cell patterns as monitored by computerized analysis of digitized whole-slide images. Image analysis provided for the first time qualitative evaluation of the extent to which PDX models retain the histological features from their original human donors. CONCLUSIONS Deep phenotyping of PDX models in a humanized setting by combinations of computational pathology, immunohistochemistry, flow cytometry and proteomics enables the exhaustive analysis of innovative preclinical models and paves the way towards the development of translational biomarkers for immuno-oncology drugs.
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Affiliation(s)
- Eva Oswald
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Daniel Bug
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Anne Grote
- Department of Pathology, Hannover Medical School, Hannover, Germany
| | - Kanstantsin Lashuk
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Nassim Bouteldja
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Dorothee Lenhard
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Anne Löhr
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Anke Behnke
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Volker Knauff
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Anna Edinger
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Kerstin Klingner
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Simone Gaedicke
- Department of Radiation Oncology, Medical Center-University of Freiburg, Freiburg, Germany
| | - Gabriele Niedermann
- Department of Radiation Oncology, Medical Center-University of Freiburg, Freiburg, Germany.,German Cancer Consortium, Heidelberg, Germany
| | - Dorit Merhof
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | | | - Julia Schueler
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
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Kuklyte J, Fitzgerald J, Nelissen S, Wei H, Whelan A, Power A, Ahmad A, Miarka M, Gregson M, Maxwell M, Raji R, Lenihan J, Finn-Moloney E, Rafferty M, Cary M, Barale-Thomas E, O’Shea D. Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies. Toxicol Pathol 2021; 49:815-842. [PMID: 33618634 PMCID: PMC8091423 DOI: 10.1177/0192623320986423] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist's workflow through screening and prioritizing slides with lesions and highlighting areas with specific lesions for review. Herein, we describe the comparison of various single- and multi-magnification convolutional neural network (CNN) architectures to accelerate the detection of lesions in tissues. Different models were evaluated for defining performance characteristics and efficiency in accurately identifying lesions in 5 key rat organs (liver, kidney, heart, lung, and brain). Cohorts for liver and kidney were collected from TG-GATEs open-source repository, and heart, lung, and brain from internally selected R&D studies. Annotations were performed, and models were trained on each of the available lesion classes in the available organs. Various class-consolidation approaches were evaluated from generalized lesion detection to individual lesion detections. The relationship between the amount of annotated lesions and the precision/accuracy of model performance is elucidated. The utility of multi-magnification CNN implementations in specific tissue subtypes is also demonstrated. The use of these CNN-based models offers users the ability to apply generalized lesion detection to whole-slide images, with the potential to generate novel quantitative data that would not be possible with conventional image analysis techniques.
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Affiliation(s)
| | | | | | - Haolin Wei
- Deciphex, Dublin City University, Dublin, Ireland
| | - Aoife Whelan
- Deciphex, Dublin City University, Dublin, Ireland
| | - Adam Power
- Deciphex, Dublin City University, Dublin, Ireland
| | - Ajaz Ahmad
- Deciphex, Dublin City University, Dublin, Ireland
| | | | - Mark Gregson
- Deciphex, Dublin City University, Dublin, Ireland
| | | | - Ruka Raji
- Deciphex, Dublin City University, Dublin, Ireland
| | | | | | | | - Maurice Cary
- Pathology Experts GmbH, Technologie Zentrum Witterswil, Witters, Switzerland
| | | | - Donal O’Shea
- Deciphex, Dublin City University, Dublin, Ireland
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Jiao Y, Li J, Qian C, Fei S. Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 204:106047. [PMID: 33789213 DOI: 10.1016/j.cmpb.2021.106047] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 03/06/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Colon cancer is a fatal disease, and a comprehensive understanding of the tumor microenvironment (TME) could lead to better risk stratification, prognosis prediction, and therapy management. In this paper, we focused on the automatic evaluation of TME in giga-pixel digital histopathology whole-slide images. METHODS A convolutional neural network is used to recognize nine different content presented in colon cancer whole-slide images. Several implementation details, including the foreground filtering and stain normalization are discussed. Based on the whole-slide segmentation, several TME descriptors are quantified and correlated with the clinical outcome by Kaplan-Meier analysis and Cox regression. Specifically, the stroma, tumor, necrosis, and lymphocyte components are discussed. RESULTS We validated the method on colon adenocarcinoma cases from The Cancer Genome Atlas project. The result shows that the stroma is an independent predictor of progression-free interval (PFI) after corrected by age and pathological stage, with a hazard ratio of 1.665 (95%CI: 1.110~2.495, p = 0.014). High-level necrosis component and lymphocytes component tend to be correlated with poor PFI, with a hazard ratio of 1.552 (95%CI: 0.943~2.554, p = 0.084) and 1.512 (95%CI: 0.979~2.336, p = 0.062), respectively. CONCLUSIONS The result reveals the complex role of the tumor microenvironment in colon adenocarcinoma, and the quantified descriptors are potential predictors of disease progression. The method could be considered for risk stratification and targeted therapy and extend to other types of cancer, leading to a better understanding of the tumor microenvironment.
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Affiliation(s)
- Yiping Jiao
- Shool of Automation, Southeast University, 2nd Sipailou Road, Nanjing, China.
| | - Junhong Li
- Luoyang Central Hospital affiliated to Zhengzhou University, Luoyang, China
| | - Chenqi Qian
- Jiangsu Chunyu Education Group CO., 88th Zhongshan North Road, Nanjing, China
| | - Shumin Fei
- Shool of Automation, Southeast University, 2nd Sipailou Road, Nanjing, China.
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Sobhani F, Robinson R, Hamidinekoo A, Roxanis I, Somaiah N, Yuan Y. Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology. Biochim Biophys Acta Rev Cancer 2021; 1875:188520. [PMID: 33561505 PMCID: PMC9062980 DOI: 10.1016/j.bbcan.2021.188520] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 01/04/2021] [Accepted: 01/30/2021] [Indexed: 02/08/2023]
Abstract
The field of immuno-oncology has expanded rapidly over the past decade, but key questions remain. How does tumour-immune interaction regulate disease progression? How can we prospectively identify patients who will benefit from immunotherapy? Identifying measurable features of the tumour immune-microenvironment which have prognostic or predictive value will be key to making meaningful gains in these areas. Recent developments in deep learning enable big-data analysis of pathological samples. Digital approaches allow data to be acquired, integrated and analysed far beyond what is possible with conventional techniques, and to do so efficiently and at scale. This has the potential to reshape what can be achieved in terms of volume, precision and reliability of output, enabling data for large cohorts to be summarised and compared. This review examines applications of artificial intelligence (AI) to important questions in immuno-oncology (IO). We discuss general considerations that need to be taken into account before AI can be applied in any clinical setting. We describe AI methods that have been applied to the field of IO to date and present several examples of their use.
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Affiliation(s)
- Faranak Sobhani
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK; Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
| | - Ruth Robinson
- Division of Radiotherapy and Imaging, Institute of Cancer Research, The Royal Marsden NHS Foundation Trust, London, UK.
| | - Azam Hamidinekoo
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK; Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
| | - Ioannis Roxanis
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
| | - Navita Somaiah
- Division of Radiotherapy and Imaging, Institute of Cancer Research, The Royal Marsden NHS Foundation Trust, London, UK.
| | - Yinyin Yuan
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK; Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
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Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020; 4:24. [PMID: 34191197 PMCID: PMC8218106 DOI: 10.1186/s41824-020-00094-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/26/2020] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
- Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Francesco Bartoli
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Andrea Marciano
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberta Zanca
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Riemer H J A Slart
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Paola A Erba
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands.
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Srivastava A, Hanig JP. Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach. J Appl Toxicol 2020; 41:996-1006. [PMID: 33140470 DOI: 10.1002/jat.4098] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 10/17/2020] [Indexed: 12/17/2022]
Abstract
Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the brain across a permeable blood brain barrier damages neurons and other supporting cells such as astrocytes. This could, in turn, lead to various neurological disorders such as Parkinson's or Huntington's disease as well as various dementias. Toxicity assessment is often done by pathologists after these exposures by qualitatively or semiquantitatively grading the severity of neurotoxicity in histopathology slides. Quantification of the extent of neurotoxicity supports qualitative histopathological analysis and provides a better understanding of the global extent of brain damage. Stereological techniques such as the utilization of an optical fractionator provide an unbiased quantification of the neuronal damage; however, the process is time-consuming. Advent of whole slide imaging (WSI) introduced digital image analysis which made quantification of neurotoxicity automated, faster and with reduced bias, making statistical comparisons possible. Although automated to a certain level, simple digital image analysis requires manual efforts of experts which is time-consuming and limits analysis of large datasets. Digital image analysis coupled with a deep learning artificial intelligence model provides a good alternative solution to time-consuming stereological and simple digital analysis. Deep learning models could be trained to identify damaged or dead neurons in an automated fashion. This review has focused on and discusses studies demonstrating the role of deep learning in segmentation of brain regions, toxicity detection and quantification of degenerated neurons as well as the estimation of area/volume of degeneration.
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Affiliation(s)
- Anshul Srivastava
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Joseph P Hanig
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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10
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Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020; 196:879-887. [PMID: 32367456 DOI: 10.1007/s00066-020-01625-9] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 04/15/2020] [Indexed: 02/07/2023]
Abstract
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy.
| | | | - Giovanni Pirrone
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
| | - Giovanna Sartor
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
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11
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Cong L, Feng W, Yao Z, Zhou X, Xiao W. Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer. J Cancer 2020; 11:3615-3622. [PMID: 32284758 PMCID: PMC7150458 DOI: 10.7150/jca.43268] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 03/05/2020] [Indexed: 01/01/2023] Open
Abstract
Lung cancer is one of the main causes of cancer-related death in the world. The identification and characteristics of malignant cells are essential for the diagnosis and treatment of primary or metastatic cancers. Deep learning is a new field of artificial intelligence, which can be used for computer aided diagnosis and scientific research of lung cancer pathology by analyzing and learning through establishment and simulation of human brain. In this review, we will introduce the application, progress and problems of deep learning in pathology of lung cancer and make prospects for its future development.
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Affiliation(s)
- Lei Cong
- Department of Oncology, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
| | - Wanbing Feng
- Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhigang Yao
- Department of Pathology, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
| | - Xiaoming Zhou
- Department of Scientific Research, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
| | - Wei Xiao
- Department of Scientific Research, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
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