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JOURNAL ARTICLE
Ioannis Prassas, Blaise Clarke, Timothy Youssef, Juliana Phlamon, Lampros Dimitrakopoulos, Andrew Rofaeil, George M Yousef
The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health...
April 23, 2024: Clinical Chemistry and Laboratory Medicine: CCLM
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JOURNAL ARTICLE
Rajesh Nachiappa Ganesh, Edward A Graviss, Duc Nguyen, Ziad El-Zaatari, Lillian Gaber, Roberto Barrios, Luan Truong, Alton B Farris
INTRODUCTION: Semi-quantitative scoring of various parameters in renal biopsy is accepted as an important tool to assess disease activity and prognostication. There are concerns on the impact of inter-observer variability in its prognostic utility, generating a need for computerized quantification. METHODS: We studied 94 patients with renal biopsies, 45 with native diseases and 49 transplant patients with index biopsies for Polyomavirus nephropathy. Chronicity scores were evaluated using two methods...
April 17, 2024: Human Pathology
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JOURNAL ARTICLE
Luyu Tang, Songhui Diao, Chao Li, Miaoxia He, Kun Ru, Wenjian Qin
Current methods of digital pathological images typically employ small image patches to learn local representative features to overcome the issues of computationally heavy and memory limitations. However, the global contextual features are not fully considered in whole-slide images (WSIs). Here, we designed a hybrid model that utilizes Graph Neural Network (GNN) module and Transformer module for the representation of global contextual features, called TransGNN. GNN module built a WSI-Graph for the foreground area of a WSI for explicitly capturing structural features, and the Transformer module through the self-attention mechanism implicitly learned the global context information...
April 16, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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REVIEW
Renata A Coudry, Emilio A C P Assis, Fernando Pereira Frassetto, Angela Marie Jansen, Leonard Medeiros da Silva, Rafael Parra-Medina, Mauro Saieg
The most widely accepted and used type of digital pathology (DP) is whole-slide imaging (WSI). The USFDA granted two WSI system approvals for primary diagnosis, the first in 2017. In Latin America, DP has the potential to reshape healthcare by enhancing diagnostic capabilities through artificial intelligence (AI) and standardizing pathology reports. Yet, we must tackle regulatory hurdles, training, resource availability, and unique challenges to the region. Collectively addressing these hurdles can enable the region to harness DP's advantages-enhancing disease diagnosis, medical research, and healthcare accessibility for its population...
December 2024: Journal of Pathology Informatics
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JOURNAL ARTICLE
Avri Giammanco, Andrey Bychkov, Simon Schallenberg, Tsvetan Tsvetkov, Junya Fukuoka, Alexey Pryalukhin, Fabian Mairinger, Alexander Seper, Wolfgang Hulla, Sebastian Klein, Alexander Quaas, Reinhard Büttner, Yuri Tolkach
Lymph node metastasis (LNM) detection can be automated using artificial intelligence-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer. The aim of this study was to develop of a clinical-grade digital pathology tool for LNM detection in colorectal cancer (CRC) using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from five pathology departments digitized by four different scanning systems...
April 16, 2024: Modern Pathology
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JOURNAL ARTICLE
Sherman Lin, Christopher Tran, Ela Bandari, Tommaso Romagnoli, Yueyang Li, Michael Chu, Abinaya S Amirthakatesan, Adam Dallmann, Andrii Kostiukov, Angel Panizo, Anjelica Hodgson, Anna R Laury, Antonio Polonia, Ashley E Stueck, Aswathy A Menon, Aurélien Morini, Birsen Özamrak, Caroline Cooper, Celestine Marie G Trinidad, Christian Eisenlöffel, Dauda E Suleiman, David Suster, David A Dorward, Eman A Aljufairi, Fiona Maclean, Gulen Gul, Irene Sansano, Irma E Erana-Rojas, Isidro Machado, Ivana Kholova, Jayanthi Karunanithi, Jean-Baptiste Gibier, Jefree J Schulte, Joshua J X Li, Jyoti R Kini, Katrina Collins, Laurence A Galea, Louis Muller, Luca Cima, Luiz M Nova-Camacho, Marcus Dabner, Matthew J Muscara, Matthew G Hanna, Mehdi Agoumi, Nicholas J P Wiebe, Nicola K Oswald, Nusrat Zahra, Olaleke O Folaranmi, Oleksandr Kravtsov, Orhan Semerci, Namrata N Patil, Preethi Muthusamy Sundar, Prem Charles, Priyadarshini Kumaraswamy Rajeswaran, Qi Zhang, Rachael van der Griend, Raghavendra Pillappa, Raul Perret, Raul S Gonzalez, Robyn C Reed, Sachin Patil, Xiaoyin Sara Jiang, Sumaira Qayoom, Susan Prendeville, Swikrity U Baskota, Thanh-Truc Tran, Thar-Htet San, Tiia-Maria Kukkonen, Timothy J Kendall, Toros Taskin, Tristan Rutland, Varsha Manucha, Vincent Cockenpot, Yale Rosen, Yessica P Rodriguez-Velandia, Zehra Ordulu, Matthew J Cecchini
Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area...
April 16, 2024: International Journal of Surgical Pathology
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REVIEW
Richard M Levenson, Yashbir Singh, Bastian Rieck, Quincy A Hathaway, Colleen Farrelly, Jennifer Rozenblit, Prateek Prasanna, Bradley Erickson, Ashok Choudhary, Gunnar Carlsson, Deepa Deepa
Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative, and increasingly, quantitative data...
April 14, 2024: Laboratory Investigation; a Journal of Technical Methods and Pathology
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JOURNAL ARTICLE
Dingyi Hu, Zhiguo Jiang, Jun Shi, Fengying Xie, Kun Wu, Kunming Tang, Ming Cao, Jianguo Huai, Yushan Zheng
Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited by the difficulty of gigapixels, the variable size of WSIs, and the dependence on manual annotations. In this work, we propose a novel histopathology language-image representation learning framework for fine-grained digital pathology cross-modal retrieval, which utilizes paired diagnosis reports to learn fine-grained semantics from the WSI...
April 9, 2024: Medical Image Analysis
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JOURNAL ARTICLE
Naomi Kouri, Isabelle Frankenhauser, Zhongwei Peng, Sydney A Labuzan, Baayla D C Boon, Christina M Moloney, Cyril Pottier, Daniel P Wickland, Kelsey Caetano-Anolles, Nick Corriveau-Lecavalier, Jessica F Tranovich, Ashley C Wood, Kelly M Hinkle, Sarah J Lincoln, A J Spychalla, Matthew L Senjem, Scott A Przybelski, Erica Engelberg-Cook, Christopher G Schwarz, Rain S Kwan, Elizabeth R Lesser, Julia E Crook, Rickey E Carter, Owen A Ross, Christian Lachner, Nilüfer Ertekin-Taner, Tanis J Ferman, Julie A Fields, Mary M Machulda, Vijay K Ramanan, Aivi T Nguyen, R Ross Reichard, David T Jones, Jonathan Graff-Radford, Bradley F Boeve, David S Knopman, Ronald C Petersen, Clifford R Jack, Kejal Kantarci, Gregory S Day, Ranjan Duara, Neill R Graff-Radford, Dennis W Dickson, Val J Lowe, Prashanthi Vemuri, Melissa E Murray
IMPORTANCE: Factors associated with clinical heterogeneity in Alzheimer disease (AD) lay along a continuum hypothesized to associate with tangle distribution and are relevant for understanding glial activation considerations in therapeutic advancement. OBJECTIVES: To examine clinicopathologic and neuroimaging characteristics of disease heterogeneity in AD along a quantitative continuum using the corticolimbic index (CLix) to account for individuality of spatially distributed tangles found at autopsy...
April 15, 2024: JAMA Neurology
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JOURNAL ARTICLE
Shaoxu Wu, Yun Wang, Guibin Hong, Yun Luo, Zhen Lin, Runnan Shen, Hong Zeng, Abai Xu, Peng Wu, Mingzhao Xiao, Xiaoyang Li, Peng Rao, Qishen Yang, Zhengyuan Feng, Quanhao He, Fan Jiang, Ye Xie, Chengxiao Liao, Xiaowei Huang, Rui Chen, Tianxin Lin
BACKGROUND: The pathological examination of lymph node metastasis (LNM) is crucial for treating prostate cancer (PCa). However, the limitations with naked-eye detection and pathologist workload contribute to a high missed-diagnosis rate for nodal micrometastasis. We aimed to develop an artificial intelligence (AI)-based, time-efficient, and high-precision PCa LNM detector (ProCaLNMD) and evaluate its clinical application value. METHODS: In this multicentre, retrospective, diagnostic study, consecutive patients with PCa who underwent radical prostatectomy and pelvic lymph node dissection at five centres between Sep 2, 2013 and Apr 28, 2023 were included, and histopathological slides of resected lymph nodes were collected and digitised as whole-slide images for model development and validation...
May 2024: EClinicalMedicine
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REVIEW
Francesco Tucci, Arvydas Laurinavicius, Jakob Nikolas Kather, Catarina Eloy
Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology...
April 12, 2024: Tumori
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EDITORIAL
Fnu Sandeep, Nfn Kiran, Zubair Rahaman, Pooja Devi, Ahmed Bendari
The evolution of pathology from its rudimentary beginnings around 1700 BC to the present day has been marked by profound advancement in understanding and diagnosing diseases. This journey, from the earliest dissections to the modern era of histochemical analysis, sets the stage for the next transformative leap to the integration of artificial intelligence (AI) in pathology. Recent research highlights AI's significant potential to revolutionize healthcare within the next decade, with a particular impact on diagnostic processes...
March 2024: Curēus
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REVIEW
Roman David Bülow, Yu-Chia Lan, Kerstin Amann, Peter Boor
BACKGROUND: Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology. AIM: Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook. MATERIALS AND METHODS: Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney"...
April 10, 2024: Pathologie (Heidelb)
#14
REVIEW
Lingxuan Zhu, Jiahua Pan, Weiming Mou, Longxin Deng, Yinjie Zhu, Yanqing Wang, Gyan Pareek, Elias Hyams, Benedito A Carneiro, Matthew J Hadfield, Wafik S El-Deiry, Tao Yang, Tao Tan, Tong Tong, Na Ta, Yan Zhu, Yisha Gao, Yancheng Lai, Liang Cheng, Rui Chen, Wei Xue
Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations...
April 3, 2024: Cell reports medicine
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JOURNAL ARTICLE
Arne Schmidt, Pablo Morales-Álvarez, Lee Ad Cooper, Lee A Newberg, Andinet Enquobahrie, Rafael Molina, Aggelos K Katsaggelos
Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing AL methods often struggle in realistic settings with artifacts, ambiguities, and class imbalances, as commonly seen in the medical field. The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value. To address these challenges, we propose Focused Active Learning (FocAL), which combines a Bayesian Neural Network with Out-of-Distribution detection to estimate different uncertainties for the acquisition function...
April 4, 2024: Medical Image Analysis
#16
JOURNAL ARTICLE
Yinxi Wang, Wenwen Sun, Emelie Karlsson, Sandy Kang Lövgren, Balázs Ács, Mattias Rantalainen, Stephanie Robertson, Johan Hartman
PURPOSE: To evaluate the Stratipath Breast tool for image-based risk profiling and compare it with an established prognostic multigene assay for risk profiling in a real-world case series of estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer patients categorized as intermediate risk based on classic clinicopathological variables and eligible for chemotherapy. METHODS: In a case series comprising 234 invasive ER-positive/HER2-negative tumors, clinicopathological data including Prosigna results and corresponding HE-stained tissue slides were retrieved...
April 9, 2024: Breast Cancer Research and Treatment
#17
JOURNAL ARTICLE
Valentina Canini, Albino Eccher, Giulia d'Amati, Nicola Fusco, Fausto Maffini, Daniela Lepanto, Maurizio Martini, Giorgio Cazzaniga, Panagiotis Paliogiannis, Renato Lobrano, Vincenzo L'Imperio, Fabio Pagni
The assessment of programmed death-ligand 1 (PD-L1) combined positive scoring (CPS) in head and neck squamous cell carcinoma (HNSCC) is challenged by pre-analytical and inter-observer variabilities. An educational program to compare the diagnostic performances between local pathologists and a board of pathologists on 11 challenging cases from different Italian pathology centers stained with PD-L1 immunohistochemistry on a digital pathology platform is reported. A laboratory-developed test (LDT) using both 22C3 (Dako) and SP263 (Ventana) clones on Dako or Ventana platforms was compared with the companion diagnostic (CDx) Dako 22C3 pharm Dx assay...
February 22, 2024: Journal of Clinical Medicine
#18
REVIEW
I A Solovev
The review presents key concepts and global developments in the field of artificial intelligence used in pathological anatomy. The work examines two types of artificial intelligence (AI): weak and strong ones. A review of experimental algorithms using both deep machine learning and computer vision technologies to work with WSI images of preparations, diagnose and make a prognosis for various malignant neoplasms is carried out. It has been established that weak artificial intelligence at this stage of development of computer (digital) pathological anatomy shows significantly better results in speeding up and refining diagnostic procedures than strong artificial intelligence having signs of general intelligence...
2024: Arkhiv Patologii
#19
JOURNAL ARTICLE
Anne-Marie Kanstrup Fiehn, Peter Johan Heiberg Engel, Ulla Engel, Dea Natalie Munch Jepsen, Thomas Blixt, Julie Rasmussen, Signe Wildt, Wojciech Cebula, Andreea-Raluca Diac, Lars Kristian Munck
Chronic watery diarrhea is a frequent symptom. In approximately 10% of the patients, a diagnosis of microscopic colitis (MC) is established. The diagnosis relies on specific, but sometimes subtle, histopathological findings. As the histology of normal intestinal mucosa vary, discriminating subtle features of MC from normal tissue can be challenging and therefore auxiliary stainings are increasingly used. The aim of this study was to determine the variance in number of intraepithelial lymphocytes (IELs) and presence of a subepithelial band in normal ileum and colonic mucosa, according to different stains and digital assessment...
December 2024: Journal of Pathology Informatics
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JOURNAL ARTICLE
Rushin H Gindra, Yi Zheng, Emily J Green, Mary E Reid, Sarah A Mazzilli, Daniel T Merrick, Eric J Burks, Vijaya B Kolachalama, Jennifer E Beane
Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. In this context, we present a novel computational approach, the Graph Perceiver Network (GRAPE-Net), leveraging hematoxylin and eosin (H&E) stained whole slide images (WSIs) to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues...
April 6, 2024: American Journal of Pathology
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