A deep learning model for breast ductal carcinoma in situ classification in whole slide images

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images

The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.

Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy

Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning

The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1 scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained convolutional neural networks improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey

Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.

Interpretable HER2 scoring by evaluating clinical guidelines through a weakly supervised, constrained deep learning approach

The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment selection. However, HER2 scoring has notoriously high interobserver variability due to stain variations between centers and the need to estimate visually the staining intensity in specific percentages of tumor area. In this paper, focusing on the interpretability of HER2 scoring by a pathologist, we propose a semi-automatic, two-stage deep learning approach that directly evaluates the clinical HER2 guidelines defined by the American Society of Clinical Oncology/ College of American Pathologists (ASCO/CAP). In the first stage, we segment the invasive tumor over the user-indicated Region of Interest (ROI). Then, in the second stage, we classify the tumor tissue into four HER2 classes. For the classification stage, we use weakly supervised, constrained optimization to find a model that classifies cancerous patches such that the tumor surface percentage meets the guidelines specification of each HER2 class. We end the second stage by freezing the model and refining its output logits in a supervised way to all slide labels in the training set. To ensure the quality of our dataset's labels, we conducted a multi-pathologist HER2 scoring consensus. For the assessment of doubtful cases where no consensus was found, our model can help by interpreting its HER2 class percentages output. We achieve a performance of 0.78 in F1-score on the test set while keeping our model interpretable for the pathologist, hopefully contributing to interpretable AI models in digital pathology.

Oral biosciences: The annual review 2022

BACKGROUND

The Journal of Oral Biosciences is devoted to advancing and disseminating fundamental knowledge concerning every aspect of oral biosciences.

HIGHLIGHT

This review features review articles in the fields of "Bone Cell Biology," "Tooth Development & Regeneration," "Tooth Bleaching," "Adipokines," "Milk Thistle," "Epithelial-Mesenchymal Transition," "Periodontitis," "Diagnosis," "Salivary Glands," "Tooth Root," "Exosome," "New Perspectives of Tooth Identification," "Dental Pulp," and "Saliva" in addition to the review articles by the winner of the "Lion Dental Research Award" ("Plastic changes in nociceptive pathways contributing to persistent orofacial pain") presented by the Japanese Association for Oral Biology.

CONCLUSION

The review articles in the Journal of Oral Biosciences have inspired its readers to broaden their knowledge about various aspects of oral biosciences. The current editorial review introduces these exciting review articles.

Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer

BACKGROUND

Prostate cancer is often a slowly progressive indolent disease. Unnecessary treatments from overdiagnosis are a significant concern, particularly low-grade disease. Active surveillance has being considered as a risk management strategy to avoid potential side effects by unnecessary radical treatment. In 2016, American Society of Clinical Oncology (ASCO) endorsed the Cancer Care Ontario (CCO) Clinical Practice Guideline on active surveillance for the management of localized prostate cancer.

METHODS

Based on this guideline, we developed a deep learning model to classify prostate adenocarcinoma into indolent (applicable for active surveillance) and aggressive (necessary for definitive therapy) on core needle biopsy whole slide images (WSIs). In this study, we trained deep learning models using a combination of transfer, weakly supervised, and fully supervised learning approaches using a dataset of core needle biopsy WSIs (n=1300). In addition, we performed an inter-rater reliability evaluation on the WSI classification.

RESULTS

We evaluated the models on a test set (n=645), achieving ROC-AUCs of 0.846 for indolent and 0.980 for aggressive. The inter-rater reliability evaluation showed s-scores in the range of 0.10 to 0.95, with the lowest being on the WSIs with both indolent and aggressive classification by the model, and the highest on benign WSIs.

CONCLUSION

The results demonstrate the promising potential of deployment in a practical prostate adenocarcinoma histopathological diagnostic workflow system.

A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning

The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately screen large numbers of histopathological prostate needle biopsy specimens. Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs) would be of great benefit for routine pathological practice. In this paper, we trained deep learning models capable of classifying needle biopsy WSIs into adenocarcinoma and benign (non-neoplastic) lesions. We evaluated the models on needle biopsy, transurethral resection of the prostate (TUR-P), and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.978 in needle biopsy test sets and up to 0.9873 in TCGA test sets for adenocarcinoma.

Deep learning models in medical image analysis

BACKGROUND

Deep learning is a state-of-the-art technology that has rapidly become the method of choice for medical image analysis. Its fast and robust object detection, segmentation, tracking, and classification of pathophysiological anatomical structures can support medical practitioners during routine clinical workflow. Thus, deep learning-based applications for diseases diagnosis will empower physicians and allow fast decision-making in clinical practice.

HIGHLIGHT

Deep learning can be more robust with various features for differentiating classes, provided the training set is large and diverse for analysis. However, sufficient medical images for training sets are not always available from medical institutions, which is one of the major limitations of deep learning in medical image analysis. This review article presents some solutions for this issue and discusses efforts needed to develop robust deep learning-based computer-aided diagnosis applications for better clinical workflow in endoscopy, radiology, pathology, and dentistry.

CONCLUSION

The introduction of deep learning-based applications will enhance the traditional role of medical practitioners in ensuring accurate diagnoses and treatment in terms of precision, reproducibility, and scalability.