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S postupující digitalizací patologie se do popředí zájmu dostávají i aplikace metod strojového učení a umělé inteligence. Výzkum a vývoj v této oblasti je velmi rychlý, ale aplikace učících systémů v klinické praxi stále zaostávají. Cílem tohoto textu je přiblížit proces tvorby a nasazení učících systémů v digitální patologii. Začneme popisem základních vlastností dat produkovaných v rámci digitální patologie. Konkrétně pojednáme o skenerech a skenování vzorků, o ukládání a přenosu dat, o kontrole jejich kvality a přípravě pro zpracování pomocí učících systémů, zejména o anotacích. Naším cílem je prezentovat aktuální přístupy k řešení technických problémů a zároveň upozornit na úskalí, na která lze narazit při zpracování dat z digitální patologie. V první části také naznačíme, jak vypadají aktuální softwarová řešení pro prohlížení naskenovaných vzorků a implementace diagnostických postupů zahrnujících učící systémy. Ve druhé části textu popíšeme obvyklé úlohy digitální patologie a naznačíme obvyklé přístupy k jejich řešení. V této části zejména vysvětlíme, jak je nutné modifikovat standardní metody strojového učení pro zpracování velkých skenů a pojednáme o konkrétních aplikacích v diagnostice. Na závěr textu poskytneme rychlý náhled dalšího možného vývoje učících systémů v digitální patologii. Zejména ilustrujeme podstatu přechodu na velké základní modely a naznačíme problematiku virtuálního barvení vzorků. Doufáme, že tento text přispěje k lepší orientaci v rapidně se vyvíjející oblasti strojového učení v digitální patologii a tím přispěje k rychlejší adopci učících metod v této oblasti.
With the advancing digitalization of pathology, the application of machine learning and artificial intelligence methods is becoming increasingly important. Research and development in this field are progressing rapidly, but the clinical implementation of learning systems still lags behind. The aim of this text is to provide an overview of the process of developing and deploying learning systems in digital pathology. We begin by describing the fundamental characteristics of data produced in digital pathology. Specifically, we discuss scanners and sample scanning, data storage and transmission, quality control, and preparation for processing by learning systems, with a particular focus on annotations. Our goal is to present current approaches to addressing technical challenges while also highlighting potential pitfalls in processing digital pathology data. In the first part of the text, we also outline existing software solutions for viewing scanned samples and implementing diagnostic procedures that incorporate learning systems. In the second part of the text, we describe common tasks in digital pathology and outline typical approaches to solving them. Here, we explain the necessary modifications to standard machine learning methods for processing large scans and discuss specific diagnostic applications. Finally, we provide a brief overview of the potential future development of learning systems in digital pathology. We illustrate the transition to large foundational models and introduce the topic of virtual staining of samples. We hope that this text will contribute to a better understanding of the rapidly evolving field of machine learning in digital pathology and, in turn, facilitate the faster adoption of learning-based methods in this domain.
Colorectal cancer (CRC) is a disease with constantly increasing incidence and high mortality. The treatment efficacy could be curtailed by drug resistance resulting from poor drug penetration into tumor tissue and the tumor-specific microenvironment, such as hypoxia and acidosis. Furthermore, CRC tumors can be exposed to different pH depending on the position in the intestinal tract. CRC tumors often share upregulation of the Akt signaling pathway. In this study, we investigated the role of external pH in control of cytotoxicity of perifosine, the Akt signaling pathway inhibitor, to CRC cells using 2D and 3D tumor models. In 3D settings, we employed an innovative strategy for simultaneous detection of spatial drug distribution and biological markers of proliferation/apoptosis using a combination of mass spectrometry imaging and immunohistochemistry. In 3D conditions, low and heterogeneous penetration of perifosine into the inner parts of the spheroids was observed. The depth of penetration depended on the treatment duration but not on the external pH. However, pH alteration in the tumor microenvironment affected the distribution of proliferation- and apoptosis-specific markers in the perifosine-treated spheroid. Accurate co-registration of perifosine distribution and biological response in the same spheroid section revealed dynamic changes in apoptotic and proliferative markers occurring not only in the perifosine-exposed cells, but also in the perifosine-free regions. Cytotoxicity of perifosine to both 2D and 3D cultures decreased in an acidic environment below pH 6.7. External pH affects cytotoxicity of the other Akt inhibitor, MK-2206, in a similar way. Our innovative approach for accurate determination of drug efficiency in 3D tumor tissue revealed that cytotoxicity of Akt inhibitors to CRC cells is strongly dependent on pH of the tumor microenvironment. Therefore, the effect of pH should be considered during the design and pre-clinical/clinical testing of the Akt-targeted cancer therapy.
- Publikační typ
- časopisecké články MeSH
Spheroids-three-dimensional aggregates of cells grown from a cancer cell line-represent a model of living tissue for chemotherapy investigation. Distribution of chemotherapeutics in spheroid sections was determined using the matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). Proliferating or apoptotic cells were immunohistochemically labeled and visualized by laser scanning confocal fluorescence microscopy (LSCM). Drug efficacy was evaluated by comparing coregistered MALDI MSI and LSCM data of drug-treated spheroids with LSCM only data of untreated control spheroids. We developed a fiducial-based workflow for coregistration of low-resolution MALDI MS with high-resolution LSCM images. To allow comparison of drug and cell distribution between the drug-treated and untreated spheroids of different shapes or diameters, we introduced a common diffusion-related coordinate, the distance from the spheroid boundary. In a procedure referred to as "peeling", we correlated average drug distribution at a certain distance with the average reduction in the affected cells between the untreated and the treated spheroids. This novel approach makes it possible to differentiate between peripheral cells that died due to therapy and the innermost cells which died naturally. Two novel algorithms-for MALDI MS image denoising and for weighting of MALDI MSI and LSCM data by the presence of cell nuclei-are also presented.
- MeSH
- buněčné sféroidy účinky léků MeSH
- konfokální mikroskopie metody MeSH
- lidé MeSH
- nádory farmakoterapie MeSH
- protinádorové látky farmakokinetika farmakologie MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice metody MeSH
- teoretické modely MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Extracellular vesicles (EVs) function as important conveyers of information between cells and thus can be exploited as drug delivery systems or disease biomarkers. Transmission electron microscopy (TEM) remains the gold standard method for visualisation of EVs, however the analysis of individual EVs in TEM images is time-consuming if performed manually. Therefore, we present here a software tool for computer-assisted evaluation of EVs in TEM images. TEM ExosomeAnalyzer detects EVs based on their shape and edge contrast criteria and subsequently analyses their size and roundness. The software tool is compatible with common negative staining protocols and isolation methods used in the field of EV research; even with challenging TEM images (EVs both lighter and darker than the background, images containing artefacts or precipitated stain, etc.). If the fully-automatic analysis fails to produce correct results, users can promptly adjust the detected seeds of EVs as well as their boundaries manually. The performance of our tool was evaluated for three different modes with variable levels of human interaction, using two datasets with various heterogeneity. The semi-automatic mode analyses EVs with high success rate in the homogenous dataset (F1 score 0.9094, Jaccard coefficient 0.8218) as well as in the highly heterogeneous dataset containing EVs isolated from cell culture medium and patient samples (F1 score 0.7619, Jaccard coefficient 0.7553). Moreover, the extracted size distribution profiles of EVs isolated from malignant ascites of ovarian cancer patients overlap with those derived by cryo-EM and are comparable to NTA- and TRPS-derived data. In summary, TEM ExosomeAnalyzer is an easy-to-use software tool for evaluation of many types of vesicular microparticles and is available at http://cbia.fi.muni.cz/exosome-analyzer free of charge for non-commercial and research purposes. The web page contains also detailed description how to use the software tool including a video tutorial.
- Publikační typ
- časopisecké články MeSH
Reliable 3D detection of diffraction-limited spots in fluorescence microscopy images is an important task in subcellular observation. Generally, fluorescence microscopy images are heavily degraded by noise and non-specifically stained background, making reliable detection a challenging task. In this work, we have studied the performance and parameter sensitivity of eight recent methods for 3D spot detection. The study is based on both 3D synthetic image data and 3D real confocal microscopy images. The synthetic images were generated using a simulator modeling the complete imaging setup, including the optical path as well as the image acquisition process. We studied the detection performance and parameter sensitivity under different noise levels and under the influence of uneven background signal. To evaluate the parameter sensitivity, we propose a novel measure based on the gradient magnitude of the F1 score. We measured the success rate of the individual methods for different types of the image data and found that the type of image degradation is an important factor. Using the F1 score and the newly proposed sensitivity measure, we found that the parameter sensitivity is not necessarily proportional to the success rate of a method. This also provided an explanation why the best performing method for synthetic data was outperformed by other methods when applied to the real microscopy images. On the basis of the results obtained, we conclude with the recommendation of the HDome method for data with relatively low variations in quality, or the Sorokin method for image sets in which the quality varies more. We also provide alternative recommendations for high-quality images, and for situations in which detailed parameter tuning might be deemed expensive.
