Pathohistological classification systems in gastric cancer:Diagnostic relevance and prognostic value

Several pathohistological classification systems exist for the diagnosis of gastric cancer. Many studies have investigated the correlation between the pathohistological characteristics in gastric cancer and patient characteristics, disease specific criteria and overall outcome. It is still controversial as to which classification system imparts the most reliable information, and therefore, the choice of system may vary in clinical routine. In addition to the most common classification systems, such as the Laurén and the World Health Organization (WHO) classifications, other authors have tried to characterize and classify gastric cancer based on the microscopic morphology and in reference to the clinical outcome of the patients. In more than 50 years of systematic classification of the pathohistological characteristics of gastric cancer, there is no sole classification system that is consistently used worldwide in diagnostics and research. However, several national guidelines for the treatment of gastric cancer refer to the Laurén or the WHO classifications regarding therapeutic decision-making, which underlines the importance of a reliable classification system for gastric cancer. The latest results from gastric cancer studies indicate that it might be useful to integrate DNA- and RNA-based features of gastric cancer into the classification systems to establish prognostic relevance. This article reviews the diagnostic relevance and the prognostic value of different pathohistological classification systems in gastric cancer.

The classification of plants by laser-induced breakdown spectroscopy based on two chemometric methods

The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.

Source Rock Classification and the Basic Structure of Coal and Kerogen

In accordance with the confusion on classification of source rocks, the authors raised a source rock classification for its enriched and dispersed organic matter types based on both Alpern’s idea and maceral genesis/composition. The determined rock type is roughly similar to palynofacies of Combaz , whereas it is "rock maceral facies (for coal viz. coal facies)" in strictly speaking. Therefore, it is necessary to use the organic ingredients classification proposed by the authors so that it can be used for both maceral analysis and environment research . This source rock classification not only shows sedimentology and diagenetic changes but also acquires organic matter type even if hydrocarbon potential derived from maceral’s geochemical parameters. So, it is considered as genetic classification. The "rock maceral facies" may be transformed to sedimentary organic facies , which is used as quantitative evaluation means if research being perfect.Now, there are many models in terms of structure either for coal or for kerogen. In our opinion, whatever coal or kerogen ought be polymer, then we follow Combaz’s thought and study structure of amorphous kerogens which are accordance with genetic mechanism showing biochemical and geochemical process perfectly. Here, we use the time of flight secondary ion mass spectrometry (TOFSIMS) to expand Combaz’s models from three to five. They are also models for coal.

Pancreatic neuroendocrine neoplasms: Magnetic resonance imaging features according to grade and stage

AIMTo describe magnetic resonance (MR) imaging features of pancreatic neuroendocrine neoplasms (PanNENs) according to their grade and tumor-nodes-metastases stage by comparing them to histopathology and to determine the accuracy of MR imaging features in predicting their biological behavior.METHODSThis study was approved by our institutional review board; requirement for informed patient consent was waived due to the retrospective nature of the study. Preoperative MR examinations of 55 PanNEN patients (29 men, 26 women; mean age of 57.6 years, range 21-83 years) performed between June 2013 and December 2015 were reviewed. Qualitative and quantitative features were compared between tumor grades and stages determined by histopathological analysis.RESULTSIll defined margins were more common in G2-3 and stage III-IV PanNENs than in G1 and low-stage tumors (P < 0.001); this feature had high specificity in the identification of G2-3 and stage III-IV tumors (90.3% and 96%, 95%CI: 73.1-97.5 and 77.7-99.8). The mean apparent diffusion coefficient value was significantly lower in G2-3 and stage III-IV lesions compared to well differentiated and low-stage tumors (1.09 × 10-3 mm2/s vs 1.45 × 10-3 mm2/s and 1.10 × 10-3 mm2/s vs 1.53 × 10-3 mm2/s, P = 0.003 and 0.001). Receiving operator characteristic analysis determined optimal cut-offs of 1.21 and 1.28 × 10-3 mm2/s for the identification of G2-3 and stage III-IV tumors, with sensitivity and specificity values of 70.8/80.7% and 64.5/64% (95%CI: 48.7-86.6/60-92.7 and 45.4-80.2/42.6-81.3).CONCLUSIONMR features of PanNENs vary according to their grade of differentiation and their stage at diagnosis and could predict the biological behavior of these tumors.

Pathologic research update of colorectal neuroendocrine tumors

Colorectal neuroendocrine tumors (NETs) originate from neuroendocrine cells in the intestinal tract, and represent a small area within oncology, but one which has provided increasing new data during the past years. Although the World Health Organization has determined clinical and histological features to predict prognosis for such tumors, they may not be valid on an individual basis. We aim to give an overview of the recent findings with regard to pathology, molecular genetics and diagnosis of NETs.

New insights in diagnosis and treatment of gastroenteropancreatic neuroendocrine neoplasms

Gastroenteropancreatic neuroendocrine neoplasms(GEP-NENs)are rare epithelial neoplasms derived from pluripotent endocrine cells along the gastrointestinal tract and pancreas.GEP-NENs are classified into well-differentiated neuroendocrine tumors and poorly differentiated neuroendocrine carcinomas.Despite overlapping morphological features,GEP-NENs vary in molecular biology,epigenetic,clinical behavior,treatment response,and prognosis features and remain an unmet clinical challenge.In this review,we introduce recent updates on the histopathologic classification,including the tumor grading and staging system,molecular genetics,and systemic evaluation of the diagnosis and treatment of GEP-NENs at different anatomic sites,together with some insights into the diagnosis of challenging and unusual cases.We also discuss the application of novel therapeutic approaches for GEP-NENs,including peptide receptor radionuclide therapy,targeted therapy,and immunotherapy with immune checkpoint inhibitors.These findings will help improve patient care with precise diagnosis and individualized treatment of patients with GEP-NENs.

A Fatal Case of Chronic Eosinophilic Leukemia Not Otherwise Specified (CEL-NOS) in a 19-Year-Old Male with Acute Transformation into Blast Crisis

Chronic eosinophilic leukemia (CEL) is a rare disorder that is characterized by hypereosinophilia with increased number of blood or marrow blasts (>5% and <20%). CEL is distinguished from hypereosinophilic syndrome (HES) by the presence of eosinophilic clonality. Chronic eosinophilic leukemia not otherwise specified (CEL-NOS) diagnosis is made when no fusion genes are detected by most modern molecular testing, particularly the most common fusion gene FIP1L-1/PDGFRA (Factor Interacting with PAP like-1/Platelet-Derived Growth Factor Receptor Alpha). This disease is very rare, and its description in the literature is not well characterized. We report a fetal case of severe CEL-NOS in a 19-year-old male who presented with a plethora of clinical features consists of constitutional symptoms, pancytopenia, intravascular thrombosis, acute stroke and endomyocardial infiltrates. The course of his disease was aggressive and resistant to conventional treatment. After a short period of improvement, an acute transformation into blast crisis (BC) had occurred. The diagnosis was confirmed by morphology and immunophenotyping of bone marrow biopsy. The patient eventually died of heart failure and sepsis. To our knowledge this is the first case report of fatal CEL-NOS transforming into severe blast crisis.

Three dimensional apple tree organs classification and yield estimation algorithm based on multifeatures fusion and support vector machine

The automatic classification of apple tree organs is of great significance for automatic pruning of apple trees,automatic picking of apple fruits,and estimation of fruit yield.How-ever,there are some problems of dense foliage,partial occlusion and clustering of apple fruits.All of the problems above would contribute to the difficulties of organs classification and yield estimation of the apple trees.In this paper a method based on Color and Shape Multi-features Fusion and Support Vector Machine(SVM)for 3D apple tree organs classifi-cation and yield estimation was proposed.The method was designed for dwarf and densely planted apple trees at the early and late maturity stages.196-dimensional feature vectors composed with Red Green Blue(RGB),Hue Saturation Value(HSV),Curvatures,Fast Point Feature Histogram(FPFH),and Spin Image were extracted firstly.And then the SVM based on linear kernel function was trained,after that the trained SVM was used for apple tree organs classification.Then the position weighted smoothing algorithm was used for clas-sified apple tree organs smoothing.Then the agglomerative hierarchical clustering algo-rithm was used to recognize single apple fruit for yield estimation.On the same training and test set the experimental results showed that the SVM based on linear kernel function outperformed the KNN algorithm and Ensemble algorithm.The Recall,Precision and F1 score of the proposed method for yield estimation were 93.75%,96.15%and 94.93%respec-tively.In summary,to solve the problems of apple tree organs classification and yield esti-mation in natural apple orchard,a novelty method based on multi-features fusion and SVM was proposed and achieve good performance.Moreover,the proposed method could pro-vide technical support for automatic apple picking,automatic pruning of fruit trees,and automatic information acquisition and management in orchards.

Epidemiological characteristics of central nervous system tumors in children: a 5-year review of 3180 cases from Beijing Tiantan Hospital

Background:To describe the epidemiological characteristics of central nervous system(CNS)tumors in children,based on the neurosurgery department of Beijing Tiantan Hospital.Methods:From January 2015 to December 2019,3180 children were histopathologically diagnosed with CNS tumors based on the 2016 World Health Organization(WHO)classification of tumors.Patients were 0 to 15 years old.We analyzed age-related gender preferences,tumor locations,and the histological grades of the tumors.In addition,the epidemiological characteristics of the five most common intracranial tumors were compared to the previous studies.Results:In this study,intracranial and spinal tumors account for 96.4%(3066)and 3.6%(114)of all tumors,with a preponderance of supratentorial tumors(57.9%).Among all pediatric patients,low-grade tumors comprise 67.1%(2135).The integral gender ratio of males to females is 1.47:1 and the average age of patients is 7.59 years old.The five most common intracranial tumors are craniopharyngioma(15.4%),medulloblastoma(14.3%),pilocytic astrocytoma(11.8%),diffuse astrocytoma(9.8%),and anaplastic ependymoma(4.8%).Conclusions:Due to the lack of national data on childhood brain tumors,we used a large nationally representative population sample based on the largest pediatric neurosurgery center in China.We analyzed the data of the past 5 years,reflecting the incidence of CNS tumors in Chinese children to a certain extent,and laying a data foundation for subsequent clinical studies.