Thyroid nodules are common in the adult population where a majority are benign and only 4.0% to 6.5% are malignant. Fine needle aspiration (FNA) is a key method used in the early stages to evaluate ...and triage patients with thyroid nodules. While a definitive cytological diagnosis is provided in more than 70-75% of all thyroid FNA cases, the group of indeterminate lesions offers a challenge in terms of interpretation and clinical management. Molecular testing platforms have been developed, are recognized as an option by the 2015 American Thyroid Association Guidelines, and are frequently used in conjunction with FNA as an integral part of the cytologic evaluation. In this review, the utility of molecular testing options for nodules assigned to the group of indeterminate thyroid FNAs is described.
Almost 20 years have passed since the commercial introduction of whole-slide imaging (WSI) scanners. During this time, the creation of various WSI devices with the ability to digitize an entire glass ...slide has transformed the field of pathology. Parallel advances in computational technology and storage have permitted rapid processing of large-scale WSI datasets. This article provides an overview of important past and present efforts related to WSI. An account of how the virtual microscope evolved from the need to visualize and manage satellite data for earth science applications is provided. The article also discusses important milestones beginning from the first WSI scanner designed by Bacus to the Food and Drug Administration approval of the first digital pathology system for primary diagnosis in surgical pathology. As pathology laboratories commit to going fully digitalize, the need has emerged to include WSIs into an enterprise-level vendor-neutral archive (VNA). The different types of VNAs available are reviewed as well as how best to implement them and how pathology can benefit from participating in this effort. Differences between traditional image algorithms that extract pixel-, object-, and semantic-level features versus deep learning methods are highlighted. The need for large-scale data management, analysis, and visualization in computational pathology is also addressed.
Tumor budding and CD8-positive (+) T-cells are recognized as prognostic factors in colorectal adenocarcinoma. We assessed CD8+ T-cell density and intratumoral budding in pretreatment rectal cancer ...biopsies to determine if they are predictive biomarkers for response to neoadjuvant therapy and survival. Pretreatment biopsies of locally advanced rectal adenocarcinoma from 117 patients were evaluated for CD8+ T-cell density using automated quantitative digital image analysis and for intratumoral budding and correlated with clinicopathological variables on postneoadjuvant surgical resection specimens, response to neoadjuvant therapy, and survival. Patients with high CD8+ T-cell density (≥157 per mm2) on biopsy were significantly more likely to exhibit complete/near complete response to neoadjuvant therapy (66% vs. 33%, p = 0.001) and low tumor stage (0 or I) on resection (62% vs. 30%, p = 0.001) compared with patients with low CD8+ T-cell density. High CD8+ T-cell density was an independent predictor of response to neoadjuvant therapy with a 2.63 higher likelihood of complete response (95% CI 1.04–6.65, p = 0.04) and a 3.66 higher likelihood of complete/near complete response (95% CI 1.60–8.38, p = 0.002). The presence of intratumoral budding on biopsy was significantly associated with a reduced likelihood of achieving complete/near complete response to neoadjuvant therapy (odds ratio 0.36, 95% CI 0.13–0.97, p = 0.048). Patients with intratumoral budding on biopsy had a significantly reduced disease-free survival compared with patients without intratumoral budding (5-year survival 39% vs 87%, p < 0.001). In the multivariable model, the presence of intratumoral budding on biopsy was associated with a 3.35-fold increased risk of tumor recurrence (95% CI 1.25–8.99, p = 0.02). In conclusion, CD8+ T-cell density and intratumoral budding in pretreatment biopsies of rectal adenocarcinoma are independent predictive biomarkers of response to neoadjuvant therapy and intratumoral budding associates with patient survival. These biomarkers may be helpful in selecting patients who will respond to neoadjuvant therapy and identifying patients at risk for recurrence.
Artificial intelligence applied to breast pathology Yousif, Mustafa; van Diest, Paul J.; Laurinavicius, Arvydas ...
Virchows Archiv : an international journal of pathology,
2022/1, Letnik:
480, Številka:
1
Journal Article
Recenzirano
The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly ...positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns from labeled digital data based on “deep learning” neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.
Bringing digital teaching materials into residency training programs has seen slow adoption, expected for many new technologies. The COVID-19 pandemic dramatically shifted the paradigm for many ...resident teaching modalities as institutions instituted social distancing to prevent spread of the novel coronavirus. The impact of this shift on pathology trainee education has not been well studied. We conducted an online survey of pathology trainees, program directors, and faculty to assess pre- and post-COVID-19 use of, and response to, various digital pathology modalities. Responses were solicited through both social media and directed appeals. A total of 261 respondents (112 faculty, 52 program directors, and 97 trainees) reported a dramatic and significant increase in the use of digital pathology-related education tools. A significant majority of faculty and program directors agreed that this shift had adversely affected the quality (59% and 62%, respectively) and effectiveness (66%) of their teaching. This perception was similar among learners relative to the impact on quality (59%) and effectiveness (64%) of learning. Most respondents (70%-92%) anticipate that their use of digital pathology education tools will increase or remain the same post-COVID. The global COVID-19 pandemic created a unique opportunity and challenge for pathology training programs. Digital pathology resources were accordingly readily adopted to continue supporting educational activities. The learning curve and utilization of this technology was perceived to impair the quality and effectiveness of teaching and learning. Since the use of digital tools appears poised to continue to grow post-COVID19, challenges due to impaired quality and effectiveness will need to be addressed.
On image search in histopathology Tizhoosh, H.R.; Pantanowitz, Liron
Journal of pathology informatics,
12/2024, Letnik:
15
Journal Article
Recenzirano
Odprti dostop
Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant ...potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.
Background
Unlike Papanicolaou tests, there are no commercially available computer‐assisted automated screening systems for urine specimens. Despite The Paris System for Reporting Urinary Cytology, ...there still is poor interobserver agreement with urine cytology and many cases in which a definitive diagnosis cannot be made. In the current study, the authors have reported on the development of an image algorithm that applies computational methods to digitized liquid‐based urine cytology slides.
Methods
A total of 2405 archival ThinPrep glass slides, including voided and instrumented urine cytology cases, were digitized. A deep learning computational pipeline with multiple tiers of convolutional neural network models was developed for processing whole slide images (WSIs) and predicting diagnoses. The algorithm was validated using a separate test data set comprised of consecutive cases encountered in routine clinical practice.
Results
There were 1.9 million urothelial cells analyzed. An average of 5400 urothelial cells were identified in each WSI. The algorithm achieved an area under the curve of 0.88 (95% CI, 0.83‐0.93). Using the optimal operating point, the algorithm's sensitivity was 79.5% (95% CI, 64.7%‐90.2%) and the specificity was 84.5% (95% CI, 81.6%‐87.1%) for high‐grade urothelial carcinoma.
Conclusions
The authors successfully developed a computational algorithm capable of accurately analyzing WSIs of urine cytology cases. Compared with prior studies, this effort used a much larger data set, exploited whole slide–level and not just cell‐level features, and used a cell gallery to display the algorithm's output for easy end‐user review. This algorithm provides computer‐assisted interpretation of urine cytology cases, akin to the machine learning technology currently used for automated Papanicolaou test screening.
The authors report the successful development of an image algorithm that applies computational methods to digitized liquid‐based urine cytology slides. The algorithm is reported to achieve an area under the curve of 0.88, a sensitivity of 79.5%, and a specificity of 84.5% for high‐grade urothelial carcinoma.
Introduction
Distinguishing small cell lung carcinoma (SCLC) from large cell neuroendocrine carcinoma (LCNEC) in cytology is challenging. Our aim was to design a deep learning algorithm for ...classifying high‐grade neuroendocrine carcinomas in fine needle aspirations.
Methods
Archival cytology cases of high‐grade neuroendocrine carcinoma (17 small cell, 13 large cell, 10 mixed/unclassifiable) were retrieved. Each case included smears (Diff‐Quik® and Papanicolaou stains) and cell block or concomitant core biopsies (haematoxylin and eosin H&E stain). All slides (n = 114) were scanned at 40× magnification, randomised and split into training (11 large, nine small) and test (two large, eight small, 10 mixed) groups. Tumour was annotated using QuPath and exported as JPEG image tiles. Three distinct deep learning convolutional neural networks, one for each preparation/stain, were designed to classify each tile and provide an overall diagnosis for each slide.
Results
The H&E‐trained algorithm correctly classified 7/8 (87.5%) SCLC cases and 2/2 (100%) LCNEC cases. The Papanicolaou stain algorithm correctly classified 6/7 (85.7%) SCLC. and 1/1 (100%) LCNEC cases. The algorithm trained on Diff‐Quik® stained images correctly classified 7/8 (87.5%) SCLC and 1/1 (100%) LCNEC cases.
Conclusion
Using open source software, it was feasible to design a deep learning algorithm to distinguish between SCLC and LCNEC. The algorithm showed high precision in distinguishing between these two categories on H&E sectioned material and direct smears. Although the dataset was limited, our deep learning models show promising results in the classification of LCNEC and SCLC. Additional work using a larger dataset is necessary to improve the algorithm's performance.
Distinguishing small cell lung carcinoma from large cell neuroendocrine carcinoma is important for clinical management. However, making the distinction in cytology material can sometimes be challenging. Using open‐source software, the authors designed a deep learning algorithm for classifying high‐grade neuroendocrine carcinomas in fine‐needle aspirations (FNA).
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