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Mukund A, Afridi MA, Karolak A, Park MA, Permuth JB, Rasool G. Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence. Cancers (Basel) 2024; 16:2240. [PMID: 38927945 DOI: 10.3390/cancers16122240] [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: 05/07/2024] [Revised: 06/03/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI's potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient's journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.
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Affiliation(s)
- Ashwin Mukund
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA
| | - Muhammad Ali Afridi
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Aleksandra Karolak
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA
| | - Margaret A Park
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA
| | - Jennifer B Permuth
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA
| | - Ghulam Rasool
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA
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Norose T, Ohike N, Nakaya D, Kamiya K, Sugiura Y, Takatsuki M, Koizumi H, Okawa C, Ohya A, Sasaki M, Aoki R, Nakahara K, Kobayashi S, Tateishi K, Koike J. Investigation of the usefulness of a bile duct biopsy and bile cytology using a hyperspectral camera and machine learning. Pathol Int 2024. [PMID: 38787324 DOI: 10.1111/pin.13438] [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: 12/24/2023] [Revised: 04/15/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024]
Abstract
To improve the efficiency of pathological diagnoses, the development of automatic pathological diagnostic systems using artificial intelligence (AI) is progressing; however, problems include the low interpretability of AI technology and the need for large amounts of data. We herein report the usefulness of a general-purpose method that combines a hyperspectral camera with machine learning. As a result of analyzing bile duct biopsy and bile cytology specimens, which are especially difficult to determine as benign or malignant, using multiple machine learning models, both were able to identify benign or malignant cells with an accuracy rate of more than 80% (93.3% for bile duct biopsy specimens and 83.2% for bile cytology specimens). This method has the potential to contribute to the diagnosis and treatment of bile duct cancer and is expected to be widely applied and utilized in general pathological diagnoses.
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Affiliation(s)
- Tomoko Norose
- Division of Molecular Pathology, Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Nobuyuki Ohike
- Division of Molecular Pathology, Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan
| | | | | | - Yoshiya Sugiura
- Division of Molecular Pathology, Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Misato Takatsuki
- Division of Molecular Pathology, Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Hirotaka Koizumi
- Division of Molecular Pathology, Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Chie Okawa
- Division of Pathology, St. Marianna University Hospital, Kawasaki, Japan
| | - Aya Ohya
- Division of Pathology, St. Marianna University Hospital, Kawasaki, Japan
| | - Miyu Sasaki
- Division of Pathology, St. Marianna University Hospital, Kawasaki, Japan
| | - Ruka Aoki
- Division of Pathology, St. Marianna University Hospital, Kawasaki, Japan
| | - Kazunari Nakahara
- Department of Gastroenterology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Shinjiro Kobayashi
- Department of Gastroenterological and General Surgery, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Keisuke Tateishi
- Department of Gastroenterology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Junki Koike
- Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan
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McGenity C, Clarke EL, Jennings C, Matthews G, Cartlidge C, Freduah-Agyemang H, Stocken DD, Treanor D. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med 2024; 7:114. [PMID: 38704465 PMCID: PMC11069583 DOI: 10.1038/s41746-024-01106-8] [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: 06/17/2023] [Accepted: 04/12/2024] [Indexed: 05/06/2024] Open
Abstract
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.
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Affiliation(s)
- Clare McGenity
- University of Leeds, Leeds, UK.
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - Emily L Clarke
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Charlotte Jennings
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | | | | | | | - Darren Treanor
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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Anghel C, Grasu MC, Anghel DA, Rusu-Munteanu GI, Dumitru RL, Lupescu IG. Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images. Diagnostics (Basel) 2024; 14:438. [PMID: 38396476 PMCID: PMC10887967 DOI: 10.3390/diagnostics14040438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/10/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.
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Affiliation(s)
- Cristian Anghel
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Mugur Cristian Grasu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Denisa Andreea Anghel
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Gina-Ionela Rusu-Munteanu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Radu Lucian Dumitru
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Ioana Gabriela Lupescu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
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Yamaguchi R, Morikawa H, Akatsuka J, Numata Y, Noguchi A, Kokumai T, Ishida M, Mizuma M, Nakagawa K, Unno M, Miyake A, Tamiya G, Yamamoto Y, Furukawa T. Machine Learning of Histopathological Images Predicts Recurrences of Resected Pancreatic Ductal Adenocarcinoma With Adjuvant Treatment. Pancreas 2024; 53:e199-e204. [PMID: 38127849 DOI: 10.1097/mpa.0000000000002289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
OBJECTIVES Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence-assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate. MATERIALS AND METHODS Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence. RESULTS Areas under the curves obtained were 0.73 (95% confidence interval, 0.59-0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73-0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence. CONCLUSIONS Results indicate that machine learning with the integration of artificial intelligence-driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma.
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Affiliation(s)
- Ruri Yamaguchi
- From the Department of Investigative Pathology, Tohoku University Graduate School of Medicine, Sendai
| | - Hiromu Morikawa
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo
| | - Jun Akatsuka
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo
| | - Yasushi Numata
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo
| | - Aya Noguchi
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Takashi Kokumai
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Masaharu Ishida
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Masamichi Mizuma
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Kei Nakagawa
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Michiaki Unno
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Akimitsu Miyake
- Department of AI and Innovative Medicine, Tohoku University Graduate School of Medicine, Sendai
| | | | | | - Toru Furukawa
- From the Department of Investigative Pathology, Tohoku University Graduate School of Medicine, Sendai
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Tripathi S, Tabari A, Mansur A, Dabbara H, Bridge CP, Daye D. From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer. Diagnostics (Basel) 2024; 14:174. [PMID: 38248051 PMCID: PMC10814554 DOI: 10.3390/diagnostics14020174] [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: 09/19/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.
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Affiliation(s)
- Satvik Tripathi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Arian Mansur
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Harika Dabbara
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
| | - Christopher P. Bridge
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
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Szymoński K, Skirlińska-Nosek K, Lipiec E, Sofińska K, Czaja M, Wilkosz N, Krupa M, Wanat F, Ulatowska-Białas M, Adamek D. Combined analytical approach empowers precise spectroscopic interpretation of subcellular components of pancreatic cancer cells. Anal Bioanal Chem 2023; 415:7281-7295. [PMID: 37906289 PMCID: PMC10684650 DOI: 10.1007/s00216-023-04997-w] [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: 09/04/2023] [Revised: 09/27/2023] [Accepted: 10/09/2023] [Indexed: 11/02/2023]
Abstract
The lack of specific and sensitive early diagnostic options for pancreatic cancer (PC) results in patients being largely diagnosed with late-stage disease, thus inoperable and burdened with high mortality. Molecular spectroscopic methodologies, such as Raman or infrared spectroscopies, show promise in becoming a leader in screening for early-stage cancer diseases, including PC. However, should such technology be introduced, the identification of differentiating spectral features between various cancer types is required. This would not be possible without the precise extraction of spectra without the contamination by necrosis, inflammation, desmoplasia, or extracellular fluids such as mucous that surround tumor cells. Moreover, an efficient methodology for their interpretation has not been well defined. In this study, we compared different methods of spectral analysis to find the best for investigating the biomolecular composition of PC cells cytoplasm and nuclei separately. Sixteen PC tissue samples of main PC subtypes (ductal adenocarcinoma, intraductal papillary mucinous carcinoma, and ampulla of Vater carcinoma) were collected with Raman hyperspectral mapping, resulting in 191,355 Raman spectra and analyzed with comparative methodologies, specifically, hierarchical cluster analysis, non-negative matrix factorization, T-distributed stochastic neighbor embedding, principal components analysis (PCA), and convolutional neural networks (CNN). As a result, we propose an innovative approach to spectra classification by CNN, combined with PCA for molecular characterization. The CNN-based spectra classification achieved over 98% successful validation rate. Subsequent analyses of spectral features revealed differences among PC subtypes and between the cytoplasm and nuclei of their cells. Our study establishes an optimal methodology for cancer tissue spectral data classification and interpretation that allows precise and cognitive studies of cancer cells and their subcellular components, without mixing the results with cancer-surrounding tissue. As a proof of concept, we describe findings that add to the spectroscopic understanding of PC.
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Affiliation(s)
- Krzysztof Szymoński
- Department of Pathomorphology, Medical College, Jagiellonian University, Kraków, Poland.
- Department of Pathomorphology, University Hospital, Kraków, Poland.
| | - Katarzyna Skirlińska-Nosek
- Faculty of Physics, Astronomy and Applied Computer Science, M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland
| | - Ewelina Lipiec
- Faculty of Physics, Astronomy and Applied Computer Science, M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
| | - Kamila Sofińska
- Faculty of Physics, Astronomy and Applied Computer Science, M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
| | - Michał Czaja
- Faculty of Physics, Astronomy and Applied Computer Science, M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland
| | - Natalia Wilkosz
- Faculty of Physics, Astronomy and Applied Computer Science, M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
- AGH University of Krakow, Faculty of Physics and Applied Computer Science, Kraków, Poland
| | - Matylda Krupa
- Department of Pathomorphology, Medical College, Jagiellonian University, Kraków, Poland
| | - Filip Wanat
- Department of Pathomorphology, Medical College, Jagiellonian University, Kraków, Poland
| | - Magdalena Ulatowska-Białas
- Department of Pathomorphology, Medical College, Jagiellonian University, Kraków, Poland
- Department of Pathomorphology, University Hospital, Kraków, Poland
| | - Dariusz Adamek
- Department of Pathomorphology, Medical College, Jagiellonian University, Kraków, Poland
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Jiang L, Huang S, Luo C, Zhang J, Chen W, Liu Z. An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images. Front Oncol 2023; 13:1240645. [PMID: 38023227 PMCID: PMC10679330 DOI: 10.3389/fonc.2023.1240645] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Deep learning-based solutions for histological image classification have gained attention in recent years due to their potential for objective evaluation of histological images. However, these methods often require a large number of expert annotations, which are both time-consuming and labor-intensive to obtain. Several scholars have proposed generative models to augment labeled data, but these often result in label uncertainty due to incomplete learning of the data distribution. Methods To alleviate these issues, a method called InceptionV3-SMSG-GAN has been proposed to enhance classification performance by generating high-quality images. Specifically, images synthesized by Multi-Scale Gradients Generative Adversarial Network (MSG-GAN) are selectively added to the training set through a selection mechanism utilizing a trained model to choose generated images with higher class probabilities. The selection mechanism filters the synthetic images that contain ambiguous category information, thus alleviating label uncertainty. Results Experimental results show that compared with the baseline method which uses InceptionV3, the proposed method can significantly improve the performance of pathological image classification from 86.87% to 89.54% for overall accuracy. Additionally, the quality of generated images is evaluated quantitatively using various commonly used evaluation metrics. Discussion The proposed InceptionV3-SMSG-GAN method exhibited good classification ability, where histological image could be divided into nine categories. Future work could focus on further refining the image generation and selection processes to optimize classification performance.
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Affiliation(s)
- Liwen Jiang
- Department of Pathology, Affiliated Cancer Hospital and Institution of Guangzhou Medical University, Guangzhou, China
| | - Shuting Huang
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - Chaofan Luo
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - Jiangyu Zhang
- Department of Pathology, Affiliated Cancer Hospital and Institution of Guangzhou Medical University, Guangzhou, China
| | - Wenjing Chen
- Department of Pathology, Guangdong Women and Children Hospital, Guangzhou, China
| | - Zhenyu Liu
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
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Gandikota HP, S. A, M. SK. CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm. PLoS One 2023; 18:e0292785. [PMID: 37930963 PMCID: PMC10627451 DOI: 10.1371/journal.pone.0292785] [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: 08/07/2023] [Accepted: 09/25/2023] [Indexed: 11/08/2023] Open
Abstract
Pancreatic cancer (PC) is a very lethal disease with a low survival rate, making timely and accurate diagnoses critical for successful treatment. PC classification in computed tomography (CT) scans is a vital task that aims to accurately discriminate between tumorous and non-tumorous pancreatic tissues. CT images provide detailed cross-sectional images of the pancreas, which allows oncologists and radiologists to analyse the characteristics and morphology of the tissue. Machine learning (ML) approaches, together with deep learning (DL) algorithms, are commonly explored to improve and automate the performance of PC classification in CT scans. DL algorithms, particularly convolutional neural networks (CNNs), are broadly utilized for medical image analysis tasks, involving segmentation and classification. This study explores the design of a tunicate swarm algorithm with deep learning-based pancreatic cancer segmentation and classification (TSADL-PCSC) technique on CT scans. The purpose of the TSADL-PCSC technique is to design an effectual and accurate model to improve the diagnostic performance of PC. To accomplish this, the TSADL-PCSC technique employs a W-Net segmentation approach to define the affected region on the CT scans. In addition, the TSADL-PCSC technique utilizes the GhostNet feature extractor to create a group of feature vectors. For PC classification, the deep echo state network (DESN) model is applied in this study. Finally, the hyperparameter tuning of the DESN approach occurs utilizing the TSA which assists in attaining improved classification performance. The experimental outcome of the TSADL-PCSC method was tested on a benchmark CT scan database. The obtained outcomes highlighted the significance of the TSADL-PCSC technique over other approaches to PC classification.
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Affiliation(s)
- Hari Prasad Gandikota
- Department of Computer Science & Engineering, Annamalai University, Chidambaram, Tamilnadu, India
| | - Abirami S.
- Department of Computer Science & Engineering, Annamalai University, Chidambaram, Tamilnadu, India
| | - Sunil Kumar M.
- School of Computing, Mohan Babu University, Tirupati, Andhra Pradesh, India
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10
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Sijithra PC, Santhi N, Ramasamy N. A review study on early detection of pancreatic ductal adenocarcinoma using artificial intelligence assisted diagnostic methods. Eur J Radiol 2023; 166:110972. [PMID: 37454557 DOI: 10.1016/j.ejrad.2023.110972] [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: 06/26/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, chemo-refractory and recalcitrant cancer and increases the number of deaths. With just around 1 in 4 individuals having respectable tumours, PDAC is frequently discovered when it is in an advanced stage. Accordingly, ED of PDAC improves patient survival. Subsequently, this paper reviews the early detection of PDAC, initially, the work presented an overview of PDAC. Subsequently, it reviews the molecular biology of pancreatic cancer and the development of molecular biomarkers are represented. This article illustrates the importance of identifying PDCA, the Immune Microenvironment of Pancreatic Cancer. Consequently, in this review, traditional and non-traditional imaging techniques are elucidated, traditional and non-traditional methods like endoscopic ultrasound, Multidetector CT, CT texture analysis, PET-CT, magnetic resonance imaging, diffusion-weighted imaging, secondary signs of pancreatic cancer, and molecular imaging. The use of artificial intelligence in pancreatic cancer, novel MRI techniques, and the future directions of AI for PDAC detection and prognosis is then described. Additionally, the research problem definition and motivation, current trends and developments, state of art of survey, and objective of the research are demonstrated in the review. Consequently, this review concluded that Artificial Intelligence Assisted Diagnostic Methods with MRI images can be proposed in future to improve the specificity and the sensitivity of the work, and to classify malignant PDAC with greater accuracy.
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Affiliation(s)
- P C Sijithra
- Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kanyakumari District, Tamilnadu, India.
| | - N Santhi
- Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kanyakumari District, Tamilnadu, India
| | - N Ramasamy
- Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kanyakumari District, Tamilnadu, India
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Kaczmarzyk JR, Gupta R, Kurc TM, Abousamra S, Saltz JH, Koo PK. ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 239:107631. [PMID: 37271050 PMCID: PMC11093625 DOI: 10.1016/j.cmpb.2023.107631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/23/2023] [Accepted: 05/28/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Histopathology is the gold standard for diagnosis of many cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for many tasks, including the detection of immune cells and microsatellite instability. However, it remains difficult to identify optimal models and training configurations for different histopathology classification tasks due to the abundance of available architectures and the lack of systematic evaluations. Our objective in this work is to present a software tool that addresses this need and enables robust, systematic evaluation of neural network models for patch classification in histology in a light-weight, easy-to-use package for both algorithm developers and biomedical researchers. METHODS Here we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible evaluation toolkit that is a one-stop-shop to train and evaluate deep neural networks for patch classification. ChampKit curates a broad range of public datasets. It enables training and evaluation of models supported by timm directly from the command line, without the need for users to write any code. External models are enabled through a straightforward API and minimal coding. As a result, Champkit facilitates the evaluation of existing and new models and deep learning architectures on pathology datasets, making it more accessible to the broader scientific community. To demonstrate the utility of ChampKit, we establish baseline performance for a subset of possible models that could be employed with ChampKit, focusing on several popular deep learning models, namely ResNet18, ResNet50, and R26-ViT, a hybrid vision transformer. In addition, we compare each model trained either from random weight initialization or with transfer learning from ImageNet pretrained models. For ResNet18, we also consider transfer learning from a self-supervised pretrained model. RESULTS The main result of this paper is the ChampKit software. Using ChampKit, we were able to systemically evaluate multiple neural networks across six datasets. We observed mixed results when evaluating the benefits of pretraining versus random intialization, with no clear benefit except in the low data regime, where transfer learning was found to be beneficial. Surprisingly, we found that transfer learning from self-supervised weights rarely improved performance, which is counter to other areas of computer vision. CONCLUSIONS Choosing the right model for a given digital pathology dataset is nontrivial. ChampKit provides a valuable tool to fill this gap by enabling the evaluation of hundreds of existing (or user-defined) deep learning models across a variety of pathology tasks. Source code and data for the tool are freely accessible at https://github.com/SBU-BMI/champkit.
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Affiliation(s)
- Jakub R Kaczmarzyk
- Department of Biomedical Informatics, Stony Brook Medicine, 101 Nicolls Rd, Stony Brook, 11794, NY, USA; Simons Center for Quantitative Biology, 1 Bungtown Rd, Cold Spring Harbor, 11724, NY, USA.
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook Medicine, 101 Nicolls Rd, Stony Brook, 11794, NY, USA
| | - Tahsin M Kurc
- Department of Biomedical Informatics, Stony Brook Medicine, 101 Nicolls Rd, Stony Brook, 11794, NY, USA
| | - Shahira Abousamra
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Joel H Saltz
- Department of Biomedical Informatics, Stony Brook Medicine, 101 Nicolls Rd, Stony Brook, 11794, NY, USA.
| | - Peter K Koo
- Simons Center for Quantitative Biology, 1 Bungtown Rd, Cold Spring Harbor, 11724, NY, USA.
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12
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Carrillo-Perez F, Ortuno FM, Börjesson A, Rojas I, Herrera LJ. Performance comparison between multi-center histopathology datasets of a weakly-supervised deep learning model for pancreatic ductal adenocarcinoma detection. Cancer Imaging 2023; 23:66. [PMID: 37365659 PMCID: PMC10294485 DOI: 10.1186/s40644-023-00586-3] [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: 02/28/2023] [Accepted: 06/21/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Pancreatic ductal carcinoma patients have a really poor prognosis given its difficult early detection and the lack of early symptoms. Digital pathology is routinely used by pathologists to diagnose the disease. However, visually inspecting the tissue is a time-consuming task, which slows down the diagnostic procedure. With the advances occurred in the area of artificial intelligence, specifically with deep learning models, and the growing availability of public histology data, clinical decision support systems are being created. However, the generalization capabilities of these systems are not always tested, nor the integration of publicly available datasets for pancreatic ductal carcinoma detection (PDAC). METHODS In this work, we explored the performace of two weakly-supervised deep learning models using the two more widely available datasets with pancreatic ductal carcinoma histology images, The Cancer Genome Atlas Project (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). In order to have sufficient training data, the TCGA dataset was integrated with the Genotype-Tissue Expression (GTEx) project dataset, which contains healthy pancreatic samples. RESULTS We showed how the model trained on CPTAC generalizes better than the one trained on the integrated dataset, obtaining an inter-dataset accuracy of 90.62% ± 2.32 and an outer-dataset accuracy of 92.17% when evaluated on TCGA + GTEx. Furthermore, we tested the performance on another dataset formed by tissue micro-arrays, obtaining an accuracy of 98.59%. We showed how the features learned in an integrated dataset do not differentiate between the classes, but between the datasets, noticing that a stronger normalization might be needed when creating clinical decision support systems with datasets obtained from different sources. To mitigate this effect, we proposed to train on the three available datasets, improving the detection performance and generalization capabilities of a model trained only on TCGA + GTEx and achieving a similar performance to the model trained only on CPTAC. CONCLUSIONS The integration of datasets where both classes are present can mitigate the batch effect present when integrating datasets, improving the classification performance, and accurately detecting PDAC across different datasets.
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Affiliation(s)
- Francisco Carrillo-Perez
- Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain.
| | - Francisco M Ortuno
- Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain
| | - Alejandro Börjesson
- Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
| | - Ignacio Rojas
- Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
| | - Luis Javier Herrera
- Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
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13
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Saillard C, Delecourt F, Schmauch B, Moindrot O, Svrcek M, Bardier-Dupas A, Emile JF, Ayadi M, Rebours V, de Mestier L, Hammel P, Neuzillet C, Bachet JB, Iovanna J, Dusetti N, Blum Y, Richard M, Kermezli Y, Paradis V, Zaslavskiy M, Courtiol P, Kamoun A, Nicolle R, Cros J. Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma. Nat Commun 2023; 14:3459. [PMID: 37311751 DOI: 10.1038/s41467-023-39026-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 05/25/2023] [Indexed: 06/15/2023] Open
Abstract
Two tumor (Classical/Basal) and stroma (Inactive/active) subtypes of Pancreatic adenocarcinoma (PDAC) with prognostic and theragnostic implications have been described. These molecular subtypes were defined by RNAseq, a costly technique sensitive to sample quality and cellularity, not used in routine practice. To allow rapid PDAC molecular subtyping and study PDAC heterogeneity, we develop PACpAInt, a multi-step deep learning model. PACpAInt is trained on a multicentric cohort (n = 202) and validated on 4 independent cohorts including biopsies (surgical cohorts n = 148; 97; 126 / biopsy cohort n = 25), all with transcriptomic data (n = 598) to predict tumor tissue, tumor cells from stroma, and their transcriptomic molecular subtypes, either at the whole slide or tile level (112 µm squares). PACpAInt correctly predicts tumor subtypes at the whole slide level on surgical and biopsies specimens and independently predicts survival. PACpAInt highlights the presence of a minor aggressive Basal contingent that negatively impacts survival in 39% of RNA-defined classical cases. Tile-level analysis ( > 6 millions) redefines PDAC microheterogeneity showing codependencies in the distribution of tumor and stroma subtypes, and demonstrates that, in addition to the Classical and Basal tumors, there are Hybrid tumors that combine the latter subtypes, and Intermediate tumors that may represent a transition state during PDAC evolution.
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Affiliation(s)
| | - Flore Delecourt
- Université Paris Cité, Dpt of Pathology - FHU MOSAIC, Beaujon Hospital, INSERM U1149, Clichy, France
| | | | | | - Magali Svrcek
- Dpt of Pathology, Saint-Antoine Hospital - Sorbonne Universités, Paris, France
| | | | - Jean Francois Emile
- Dpt of Pathology, Ambroise Paré Hospital - Université Saint Quentin en Yvelines, Paris, France
| | - Mira Ayadi
- Integragen, Genomic Services & Precision Medicine, Paris, France
| | - Vinciane Rebours
- Université Paris Cité, Dpt of Pancreatology - FHU MOSAIC, Beaujon Hospital, INSERM U1149, Clichy, France
| | - Louis de Mestier
- Université Paris Cité, Dpt of Pancreatology - FHU MOSAIC, Beaujon Hospital, INSERM U1149, Clichy, France
| | - Pascal Hammel
- Dpt of Medical oncology, Paul Brousse Hospital, Villejuif, France
| | | | - Jean Baptiste Bachet
- Dpt of Gastroenterology, Pitié-Salpêtrière Hospital - Sorbonne Universités, Paris, France
| | - Juan Iovanna
- Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, Institut Paoli-Calmettes, Aix Marseille Université, CNRS UMR 7258, Marseille, France
| | - Nelson Dusetti
- Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, Institut Paoli-Calmettes, Aix Marseille Université, CNRS UMR 7258, Marseille, France
| | - Yuna Blum
- Institut Génétique et Développement de Rennes (IGDR), CNRS, Université de Rennes 1, UMR 6290, Rennes, France
| | - Magali Richard
- Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications Grenoble (TIMC-IMAG), CNRS, Université Grenoble-Alpes, UMR5525, Grenoble, France
| | - Yasmina Kermezli
- Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications Grenoble (TIMC-IMAG), CNRS, Université Grenoble-Alpes, UMR5525, Grenoble, France
| | - Valerie Paradis
- Université Paris Cité, Dpt of Pathology - FHU MOSAIC, Beaujon Hospital, INSERM U1149, Clichy, France
| | | | | | | | - Remy Nicolle
- Université Paris Cité, FHU MOSAIC, Centre de Recherche sur l'Inflammation (CRI), INSERM, U1149, CNRS, ERL 8252, F-75018, Paris, France
| | - Jerome Cros
- Université Paris Cité, Dpt of Pathology - FHU MOSAIC, Beaujon Hospital, INSERM U1149, Clichy, France.
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Faur AC, Lazar DC, Ghenciu LA. Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis. World J Gastroenterol 2023; 29:1811-1823. [PMID: 37032728 PMCID: PMC10080704 DOI: 10.3748/wjg.v29.i12.1811] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/23/2022] [Accepted: 03/16/2023] [Indexed: 03/28/2023] Open
Abstract
Pancreatic cancer (PC) has a low incidence rate but a high mortality, with patients often in the advanced stage of the disease at the time of the first diagnosis. If detected, early neoplastic lesions are ideal for surgery, offering the best prognosis. Preneoplastic lesions of the pancreas include pancreatic intraepithelial neoplasia and mucinous cystic neoplasms, with intraductal papillary mucinous neoplasms being the most commonly diagnosed. Our study focused on predicting PC by identifying early signs using noninvasive techniques and artificial intelligence (AI). A systematic English literature search was conducted on the PubMed electronic database and other sources. We obtained a total of 97 studies on the subject of pancreatic neoplasms. The final number of articles included in our study was 44, 34 of which focused on the use of AI algorithms in the early diagnosis and prediction of pancreatic lesions. AI algorithms can facilitate diagnosis by analyzing massive amounts of data in a short period of time. Correlations can be made through AI algorithms by expanding image and electronic medical records databases, which can later be used as part of a screening program for the general population. AI-based screening models should involve a combination of biomarkers and medical and imaging data from different sources. This requires large numbers of resources, collaboration between medical practitioners, and investment in medical infrastructures.
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Affiliation(s)
- Alexandra Corina Faur
- Department of Anatomy and Embriology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Timișoara 300041, Timiș, Romania
| | - Daniela Cornelia Lazar
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, University of Medicine and Pharmacy “Victor Babes” Timișoara, Timișoara 300041, Timiș, Romania
| | - Laura Andreea Ghenciu
- Department III, Discipline of Pathophysiology, “Victor Babeș” University of Medicine and Pharmacy, Timișoara 300041, Timiș, Romania
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15
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Abstract
Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.
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Affiliation(s)
- Siddhi Ramesh
- Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA
| | - James M Dolezal
- Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA
| | - Alexander T Pearson
- Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; University of Chicago Comprehensive Cancer Center, Chicago, IL, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA.
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16
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Qiao Y, Zhao L, Luo C, Luo Y, Wu Y, Li S, Bu D, Zhao Y. Multi-modality artificial intelligence in digital pathology. Brief Bioinform 2022; 23:6702380. [PMID: 36124675 PMCID: PMC9677480 DOI: 10.1093/bib/bbac367] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/27/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022] Open
Abstract
In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.
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Affiliation(s)
- Yixuan Qiao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lianhe Zhao
- Corresponding authors: Yi Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences; Shandong First Medical University & Shandong Academy of Medical Sciences. Tel.: +86 10 6260 0822; Fax: +86 10 6260 1356; E-mail: ; Lianhe Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences. Tel.: +86 18513983324; E-mail:
| | - Chunlong Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yufan Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Shengtong Li
- Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yi Zhao
- Corresponding authors: Yi Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences; Shandong First Medical University & Shandong Academy of Medical Sciences. Tel.: +86 10 6260 0822; Fax: +86 10 6260 1356; E-mail: ; Lianhe Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences. Tel.: +86 18513983324; E-mail:
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17
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Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers (Basel) 2022; 14:cancers14143498. [PMID: 35884559 PMCID: PMC9316850 DOI: 10.3390/cancers14143498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed. Abstract Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
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Affiliation(s)
- Megan Schuurmans
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Natália Alves
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Pierpaolo Vendittelli
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - John Hermans
- Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands;
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18
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Li J, Mi W, Guo Y, Ren X, Fu H, Zhang T, Zou H, Liang Z. Artificial intelligence for histological subtype classification of breast cancer: combining multi‐scale feature maps and recurrent attention model. Histopathology 2021; 80:836-846. [DOI: 10.1111/his.14613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 11/09/2021] [Accepted: 12/22/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Junjie Li
- Department of Pathology, State Key Laboratory of Complex Severe and Rare Diseases, Molecular Pathology Research Center Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College Beijing 100730 China
| | - Weiming Mi
- Department of Automation, School of Information Science and Technology Tsinghua University Beijing 100084 China
| | - Yucheng Guo
- Tsimage Medical Technology Yihai Center No. 2039 Shenyan Road, Yantian District Shenzhen 518081 China
- Center for Intelligent Medical Imaging & Health Research Institute of Tsinghua University in Shenzhen Shenzhen 518057 China
| | - Xinyu Ren
- Department of Pathology, State Key Laboratory of Complex Severe and Rare Diseases, Molecular Pathology Research Center Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College Beijing 100730 China
| | - Hao Fu
- Tsimage Medical Technology Yihai Center No. 2039 Shenyan Road, Yantian District Shenzhen 518081 China
| | - Tao Zhang
- Department of Automation, School of Information Science and Technology Tsinghua University Beijing 100084 China
| | - Hao Zou
- Tsimage Medical Technology Yihai Center No. 2039 Shenyan Road, Yantian District Shenzhen 518081 China
- Center for Intelligent Medical Imaging & Health Research Institute of Tsinghua University in Shenzhen Shenzhen 518057 China
| | - Zhiyong Liang
- Department of Pathology, State Key Laboratory of Complex Severe and Rare Diseases, Molecular Pathology Research Center Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College Beijing 100730 China
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19
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Ju J, Wismans LV, Mustafa DAM, Reinders MJT, van Eijck CHJ, Stubbs AP, Li Y. Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients. iScience 2021; 24:103415. [PMID: 34901786 PMCID: PMC8637475 DOI: 10.1016/j.isci.2021.103415] [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: 06/07/2021] [Revised: 09/27/2021] [Accepted: 11/05/2021] [Indexed: 02/07/2023] Open
Abstract
A major challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is the unpredictability of their prognoses due to high heterogeneity. We present Multi-Omics DEep Learning for Prognosis-correlated subtyping (MODEL-P) to identify PDAC subtypes and to predict prognoses of new patients. MODEL-P was trained on autoencoder integrated multi-omics of 146 patients with PDAC together with their survival outcome. Using MODEL-P, we identified two PDAC subtypes with distinct survival outcomes (median survival 10.1 and 22.7 months, respectively, log rank p = 1 × 10−6), which correspond to DNA damage repair and immune response. We rigorously validated MODEL-P by stratifying patients in five independent datasets into these two survival groups and achieved significant survival difference, which is superior to current practice and other subtyping schemas. We believe the subtype-specific signatures would facilitate PDAC pathogenesis discovery, and MODEL-P can provide clinicians the prognoses information in the treatment decision-making to better gauge the benefits versus the risks. We developed DL-based MODEL-P to identify prognosis-correlated PDAC subtypes The identified subtypes related to DNA damage repair and immune response processes MODEL-P stratified patients from independent datasets into distinct survival groups MODEL-P could be used in clinics to aid treatment decision-making
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Affiliation(s)
- Jie Ju
- Department of Pathology & Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Leonoor V Wismans
- Department of Surgery, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Dana A M Mustafa
- Department of Pathology & Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marcel J T Reinders
- The Delft Bioinformatics Lab, Delft University of Technology, Rotterdam, the Netherlands
| | - Casper H J van Eijck
- Department of Surgery, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Andrew P Stubbs
- Department of Pathology & Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Yunlei Li
- Department of Pathology & Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
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