scholarly journals A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images

Biostatistics ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 565-581
Author(s):  
Qiwei Li ◽  
Xinlei Wang ◽  
Faming Liang ◽  
Faliu Yi ◽  
Yang Xie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Spatial Patterns ◽  
Cancer Progression ◽  
Large Scale ◽  
Digital Pathology ◽  
Interaction Strength ◽  
Monte Carlo Sampling ◽  
Square Lattice ◽  
Different Types ◽  
Pathology Image

Summary Digital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology images analysis at a large scale. This creates new opportunities to study the spatial patterns of and interactions among different types of cells. Reliable statistical approaches to modeling such spatial patterns and interactions can provide insight into tumor progression and shed light on the biological mechanisms of cancer. In this article, we consider the problem of modeling a pathology image with irregular locations of three different types of cells: lymphocyte, stromal, and tumor cells. We propose a novel Bayesian hierarchical model, which incorporates a hidden Potts model to project the irregularly distributed cells to a square lattice and a Markov random field prior model to identify regions in a heterogeneous pathology image. The model allows us to quantify the interactions between different types of cells, some of which are clinically meaningful. We use Markov chain Monte Carlo sampling techniques, combined with a double Metropolis–Hastings algorithm, in order to simulate samples approximately from a distribution with an intractable normalizing constant. The proposed model was applied to the pathology images of $205$ lung cancer patients from the National Lung Screening trial, and the results show that the interaction strength between tumor and stromal cells predicts patient prognosis (P = $0.005$). This statistical methodology provides a new perspective for understanding the role of cell–cell interactions in cancer progression.

scholarly journals Prognosis of Different Types of Non-Small Cell Lung Cancer Progression: Current State and Perspectives

2021 ◽  
Vol 55 (S2) ◽  
pp. 29-48
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Progression ◽  
Cell Lung Cancer ◽  
Molecular Mechanisms ◽  
Distant Recurrence ◽  
Small Cell ◽  
Differential Analysis ◽  
Small Cell Lung ◽  
Different Types

Despite advances in diagnostics and therapy of non-small cell lung cancer (NSCLC), the problem of prognosis and prevention of tumor progression is still highly important. Even if NSCLC is diagnosed in the early stages, almost a quarter of patients develop relapse; most of them die from recurrent disease. A large number of different markers have been proposed to predict the risk of NSCLC progression; however, none of them are used in clinical practice. It is obvious that this situation is related to the economic and methodological complexity of the proposed markers and/or their insufficient efficiency due to a lack of effective study models and tumor heterogeneity. Another reason may be that potential markers are developed for NSCLC progression in general, which is represented by at least four pathogenetically-distinct processes: synchronous lymph node metastasis, local, regional, and distant recurrence. In this review, we summarize data from published literature on clinicopathological, genetic, and molecular factors associated with different types of NSCLC progression and emphasize challenges and approaches to developing prognostic factors. In conclusion, we highlight the importance of further studies to reveal molecular mechanisms of NSCLC progression and the need for differential analysis of markers of local, regional, and distant recurrences for disease prognosis.

scholarly journals LINC00261 and the Adjacent Gene FOXA2 Are Epithelial Markers and Are Suppressed during Lung Cancer Tumorigenesis and Progression

2018 ◽  
Vol 5 (1) ◽  
pp. 2 ◽  
Author(s):  
Sonam Dhamija ◽  
Andrea Becker ◽  
Yogita Sharma ◽  
Ksenia Myacheva ◽  
Jeanette Seiler ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Progression ◽  
Large Scale ◽  
Patient Survival ◽  
Cancer Cell Lines ◽  
Specific Marker ◽  
Lung Cancer Cell ◽  
Mesenchymal Transition

Lung cancer continues to be the leading cause of cancer-related deaths worldwide, with little improvement in patient survival rates in the past decade. Long non-coding RNAs (lncRNAs) are gaining importance as possible biomarkers with prognostic potential. By large-scale data mining, we identified LINC00261 as a lncRNA which was significantly downregulated in lung cancer. Low expression of LINC00261 was associated with recurrence and poor patient survival in lung adenocarcinoma. Moreover, the gene pair of LINC00261 and its neighbor FOXA2 were significantly co-regulated. LINC00261 as well as FOXA2 negatively correlated with markers for epithelial-to-mesenchymal transition (EMT) and were suppressed by the EMT inducer TGFβ. Hierarchical clustering of gene expression data from lung cancer cell lines could further verify the association of high LINC00261/FOXA2 expression to an epithelial gene signature. Furthermore, higher expression of the LINC00261/FOXA2 locus was associated with lung cancer cell lines with lower migratory capacity. All these data establish LINC00261 and FOXA2 as an epithelial-specific marker pair, downregulated during EMT and lung cancer progression, and associated with lower cell migration potential in lung cancer cells.

scholarly journals Artificial Intelligence in Lung Cancer Pathology Image Analysis

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1673 ◽  
Author(s):  
Shidan Wang ◽  
Donghan M. Yang ◽  
Ruichen Rong ◽  
Xiaowei Zhan ◽  
Junya Fujimoto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Image Analysis ◽  
Deep Learning ◽  
Digital Pathology ◽  
Diagnosis And Prognosis ◽  
Model Interpretation ◽  
Lung Cancer Diagnosis ◽  
Cancer Pathology ◽  
Pathology Image

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps

2020 ◽  
Vol 2020 (10) ◽  
pp. 64-1-64-5
Author(s):  
Mustafa I. Jaber ◽  
Christopher W. Szeto ◽  
Bing Song ◽  
Liudmila Beziaeva ◽  
Stephen C. Benz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Density ◽  
Majority Voting ◽  
Training Set ◽  
Density Maps ◽  
Color Deconvolution ◽  
Map Generation ◽  
Density Map ◽  
Pathology Image ◽  
Whole Slide Images

In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.

The value of sperm-associated antigen 9 in lung cancer progression

2018 ◽  
Author(s):  
Merih Tepeoglu ◽  
Ebru Sebnem Ayva ◽  
B. Handan Ozdemir
Keyword(s):  
Lung Cancer ◽  
Cancer Progression

Carbazole Derivatives as Kinase-Targeting Inhibitors for Cancer Treatment

2020 ◽  
Vol 20 (6) ◽  
pp. 444-465 ◽  
Author(s):  
Jessica Ceramella ◽  
Domenico Iacopetta ◽  
Alexia Barbarossa ◽  
Anna Caruso ◽  
Fedora Grande ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Drug Targets ◽  
Mechanisms Of Action ◽  
Biological Pathways ◽  
G Protein Coupled Receptors ◽  
Ability To Act ◽  
Different Types ◽  
Activity Of Enzymes ◽  
Intercalating Agents ◽  
G Protein Coupled

Protein Kinases (PKs) are a heterogeneous family of enzymes that modulate several biological pathways, including cell division, cytoskeletal rearrangement, differentiation and apoptosis. In particular, due to their crucial role during human tumorigenesis and cancer progression, PKs are ideal targets for the design and development of effective and low toxic chemotherapeutics and represent the second group of drug targets after G-protein-coupled receptors. Nowadays, several compounds have been claimed to be PKs inhibitors, and some of them, such as imatinib, erlotinib and gefitinib, have already been approved for clinical use, whereas more than 30 others are in various phases of clinical trials. Among them, some natural or synthetic carbazole-based molecules represent promising PKs inhibitors due to their capability to interfere with PK activity by different mechanisms of action including the ability to act as DNA intercalating agents, interfere with the activity of enzymes involved in DNA duplication, such as topoisomerases and telomerases, and inhibit other proteins such as cyclindependent kinases or antagonize estrogen receptors. Thus, carbazoles can be considered a promising this class of compounds to be adopted in targeted therapy of different types of cancer.

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