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Nagatani Y, Kiyota N, Imamura Y, Koyama T, Funakoshi Y, Komatsu M, Itoh T, Teshima M, Nibu KI, Sakai K, Nishio K, Shimomura M, Nakatsura T, Ikarashi D, Nakayama T, Kitano S, Minami H. Different characteristics of the tumor immune microenvironment among subtypes of salivary gland cancer. Asia Pac J Clin Oncol 2024. [PMID: 39233454 DOI: 10.1111/ajco.14108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 08/20/2024] [Accepted: 08/21/2024] [Indexed: 09/06/2024]
Abstract
AIM Although immune checkpoint inhibitors (ICPi) for salivary gland cancer (SGC) have been investigated in clinical trials, details of the tumor immune microenvironment (TIME) remain unclear. This research aimed to elucidate the TIME of SGC and its relationship with tumor mutation burden (TMB) and to explore the rationale for the applicability of ICPi. MATERIALS AND METHODS We selected five pathological types, namely adenoid cystic carcinoma (ACC); adenocarcinoma, not otherwise specified (ANOS); salivary duct carcinoma (SDC); and low/high-grade mucoepidermoid carcinoma (MEClow/high). We investigated the TIME and TMB of each pathological type. TIME was evaluated by multiplexed fluorescent immunohistochemistry. TMB was measured by next-generation sequencing. RESULTS ACC and MEChigh showed the lowest and highest infiltration of immune effector and suppressor cells in both tumor and stroma. ANOS, SDC, and MEClow showed modest infiltration of immune effector cells in tumors. Correlation analysis showed a positive correlation between CD3+CD8+ T cells in tumor and TMB (r = 0.647). CD3+CD8+ T cells in tumors showed a positive correlation with programmed cell death-ligand 1 expression in tumor cells (r = 0.513) and a weak positive correlation with CD3+CD4+Foxp3+ cells in tumors (r = 0.399). However, no correlation was observed between CD3+CD8+ T cells and CD204+ cells in tumors (r = -0.049). CONCLUSION The TIME of ACC was the so-called immune desert type, which may explain the mechanisms of the poor response to ICPi in previous clinical trials. On the other hand, MEChigh was the immune-inflamed type, and this may support the rationale of ICPi for this pathological subtype.
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Affiliation(s)
- Yoshiaki Nagatani
- Department of Medical Oncology and Hematology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Naomi Kiyota
- Department of Medical Oncology and Hematology, Kobe University Graduate School of Medicine, Kobe, Japan
- Kobe University Hospital Cancer Center, Kobe, Japan
| | - Yoshinori Imamura
- Department of Medical Oncology and Hematology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Taiji Koyama
- Department of Medical Oncology and Hematology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Yohei Funakoshi
- Department of Medical Oncology and Hematology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Masato Komatsu
- Department of Diagnostic Pathology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Tomoo Itoh
- Department of Diagnostic Pathology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Masanori Teshima
- Department of Otorhinolaryngology-Head and Neck Surgery, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Ken-Ichi Nibu
- Department of Otorhinolaryngology-Head and Neck Surgery, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Kazuko Sakai
- Department of Genome Biology, Kindai University Faculty of Medicine, Sayama, Japan
| | - Kazuto Nishio
- Department of Genome Biology, Kindai University Faculty of Medicine, Sayama, Japan
| | - Manami Shimomura
- Division of Cancer Immunotherapy (Kashiwa), Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Tetsuya Nakatsura
- Division of Cancer Immunotherapy (Kashiwa), Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Daiki Ikarashi
- Division of Cancer Immunotherapy (Kashiwa), Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Takayuki Nakayama
- Division of Cancer Immunotherapy (Kashiwa), Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Shigehisa Kitano
- Division of Cancer Immunotherapy Development, Center for Advanced Medical Development, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hironobu Minami
- Department of Medical Oncology and Hematology, Kobe University Graduate School of Medicine, Kobe, Japan
- Kobe University Hospital Cancer Center, Kobe, Japan
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2
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Chang J, Hatfield B. Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond. Adv Cancer Res 2024; 161:431-478. [PMID: 39032956 DOI: 10.1016/bs.acr.2024.05.006] [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] [Indexed: 07/23/2024]
Abstract
The integration of computer vision into pathology through slide digitalization represents a transformative leap in the field's evolution. Traditional pathology methods, while reliable, are often time-consuming and susceptible to intra- and interobserver variability. In contrast, computer vision, empowered by artificial intelligence (AI) and machine learning (ML), promises revolutionary changes, offering consistent, reproducible, and objective results with ever-increasing speed and scalability. The applications of advanced algorithms and deep learning architectures like CNNs and U-Nets augment pathologists' diagnostic capabilities, opening new frontiers in automated image analysis. As these technologies mature and integrate into digital pathology workflows, they are poised to provide deeper insights into disease processes, quantify and standardize biomarkers, enhance patient outcomes, and automate routine tasks, reducing pathologists' workload. However, this transformative force calls for cross-disciplinary collaboration between pathologists, computer scientists, and industry innovators to drive research and development. While acknowledging its potential, this chapter addresses the limitations of AI in pathology, encompassing technical, practical, and ethical considerations during development and implementation.
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Affiliation(s)
- Justin Chang
- Virginia Commonwealth University Health System, Richmond, VA, United States
| | - Bryce Hatfield
- Virginia Commonwealth University Health System, Richmond, VA, United States.
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3
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Williams DKA, Graifman G, Hussain N, Amiel M, Tran P, Reddy A, Haider A, Kavitesh BK, Li A, Alishahian L, Perera N, Efros C, Babu M, Tharakan M, Etienne M, Babu BA. Digital pathology, deep learning, and cancer: a narrative review. Transl Cancer Res 2024; 13:2544-2560. [PMID: 38881914 PMCID: PMC11170525 DOI: 10.21037/tcr-23-964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 03/24/2024] [Indexed: 06/18/2024]
Abstract
Background and Objective Cancer is a leading cause of morbidity and mortality worldwide. The emergence of digital pathology and deep learning technologies signifies a transformative era in healthcare. These technologies can enhance cancer detection, streamline operations, and bolster patient care. A substantial gap exists between the development phase of deep learning models in controlled laboratory environments and their translations into clinical practice. This narrative review evaluates the current landscape of deep learning and digital pathology, analyzing the factors influencing model development and implementation into clinical practice. Methods We searched multiple databases, including Web of Science, Arxiv, MedRxiv, BioRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Semantic Scholar, and Cochrane, targeting articles on whole slide imaging and deep learning published from 2014 and 2023. Out of 776 articles identified based on inclusion criteria, we selected 36 papers for the analysis. Key Content and Findings Most articles in this review focus on the in-laboratory phase of deep learning model development, a critical stage in the deep learning lifecycle. Challenges arise during model development and their integration into clinical practice. Notably, lab performance metrics may not always match real-world clinical outcomes. As technology advances and regulations evolve, we expect more clinical trials to bridge this performance gap and validate deep learning models' effectiveness in clinical care. High clinical accuracy is vital for informed decision-making throughout a patient's cancer care. Conclusions Deep learning technology can enhance cancer detection, clinical workflows, and patient care. Challenges may arise during model development. The deep learning lifecycle involves data preprocessing, model development, and clinical implementation. Achieving health equity requires including diverse patient groups and eliminating bias during implementation. While model development is integral, most articles focus on the pre-deployment phase. Future longitudinal studies are crucial for validating models in real-world settings post-deployment. A collaborative approach among computational pathologists, technologists, industry, and healthcare providers is essential for driving adoption in clinical settings.
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Affiliation(s)
| | | | - Nowair Hussain
- Department of Internal Medicine, Overlook Medical Center, Summit, NJ, USA
| | | | | | - Arjun Reddy
- Applied Mathematics & Statistics Stony Brook University, Stony Brook, NY, USA
| | - Ali Haider
- Department of Artificial Intelligence, Yeshiva University, New York, NY, USA
| | - Bali Kumar Kavitesh
- Centre for Frontier AI Research (CFAR), Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore
| | - Austin Li
- New York Medical College, Valhalla, NY, USA
| | | | | | | | - Myoungmee Babu
- Artificial Intelligence and Mathematics, New York City Department of Education, New York, NY, USA
| | | | - Mill Etienne
- Department of Neurology, New York Medical College, Valhalla, NY, USA
| | - Benson A Babu
- New York Medical College, Valhalla, NY, USA
- Department of Hospital Medicine, Wyckoff, Medical Center, New York, NY, USA
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4
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Xirou V, Moutafi M, Bai Y, Nwe Aung T, Burela S, Liu M, Kimple RJ, Shabbir Ahmed F, Schultz B, Flieder D, Connolly DC, Psyrri A, Burtness B, Rimm DL. An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers. Oral Oncol 2024; 152:106750. [PMID: 38547779 PMCID: PMC11060915 DOI: 10.1016/j.oraloncology.2024.106750] [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: 12/12/2023] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 05/01/2024]
Abstract
PURPOSE The prognostic and predictive significance of pathologist-read tumor infiltrating lymphocytes (TILs) in head and neck cancers have been demonstrated through multiple studies over the years. TILs have not been broadly adopted clinically, perhaps due to substantial inter-observer variability. In this study, we developed a machine-based algorithm for TIL evaluation in head and neck cancers and validated its prognostic value in independent cohorts. EXPERIMENTAL DESIGN A network classifier called NN3-17 was trained to identify and calculate tumor cells, lymphocytes, fibroblasts and "other" cells on hematoxylin-eosin stained sections using the QuPath software. These measurements were used to construct three predefined TIL variables. A retrospective collection of 154 head and neck squamous cell cancer cases was used as the discovery set to identify optimal association of TIL variables and survival. Two independent cohorts of 234 cases were used for validation. RESULTS We found that electronic TIL variables were associated with favorable prognosis in both the HPV-positive and -negative cases. After adjusting for clinicopathologic factors, Cox regression analysis demonstrated that electronic total TILs% (p = 0.025) in the HPV-positive and electronic stromal TILs% (p < 0.001) in the HPV-negative population were independent markers of disease specific outcomes (disease free survival). CONCLUSIONS Neural network TIL variables demonstrated independent prognostic value in validation cohorts of HPV-positive and HPV-negative head and neck cancers. These objective variables can be calculated by an open-source software and could be considered for testing in a prospective setting to assess potential clinical implications.
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Affiliation(s)
- Vasiliki Xirou
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Myrto Moutafi
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Yalai Bai
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Thazin Nwe Aung
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Sneha Burela
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Matthew Liu
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Randall J Kimple
- Department of Human Oncology and UW Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Fahad Shabbir Ahmed
- Department of Pathology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Bryant Schultz
- Biosample Repository Facility, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Douglas Flieder
- Department of Pathology, Fox Chase Cance Center, Philadelphia, PA, USA
| | - Denise C Connolly
- Biosample Repository Facility, Fox Chase Cancer Center, Philadelphia, PA, USA; Cancer Signaling and Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Amanda Psyrri
- Department of Internal Medicine (Medical Oncology), National and Kapodistrian University of Athens, Athens, Greece
| | - Barbara Burtness
- Department of Internal Medicine (Medical Oncology), Yale University School of Medicine, New Haven, CT, USA
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA; Department of Internal Medicine (Medical Oncology), Yale University School of Medicine, New Haven, CT, USA.
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5
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Yee J, Rosendahl C, Aoude LG. The role of artificial intelligence and convolutional neural networks in the management of melanoma: a clinical, pathological, and radiological perspective. Melanoma Res 2024; 34:96-104. [PMID: 38141179 PMCID: PMC10906187 DOI: 10.1097/cmr.0000000000000951] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/29/2023] [Indexed: 12/25/2023]
Abstract
Clinical dermatoscopy and pathological slide assessment are essential in the diagnosis and management of patients with cutaneous melanoma. For those presenting with stage IIC disease and beyond, radiological investigations are often considered. The dermatoscopic, whole slide and radiological images used during clinical care are often stored digitally, enabling artificial intelligence (AI) and convolutional neural networks (CNN) to learn, analyse and contribute to the clinical decision-making. A keyword search of the Medline database was performed to assess the progression, capabilities and limitations of AI and CNN and its use in diagnosis and management of cutaneous melanoma. Full-text articles were reviewed if they related to dermatoscopy, pathological slide assessment or radiology. Through analysis of 95 studies, we demonstrate that diagnostic accuracy of AI/CNN can be superior (or at least equal) to clinicians. However, variability in image acquisition, pre-processing, segmentation, and feature extraction remains challenging. With current technological abilities, AI/CNN and clinicians synergistically working together are better than one another in all subspecialty domains relating to cutaneous melanoma. AI has the potential to enhance the diagnostic capabilities of junior dermatology trainees, primary care skin cancer clinicians and general practitioners. For experienced clinicians, AI provides a cost-efficient second opinion. From a pathological and radiological perspective, CNN has the potential to improve workflow efficiency, allowing clinicians to achieve more in a finite amount of time. Until the challenges of AI/CNN are reliably met, however, they can only remain an adjunct to clinical decision-making.
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Affiliation(s)
- Joshua Yee
- Faculty of Medicine, University of Queensland, St Lucia
| | - Cliff Rosendahl
- Primary Care Clinical Unit, Medical School, The University of Queensland, Herston
| | - Lauren G. Aoude
- Frazer Institute, The University of Queensland, Woolloongabba, QLD, Australia
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6
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Neimy H, Helmy JE, Snyder A, Valdebran M. Artificial Intelligence in Melanoma Dermatopathology: A Review of Literature. Am J Dermatopathol 2024; 46:83-94. [PMID: 37982502 DOI: 10.1097/dad.0000000000002593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
ABSTRACT Pathology serves as a promising field to integrate artificial intelligence into clinical practice as a powerful screening tool. Melanoma is a common skin cancer with high mortality and morbidity, requiring timely and accurate histopathologic diagnosis. This review explores applications of artificial intelligence in melanoma dermatopathology, including differential diagnostics, prognosis prediction, and personalized medicine decision-making.
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Affiliation(s)
- Hannah Neimy
- College of Medicine, Medical University of South Carolina, Charleston, SC; and
| | - John Elia Helmy
- College of Medicine, Medical University of South Carolina, Charleston, SC; and
| | - Alan Snyder
- Department of Dermatology & Dermatologic Surgery, Medical University of South Carolina, Charleston, SC
| | - Manuel Valdebran
- Department of Dermatology & Dermatologic Surgery, Medical University of South Carolina, Charleston, SC
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7
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Tan SX, Aung TN, Claeson M, Acs B, Zhou C, Brown S, Lambie D, Baade PD, Pandeya N, Soyer HP, Smithers BM, Whiteman DC, Rimm DL, Khosrotehrani K. Automated scoring of tumor-infiltrating lymphocytes informs risk of death from thin melanoma: A nested case-case study. J Am Acad Dermatol 2024; 90:179-182. [PMID: 37730017 DOI: 10.1016/j.jaad.2023.09.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/14/2023] [Accepted: 09/02/2023] [Indexed: 09/22/2023]
Affiliation(s)
- Samuel X Tan
- Frazer Institute, University of Queensland, Brisbane, Australia
| | - Thazin N Aung
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Magdalena Claeson
- Frazer Institute, University of Queensland, Brisbane, Australia; Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Balazs Acs
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut; Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Chenhao Zhou
- Frazer Institute, University of Queensland, Brisbane, Australia
| | - Susan Brown
- Frazer Institute, University of Queensland, Brisbane, Australia
| | - Duncan Lambie
- Pathology Queensland, Princess Alexandra Hospital, Brisbane, Australia
| | | | - Nirmala Pandeya
- Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Public Health, Faculty of Medicine, University of Queensland
| | - H Peter Soyer
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Australia; Department of Dermatology, Princess Alexandra Hospital, Brisbane, Australia
| | - B Mark Smithers
- Queensland Melanoma Project, University of Queensland, Princess Alexandra Hospital, Brisbane, Australia
| | - David C Whiteman
- Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Kiarash Khosrotehrani
- Frazer Institute, University of Queensland, Brisbane, Australia; Department of Dermatology, Princess Alexandra Hospital, Brisbane, Australia.
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8
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Ugolini F, De Logu F, Iannone LF, Brutti F, Simi S, Maio V, de Giorgi V, Maria di Giacomo A, Miracco C, Federico F, Peris K, Palmieri G, Cossu A, Mandalà M, Massi D, Laurino M. Tumor-Infiltrating Lymphocyte Recognition in Primary Melanoma by Deep Learning Convolutional Neural Network. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:2099-2110. [PMID: 37734590 DOI: 10.1016/j.ajpath.2023.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/25/2023] [Accepted: 08/02/2023] [Indexed: 09/23/2023]
Abstract
The presence of tumor-infiltrating lymphocytes (TILs) is associated with a favorable prognosis of primary melanoma (PM). Recently, artificial intelligence (AI)-based approach in digital pathology was proposed for the standardized assessment of TILs on hematoxylin and eosin-stained whole slide images (WSIs). Herein, the study applied a new convolution neural network (CNN) analysis of PM WSIs to automatically assess the infiltration of TILs and extract a TIL score. A CNN was trained and validated in a retrospective cohort of 307 PMs including a training set (237 WSIs, 57,758 patches) and an independent testing set (70 WSIs, 29,533 patches). An AI-based TIL density index (AI-TIL) was identified after the classification of tumor patches by the presence or absence of TILs. The proposed CNN showed high performance in recognizing TILs in PM WSIs, showing 100% specificity and sensitivity on the testing set. The AI-based TIL index correlated with conventional TIL evaluation and clinical outcome. The AI-TIL index was an independent prognostic marker associated directly with a favorable prognosis. A fully automated and standardized AI-TIL appeared to be superior to conventional methods at differentiating the PM clinical outcome. Further studies are required to develop an easy-to-use tool to assist pathologists to assess TILs in the clinical evaluation of solid tumors.
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Affiliation(s)
- Filippo Ugolini
- Section of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy
| | - Francesco De Logu
- Section of Clinical Pharmacology and Oncology, Department of Health Sciences, University of Florence, Florence, Italy
| | - Luigi Francesco Iannone
- Section of Clinical Pharmacology and Oncology, Department of Health Sciences, University of Florence, Florence, Italy
| | - Francesca Brutti
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Sara Simi
- Section of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy
| | - Vincenza Maio
- Section of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy
| | - Vincenzo de Giorgi
- Section of Dermatology, Department of Health Sciences, University of Florence, Florence, Italy
| | - Anna Maria di Giacomo
- Center for Immuno-Oncology, Medical Oncology and Immunotherapy, University of Siena, Siena, Italy
| | - Clelia Miracco
- Center for Immuno-Oncology, Medical Oncology and Immunotherapy, University of Siena, Siena, Italy
| | | | - Ketty Peris
- Institute of Dermatology, Sacred Heart Catholic University, Rome, Italy
| | - Giuseppe Palmieri
- Unit of Cancer Genetics, Institute of Genetic and Biomedical Research, National Research Council, Sassari, Italy
| | - Antonio Cossu
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Mario Mandalà
- Oncology Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Daniela Massi
- Section of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy
| | - Marco Laurino
- Institute of Clinical Physiology, National Research Council, Pisa, Italy.
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9
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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10
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Adeuyan O, Gordon ER, Kenchappa D, Bracero Y, Singh A, Espinoza G, Geskin LJ, Saenger YM. An update on methods for detection of prognostic and predictive biomarkers in melanoma. Front Cell Dev Biol 2023; 11:1290696. [PMID: 37900283 PMCID: PMC10611507 DOI: 10.3389/fcell.2023.1290696] [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: 09/07/2023] [Accepted: 10/04/2023] [Indexed: 10/31/2023] Open
Abstract
The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.
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Affiliation(s)
- Oluwaseyi Adeuyan
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Emily R. Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Divya Kenchappa
- Albert Einstein College of Medicine, Bronx, NY, United States
| | - Yadriel Bracero
- Albert Einstein College of Medicine, Bronx, NY, United States
| | - Ajay Singh
- Albert Einstein College of Medicine, Bronx, NY, United States
| | | | - Larisa J. Geskin
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, United States
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Chatziioannou E, Roßner J, Aung TN, Rimm DL, Niessner H, Keim U, Serna-Higuita LM, Bonzheim I, Kuhn Cuellar L, Westphal D, Steininger J, Meier F, Pop OT, Forchhammer S, Flatz L, Eigentler T, Garbe C, Röcken M, Amaral T, Sinnberg T. Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases. EBioMedicine 2023; 93:104644. [PMID: 37295047 PMCID: PMC10363450 DOI: 10.1016/j.ebiom.2023.104644] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 05/15/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic and predictive relevance in cutaneous melanoma. METHODS We included stage I to IV cutaneous melanoma patients and used hematoxylin-eosin-stained slides for TIL analysis. We assessed eTILs as a continuous and categorical variable using the published cut-off of 16.6% and applied Cox regression models to evaluate associations of eTILs with relapse-free, distant metastasis-free, and overall survival. We compared eTILs of the primaries with matched metastasis. Moreover, we assessed the predictive relevance of eTILs in therapy-naïve metastases according to the first-line therapy. FINDINGS We analysed 321 primary cutaneous melanomas and 191 metastatic samples. In simple Cox regression, tumour thickness (p < 0.0001), presence of ulceration (p = 0.0001) and eTILs ≤16.6% (p = 0.0012) were found to be significant unfavourable prognostic factors for RFS. In multiple Cox regression, eTILs ≤16.6% (p = 0.0161) remained significant and downgraded the current staging. Lower eTILs in the primary tissue was associated with unfavourable relapse-free (p = 0.0014) and distant metastasis-free survival (p = 0.0056). In multiple Cox regression adjusted for tumour thickness and ulceration, eTILs as continuous remained significant (p = 0.019). When comparing TILs in primary tissue and corresponding metastasis of the same patient, eTILs in metastases was lower than in primary melanomas (p < 0.0001). In therapy-naïve metastases, an eTILs >12.2% was associated with longer progression-free survival (p = 0.037) and melanoma-specific survival (p = 0.0038) in patients treated with anti-PD-1-based immunotherapy. In multiple Cox regression, lactate dehydrogenase (p < 0.0001) and eTILs ≤12.2% (p = 0.0130) were significantly associated with unfavourable melanoma-specific survival. INTERPRETATION Assessment of TILs is prognostic in primary melanoma samples, and the eTILs complements staging. In therapy-naïve metastases, eTILs ≤12.2% is predictive of unfavourable survival outcomes in patients receiving anti-PD-1-based therapy. FUNDING See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.
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Affiliation(s)
- Eftychia Chatziioannou
- Department of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany
| | - Jana Roßner
- Department of Dermatology, University of Heidelberg, Im Neuenheimer Feld 440, 69120 Heidelberg, Germany
| | - Thazin New Aung
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Heike Niessner
- Department of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany
| | - Ulrike Keim
- Department of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany
| | - Lina Maria Serna-Higuita
- Department of Clinical Epidemiology and Applied Biostatistics, Eberhard Karls University of Tübingen, 72076 Tübingen, Germany
| | - Irina Bonzheim
- Institute of Pathology and Neuropathology, Eberhard Karls University of Tübingen, 72076 Tübingen, Germany
| | - Luis Kuhn Cuellar
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
| | - Dana Westphal
- Department of Dermatology, Faculty of Medicine and University Hospital Carl Gustav Carus, Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases, Technical University Dresden, 01307 Dresden, Germany
| | - Julian Steininger
- Department of Dermatology, Faculty of Medicine and University Hospital Carl Gustav Carus, Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases, Technical University Dresden, 01307 Dresden, Germany
| | - Friedegund Meier
- Department of Dermatology, Faculty of Medicine and University Hospital Carl Gustav Carus, Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases, Technical University Dresden, 01307 Dresden, Germany
| | - Oltin Tiberiu Pop
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Stephan Forchhammer
- Department of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany
| | - Lukas Flatz
- Department of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Thomas Eigentler
- Department of Dermatology, Venereology and Allergology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Claus Garbe
- Department of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany
| | - Martin Röcken
- Department of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany
| | - Teresa Amaral
- Department of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany
| | - Tobias Sinnberg
- Department of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany; Department of Dermatology, Venereology and Allergology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
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12
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Verdicchio M, Brancato V, Cavaliere C, Isgrò F, Salvatore M, Aiello M. A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images. Heliyon 2023; 9:e14371. [PMID: 36950640 PMCID: PMC10025040 DOI: 10.1016/j.heliyon.2023.e14371] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 03/11/2023] Open
Abstract
Background and objectives The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature. Methods A dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves. Results 21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique. Conclusions The presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.
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Affiliation(s)
| | | | - Carlo Cavaliere
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
| | - Francesco Isgrò
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Claudio 21, Naples, 80125, Italy
| | - Marco Salvatore
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
| | - Marco Aiello
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
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Abstract
Gene therapy is a powerful biological tool that is reshaping therapeutic landscapes for several diseases. Researchers are using both non-viral and viral-based gene therapy methods with success in the lab and the clinic. In the cancer biology field, gene therapies are expanding treatment options and the possibility of favorable outcomes for patients. While cellular immunotherapies and oncolytic virotherapies have paved the way in cancer treatments based on genetic engineering, recombinant adeno-associated virus (rAAV), a viral-based module, is also emerging as a potential cancer therapeutic through its malleability, specificity, and broad application to common as well as rare tumor types, tumor microenvironments, and metastatic disease. A wide range of AAV serotypes, promoters, and transgenes have been successful at reducing tumor growth and burden in preclinical studies, suggesting more groundbreaking advances using rAAVs in cancer are on the horizon.
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Affiliation(s)
- Patrick L. Mulcrone
- Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN 46202, USA
- Department of Pediatrics, Indiana University, Indianapolis, IN 46202, USA
| | - Roland W. Herzog
- Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN 46202, USA
| | - Weidong Xiao
- Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN 46202, USA
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Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms. Diagnostics (Basel) 2022; 12:diagnostics12102340. [PMID: 36292028 PMCID: PMC9600129 DOI: 10.3390/diagnostics12102340] [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: 08/30/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 12/03/2022] Open
Abstract
Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.
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