Pathological Images Research Articles

Optical coherence tomography otoscope for imaging of tympanic membrane and middle ear pathology.

Pathologies within the tympanic membrane (TM) and middle ear (ME) can lead to hearing loss. Imaging tools available in the hearing clinic for diagnosis and management are limited to visual inspection using the classic otoscope. The otoscopic view is limited to the surface of the TM, especially in diseased ears where the TM is opaque. An integrated optical coherence tomography (OCT) otoscope can provide images of the interior of the TM and ME space as well as an otoscope image. This enables the clinicians to correlate the standard otoscopic view with OCT and then use the new information to improve the diagnostic accuracy and management. We aim to develop an OCT otoscope that can easily be used in the hearing clinic and demonstrate the system in the hearing clinic, identifying relevant image features of various pathologies not apparent in the standard otoscopic view. We developed a portable OCT otoscope device featuring an improved field of view and form-factor that can be operated solely by the clinician using an integrated foot pedal to control image acquisition. The device was used to image patients at a hearing clinic. The field of view of the imaging system was improved to a 7.4mm diameter, with lateral and axial resolutions of and , respectively. We developed algorithms to resample the images in Cartesian coordinates after collection in spherical polar coordinates and correct the image aberration. We imaged over 100 patients in the hearing clinic at USC Keck Hospital. Here, we identify some of the pathological features evident in the OCT images and highlight cases in which the OCT image provided clinically relevant information that was not available from traditional otoscopic imaging. The developed OCT otoscope can readily fit into the hearing clinic workflow and provide new relevant information for diagnosing and managing TM and ME disease.

Update on Pathologic Conditions, Imaging Findings, Prevention, and Management of Human Papillomavirus-related Neoplasms.

Human papillomavirus (HPV) is the most common sexually transmitted infection that proliferates in the squamous epithelium and is the most common source of viral-related neoplasms. Low-risk subtypes (HPV-6 and -11) cause respiratory papillomas (laryngeal, tracheal, and bronchial) and condyloma acuminata of the penis, anus, and perineal region (anogenital warts). High-risk subtypes (HPV-16, -18, -31, and -33) are responsible for oropharyngeal squamous cell carcinoma (SCC) that involves the tongue base, tonsils, posterior pharyngeal wall, and larynx and malignancies of the anogenital region (cancers of the cervix, vagina, vulva, penis, and anal canal). Recent studies have increasingly shown a favorable treatment response and substantial differences in the overall prognosis associated with HPV-associated oropharyngeal cancers. Given this fact, oropharyngeal, cervical, and penile SCCs are classified as HPV-associated and HPV-independent cancers in the current World Health Organization classification. Imaging is essential in the early detection, diagnosis, and staging of HPV-associated cancers. Imaging also helps assess treatment response and postoperative complications and is used for long-term surveillance. HPV-associated oropharyngeal SCCs have well-defined borders and solid and cystic nodal metastases at imaging. Updated screening and vaccination guidelines are currently available that have great potential to decrease the overall disease burden and help control this worldwide public health concern. Novel therapeutic strategies, such as immunotherapies, are being explored, and imaging biomarkers that can predict treatment response and prognosis are being investigated; radiologists play a pivotal role in these efforts. ©RSNA, 2024 Supplemental material is available for this article.

Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.

If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the recurrence rate is low. Our study aims to predict ER at 6-24 months after RAI by using machine learning (ML) methods in which clinicopathological parameters are included in patients with DTC without distant metastasis. Treatment response of 151 patients with DTC without distant metastasis and who received RAI treatment was determined (ER/nonER). Thyroidectomy ± neck dissection pathology data, laboratory, and imaging findings before and after RAI treatment were introduced to ML models. After RAI treatment, 118 patients had ER and 33 had nonER. Before RAI treatment, TgAb was positive in 29% of patients with ER and 55% of patients with nonER (p = 0.007). Eight of the ML models predicted ER with high area under the ROC curve (AUC) values (> 0.700). The model with the highest AUC value was extreme gradient boosting (AUC = 0.871), the highest accuracy shown by gradient boosting (81%). ML models may be used to predict ER in patients with DTC without distant metastasis.

Breast Cancer Classification From Digital Pathology Images via Connectivity-Aware Graph Transformer.

Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT.

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images

Delayed diagnosis and treatment resistance make pancreatic ductal adenocarcinoma (PDAC) mortality rates high. Identifying molecular subtypes can improve treatment, but current methods are costly and time-consuming. In this study, deep learning models were utilized to identify histological features that classify PDAC molecular subtypes based on routine hematoxylin-eosin (H&E)-stained histopathological slides. 97 histopathology slides associated with resectable PDAC from the Cancer Genome Atlas (TCGA) project were utilized to train a deep learning model and tested the performance on 44 needle biopsy material (110 slides) from a local annotated patient cohort. The model achieved balanced accuracy of 96.19% and 83.03% in identifying the classical and basal subtypes of PDAC in the TCGA and the local cohort, respectively. This study provides a promising method to cost-effectively and rapidly classifying PDAC molecular subtypes based on routine H&E slides, potentially leading to more effective clinical management of this disease.

Fast Metabolic Glycan Labeling for Click-Labeling and Imaging of Sialoglycans in Living Acute Brain Slices from Mice and Human Patients.

Living acute brain slices provide a practical platform for imaging sialylation in human brain pathology. However, the limited lifespan of acute brain slices has impeded the use of metabolic glycan labeling (MGL), which requires long-term incubation of clickable unnatural sugars such as N-azidoacetylmannosamine (ManNAz) to metabolically incorporate azides into sialoglycans. Here, we report a fast variant of MGL (fMGL), in which ManNAz-6-phosphate enables efficient azidosugar incorporation within 12 h by bypassing the bottleneck step in the sialic acid biosynthesis pathway, followed by click-labeling with fluorophores and imaging of sialoglycans in acute brain slices from mice and human patients. In the clinical samples of ganglioglioma, fMGL-based imaging reveals specific upregulation of sialylation in astrocyte-like but not neuron-like tumor cells. In addition, fMGL is integrated with click-expansion microscopy for high-resolution imaging of sialoglycans in brain slices. The fMGL strategy should find broad applications in the tissue imaging of glycans and surgical pathology.

Discovery and Evaluation of Imidazo[2,1-b][1,3,4]thiadiazole Derivatives as New Candidates for α-Synuclein PET Imaging.

α-synuclein (α-syn) pathologies are central to the development of synucleinopathies including Parkinson's disease (PD). Positron emission tomography (PET) imaging of α-syn pathologies is one strategy to facilitate the diagnosis, understanding, and treatment of synucleinopathies, but has been restricted by the lack of specific α-syn PET probes. In this work, we identified 2,6-disubstituted imidazo[2,1-b][1,3,4]thiadiazole (ITA) as a new α-syn-binding scaffold. Through autoradiography studies, we discovered an iodinated lead compound [125I]ITA-3, with moderate binding affinity (IC50 = 55 nM) to α-syn pathologies in human PD brain sections. Modified from [125I]ITA-3, we developed a potential PET tracer, [18F]FITA-2 (radiochemical yield >25%, molar activity >110 GBq/μmol), which demonstrated clear signals in α-syn-rich regions in human PD brain tissues (IC50 = 245 nM), good brain uptake (SUVpeak = 2.80 ± 0.45), and fast clearance rate in rats. Overall, [18F]FITA-2 appears to be a promising candidate for α-syn PET imaging and merits further development.

MR imaging of penile pathology and prostheses.

Penile MRI is a vital yet underutilized diagnostic tool that provides detailed information crucial for managing various penile pathologies. Due to its infrequent use, many radiology trainees lack confidence in interpreting these exams. This article reviews the anatomy, key technical considerations, and interpretive pearls for penile trauma, Peyronie's disease, priapism, penile neoplasms, prosthesis evaluation, and a few miscellaneous conditions. Through illustrative case examples, this review aims to enhance the understanding and proficiency of radiologists in performing and interpreting penile MRI in these clinical scenarios.

Predictive models for personalized precision medical intervention in spontaneous regression stages of cervical precancerous lesions

BackgroundDuring the prolonged period from Human Papillomavirus (HPV) infection to cervical cancer development, Low-Grade Squamous Intraepithelial Lesion (LSIL) stage provides a critical opportunity for cervical cancer prevention, giving the high potential for reversal in this stage. However, there is few research and a lack of clear guidelines on appropriate intervention strategies at this stage, underscoring the need for real-time prognostic predictions and personalized treatments to promote lesion reversal.MethodsWe have established a prospective cohort. Since 2018, we have been collecting clinical data and pathological images of HPV-infected patients, followed by tracking the progression of their cervical lesions. In constructing our predictive models, we applied logistic regression and six machine learning models, evaluating each model’s predictive performance using metrics such as the Area Under the Curve (AUC). We also employed the SHAP method for interpretative analysis of the prediction results. Additionally, the model identifies key factors influencing the progression of the lesions.ResultsModel comparisons highlighted the superior performance of Random Forests (RF) and Support Vector Machines (SVM), both in clinical parameter and pathological image-based predictions. Notably, the RF model, which integrates pathological images and clinical multi-parameters, achieved the highest AUC of 0.866. Another significant finding was the substantial impact of sleep quality on the spontaneous clearance of HPV and regression of LSIL.ConclusionsIn contrast to current cervical cancer prediction models, our model’s prognostic capabilities extend to the spontaneous regression stage of cervical cancer. This model aids clinicians in real-time monitoring of lesions and in developing personalized treatment or follow-up plans by assessing individual risk factors, thus fostering lesion spontaneous reversal and aiding in cervical cancer prevention and reduction.Graphical

A-ReSEUnet: Achieve no-label binary segmentation of nuclei in histology images

Histopathological images reveal a lot of information about tissue health. The task of nucleus segmentation can be applied to more diverse studies. The potential of deep learning has been demonstrated for the task of nuclei segmentation. However, the cost and time required for labeling data, as well as the lack of annotators, makes it difficult to keep up with the continuous generation of new pathological images. This paper proposes a method that combines attention mechanisms and self-supervised learning to train a model for nuclei segmentation on unlabeled data. The method is mainly divided into two stages. After the initial preprocessing of the image, the enhanced image of the nucleus area can be obtained in the first stage. Subsequently, the original image and the augmented image needed for self-supervised learning undergo feature extraction in the second stage. The results of this study show that the proposed method can achieve dice scores exceeding 0.7 in the nucleus binary segmentation task without labels. Fine-tuning the model for downstream tasks can also achieve the effect of F1 score 0.7 multi-category segmentation. Using the same experimental setting, the method proposed in this study achieves a dice score of 0.779 on the MoNuSeg dataset, which is only 0.03 lower than the highest dice score of 0.809 achieved by the compared supervised learning methods. In comparison to other unsupervised models, this method demonstrates superior segmentation performance. Moreover, this study applies the training weights to other histopathology image datasets, enabling the generation of the same binary segmentation effect.© 2017 Elsevier Inc. All rights reserved.

A foundation model for clinical-grade computational pathology and rare cancers detection.

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow's performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data.

Artificial Intelligence in Pancreatic Image Analysis: A Review.

Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.

Identification of pathological characteristics in pulmonary tuberculosis using polarization-sensitive optical coherence tomography

The low detection rate of Mycobacterium tuberculosis in clinical practice leads to a high rate of missed diagnosis for pulmonary tuberculosis (PTB). As a noninvasive, high-resolution, real-time imaging technology, polarization-sensitive optical coherence tomography (PS-OCT) may be feasible for the rapid identification of pathological feature. This study aimed to explore the feasibility of using PS-OCT to identify pathological features of PTB. In the experiments, PTB samples containing some surrounding lung tissues were imaged using PS-OCT. It is demonstrated that PS-OCT images showed good consistency with the corresponding pathological images and were able to identify PTB-related characteristic pathological regions. We think PS-OCT can serve as an effective supplement for the diagnosis of PTB, enabling rapid and accurate diagnosis, and aiding in the understanding of the pathological characteristics and pathophysiological processes of PTB.

Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images

Lupus Nephritis (LN) has been considered as the most prevalent form of systemic lupus erythematosus. Medical imaging plays an important role in diagnosing and treating LN, which can help doctors accurately assess the extent and extent of the lesion. However, relying solely on visual observation and judgment can introduce subjectivity and errors, especially for complex pathological images. Image segmentation techniques are used to differentiate various tissues and structures in medical images to assist doctors in diagnosis. Multi-threshold Image Segmentation (MIS) has gained widespread recognition for its direct and practical application. However, existing MIS methods still have some issues. Therefore, this study combines non-local means, 2D histogram, and 2D Renyi’s entropy to improve the performance of MIS methods. Additionally, this study introduces an improved variant of the Whale Optimization Algorithm (GTMWOA) to optimize the aforementioned MIS methods and reduce algorithm complexity. The GTMWOA fusions Gaussian Exploration (GE), Topology Mapping (TM), and Magnetic Liquid Climbing (MLC). The GE effectively amplifies the algorithm’s proficiency in local exploration and quickens the convergence rate. The TM facilitates the algorithm in escaping local optima, while the MLC mechanism emulates the physical phenomenon of MLC, refining the algorithm’s convergence precision. This study conducted an extensive series of tests using the IEEE CEC 2017 benchmark functions to demonstrate the superior performance of GTMWOA in addressing intricate optimization problems. Furthermore, this study executed an experiment using Berkeley images and LN images to verify the superiority of GTMWOA in MIS. The ultimate outcomes of the MIS experiments substantiate the algorithm’s advanced capabilities and robustness in handling complex optimization problems.

Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features

BackgroundCancer pathology shows disease development and associated molecular features. It provides extensive phenotypic information that is cancer-predictive and has potential implications for planning treatment. Based on the exceptional performance of computational approaches in the field of digital pathogenic, the use of rich phenotypic information in digital pathology images has enabled us to identify low-level gliomas (LGG) from high-grade gliomas (HGG). Because the differences between the textures are so slight, utilizing just one feature or a small number of features produces poor categorization results.MethodsIn this work, multiple feature extraction methods that can extract distinct features from the texture of histopathology image data are used to compare the classification outcomes. The successful feature extraction algorithms GLCM, LBP, multi-LBGLCM, GLRLM, color moment features, and RSHD have been chosen in this paper. LBP and GLCM algorithms are combined to create LBGLCM. The LBGLCM feature extraction approach is extended in this study to multiple scales using an image pyramid, which is defined by sampling the image both in space and scale. The preprocessing stage is first used to enhance the contrast of the images and remove noise and illumination effects. The feature extraction stage is then carried out to extract several important features (texture and color) from histopathology images. Third, the feature fusion and reduction step is put into practice to decrease the number of features that are processed, reducing the computation time of the suggested system. The classification stage is created at the end to categorize various brain cancer grades. We performed our analysis on the 821 whole-slide pathology images from glioma patients in the Cancer Genome Atlas (TCGA) dataset. Two types of brain cancer are included in the dataset: GBM and LGG (grades II and III). 506 GBM images and 315 LGG images are included in our analysis, guaranteeing representation of various tumor grades and histopathological features.ResultsThe fusion of textural and color characteristics was validated in the glioma patients using the 10-fold cross-validation technique with an accuracy equals to 95.8%, sensitivity equals to 96.4%, DSC equals to 96.7%, and specificity equals to 97.1%. The combination of the color and texture characteristics produced significantly better accuracy, which supported their synergistic significance in the predictive model. The result indicates that the textural characteristics can be an objective, accurate, and comprehensive glioma prediction when paired with conventional imagery.ConclusionThe results outperform current approaches for identifying LGG from HGG and provide competitive performance in classifying four categories of glioma in the literature. The proposed model can help stratify patients in clinical studies, choose patients for targeted therapy, and customize specific treatment schedules.

Sequential Patching Lattice for Image Classification and Enquiry: Streamlining Digital Pathology Image Processing

Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of Whole Slide Images (WSIs), there’s a growing demand for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this paper, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a “collage” of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting nonredundant representative features. We evaluated SPLICE for search and match applications, demonstrating improved accuracy, reduced computation time, and storage requirements compared to existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%. This reduction enables numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.

Prediction of HER2 Status Based on Deep Learning in H&E-Stained Histopathology Images of Bladder Cancer.

Epidermal growth factor receptor 2 (HER2) has been widely recognized as one of the targets for bladder cancer immunotherapy. The key to implementing personalized treatment for bladder cancer patients lies in achieving rapid and accurate diagnosis. To tackle this challenge, we have pioneered the application of deep learning techniques to predict HER2 expression status from H&E-stained pathological images of bladder cancer, bypassing the need for intricate IHC staining or high-throughput sequencing methods. Our model, when subjected to rigorous testing within the cohort from the People's Hospital of Wuhan University, which encompasses 106 cases, has exhibited commendable performance on both the validation and test datasets. Specifically, the validation set yielded an AUC of 0.92, an accuracy of 0.86, a sensitivity of 0.87, a specificity of 0.83, and an F1 score of 86.7%. The corresponding metrics for the test set were 0.88 for AUC, 0.67 for accuracy, 0.56 for sensitivity, 0.75 for specificity, and 77.8% for F1 score. Additionally, in a direct comparison with pathologists, our model demonstrated statistically superior performance, with a p-value less than 0.05, highlighting its potential as a powerful diagnostic tool.

Radiological Imaging in Forensic Pathology: Exploring Nonlinear Bullet Trajectories.

Radiological Imaging in Forensic Pathology: Exploring Nonlinear Bullet Trajectories.

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Clinical and diagnostic implications of Alzheimer's disease copathology in Lewy body disease.

Concomitant Alzheimer's disease (AD) pathology is a frequent event in the context of Lewy body disease (LBD), occurring in approximately half of all cases. Evidence shows that LBD patients with AD copathology show an accelerated disease course, a greater risk of cognitive decline and an overall poorer prognosis. However, LBD-AD cases may show heterogeneous motor and non-motor phenotypes with a higher risk of dementia and, consequently, be not rarely misdiagnosed. In this review, we summarize the current understanding of LBD-AD by discussing the synergistic effects of AD neuropathological changes and Lewy pathology and their clinical relevance. Furthermore, we provide an extensive overview of neuroimaging and fluid biomarkers under assessment for use in LBD-AD and their possible diagnostic and prognostic values. AD pathology can be predicted in vivo by means of CSF, MRI and PET markers, whereas the most promising technique to date for identifying Lewy pathology in different biological tissues is the α-synuclein seed amplification assay. Pathological imaging and CSF AD biomarkers are associated with a higher likelihood of cognitive decline in LBD but do not always mirror the neuropathological severity as in pure AD. Implementing the use of blood-based AD biomarkers might allow faster screening of LBD patients for AD copathology, thus improving the overall diagnostic sensitivity for LBD-AD. Finally, we discuss the literature on novel candidate biomarkers being exploited in LBD-AD to investigate other aspects of neurodegeneration, such as neuroaxonal injury, glial activation and synaptic dysfunction. The thorough characterization of AD copathology in LBD should be taken into account when considering differential diagnoses of dementia syndromes, to allow prognostic evaluation on an individual level, and to guide symptomatic and disease-modifying therapies.