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. 

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

Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network

This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review’s findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.

Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis

GraphLSurv: A scalable survival prediction network with adaptive and sparse structure learning for histopathological whole-slide images

The rapid development of computational pathology has brought new opportunities for prognosis prediction using histopathological images. However, the existing deep learning frameworks lack exploration of the relationship between images and other prognostic information, resulting in poor interpretability. Tumor mutation burden (TMB) is a promising biomarker for predicting the survival outcomes of cancer patients, but its measurement is costly. Its heterogeneity may be reflected in histopathological images. Here, we report a two-step framework for prognostic prediction using whole-slide images (WSIs). First, the framework adopts a deep residual network to encode the phenotype of WSIs and classifies patient-level TMB by the deep features after aggregation and dimensionality reduction. Then, the patients' prognosis is stratified by the TMB-related information obtained during the classification model development. Deep learning feature extraction and TMB classification model construction are performed on an in-house dataset of 295 Haematoxylin & Eosin stained WSIs of clear cell renal cell carcinoma (ccRCC). The development and evaluation of prognostic biomarkers are performed on The Cancer Genome Atlas-Kidney ccRCC (TCGA-KIRC) project with 304 WSIs. Our framework achieves good performance for TMB classification with an area under the receiver operating characteristic curve (AUC) of 0.813 on the validation set. Through survival analysis, our proposed prognostic biomarkers can achieve significant stratification of patients' overall survival (P 0.05) and outperform the original TMB signature in risk stratification of patients with advanced disease. The results indicate the feasibility of mining TMB-related information from WSI to achieve stepwise prognosis prediction. 

Development of Prognostic Biomarkers by TMB-Guided WSI Analysis: A Two-Step Approach

To the Editor: Acute lobar nephronia (ALN), also known as acute focal bacterial nephritis, is a severe form of upper urinary tract infection (UTI) that is characterized by the presence of an in ﬂ ammatory mass lesion in the kidney without abscess formation. It is considered to be the midpoint between acute pyelonephritis and renal abscess. [1] ALN was ﬁ rst described in 1979 as a radiologic diagnosis in ten adults and two children. [2] the kidney; and a poorly de ﬁ striated or wedge-shaped lesion with decreased enhancement on computed tomography (CT)

https://doi.org/10.1097/cm9.0000000000001997

Clinical characteristics of acute lobar nephronia in renal allograft

89P Radiotherapy followed by camrelizumab for unresectable biliary tract cancer: A phase II clinical trial

How eluents define proteomic fingerprinting of protein corona on nanoparticles.

The Molecular Subtypes and Immune Microenvironment of Mucinous Adenocarcinoma of the Colon

Abstract Background Lung cancer is a malignant tumour with the fastest increase in morbidity and mortality around the world. The clinical treatments available have significant side effects, thus it is desirable to identify alternative modalities to treat lung cancer. Shashen Maidong decoction (SMD) is a commonly used traditional Chinese medicine (TCM) formula for treating lung cancer in the clinic. While the key functional components (KFC) and the underlying mechanisms of SMD treating lung cancer are still unclear. Methods We propose a new integrated pharmacology model, which combines a novel node-importance calculation method and the contribution decision rate (CDR) model, to identify the KFC of SMD and to deduce their mechanisms in the treatment of lung cancer. Results The enriched effective Gene Ontology (GO) terms selected from our proposed node importance detection method could cover 97.66% of enriched GO terms of reference targets. After calculating CDR of active components in key functional network, the first 82 components covered 90.25% of the network information, which were defined as KFC. 82 KFC were subjected to functional analysis and experimental validation. 5–40 μM protocatechuic acid, 100–400 μM paeonol or caffeic acid exerted significant inhibitory activity on the proliferation of A549 cells. The results show that KFC play an important therapeutic role in the treatment of lung cancer by targeting Ras, AKT, IKK, Raf1, MEK, and NF-κB in the PI3K-Akt, MAPK, SCLC, and NSCLC signaling pathways active in lung cancer. Conclusions This study provides a methodological reference for the optimization and secondary development of TCM formulas. The strategy proposed in this study can be used to identify key compounds in the complex network and provides an operable test range for subsequent experimental verification, which greatly reduces the experimental workload. 

/pdf/decoding-the-key-compounds-and-mechanism-of-shashen-maidong-2xxppwym.pdf

Decoding the key compounds and mechanism of Shashen Maidong decoction in the treatment of lung cancer

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