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Journal articles on the topic 'Histopathologie digitale'

Author: Grafiati

Published: 23 November 2024

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1

Braun, Stephan A., and Doris Helbig. "Infantile digitale Fibromatose: ein seltener myofibrozytärer Tumor mit charakteristischer Histopathologie." JDDG: Journal der Deutschen Dermatologischen Gesellschaft 12, no. 12 (December 2014): 1141–42. http://dx.doi.org/10.1111/ddg.12450_suppl.

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2

Cummins, Donna M., Iskander H. Chaudhry, and Matthew Harries. "Scarring Alopecias: Pathology and an Update on Digital Developments." Biomedicines 9, no. 12 (November 24, 2021): 1755. http://dx.doi.org/10.3390/biomedicines9121755.

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Primary cicatricial alopecias (PCA) represent a challenging group of disorders that result in irreversible hair loss from the destruction and fibrosis of hair follicles. Scalp skin biopsies are considered essential in investigating these conditions. Unfortunately, the recognised complexity of histopathologic interpretation is compounded by inadequate sampling and inappropriate laboratory processing. By sharing our successes in developing the communication pathway between the clinician, laboratory and histopathologist, we hope to mitigate some of the difficulties that can arise in managing these conditions. We provide insight from clinical and pathology practice into how diagnoses are derived and the key histological features observed across the most common PCAs seen in practice. Additionally, we highlight the opportunities that have emerged with advances in digital pathology and how these technologies may be used to develop clinicopathological relationships, improve working practices, enhance remote learning, reduce inefficiencies, optimise diagnostic yield, and harness the potential of artificial intelligence (AI).

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Tawfeeq, Furat Nidhal, Nada A. S. Alwan, and Basim M. Khashman. "Optimization of Digital Histopathology Image Quality." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 2 (April 20, 2018): 71. http://dx.doi.org/10.11591/ijai.v7.i2.pp71-77.

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<span lang="EN-US">One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis. Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather than using difficult numerical equation. Mainly there are five parts, starting with accepting the image, passing through removing the bubbles, and ending with predict the tissues. These were implemented by defining membership functions between colours range using MATLAB. Results: 50 histopathological images were tested on four types of membership functions (MF); the results show that (nine-triangular) MF get 75.4% correctly predicted pixels versus 69.1, 72.31 and 72% for (five- triangular), (five-Gaussian) and (nine-Gaussian) respectively. Conclusions: In line with the era of digitally driven e-pathology, this process is essentially recommended to ensure quality interpretation and analyses of the processed slides; thus overcoming relevant limitations.</span>

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Amgad Mohamed Khater, Nesma. "Review on Advancements in Histopathology Education through Virtual Labs, Digital Microscopy and AI." International Journal of Science and Research (IJSR) 13, no. 11 (November 5, 2024): 807–8. http://dx.doi.org/10.21275/sr241113034953.

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Min, Eunjung, Nurbolat Aimakov, Sangjin Lee, Sungbea Ban, Hyunmo Yang, Yujin Ahn, Joon S. You, and Woonggyu Jung. "Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining." Biomedical Optics Express 14, no. 5 (April 18, 2023): 2068. http://dx.doi.org/10.1364/boe.484516.

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Quantitative phase imaging (QPI) has emerged as a new digital histopathologic tool as it provides structural information of conventional slide without staining process. It is also capable of imaging biological tissue sections with sub-nanometer sensitivity and classifying them using light scattering properties. Here we extend its capability further by using optical scattering properties as imaging contrast in a wide-field QPI. In our first step towards validation, QPI images of 10 major organs of a wild-type mouse have been obtained followed by H&E-stained images of the corresponding tissue sections. Furthermore, we utilized deep learning model based on generative adversarial network (GAN) architecture for virtual staining of phase delay images to a H&E-equivalent brightfield (BF) image analogues. Using the structural similarity index, we demonstrate similarities between virtually stained and H&E histology images. Whereas the scattering-based maps look rather similar to QPI phase maps in the kidney, the brain images show significant improvement over QPI with clear demarcation of features across all regions. Since our technology provides not only structural information but also unique optical property maps, it could potentially become a fast and contrast-enriched histopathology technique.

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Ciga, Ozan, Tony Xu, and Anne Louise Martel. "Self supervised contrastive learning for digital histopathology." Machine Learning with Applications 7 (March 2022): 100198. http://dx.doi.org/10.1016/j.mlwa.2021.100198.

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Amrania, Hemmel, Giuseppe Antonacci, Che-Hung Chan, Laurence Drummond, William R. Otto, Nicholas A. Wright, and Chris Phillips. "Digistain: a digital staining instrument for histopathology." Optics Express 20, no. 7 (March 15, 2012): 7290. http://dx.doi.org/10.1364/oe.20.007290.

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8

Huss, Ralf, and Sarah E. Coupland. "Software‐assisted decision support in digital histopathology." Journal of Pathology 250, no. 5 (February 25, 2020): 685–92. http://dx.doi.org/10.1002/path.5388.

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Martines, Roosecelis B., Jana M. Ritter, Joy Gary, Wun-Ju Shieh, Jaume Ordi, Martin Hale, Carla Carrilho, et al. "Pathology and Telepathology Methods in the Child Health and Mortality Prevention Surveillance Network." Clinical Infectious Diseases 69, Supplement_4 (October 9, 2019): S322—S332. http://dx.doi.org/10.1093/cid/ciz579.

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Abstract This manuscript describes the Child Health and Mortality Prevention Surveillance (CHAMPS) network approach to pathologic evaluation of minimally invasive tissue sampling (MITS) specimens, including guidelines for histopathologic examination and further diagnostics with special stains, immunohistochemistry, and molecular testing, as performed at the CHAMPS Central Pathology Laboratory (CPL) at the Centers for Disease Control and Prevention, as well as techniques for virtual discussion of these cases (telepathology) with CHAMPS surveillance locations. Based on review of MITS from the early phase of CHAMPS, the CPL has developed standardized histopathology-based algorithms for achieving diagnoses from MITS and telepathology procedures in conjunction with the CHAMPS sites, with the use of whole slide scanners and digital image archives, for maximizing concurrence and knowledge sharing between site and CPL pathologists. These algorithms and procedures, along with lessons learned from initial implementation of these approaches, guide pathologists at the CPL and CHAMPS sites through standardized diagnostics of MITS cases, and allow for productive, real-time case discussions and consultations.

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Mungenast, Felicitas, Achala Fernando, Robert Nica, Bogdan Boghiu, Bianca Lungu, Jyotsna Batra, and Rupert C. Ecker. "Next-Generation Digital Histopathology of the Tumor Microenvironment." Genes 12, no. 4 (April 7, 2021): 538. http://dx.doi.org/10.3390/genes12040538.

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Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology—which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.

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Schnell, Martin, Shachi Mittal, Kianoush Falahkheirkhah, Anirudh Mittal, Kevin Yeh, Seth Kenkel, Andre Kajdacsy-Balla, P. Scott Carney, and Rohit Bhargava. "All-digital histopathology by infrared-optical hybrid microscopy." Proceedings of the National Academy of Sciences 117, no. 7 (February 3, 2020): 3388–96. http://dx.doi.org/10.1073/pnas.1912400117.

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Optical microscopy for biomedical samples requires expertise in staining to visualize structure and composition. Midinfrared (mid-IR) spectroscopic imaging offers label-free molecular recording and virtual staining by probing fundamental vibrational modes of molecular components. This quantitative signal can be combined with machine learning to enable microscopy in diverse fields from cancer diagnoses to forensics. However, absorption of IR light by common optical imaging components makes mid-IR light incompatible with modern optical microscopy and almost all biomedical research and clinical workflows. Here we conceptualize an IR-optical hybrid (IR-OH) approach that sensitively measures molecular composition based on an optical microscope with wide-field interferometric detection of absorption-induced sample expansion. We demonstrate that IR-OH exceeds state-of-the-art IR microscopy in coverage (10-fold), spatial resolution (fourfold), and spectral consistency (by mitigating the effects of scattering). The combined impact of these advances allows full slide infrared absorption images of unstained breast tissue sections on a visible microscope platform. We further show that automated histopathologic segmentation and generation of computationally stained (stainless) images is possible, resolving morphological features in both color and spatial detail comparable to current pathology protocols but without stains or human interpretation. IR-OH is compatible with clinical and research pathology practice and could make for a cost-effective alternative to conventional stain-based protocols for stainless, all-digital pathology.

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12

Asbeutah, Akram M., Nouralhuda Karmani, AbdulAziz A. Asbeutah, Yasmin A. Echreshzadeh, Abdullah A. AlMajran, and Khalid H. Al-Khalifah. "Comparison of Digital Breast Tomosynthesis and Digital Mammography for Detection of Breast Cancer in Kuwaiti Women." Medical Principles and Practice 28, no. 1 (November 26, 2018): 10–15. http://dx.doi.org/10.1159/000495753.

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Objective: To investigate the sensitivity and specificity of digital mammography (DM) and digital breast tomosynthesis (DBT) for the detection of breast cancer in comparison to histopathology findings. Subjects and Methods: We included 65 breast lesions in 58 women, each detected by two diagnostic mammography techniques – DM and DBT using Senographe Essential (GE Healthcare, Buc, France) – and subsequently confirmed by histopathology. The Breast Imaging Reporting and Data System was used for characterizing the lesions. Results: The average age of women was 48.3 years (range 26–81 years). There were 34 malignant and 31 benign breast lesions. The sensitivity of DM and DBT was 73.5 and 100%, respectively, while the specificity was 67.7 and 94%, respectively. Receiver operating characteristic curve analysis showed an overall diagnostic advantage of DBT over DM, with a significant difference between DBT and DM (p < 0.001). By performing Cohen’s kappa test, we found that there was a strong level of agreement according to Altman guidelines between DBT and histopathology findings (0.97), but a weak agreement between DM and histopathology findings (0.47). Conclusion: DBT improves the clinical accuracy of mammography by increasing both sensitivity and specificity. We believe that this improvement is due to improved image visibility and quality. These results could be of interest to health care institutions as they may impact their decision on whether to upgrade to DBT not only for diagnosis, but also for screening.

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Orr, Brent A., Zahangir Alom, and Quyhn T. Tran. "PATH-13. LEARNED RESIZING WITH EFFICIENT TRAINING (LRET) FACILITATES IMPROVED PERFORMANCE OF LARGE-SCALE BRAIN TUMOR HISTOLOGY IMAGE CLASSIFICATION MODELS." Neuro-Oncology 26, Supplement_4 (June 18, 2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.716.

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Abstract BACKGROUND Histologic examination is vital in oncology research and diagnostics. The adoption of digital scanning of whole slide images (WSI) has created an opportunity to leverage deep learning-based image classification methods to enhance diagnosis and risk stratification. However, technical limitations prevent training and deployment of accurate comprehensive multiclass deep convolutional neural networks (DCNN) models for histopathology image classification. The input dimensions of DCNN architectures are small compared to the typical pathologist field of view, degrading performance by excluding important architectural features. Furthermore, data requirements for comprehensive models are sufficiently large to overwhelm the system memory during training. METHODS A method termed Learned Resizing with Efficient Training (LRET) was developed to address the main limitations of traditional histopathology classification model training. The LRET method couples efficient training techniques with image resizing to facilitate seamless integration of larger histology image patches into state-of-the-art classification models while preserving important structural information. The LRET method was coupled with two distinct resizing techniques to train three diverse histology image datasets using five different DCNN architectures. Performance metrics were compared on cross validation and hold out test sets. RESULTS LRET-trained models were flexible to multiple input patch dimensions and DCNN models. We demonstrated performance improvement across all datasets while significantly reducing the training time and resources over traditional methods. Using a large-scale, multiclass brain tumor classification dataset consisting of 74 distinct histopathologic classes, LRET-trained models outperformed existing methods by 15-28% in accuracy, yielding 94% accuracy for the best model. CONCLUSION The LRET method for DCNN training significantly enhances the performance of large-scale multiclass histopathology image classification. The implications of this work extend to broader applications within medical imaging and beyond, where efficient integration of high-resolution images into deep learning pipelines is paramount for driving advancements research and clinical practice.

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Rivenson, Yair, Kevin de Haan, W. Dean Wallace, and Aydogan Ozcan. "Emerging Advances to Transform Histopathology Using Virtual Staining." BME Frontiers 2020 (August 25, 2020): 1–11. http://dx.doi.org/10.34133/2020/9647163.

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In an age where digitization is widespread in clinical and preclinical workflows, pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides. Over the last decade, new high throughput digital scanning microscopes have ushered in the era of digital pathology that, along with recent advances in machine vision, have opened up new possibilities for Computer-Aided-Diagnoses. Despite these advances, the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption. Here, we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology.

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15

Sperandio, CP, and DJ McCarthy. "Digital arterial embolism-true blue toe syndrome. A histopathologic analysis." Journal of the American Podiatric Medical Association 78, no. 11 (November 1, 1988): 593–98. http://dx.doi.org/10.7547/87507315-78-11-593.

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16

Bandyopadhyay, Samir. "Analysis of digital histopathology images for breast cancer diagnosis." International Medicine 1, no. 2 (2019): 90. http://dx.doi.org/10.5455/im.41366.

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17

ARAÚJO, ANNA LUÍZA DAMACENO, GLEYSON KLEBER DO AMARAL-SILVA, FELIPE PAIVA FONSECA, MARCIO AJUDARTE LOPES, OSLEI PAES DE ALMEIDA, PABLO AGUSTIN VARGAS, and ALAN ROGER SANTOS-SILVA. "VALIDATION OF DIGITAL MICROSCOPY IN THE HISTOPATHOLOGIC DIAGNOSES OF ORAL DISEASES." Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 129, no. 1 (January 2020): e146. http://dx.doi.org/10.1016/j.oooo.2019.06.631.

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18

Retamero, Juan Antonio, Jose Aneiros-Fernandez, and Raimundo G. del Moral. "Complete Digital Pathology for Routine Histopathology Diagnosis in a Multicenter Hospital Network." Archives of Pathology & Laboratory Medicine 144, no. 2 (July 11, 2019): 221–28. http://dx.doi.org/10.5858/arpa.2018-0541-oa.

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Context.— Complete digital pathology and whole slide imaging for routine histopathology diagnosis is currently in use in few laboratories worldwide. Granada University Hospitals, Spain, which comprises 4 hospitals, adopted full digital pathology for primary histopathology diagnosis in 2016. Objective.— To describe the methodology adopted and the resulting experience at Granada University Hospitals in transitioning to full digital diagnosis. Design.— All histopathology glass slides generated for routine diagnosis were digitized at ×40 using the Philips IntelliSite Pathology Solution, which includes an ultrafast scanner and an image management system. All hematoxylin-eosin–stained preparations and immunohistochemistry and histochemistry slides were digitized. The existing sample-tracking software and image management system were integrated to allow data interchange through the Health Level 7 protocol. Results.— Circa 160 000 specimens have been signed out using digital pathology for primary diagnosis. This comprises more than 800 000 digitized glass slides. The scanning error rate during the implementation phase was below 1.5%, and subsequent workflow optimization rendered this rate negligible. Since implementation, Granada University Hospitals pathologists have signed out 21% more cases per year on average. Conclusions.— Digital pathology is an adequate medium for primary histopathology diagnosis. Successful digitization relies on existing sample tracking and integration of the information technology infrastructure. Rapid and reliable scanning at ×40 equivalent was key to the transition to a fully digital workflow. Digital pathology resulted in efficiency gains in the preanalytical and analytical phases, and created the basis for computational pathology: the use of computer-assisted tools to aid diagnosis.

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Sauter, Daniel, Georg Lodde, Felix Nensa, Dirk Schadendorf, Elisabeth Livingstone, and Markus Kukuk. "A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology." Bioengineering 11, no. 1 (December 24, 2023): 19. http://dx.doi.org/10.3390/bioengineering11010019.

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Due to an insufficient amount of image annotation, artificial intelligence in computational histopathology usually relies on fine-tuning pre-trained neural networks. While vanilla fine-tuning has shown to be effective, research on computer vision has recently proposed improved algorithms, promising better accuracy. While initial studies have demonstrated the benefits of these algorithms for medical AI, in particular for radiology, there is no empirical evidence for improved accuracy in histopathology. Therefore, based on the ConvNeXt architecture, our study performs a systematic comparison of nine task adaptation techniques, namely, DELTA, L2-SP, MARS-PGM, Bi-Tuning, BSS, MultiTune, SpotTune, Co-Tuning, and vanilla fine-tuning, on five histopathological classification tasks using eight datasets. The results are based on external testing and statistical validation and reveal a multifaceted picture: some techniques are better suited for histopathology than others, but depending on the classification task, a significant relative improvement in accuracy was observed for five advanced task adaptation techniques over the control method, i.e., vanilla fine-tuning (e.g., Co-Tuning: P(≫) = 0.942, d = 2.623). Furthermore, we studied the classification accuracy for three of the nine methods with respect to the training set size (e.g., Co-Tuning: P(≫) = 0.951, γ = 0.748). Overall, our results show that the performance of advanced task adaptation techniques in histopathology is affected by influencing factors such as the specific classification task or the size of the training dataset.

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Mayerich, David, Michael J. Walsh, Andre Kadjacsy-Balla, Partha S. Ray, Stephen M. Hewitt, and Rohit Bhargava. "Stain-less staining for computed histopathology." TECHNOLOGY 03, no. 01 (March 2015): 27–31. http://dx.doi.org/10.1142/s2339547815200010.

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Dyes such as hematoxylin and eosin (H&E) and immunohistochemical stains have been increasingly used to visualize tissue composition in research and clinical practice. We present an alternative approach to obtain the same information using stain-free chemical imaging. Relying on Fourier transform infrared (FT-IR) spectroscopic imaging and computation, stainless computed histopathology can enable a rapid, digital, quantitative and non-perturbing visualization of morphology and multiple molecular epitopes simultaneously in a variety of research and clinical pathology applications.

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Kumar, K. Jagadish, Subramanian Ramaswamy, and Karen Saldana. "Cutaneous Polyarteritis Nodosa Presenting with Digital Gangrene." Journal of Nepal Paediatric Society 36, no. 1 (October 22, 2016): 82–84. http://dx.doi.org/10.3126/jnps.v36i1.14481.

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Cutaneous polyarteritis nodosa is a vasculitis with characteristic manifestations like tender subcutaneous nodules, livedo reticularis, cutaneous ulcers and necrosis. Diagnosis requires histopathologic evidence of necrotizing inflammation of the medium and small arteries. We report a six year old girl with cutaneous PAN with gangenous changes of the fingertips which responded to methylprednisolone. J Nepal Paediatr Soc 2016;36(1):82-84.

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22

Okuno, Taeko, Conrad Wall, and Isamu Sando. "Computerized Data Bank System for Temporal Bone Histopathology." Annals of Otology, Rhinology & Laryngology 97, no. 2 (March 1988): 195–98. http://dx.doi.org/10.1177/000348948809700219.

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A computerized data bank system to store and analyze temporal bone histopathologic data is described. This system uses the University of Pittsburgh's Digital Equipment Corporation System 10 computer and the System 1022 data base management software. Data on histology cases are divided into five files: General information, otologic information, summary, histopathologic information about the external ear and middle ear, and histopathologic information about the inner ear. Eleven general terms are used to describe pathologic findings, surgery, postmortem degeneration, and artifacts. In addition, provision is made for the inclusion of more precise qualitative information to be entered as text.

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Sauter, Daniel, Georg Lodde, Felix Nensa, Dirk Schadendorf, Elisabeth Livingstone, and Markus Kukuk. "Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology." Sensors 22, no. 14 (July 18, 2022): 5346. http://dx.doi.org/10.3390/s22145346.

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Digital histopathology poses several challenges such as label noise, class imbalance, limited availability of labelled data, and several latent biases to deep learning, negatively influencing transparency, reproducibility, and classification performance. In particular, biases are well known to cause poor generalization. Proposed tools from explainable artificial intelligence (XAI), bias detection, and bias discovery suffer from technical challenges, complexity, unintuitive usage, inherent biases, or a semantic gap. A promising XAI method, not studied in the context of digital histopathology is automated concept-based explanation (ACE). It automatically extracts visual concepts from image data. Our objective is to evaluate ACE’s technical validity following design science principals and to compare it to Guided Gradient-weighted Class Activation Mapping (Grad-CAM), a conventional pixel-wise explanation method. To that extent, we created and studied five convolutional neural networks (CNNs) in four different skin cancer settings. Our results demonstrate that ACE is a valid tool for gaining insights into the decision process of histopathological CNNs that can go beyond explanations from the control method. ACE validly visualized a class sampling ratio bias, measurement bias, sampling bias, and class-correlated bias. Furthermore, the complementary use with Guided Grad-CAM offers several benefits. Finally, we propose practical solutions for several technical challenges. In contradiction to results from the literature, we noticed lower intuitiveness in some dermatopathology scenarios as compared to concept-based explanations on real-world images.

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Braun, Stephan A., and Doris Helbig. "Infantile digital Fibromatosis: a rare myofibrocytic tumor with characteristic histopathology." JDDG: Journal der Deutschen Dermatologischen Gesellschaft 12, no. 12 (December 2014): 1141–42. http://dx.doi.org/10.1111/ddg.12450.

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Ethunandan, M., and I. P. Downie. "Digital photographs of excised lesions: An aid to histopathology reports." British Journal of Oral and Maxillofacial Surgery 46, no. 3 (April 2008): 251–52. http://dx.doi.org/10.1016/j.bjoms.2007.08.001.

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26

Brown, Peter J., Debra Fews, and Nick J. Bell. "Teaching Veterinary Histopathology: A Comparison of Microscopy and Digital Slides." Journal of Veterinary Medical Education 43, no. 1 (January 2016): 13–20. http://dx.doi.org/10.3138/jvme.0315-035r1.

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Kwak, Jin Tae, Sandeep Sankineni, Sheng Xu, Baris Turkbey, Peter L. Choyke, Peter A. Pinto, Maria Merino, and Bradford J. Wood. "Correlation of magnetic resonance imaging with digital histopathology in prostate." International Journal of Computer Assisted Radiology and Surgery 11, no. 4 (September 4, 2015): 657–66. http://dx.doi.org/10.1007/s11548-015-1287-x.

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Vallez, Noelia, Jose Luis Espinosa-Aranda, Anibal Pedraza, Oscar Deniz, and Gloria Bueno. "Deep Learning within a DICOM WSI Viewer for Histopathology." Applied Sciences 13, no. 17 (August 23, 2023): 9527. http://dx.doi.org/10.3390/app13179527.

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Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse. DICOM stands out as a standard that transcends internal image formats via metadata-driven image exchange in this context. DICOM defines imaging workflow information objects for images, patients’ studies, reports, etc. DICOM promises standards-based pathology imaging, but its clinical use is limited. No FDA-approved digital pathology system natively generates DICOM, and only one high-performance whole slide images (WSI) device has been approved for diagnostic use in Asia and Europe. In a recent series of Digital Pathology Connectathons, the interoperability of our solution was demonstrated by integrating DICOM digital pathology imaging, i.e., WSI, into PACs and enabling their visualisation. However, no system that incorporates state-of-the-art AI methods and directly applies them to DICOM images has been presented. In this paper, we present the first web viewer system that employs WSI DICOM images and AI models. This approach aims to bridge the gap by integrating AI methods with DICOM images in a seamless manner, marking a significant step towards more effective CAD WSI processing tasks. Within this innovative framework, convolutional neural networks, including well-known architectures such as AlexNet and VGG, have been successfully integrated and evaluated.

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Thom, Leonie K., Roy R. Pool, and Richard Malik. "Digital flexor musculotendinous contracture in two Devon Rex cats." Journal of Feline Medicine and Surgery 19, no. 3 (March 2017): 304–10. http://dx.doi.org/10.1177/1098612x17693503.

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Clinical summary: A 13-year-old, spayed Devon Rex with unilateral digital flexor musculotendinous contracture of the forelimb was treated by surgical tenotomy. The condition improved transiently, but recurred rapidly and became bilateral. Histopathologic analysis of necropsy tissues resulted in a morphologic diagnosis of fibromyositis of the antebrachial muscles causing contracture and flexural deformity of the carpi and phalanges of both thoracic limbs. A search for similar cases yielded the clinical notes of a second cat, a 10-year-old, spayed Devon Rex, also with bilateral disease. This second case responded well to surgical tenotomy but tissue biopsies were not obtained to permit microscopic assessment of the underlying pathologic process. Relevance and novel information: Acquired and permanent contracture of the digital flexor muscles and/or tendons of the forelimbs is a rare and poorly described condition of cats. The very limited number of documented cases describing disease affecting one or more digits (but not the carpus) infers a causal link with onychectomy, but reported histopathologic changes have been limited to the tendons. The two cases described in this report suffered contracture of the carpus and all digits bilaterally, one without previous onychectomy and the other 9 years after onychectomy. There were novel histopathologic findings in the muscles of the one case for which biopsy material was available. Information gained from these two cases provides a new perspective for the investigation and treatment of future cases. Specifically, consideration should be given to an underlying immune-mediated myopathic process and a possible genetic predisposition in the Devon Rex breed. Currently, the poorly understood etiopathogenesis hinders our ability to definitively recommend treatment options, which might include corticosteroids and other forms of immunosuppressive therapy.

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Clarke, G. M., S. Eidt, L. Sun, G. Mawdsley, J. T. Zubovits, and M. J. Yaffe. "Whole-specimen histopathology: a method to produce whole-mount breast serial sections for 3-D digital histopathology imaging." Histopathology 50, no. 2 (January 2007): 232–42. http://dx.doi.org/10.1111/j.1365-2559.2006.02561.x.

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31

Van Bockstal, Mieke R., Martine Berlière, Francois P. Duhoux, and Christine Galant. "Interobserver Variability in Ductal Carcinoma In Situ of the Breast." American Journal of Clinical Pathology 154, no. 5 (June 22, 2020): 596–609. http://dx.doi.org/10.1093/ajcp/aqaa077.

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Abstract Objectives Since most patients with ductal carcinoma in situ (DCIS) of the breast are treated upon diagnosis, evidence on its natural progression to invasive carcinoma is limited. It is estimated that around half of the screen-detected DCIS lesions would have remained indolent if they had never been detected. Many patients with DCIS are therefore probably overtreated. Four ongoing randomized noninferiority trials explore active surveillance as a treatment option. Eligibility for these trials is mainly based on histopathologic features. Hence, the call for reproducible histopathologic assessment has never sounded louder. Methods Here, the available classification systems for DCIS are discussed in depth. Results This comprehensive review illustrates that histopathologic evaluation of DCIS is characterized by significant interobserver variability. Future digitalization of pathology, combined with development of deep learning algorithms or so-called artificial intelligence, may be an innovative solution to tackle this problem. However, implementation of digital pathology is not within reach for each laboratory worldwide. An alternative classification system could reduce the disagreement among histopathologists who use “conventional” light microscopy: the introduction of dichotomous histopathologic assessment is likely to increase interobserver concordance. Conclusions Reproducible histopathologic assessment is a prerequisite for robust risk stratification and adequate clinical decision-making. Two-tier histopathologic assessment might enhance the quality of care.

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Gherardi, Alessandro, and Alessandro Bevilacqua. "Manual Stage Acquisition and Interactive Display of Digital Slides in Histopathology." IEEE Journal of Biomedical and Health Informatics 18, no. 4 (July 2014): 1413–22. http://dx.doi.org/10.1109/jbhi.2013.2291998.

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Murtaza, Ghulam, Liyana Shuib, Ainuddin Wahid Abdul Wahab, Ghulam Mujtaba, Ghulam Mujtaba, Ghulam Raza, and Nor Aniza Azmi. "Breast cancer classification using digital biopsy histopathology images through transfer learning." Journal of Physics: Conference Series 1339 (December 2019): 012035. http://dx.doi.org/10.1088/1742-6596/1339/1/012035.

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Jansen, Ilaria, Marit Lucas, C. Dilara Savci-Heijink, Sybren L. Meijer, Henk A. Marquering, Daniel M. de Bruin, and Patricia J. Zondervan. "Histopathology: ditch the slides, because digital and 3D are on show." World Journal of Urology 36, no. 4 (February 2, 2018): 549–55. http://dx.doi.org/10.1007/s00345-018-2202-1.

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Luong, Richard H. "Commentary: Digital histopathology in a private or commercial diagnostic veterinary laboratory." Journal of Veterinary Diagnostic Investigation 32, no. 3 (May 2020): 353–55. http://dx.doi.org/10.1177/1040638720919842.

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Wu, Yawen, Michael Cheng, Shuo Huang, Zongxiang Pei, Yingli Zuo, Jianxin Liu, Kai Yang, et al. "Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications." Cancers 14, no. 5 (February 25, 2022): 1199. http://dx.doi.org/10.3390/cancers14051199.

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With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

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Bassan, Paul, Miles J. Weida, Jeremy Rowlette, and Peter Gardner. "Large scale infrared imaging of tissue micro arrays (TMAs) using a tunable Quantum Cascade Laser (QCL) based microscope." Analyst 139, no. 16 (2014): 3856–59. http://dx.doi.org/10.1039/c4an00638k.

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Doherty, Trevor, Susan McKeever, Nebras Al-Attar, Tiarnán Murphy, Claudia Aura, Arman Rahman, Amanda O'Neill, et al. "Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection." Analyst 146, no. 13 (2021): 4195–211. http://dx.doi.org/10.1039/d1an00075f.

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Mezei, Tibor, Melinda Kolcsár, András Joó, and Simona Gurzu. "Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives." Journal of Imaging 10, no. 10 (October 14, 2024): 252. http://dx.doi.org/10.3390/jimaging10100252.

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Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes.

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Demichelis, Francesca, Vincenzo Della Mea, Stefano Forti, Paolo Dalla Palma, and Carlo Alberto Beltrami. "Digital storage of glass slides for quality assurance in histopathology and cytopathology." Journal of Telemedicine and Telecare 8, no. 3 (June 1, 2002): 138–42. http://dx.doi.org/10.1258/135763302320118979.

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Lee, K. J., and H. P. Soyer. "Smartphones, artificial intelligence and digital histopathology take on basal cell carcinoma diagnosis." British Journal of Dermatology 182, no. 3 (August 19, 2019): 540–41. http://dx.doi.org/10.1111/bjd.18374.

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Demichelis, Francesca, Vincenzo Della Mea, Stefano Forti, Paolo Dalla Palma, and Carlo Alberto Beltrami. "Digital Storage of Glass Slides for Quality Assurance in Histopathology and Cytopathology." Journal of Telemedicine and Telecare 8, no. 3 (June 2002): 138–42. http://dx.doi.org/10.1177/1357633x0200800303.

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Proficiency testing programmes for measuring screening skills in pathology are mainly conducted using conventional glass microscope slides. However, the availability of robotic microscopes allows an entire conventional slide to be digitized. Our experiments have shown that, using a widely available robotized microscope and a PC, the image of a single field may be acquired in 2 s on average, including stage movements, autofocus and storage. Digitizing an entire slide, a fully automated procedure, takes up to 8 h. If the image of each field is compressed at an appropriate quality level (a compression ratio of, say, 35:1) it requires about 40 kByte to be stored, resulting in a total storage requirement of about 600 MByte per slide. Thus one CD-ROM can be used to store one virtual slide, as well as a self-installing program to provide a microscope simulator facility. This allows pathologists to examine the virtual case from their computer in a similar manner to looking at a glass slide on a conventional microscope. This permits a new, computer-based approach to proficiency testing in histopathology and cytopathology. Use of virtual slides should encourage the diffusion of national quality assurance programmes, which at present suffer from certain organizational and logistical limitations.

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Ferreira, Vera Christina Camargo de Siqueira, Elba Cristina Sá de Camargo Etchebehere, José Luiz Barbosa Bevilacqua, and Nestor de Barros. "Suspicious amorphous microcalcifications detected on full-field digital mammography: correlation with histopathology." Radiologia Brasileira 51, no. 2 (March 15, 2018): 87–94. http://dx.doi.org/10.1590/0100-3984.2017.0025.

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Abstract Objective: To evaluate suspicious amorphous calcifications diagnosed on full-field digital mammography (FFDM) and establish correlations with histopathology findings. Materials and Methods: This was a retrospective study of 78 suspicious amorphous calcifications (all classified as BI-RADS® 4) detected on FFDM. Vacuum-assisted breast biopsy (VABB) was performed. The histopathological classification of VABB core samples was as follows: pB2 (benign); pB3 (uncertain malignant potential); pB4 (suspicion of malignancy); and pB5 (malignant). Treatment was recommended for pB5 lesions. To rule out malignancy, surgical excision was recommended for pB3 and pB4 lesions. Patients not submitted to surgery were followed for at least 6 months. Results: Among the 78 amorphous calcifications evaluated, the histopathological analysis indicated that 8 (10.3%) were malignant/suspicious (6 classified as pB5 and 2 classified as pB4) and 36 (46.2%) were benign (classified as pB2). The remaining 34 lesions (43.6%) were classified as pB3: 33.3% were precursor lesions (atypical ductal hyperplasia, lobular neoplasia, or flat epithelial atypia) and 10.3% were high-risk lesions. For the pB3 lesions, the underestimation rate was zero. Conclusion: The diagnosis of precursor lesions (excluding atypical ductal hyperplasia, which can be pB4 depending on the severity and extent of the lesion) should not necessarily be considered indicative of underestimation of malignancy. Suspicious amorphous calcifications correlated more often with precursor lesions than with malignant lesions, at a ratio of 3:1.

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Kwak, Jin Tae, Sandeep Sankineni, Sheng Xu, Baris Turkbey, Peter L. Choyke, Peter A. Pinto, Vanessa Moreno, Maria Merino, and Bradford J. Wood. "Prostate Cancer: A Correlative Study of Multiparametric MR Imaging and Digital Histopathology." Radiology 285, no. 1 (October 2017): 147–56. http://dx.doi.org/10.1148/radiol.2017160906.

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Dessinioti, Clio, Andriani Tsiakou, Athina Christodoulou, and Alexander J. Stratigos. "Clinical and Dermoscopic Findings of Nevi after Photoepilation: A Review." Life 13, no. 9 (August 29, 2023): 1832. http://dx.doi.org/10.3390/life13091832.

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Atypical clinical and dermoscopic findings, or changes in pigmented melanocytic lesions located on body areas treated with lasers or intense pulsed light (IPL) for hair removal (photoepilation), have been described in the literature. There are three prospective studies in a total of 79 individuals with 287 melanocytic nevi and several case reports reporting the dermoscopic findings and changes after photoepilation. Clinical changes have been reported in 20–100% of individuals, while dermoscopic changes have been observed in 48% to 93% of nevi. More frequent dermoscopic changes included bleaching, the development of pigmented globules, and irregular hyperpigmented areas and regression structures, including gray areas, gray dots/globules, and whitish structureless areas. The diagnostic approach for pigmented lesions with atypical dermoscopic findings and changes after photo-epilation included reflectance confocal microscopy, sequential digital dermoscopy follow-up, and/or excision and histopathology. Challenges pertaining to these diagnostic steps in the context of photoepilation include the detection of findings that may warrant a biopsy to exclude melanoma (ugly duckling, irregular hyperpigmented areas, blue-gray or white areas, and loss of pigment network), the potential persistence of changes at follow-up, and that a histopathologic diagnosis may not be possible due to the distortion of melanocytes or complete regression of the lesion. Furthermore, these diagnostic approaches can be time-consuming, require familiarization of the physician with dermoscopic features, may cause anxiety to the individual, and highlight that avoiding passes of the laser or IPL devices over pigmented lesions is key.

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Arevalo, John, Angel Cruz-Roa, and Fabio A. González O. "Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte." Revista Med 22, no. 2 (December 1, 2014): 79. http://dx.doi.org/10.18359/rmed.1184.

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<p>This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology.</p>

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Hipp, Jason, Jerome Cheng, Stephanie Daignault, Jefferey Sica, Michael C. Dugan, David Lucas, Yukako Yagi, Stephen Hewitt, and Ulysses J. Balis. "Automated Area Calculation of Histopathologic Features Using SIVQ." Analytical Cellular Pathology 34, no. 5 (2011): 265–75. http://dx.doi.org/10.1155/2011/606273.

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Recently, with the advent of the 7th edition of the AJCC Cancer Staging manual, at least one set of criteria (e.g. breast) were modified to now require the measurement of maximal depth of stromal invasion. With the current manual interpretive morphological approaches typically employed by surgical pathologists to assess tumor extent, the specialty now potentially has stumbled upon a crossroads of practice, where the diagnostic criteria have exceeded the capabilities of our commonly available tools. While whole slide imaging (WSI) technology holds the potential to offer many improvements in clinical workflow over conventional slide microscopy including unambiguous utility for facilitating quantitative diagnostic tasks with one important example being the determination of both linear dimension and surface area. However, the availability of histology data in digital form is of little utility if time-consuming and cumbersome manual workflow steps are necessarily imposed upon the pathologist in order to generate such measurements, especially as encountered with the complex and ill-defined shapes inherent to infiltrative tumors. In this communication, we demonstrate the utility of the recently described SIVQ algorithm to serve as the basis of a highly accurate, precise and semi-automated tool for direct surface area measurement of tumor infiltration from WSI data sets. By anticipating the current trend in cancer staging that emphasizes increasingly precise feature characterization, as witnessed by the recent publication of AJCC's 7th edition of the Cancer Staging Manual, this tool holds promise to will be of value to pathologists for clinical utility.

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Afzal, Kanza, Nadia Gul, Khalid Mehmood, Sobia Jawwad, and Bushra Iqbal. "Assessment of Diagnostic Accuracy of Digital Breast Tomosynthesis in Distinguishing Malignant and Benign Breast Lesions." Life and Science 5, no. 1 (January 15, 2024): 06. http://dx.doi.org/10.37185/lns.1.1.416.

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Objective: The study aims to determine the diagnostic accuracy of digital breast tomosynthesis in diagnosing malignant and benign lesions, keeping histopathology as the gold standard.Study Design: Cross-sectional study.Place and Duration of Study: The study was carried out at the Department of Diagnostic Radiology, POF th th Hospital, Wah Cantt, Pakistan over a period of six months from 11th July 2021 to 11th January 2022.Methods: A total of 200 women presenting with suspicion of breast malignancy were selected consecutively from the outpatient department, and Digital Breast Tomosynthesis(DBT) was performed, followed by a biopsy of the specimen to confirm the findings on histopathology. Results: The average age of the sample was 48.3 + 7.1 years, ranging between 35 and 60 years. Palpable breast lump was recorded in 44.5%, pain in 33%, and nipple discharge in 35.5%. Family history of breast Ca was present in 25.5. On Digital Breast Tomosynthesis (DBT), 58.5% of lesions were labeled as malignant, while 53.5% were labeled as malignant on follow-up histopathology. On applying the formulae for calculation, the sensitivity of DBT was found to be 86% and specificity 73.1%. The positive predictive value of the DBT is 78.6%, and the negative predictive value is 81.9%.Conclusion: In conclusion, Digital Breast Tomosynthesisis a significantly sensitive and specific tool for detecting malignant breast lesions in women suspected of breast carcinoma.How to cite this: Afzal K, Gul N, Mehmood K, Jawwad S, Iqbal B. Assessment of Diagnostic Accuracy of Digital Breast Tomosynthesis in Distinguishing Malignant and Benign Breast Lesions. Life and Science. 2024; 5(1): 3-8. doi: http://doi.org/10.37185/LnS.1.1.416

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Montague, Paul R., Margaret Meyer, and Robert Folberg. "Technique for the Digital Imaging of Histopathologic Preparations of Eyes for Research and Publication." Ophthalmology 102, no. 8 (August 1995): 1248–51. http://dx.doi.org/10.1016/s0161-6420(95)30882-2.

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Ruan, Jun, Zhikui Zhu, Chenchen Wu, Guanglu Ye, Jingfan Zhou, and Junqiu Yue. "A fast and effective detection framework for whole-slide histopathology image analysis." PLOS ONE 16, no. 5 (May 12, 2021): e0251521. http://dx.doi.org/10.1371/journal.pone.0251521.

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Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention. Thus, the working style of pathologists is also beginning to change. Computer-assisted image analysis systems have been developed to help pathologists perform basic examinations. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology images. We develop the Double Magnification Combination (DMC) classifier, which is a modified DenseNet-40 to make patch-level predictions with only 0.3 million parameters. To improve the detection performance of multiple instances, we propose an improved adaptive sampling method with superpixel segmentation and introduce a new heuristic factor, local sampling density, as the convergence condition of iterations. In postprocessing, we use a CNN model with 4 convolutional layers to regulate the patch-level predictions based on the predictions of adjacent sampling points and use linear interpolation to generate a tumor probability heatmap. The entire framework was trained and validated using the dataset from the Camelyon16 Grand Challenge and Hubei Cancer Hospital. In our experiments, the average AUC was 0.95 in the test set for pixel-level detection.

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