Refine
Year of publication
Language
- English (22) (remove)
Has Fulltext
- yes (22)
Is part of the Bibliography
- no (22)
Keywords
- Petri net (3)
- Artificial intelligence (2)
- CT (2)
- Machine learning (2)
- Prostate cancer (2)
- Radiomics (2)
- ACLF (1)
- Anemia (1)
- Angiography (1)
- Blood (1)
- CLIF-C ACLF score (1)
- CLIF-C ACLF-R score (1)
- Cell staining (1)
- Clustering (1)
- Cohort studies (1)
- Diagnostic markers (1)
- Exudates and transudates (1)
- Feasibility (1)
- Feasibility studies (1)
- Fourier-Motzkin algorithm (1)
- Gene expression (1)
- Genetics (1)
- Hematoxylin staining (1)
- Hepatocellular carcinoma (1)
- Histology (1)
- Hodgkin lymphoma (1)
- Image processing (1)
- In-TIPS thrombosis (1)
- Intensive care units (1)
- LTP (1)
- Lymph nodes (1)
- Lymphocytes (1)
- Manatee invariant (1)
- Mastoiditis (1)
- Mathematical model (1)
- Microarray (1)
- Multiparametric MRI (1)
- NF-κB pathway (1)
- PDE (1)
- Pathologists (1)
- Portal (1)
- Preclinical research (1)
- Prediction (1)
- Predictive markers (1)
- Prognostic markers (1)
- Psychology (1)
- Q-modularity (1)
- Quantitative Imaging (1)
- Quantitative features (1)
- Shunt (1)
- Signaling pathway (1)
- Statistical analysis (1)
- TIPS (1)
- Thrombosis (1)
- Transarterial chemoembolization (1)
- Transition invariant (1)
- Transjugular Intrahepatic Portosystemic Shunt (1)
- Translational research (1)
- acute-on-chronic liver failure (1)
- agent-based modeling (1)
- aging (1)
- amyloid precursor protein (1)
- artificial intelligence (1)
- community (1)
- computer vision (1)
- digital pathology (1)
- elementary mode (1)
- functional module (1)
- hippocampus (1)
- human lymph node (1)
- immune response (1)
- lymph node (1)
- maximal common transition set (1)
- mechanical ventilation (1)
- metabolic networks (1)
- minimal cut set (1)
- mitochondria (1)
- modeling (1)
- morphological filtering (1)
- ordinary differential equation (1)
- partial differential equation (1)
- presynaptic active zone (1)
- pulmonary failure (1)
- respiratory failure (1)
- shock filter (1)
- t-cluster (1)
- t-invariant (1)
- whole slide image (1)
Institute
The human immune system is determined by the functionality of the human lymph node. With the use of high-throughput techniques in clinical diagnostics, a large number of data is currently collected. The new data on the spatiotemporal organization of cells offers new possibilities to build a mathematical model of the human lymph node - a virtual lymph node. The virtual lymph node can be applied to simulate drug responses and may be used in clinical diagnosis. Here, we review mathematical models of the human lymph node from the viewpoint of cellular processes. Starting with classical methods, such as systems of differential equations, we discuss the values of different levels of abstraction and methods in the range from artificial intelligence techniques formalism.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.