Article
Pathology
Diana Montezuma, Sara P. Oliveira, Pedro C. Neto, Domingos Oliveira, Ana Monteiro, Jaime S. Cardoso, Isabel Macedo-Pinto
Summary: In this paper, the authors describe their experience in training machine learning models for AI applications in pathology, which often requires extensive annotation by human experts. They provide a simple and practical guide addressing annotation strategies for AI development in computational pathology, covering team interaction, ground-truth quality assessment, annotation types, and available software and hardware options. This guide aims to assist pathologists, researchers, and AI developers in the annotation process.
Article
Engineering, Electrical & Electronic
Sandra Morales, Kjersti Engan, Valery Naranjo
Summary: The field of digital histopathology has seen significant growth, offering important tools for healthcare, industrial, and research sectors to improve diagnostic accuracy and reduce turnaround times in pathology. The future of computational pathology is expected to involve the integration of AI with strategies such as weak labeling, active learning, and crowdsourcing to address current challenges. Areas such as explainable AI, data fusion, and secure role-based data sharing are likely to receive increasing research attention in computational pathology.
DIGITAL SIGNAL PROCESSING
(2021)
Article
Biochemistry & Molecular Biology
M. P. Humphries, P. Maxwell, M. Salto-Tellez
Summary: QuPath, created at Queen's University Belfast, is the most widely used image analysis software program globally, addressing various needs in tissue-based image analysis and serving as the system of choice for researchers in scientific research.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Review
Oncology
Vidya Sankar Viswanathan, Paula Toro, German Corredor, Sanjay Mukhopadhyay, Anant Madabhushi
Summary: The development of digital pathology and artificial intelligence has provided new opportunities for the diagnosis and treatment of lung diseases. Research has explored the application of AI methods and tools in lung diseases, including image analysis and the use of biomarkers. AI tools also have the potential to play a role in areas such as multimodal data analysis, 3D pathology, and transplant rejection in lung diseases.
JOURNAL OF PATHOLOGY
(2022)
Article
Computer Science, Information Systems
Peter J. Schueffler, Luke Geneslaw, D. Vijay K. Yarlagadda, Matthew G. Hanna, Jennifer Samboy, Evangelos Stamelos, Chad Vanderbilt, John Philip, Marc-Henri Jean, Lorraine Corsale, Allyne Manzo, Neeraj H. G. Paramasivam, John S. Ziegler, Jianjiong Gao, Juan C. Perin, Young Suk Kim, Umeshkumar K. Bhanot, Michael H. A. Roehrl, Orly Ardon, Sarah Chiang, Dilip D. Giri, Carlie S. Sigel, Lee K. Tan, Melissa Murray, Christina Virgo, Christine England, Yukako Yagi, S. Joseph Sirintrapun, David Klimstra, Meera Hameed, Victor E. Reuter, Thomas J. Fuchs
Summary: The study introduced a comprehensive digital pathology solution in a large academic medical center, including a viewer for clinical workflows, research applications, and educational processes, as well as an interconnected tool for compiling and sharing research datasets. The implementation of these solutions led to increased adoption of digital pathology and facilitated next-generation computational pathology for enhanced cancer research.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2021)
Article
Medicine, Research & Experimental
Daniel Gomes Pinto, Andrey Bychkov, Naoko Tsuyama, Junya Fukuoka, Catarina Eloy
Summary: The past 70 years have witnessed rapid advancements in computer technology, although anatomical pathology has mainly remained an analog discipline. However, with the growing adoption of digital pathology, it is becoming the present reality for some laboratories and may continue to be the future for many others.
LABORATORY INVESTIGATION
(2023)
Article
Multidisciplinary Sciences
Lei Jin, Tianyang Sun, Xi Liu, Zehong Cao, Yan Liu, Hong Chen, Yixin Ma, Jun Zhang, Yaping Zou, Yingchao Liu, Feng Shi, Dinggang Shen, Jinsong Wu
Summary: Accurate pathological classification and grading of gliomas is crucial in clinical diagnosis and treatment. In this study, deep learning techniques were used for automated histological pathology diagnosis of gliomas. The model showed high accuracy in both internal validation and multi-center testing.
Review
Oncology
Anglin Dent, Phedias Diamandis
Summary: Despite advances in cancer biology, precision medicine trials face challenges due to molecular inconsistencies and heterogeneous tumor biology. Integrating mass-spectrometry-based global proteomics and computational imaging can overcome these challenges of biologic variation in cancer.
JOURNAL OF PATHOLOGY
(2022)
Article
Computer Science, Theory & Methods
Christoph Jansen, Bjoern Lindequist, Klaus Strohmenger, Daniel Romberg, Tobias Kuester, Nick Weiss, Michael Franz, Lars Ole Schwen, Theodore Evans, Andre Homeyer, Norman Zerbe
Summary: Automated image analysis and AI are increasingly common in digital pathology software. The EMPAlA Consortium is developing an open and decentralized platform for AI apps to be integrated with clinical IT infrastructures, reducing integration efforts for vendors and providing pathologists access to advanced AI tools.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Review
Medicine, General & Internal
Inho Kim, Kyungmin Kang, Youngjae Song, Tae-Jung Kim
Summary: With the recent success of artificial intelligence (AI) in computer vision applications, many pathologists expect AI to assist them in various digital pathology tasks. Advances in deep learning have synergized with AI, enabling image-based diagnosis in the field of digital pathology. Efforts are being made to develop AI-based tools that can save time and eliminate errors for pathologists.
Article
Mathematics
Tahir Mahmood, Seung Gu Kim, Ja Hyung Koo, Kang Ryoung Park
Summary: In this paper, a novel deep learning method is proposed for the detection and phenotyping of colorectal cancer tissue. The method achieves higher accuracy and efficiency compared to state-of-the-art methods by incorporating visual and semantic information and applying data augmentation techniques.
Article
Computer Science, Artificial Intelligence
Hao Chen, Yanning Zhou, Yi Lin, Ronald C. K. Chan, Jiang Liu, Hao Jiang
Summary: Computational cytology is a critical and rapidly developing topic in medical image computing. Deep learning approaches have achieved significant advancements in cytology image analysis, leading to a substantial increase in publications. This article surveys over 120 publications on deep learning-based cytology image analysis, investigating advanced methods and comprehensive applications. It introduces different deep learning schemes, summarizes public datasets, evaluation metrics, and versatile applications, and discusses current challenges and potential research directions in computational cytology.
MEDICAL IMAGE ANALYSIS
(2023)
Review
Pathology
Sophia J. Wagner, Christian Matek, Sayedali Shetab Boushehri, Melanie Boxberg, Lorenz Lamm, Ario Sada, Dominik J. E. Winter, Carsten Marr, Tingying Peng
Summary: Computational pathology research driven by deep learning faces challenges in reproducibility and reusability. Codebase with good documentation and robustness and generalizability of models are crucial. The reuse of computational pathology algorithms is limited, and their application in clinical settings is even rarer. This study evaluates 160 peer-reviewed articles, providing criteria for data and code availability and statistical analysis of results.
Article
Oncology
Peter Leonard Schrammen, Narmin Ghaffari Laleh, Amelie Echle, Daniel Truhn, Volkmar Schulz, Titus J. Brinker, Hermann Brenner, Jenny Chang-Claude, Elizabeth Alwers, Alexander Brobeil, Matthias Kloor, Lara R. Heij, Dirk Jager, Christian Trautwein, Heike Grabsch, Philip Quirke, Nicholas P. West, Michael Hoffmeister, Jakob Nikolas Kather
Summary: SLAM is a simple yet powerful computational pathology method that utilizes a neural network to predict tumor presence and genetic alterations directly from histopathology slides, without the need for complex manual annotations. It demonstrates high reliability and accuracy in clinically relevant tasks, making it a valuable tool for disease analysis.
JOURNAL OF PATHOLOGY
(2022)
Article
Pathology
Catarina Eloy, Ana Marques, Joao Pinto, Jorge Pinheiro, Sofia Campelos, Monica Curado, Joao Vale, Antonio Polonia
Summary: Paige Prostate is a clinical-grade AI tool that assists pathologists in detecting and grading prostate cancer. In a study with 105 prostate core needle biopsies, pathologists using Paige Prostate showed improved diagnostic accuracy and efficiency compared to those without assistance. The use of Paige Prostate reduced the need for additional tests and second opinions, and decreased the time required for reading and reporting slides.
Review
Medicine, Research & Experimental
Miao Cui, Chao Cheng, Lanjing Zhang
Summary: Proteomics plays a vital role in biomedical research by providing new insights into complex biomedical problems. This mini-review summarizes existing and emerging high-throughput proteomics methodologies and discusses the importance of computational methods and statistical algorithms in maximizing the value of proteomic data. The advances in high-throughput proteomics not only enhance our understanding of disease mechanisms, but also offer potential applications in various fields such as basic research, oncology, precision medicine, and drug discovery.
LABORATORY INVESTIGATION
(2022)
Article
Biochemistry & Molecular Biology
Yingying Lu, Feng Liu, Gangling Tong, Feng Qiu, Pinhong Song, Xiaolin Wang, Xiafei Zou, Deyun Wan, Miao Cui, Yunsheng Xu, Zhihua Zheng, Peng Hong
Summary: This study found that early interferon therapy in patients with COVID-19 who were also receiving glucocorticoids was associated with earlier hospital discharge, symptom relief, and lower prevalence of prolonged viral shedding. These associations were more significant in glucocorticoid users, indicating a potential therapeutic synergy between interferons and glucocorticoids in COVID-19.
SIGNAL TRANSDUCTION AND TARGETED THERAPY
(2021)
Article
Medicine, Research & Experimental
Marijn A. Scheijde-Vermeulen, Lennart A. Kester, Liset Westera, Bastiaan B. J. Tops, Friederike A. G. Meyer-Wentrup
Summary: This study aimed to evaluate the feasibility of integrating state-of-the-art sequencing techniques and flow cytometry into the diagnostic workup of pediatric lymphoma. The results showed that this integration is not only feasible but also provides additional diagnostic information.
LABORATORY INVESTIGATION
(2024)
Article
Medicine, Research & Experimental
Enrico Berrino, Sara Erika Bellomo, Anita Chesta, Paolo Detillo, Alberto Bragoni, Amedeo Gagliardi, Alessio Naccarati, Matteo Cereda, Gianluca Witel, Anna Sapino, Benedetta Bussolati, Gianni Bussolati, Caterina Marchi
Summary: Formalin-fixed paraffin-embedded (FFPE) samples are crucial for tissue-based analysis in precision medicine, but the quality of these samples can affect the reliability of sequencing data. The use of acid-deprived fixatives guarantees the highest DNA preservation and sequencing performance, enabling more complex molecular profiling of tissue samples.
LABORATORY INVESTIGATION
(2024)
Article
Medicine, Research & Experimental
Roope A. Kallionpaa, Sirkku Peltonen, Kim My Le, Eija Martikkala, Mira Jaaskelainen, Elnaz Fazeli, Pilvi Riihila, Pekka Haapaniemi, Anne Rokka, Marko Salmi, Ilmo Leivo, Juha Peltonen
Summary: This study investigated the immune microenvironment of cutaneous neurofibromas (cNFs) in patients with neurofibromatosis 1 (NF1). The results showed that cNFs have substantial populations of T cells and macrophages, which may be tumor-specific. T cell populations in cNFs were found to be different from those in the skin, and cNFs exhibited lower expression of proteins related to T cell-mediated immunity compared to the skin.
LABORATORY INVESTIGATION
(2024)