-
Balkan Medical Journal Aug 2020Gastroenteropancreatic neuroendocrine tumors, a heterogeneous group of neoplasms, originates from the neuroendocrine system of the gastrointestinal tract and pancreas.... (Observational Study)
Observational Study
BACKGROUND
Gastroenteropancreatic neuroendocrine tumors, a heterogeneous group of neoplasms, originates from the neuroendocrine system of the gastrointestinal tract and pancreas. There are limited number of studies investigating neuroendocrine tumors in Turkey.
AIMS
To define the clinicopathologic, demographic, and survival features of patients with gastroenteropancreatic neuroendocrine tumors.
STUDY DESIGN
A retrospective observational cohort study.
METHODS
We reviewed hospital records of patients and data was analyzed retrospectively. We investigated the clinical, pathological, survival features, and prognosis of patients with gastroenteropancreatic neuroendocrine tumors (n=128) admitted to the medical oncology department between year 2003 and 2014. Survival estimation was performed by the Kaplan-Meier method. Univariate and multivariate Cox regression models were utilized to investigate the prognostic factors for survival.
RESULTS
Of 128 patients with gastroenteropancreatic neuroendocrine tumors, 61 (47.7%) were female and 67 (52.3%) were male. The most common site of the tumor was stomach (36.7%), while the most common stage of tumor at diagnosis was stage 4 (40.9%). The median follow-up period was 37 months, while the 3- and 5-year overall survival rates were 78% and 69%, respectively. The factors significantly affecting overall survival rate were clinical stage, grade, presence of metastasis at diagnosis, and Ki-67 proliferation index. These factors were associated with the 3- and 5-year overall survival rate. Moreover, grade (hazard ratio: 8.34, 95% confidence interval: 2.16-32.22, p=0.01) and presence of metastasis at diagnosis (hazard ratio: 3.18, 95% confidence interval: 1.30-7.77, p=0.01) independently predicted overall survival in multivariate model following adjustment for age and gender.
CONCLUSION
Higher-grade and presence of metastasis at diagnosis are negative independent prognostic indicators of survival in patients with gastroenteropancreatic neuroendocrine tumors.
Topics: Cohort Studies; Female; Humans; Intestinal Neoplasms; Kaplan-Meier Estimate; Male; Middle Aged; Neuroendocrine Tumors; Pancreatic Neoplasms; Pathology, Clinical; Prognosis; Retrospective Studies; Stomach Neoplasms; Turkey
PubMed: 32573179
DOI: 10.4274/balkanmedj.galenos.2020.2020.1.126 -
Cancer Imaging : the Official... Feb 2023We aimed to prospectively compare the diagnostic performance of gadoxetic acid-enhanced MRI (EOB-MRI) and contrast-enhanced Computed Tomography (CECT) for hepatocellular... (Clinical Trial)
Clinical Trial
Prospective evaluation of Gadoxetate-enhanced magnetic resonance imaging and computed tomography for hepatocellular carcinoma detection and transplant eligibility assessment with explant histopathology correlation.
BACKGROUND
We aimed to prospectively compare the diagnostic performance of gadoxetic acid-enhanced MRI (EOB-MRI) and contrast-enhanced Computed Tomography (CECT) for hepatocellular carcinoma (HCC) detection and liver transplant (LT) eligibility assessment in cirrhotic patients with explant histopathology correlation.
METHODS
In this prospective, single-institution ethics-approved study, 101 cirrhotic patients were enrolled consecutively from the pre-LT clinic with written informed consent. Patients underwent CECT and EOB-MRI alternately every 3 months until LT or study exclusion. Two blinded radiologists independently scored hepatic lesions on CECT and EOB-MRI utilizing the liver imaging reporting and data system (LI-RADS) version 2018. Liver explant histopathology was the reference standard. Pre-LT eligibility accuracies with EOB-MRI and CECT as per Milan criteria (MC) were assessed in reference to post-LT explant histopathology. Lesion-level and patient-level statistical analyses were performed.
RESULTS
Sixty patients (49 men; age 33-72 years) underwent LT successfully. One hundred four non-treated HCC and 42 viable HCC in previously treated HCC were identified at explant histopathology. For LR-4/5 category lesions, EOB-MRI had a higher pooled sensitivity (86.7% versus 75.3%, p < 0.001) but lower specificity (84.6% versus 100%, p < 0.001) compared to CECT. EOB-MRI had a sensitivity twice that of CECT (65.9% versus 32.2%, p < 0.001) when all HCC identified at explant histopathology were included in the analysis instead of imaging visible lesions only. Disregarding the hepatobiliary phase resulted in a significant drop in EOB-MRI performance (86.7 to 72.8%, p < 0.001). EOB-MRI had significantly lower pooled sensitivity and specificity versus CECT in the LR5 category with lesion size < 2 cm (50% versus 79%, p = 0.002 and 88.9% versus 100%, p = 0.002). EOB-MRI had higher sensitivity (84.8% versus 75%, p < 0.037) compared to CECT for detecting < 2 cm viable HCC in treated lesions. Accuracies of LT eligibility assessment were comparable between EOB-MRI (90-91.7%, p = 0.156) and CECT (90-95%, p = 0.158).
CONCLUSION
EOB-MRI had superior sensitivity for HCC detection; however, with lower specificity compared to CECT in LR4/5 category lesions while it was inferior to CECT in the LR5 category under 2 cm. The accuracy for LT eligibility assessment based on MC was not significantly different between EOB-MRI and CECT.
TRIAL REGISTRATION
ClinicalTrials.gov Identifier: NCT03342677 , Registered: November 17, 2017.
Topics: Adult; Aged; Humans; Male; Middle Aged; Carcinoma, Hepatocellular; Contrast Media; Gadolinium DTPA; Liver Cirrhosis; Liver Neoplasms; Magnetic Resonance Imaging; Retrospective Studies; Sensitivity and Specificity
PubMed: 36841796
DOI: 10.1186/s40644-023-00532-3 -
Clinical and pathological analysis of 10 cases of salivary gland epithelial-myoepithelial carcinoma.Medicine Oct 2020Epithelial-myoepithelial carcinoma (EMC) is a rare neoplasm of the salivary glands. The aim of this study is to review and evaluate clinicopathological features and...
Epithelial-myoepithelial carcinoma (EMC) is a rare neoplasm of the salivary glands. The aim of this study is to review and evaluate clinicopathological features and treatment of EMC of salivary gland for better sensitivity and specificity of the diagnosis.The clinical and pathological data of the 10 salivary gland EMC cases from 2008 to 2017 were analyzed.Six cases of EMC were diagnosed to be originated from parotid gland and 4 cases were from the minor salivary gland including palate, tongue, and oropharynx. Seven cases were performed radical surgery and 3 cases had radiotherapy postoperation, 2 cases had a local recurrence. The follow-up period was 4 to 104 months and the survival rate was 100%. Histopathology showed the tumors had a dominant prototypical biphasic tubular structure consisting of inner, cuboidal ductal cells and an outer layer of clear, myoepithelial cells, which grew infiltratively. The immunohistochemistry (IHC) showed the marker proteins CK, S-100, CD117, and Calponin were strongly positive in most EMC.EMC is a rare and low-grade malignant tumor with good overall survival but relatively high tendency for local recurrence. Surgery is the priority choice for EMC therapy. Complete surgical excision and negative margins are necessary for good prognosis. Imaging techniques should be used to assess the neck dissection and it is unclear whether adjuvant radiotherapy is beneficial. To ensure the sensitivity and specificity of the EMC diagnosis, we should perform both pathological and IHC analysis.
Topics: Adult; Aged; Aged, 80 and over; Carcinoma; Female; Humans; Male; Middle Aged; Myoepithelioma; Retrospective Studies; Salivary Gland Neoplasms; Salivary Glands
PubMed: 33031333
DOI: 10.1097/MD.0000000000022671 -
Journal of the American Society of... Jan 2021Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease...
BACKGROUND
Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation.
METHODS
We investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid-Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the convolutional neural network to segment six major renal structures: glomerular tuft, glomerulus including Bowman's capsule, tubules, arteries, arterial lumina, and veins. To achieve high accuracy, we performed a large number of expert-based annotations, 72,722 in total.
RESULTS
Multiclass segmentation performance was very high in all disease models. The convolutional neural network allowed high-throughput and large-scale, quantitative and comparative analyses of various models. In disease models, computational feature extraction revealed interstitial expansion, tubular dilation and atrophy, and glomerular size variability. Validation showed a high correlation of findings with current standard morphometric analysis. The convolutional neural network also showed high performance in other species used in research-including rats, pigs, bears, and marmosets-as well as in humans, providing a translational bridge between preclinical and clinical studies.
CONCLUSIONS
We developed a deep learning algorithm for accurate multiclass segmentation of digital whole-slide images of periodic acid-Schiff-stained kidneys from various species and renal disease models. This enables reproducible quantitative histopathologic analyses in preclinical models that also might be applicable to clinical studies.
Topics: Algorithms; Animals; Deep Learning; Diagnosis, Computer-Assisted; Disease Models, Animal; Image Processing, Computer-Assisted; Kidney; Kidney Diseases; Kidney Glomerulus; Male; Mice; Mice, Inbred C57BL; Neural Networks, Computer; Pattern Recognition, Automated; Periodic Acid; Reproducibility of Results; Schiff Bases; Translational Research, Biomedical
PubMed: 33154175
DOI: 10.1681/ASN.2020050597 -
Journal of Biomedical Semantics Sep 2020Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially...
BACKGROUND
Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially available data, can greatly improve healthcare. Although EHR de-identification is necessary to protect personal information, automatic de-identification of Japanese language EHRs has not been studied sufficiently. This study was conducted to raise de-identification performance for Japanese EHRs through classic machine learning, deep learning, and rule-based methods, depending on the dataset.
RESULTS
Using three datasets, we implemented de-identification systems for Japanese EHRs and compared the de-identification performances found for rule-based, Conditional Random Fields (CRF), and Long-Short Term Memory (LSTM)-based methods. Gold standard tags for de-identification are annotated manually for age, hospital, person, sex, and time. We used different combinations of our datasets to train and evaluate our three methods. Our best F1-scores were 84.23, 68.19, and 81.67 points, respectively, for evaluations of the MedNLP dataset, a dummy EHR dataset that was virtually written by a medical doctor, and a Pathology Report dataset. Our LSTM-based method was the best performing, except for the MedNLP dataset. The rule-based method was best for the MedNLP dataset. The LSTM-based method achieved a good score of 83.07 points for this MedNLP dataset, which differs by 1.16 points from the best score obtained using the rule-based method. Results suggest that LSTM adapted well to different characteristics of our datasets. Our LSTM-based method performed better than our CRF-based method, yielding a 7.41 point F1-score, when applied to our Pathology Report dataset. This report is the first of study applying this LSTM-based method to any de-identification task of a Japanese EHR.
CONCLUSIONS
Our LSTM-based machine learning method was able to extract named entities to be de-identified with better performance, in general, than that of our rule-based methods. However, machine learning methods are inadequate for processing expressions with low occurrence. Our future work will specifically examine the combination of LSTM and rule-based methods to achieve better performance. Our currently achieved level of performance is sufficiently higher than that of publicly available Japanese de-identification tools. Therefore, our system will be applied to actual de-identification tasks in hospitals.
Topics: Deep Learning; Electronic Health Records; Language; Natural Language Processing
PubMed: 32958039
DOI: 10.1186/s13326-020-00227-9 -
Scientific Reports Aug 2023Technical advances in microsurgery have enabled complex oncological reconstructions by performing free tissue transfers, nerve and lymphatic reconstructions. However,... (Randomized Controlled Trial)
Randomized Controlled Trial
Technical advances in microsurgery have enabled complex oncological reconstructions by performing free tissue transfers, nerve and lymphatic reconstructions. However, the manual abilities required to perform microsurgery can be affected by human fatigue and physiological tremor resulting in tissue damage and compromised outcomes. Robotic assistance has the potential to overcome issues of manual microsurgery by improving clinical value and anastomoses' outcomes. The Symani Surgical System, a robotic platform designed for microsurgery, was used in this in-vivo preclinical study using a rat animal model. The tests included anastomoses on veins and arteries performed by microsurgeons manually and robotically, with the latter approach using Symani. The anastomoses were assessed for patency, histopathology, and execution time. Patency results confirmed that the robotic and manual techniques for venous and arterial anastomoses were equivalent after anastomosis, however, the time to perform the anastomosis was longer with the use of the robot (p < 0.0001). Histological analysis showed less total average host reaction score at the anastomotic site in robotic anastomosis for both veins and arteries. This study demonstrates the equivalence of vessel patency after microsurgical anastomoses with the robotic system and with manual technique. Furthermore, robotic anastomosis has proven to be slightly superior to manual anastomosis in terms of decreased tissue damage, as shown by histological analysis.
Topics: Animals; Humans; Rats; Anastomosis, Surgical; Arteries; Essential Tremor; Robotic Surgical Procedures; Veins
PubMed: 37635195
DOI: 10.1038/s41598-023-41143-z -
Diagnostics (Basel, Switzerland) Jun 2022CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers...
CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathological image analysis. MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their predefined ground-truth labels, for every single mini-batch. Mixed-patches are generated using a small size of clean patches confirmed by pathologists while their ground-truth labels are defined using a proportion-based soft labeling method. Our results obtained using a large histopathological image dataset shows that the proposed method performs better and alleviates overconfidence more effectively than any other method examined in the study. More specifically, our model showed 97.06% accuracy, an increase of 1.6% to 12.18%, while achieving 0.76% of expected calibration error, a decrease of 0.6% to 6.3%, over the other models. By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis.
PubMed: 35741303
DOI: 10.3390/diagnostics12061493 -
Forensic Science, Medicine, and... Sep 2023The autopsy is considered the gold standard in death investigation. Performing an autopsy requires human and material resources that must be programmed in order to meet...
INTRODUCTION AND OBJECTIVES
The autopsy is considered the gold standard in death investigation. Performing an autopsy requires human and material resources that must be programmed in order to meet the demands of the judicial system. However, as far as we know, the cost of forensic autopsy in Spain has not been determined. Thus, the aim of this study was to estimate the cost of a standard autopsy in order to organise Forensic Pathology Services more efficiently.
MATERIAL AND METHODS
A micro-cost analysis was carried out. The nominal group technique was applied using a panel of 10 forensic experts in order to identify and quantify the resources associated with a forensic autopsy.
RESULTS
The results showed that analysis and studies are the most important item in the total cost (54.7%), followed by staff (20.5%), preservation of body (14%), single-use products (7%), equipment and stock (1.6%), cleaning and disinfection (1.5%), facilities maintenance (0.5%) and IT (0.2%).
CONCLUSIONS
The total cost of a standard autopsy was €1501.45, which is lower than the European average. This study is the first in Spain to calculate the unit price of a forensic autopsy by means of micro-cost analysis. This may help to address the way forensic pathology centres are organised at different levels of complexity.
Topics: Humans; Autopsy; Spain; Cause of Death; Forensic Medicine; Forensic Pathology
PubMed: 36342626
DOI: 10.1007/s12024-022-00534-w -
Technology in Cancer Research &... 2023Pap smear is considered to be the primary examination for the diagnosis of cervical cancer. But the analysis of pap smear slides is a time-consuming task and tedious as...
Pap smear is considered to be the primary examination for the diagnosis of cervical cancer. But the analysis of pap smear slides is a time-consuming task and tedious as it requires manual intervention. The diagnostic efficiency depends on the medical expertise of the pathologist, and human error often hinders the diagnosis. Automated segmentation and classification of cervical nuclei will help diagnose cervical cancer in earlier stages. The proposed methodology includes three models: a Residual-Squeeze-and-Excitation-module based segmentation model, a fusion-based feature extraction model, and a Multi-layer Perceptron classification model. In the fusion-based feature extraction model, three sets of deep features are extracted from these segmented nuclei using the pre-trained and fine-tuned VGG19, VGG-F, and CaffeNet models, and two hand-crafted descriptors, Bag-of-Features and Linear-Binary-Patterns, are extracted for each image. For this work, Herlev, SIPaKMeD, and ISBI2014 datasets are used for evaluation. The Herlev datasetis used for evaluating both segmentation and classification models. Whereas the SIPaKMeD and ISBI2014 are used for evaluating the classification model, and the segmentation model respectively. The segmentation network enhanced the precision and ZSI by 2.04%, and 2.00% on the Herlev dataset, and the precision and recall by 0.68%, and 2.59% on the ISBI2014 dataset. The classification approach enhanced the accuracy, recall, and specificity by 0.59%, 0.47%, and 1.15% on the Herlev dataset, and by 0.02%, 0.15%, and 0.22% on the SIPaKMed dataset. The experiments demonstrate that the proposed work achieves promising performance on segmentation and classification in cervical cytopathology cell images..
Topics: Female; Humans; Uterine Cervical Neoplasms; Cytology; Cervix Uteri; Papanicolaou Test; Neural Networks, Computer; Image Processing, Computer-Assisted
PubMed: 36744768
DOI: 10.1177/15330338221134833 -
La Clinica Terapeutica Mar 2021One of the increasingly discussed topics in forensic pathology is that concerning the quantification of the postmortem interval (PMI). The estimation of the time...
One of the increasingly discussed topics in forensic pathology is that concerning the quantification of the postmortem interval (PMI). The estimation of the time interval between the death of a person and the discovery of the body is extremely complicated, as it is affected by the influence of many factors, both endogenous and exogenous. With the advancement of knowledge in the field of molecular biology, several studies have been performed, for more than 30 years, on the degradation pattern of macromolecules, such as proteins, DNA, RNA, and the relationship with PMI. Despite initial enthusiasm, studies have shown different kind of limitations in determining PMI in the forensic field. In the last years, consequently, researchers focused their attention on the potential of microRNAs as housekeeping genes, due to their postmortem stability and resistance to degradation. MiRNAs are small, endogenous, single stranded, non-coding RNA molecules identified in plants, animals and DNA virus transcriptome. Various and growing are the fields of application: to establish time of death, to evaluate vitality of skin lesions, in cases of head trauma, and cases of acute myocardial infarction. Their use could also be particularly useful in determining late PMI (beyond 24 hours after death), as no additional markers are available in this scenario. At the moment, scientific research is still at an early stage as it is mainly based on animal models. However, the promising properties of miRNAs and their low cost may make this field of research very interesting for an increasingly precise determination of PMI in the future.
Topics: Animals; Autopsy; Forensic Medicine; Forensic Pathology; Humans; MicroRNAs; Molecular Biology; Postmortem Changes; Real-Time Polymerase Chain Reaction; Time Factors
PubMed: 33763669
DOI: 10.7417/CT.2021.2294