scholarly journals The value of GATA6 immunohistochemistry and computer-assisted diagnosis to predict clinical outcome in advanced pancreatic cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Duan ◽  
Gun-Ho Jang ◽  
Robert C. Grant ◽  
Julie M. Wilson ◽  
Faiyaz Notta ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Open Source Software ◽  
Digital Pathology ◽  
Advanced Pancreatic Cancer ◽  
Molecular Data ◽  
Ductal Adenocarcinoma ◽  
Computer Assisted ◽  
Biopsy Specimens ◽  
Assisted Diagnosis

AbstractCombination chemotherapy, either modified FOLFIRINOX (mFFX) or gemcitabine–nabpaclitaxel, are used in the treatment of most patients with advanced pancreatic ductal adenocarcinoma (PDAC), yet robust biomarkers of outcome are currently lacking to guide regimen selection. Here, we tested GATA6 immunohistochemistry (IHC) as a putative biomarker in advanced PDAC. GATA6 is a transcription factor in normal pancreas development. Two pathologists, blinded to clinical and molecular data, independently assessed GATA6 IHC in biopsy specimens of 130 patients with advanced PDAC, in 2 distinct phases (without and with computer assistance using the open source software QuPath). Low GATA6 IHC expression was associated with shorter overall survival [median OS 6.2 months for patients with GATA6 low tumors vs. 11.5 months for patients with GATA6 high tumors, HR 1.66 (95% CI 1.15–2.40), P = 0.007]. Progression appears to be higher in GATA6-low tumors compared to GATA6-high tumors in patients treated with mFFX (P = 0.024) but not in patients treated with gemcitabine regimens. GATA6 IHC expression was significantly associated with molecular subtypes (P = 0.0003). Digital assistance markedly improved interrater concordance (Cohen’s kappa scores of 0.32 vs. 0.95). Our results provide strong evidence that GATA6 IHC can be used as a single biomarker in the clinic to predict clinical outcome in advanced PDAC, warranting further investigation in prospective clinical trials. These results provide the basis for an improved classification of PDAC and future biomarker design using digital pathology workflow.

scholarly journals Detection and classification of contrast-enhancing masses by a fully automatic computer-assisted diagnosis system for breast MRI

2012 ◽  
Vol 35 (5) ◽  
pp. 1077-1088 ◽  
Author(s):  
Diane M. Renz ◽  
Joachim Böttcher ◽  
Felix Diekmann ◽  
Alexander Poellinger ◽  
Martin H. Maurer ◽  
...  
Keyword(s):  
Breast Mri ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Automatic Computer ◽  
Computer Assisted Diagnosis ◽  
Fully Automatic ◽  
Assisted Diagnosis

Dental Radiograph Segmentation and Classification—A Comparative Study of Hu’s Moments and Histogram of Oriented Gradients

2019 ◽  
Vol 16 (8) ◽  
pp. 3612-3616
Author(s):  
Rameswari Poornima Janardanan ◽  
Rajasvaran Logeswaran
Keyword(s):  
Support Vector ◽  
Computer Assisted ◽  
X Rays ◽  
Histogram Of Oriented Gradients ◽  
Moment Invariant ◽  
Feature Descriptors ◽  
Assisted Diagnosis ◽  
Classification Of Images ◽  
Comparison Of The Results

This paper proposes a method to compare two feature descriptors to classify dental X-rays, using Hu’s Moments (HM) and the Histogram of Oriented Gradients (HOG). The dental radiographs are preprocessed, and the shape features of teeth are derived using HM and HOG. Support Vector Machine (SVM) is then used for tooth classification and recognition. Comparison of the results of using the two approaches as feature descriptors revealed that regardless of its orientation, size and position, moment invariant functions are very useful for object classification. The classification of images into molar and premolar has been done on manually cropped images. This method was validated on periapical radiographs. Results obtained show that using both HM and HOG to classify and recognize teeth shape description accuracy as better than, or at least comparable, to the state-of-the-art approaches. This work aids to improve the computer-assisted diagnosis and decision in dentistry. The forensic odonatological applications of this approach are wide and of immense benefits in both forensic and biometric identification.

A computer assisted diagnosis tool for the classification of burns by depth of injury

Burns ◽  
2005 ◽  
Vol 31 (3) ◽  
pp. 275-281 ◽  
Author(s):  
Carmen Serrano ◽  
Begoña Acha ◽  
Tomás Gómez-Cía ◽  
José I. Acha ◽  
Laura M. Roa
Keyword(s):  
Computer Assisted ◽  
Computer Assisted Diagnosis ◽  
Diagnosis Tool ◽  
Assisted Diagnosis

scholarly journals Tensor Decomposition for Multiple-Instance Classification of High-Order Medical Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Thomas Papastergiou ◽  
Evangelia I. Zacharaki ◽  
Vasileios Megalooikonomou
Keyword(s):  
Health Care ◽  
Missing Values ◽  
Tensor Decomposition ◽  
Multiple Instance Learning ◽  
High Order ◽  
Clinical Diagnostics ◽  
Multidimensional Data ◽  
Computer Assisted ◽  
Assisted Diagnosis

Multidimensional data that occur in a variety of applications in clinical diagnostics and health care can naturally be represented by multidimensional arrays (i.e., tensors). Tensor decompositions offer valuable and powerful tools for latent concept discovery that can handle effectively missing values and noise. We propose a seamless, application-independent feature extraction and multiple-instance (MI) classification method, which represents the raw multidimensional, possibly incomplete, data by means of learning a high-order dictionary. The effectiveness of the proposed method is demonstrated in two application scenarios: (i) prediction of frailty in older people using multisensor recordings and (ii) breast cancer classification based on histopathology images. The proposed method outperforms or is comparable to the state-of-the-art multiple-instance learning classifiers highlighting its potential for computer-assisted diagnosis and health care support.

scholarly journals A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images

2021 ◽  
Vol 18 (5) ◽  
pp. 6978-3994
Author(s):  
Zijian Wang ◽  
◽  
Yaqin Zhu ◽  
Haibo Shi ◽  
Yanting Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Mental Disease ◽  
Computer Assisted ◽  
Multi Scale ◽  
Mental Diseases ◽  
Assisted Diagnosis ◽  
3D Cnn

<abstract> <p>Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consideration the influence of multi-scale brain connectivity disorders on some mental diseases. To improve this defect, a deep learning structure was proposed based on MRI images, which was designed to consider the brain's connections at different sizes and the attention of connections. In this work, a Multiscale View (MV) module was proposed, which was designed to detect multi-scale brain network disorders. On the basis of the MV module, the path attention module was also proposed to simulate the attention selection of the parallel paths in the MV module. Based on the two modules, we proposed a 3D Multiscale View Convolutional Neural Network with Attention (3D MVA-CNN) for classification of MRI images for mental disease. The proposed method outperformed the previous 3D CNN structures in the structural MRI data of ADHD-200 and the functional MRI data of schizophrenia. Finally, we also proposed a preliminary framework for clinical application using 3D CNN, and discussed its limitations on data accessing and reliability. This work promoted the assisted diagnosis of mental diseases based on deep learning and provided a novel 3D CNN method based on MRI data.</p> </abstract>

scholarly journals Advances in digital pathology

2018 ◽  
Vol 1 (2) ◽  
pp. 33-39
Author(s):  
Evgin Goceri
Keyword(s):  
Digital Pathology ◽  
Computer Assisted ◽  
Cell Detection ◽  
Tissue Samples ◽  
Nuclei Segmentation ◽  
New Ideas ◽  
Assisted Diagnosis ◽  
Tissue Specimens ◽  
Validation Of Results

Characterization of cancer diseases and preparation of diagnostic reports after analyzing tissue specimens and several cell samples are provided by pathologists. One of the most successful strategies in pathology is to divide tumors into different subtypes and to adapt the treatment for each tumor. However, this approach has put a big burden on pathologists, who are reviewing tissue samples under the light of the microscope. Because, tumors have about 200 subtypes and pathologies are facing a growing demand for accurate and fast diagnosis and also patient safety. Therefore, digital pathology has been important and growing rapidly. Advances in computer technology such as computing power, faster networks and cheaper storage have enabled pathologists to manage images more easily than in the last decade. Novel pathology tools have a potential for automated and faster diagnosis and also better management of data. Moreover, it enables re-reducibility, validation of results, quality assurance and sharing of new ideas at anywhere and anytime. Advances in digital pathology have been reviewed in this paper. It seems that innovations in technologies will not only provide important improvements in pathology service, but also they will change healthcare and research fundamentally despite some challenges.   Keywords: Cell detection, computer assisted diagnosis, digital pathology, image analysis, nuclei segmentation, tissue classification.          

scholarly journals Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte

Revista Med ◽  
2014 ◽  
Vol 22 (2) ◽  
pp. 79 ◽  
Author(s):  
John Arevalo ◽  
Angel Cruz-Roa ◽  
Fabio A. González O
Keyword(s):  
Image Analysis ◽  
Image Representation ◽  
Digital Pathology ◽  
Virtual Microscopy ◽  
Automatic Analysis ◽  
Computer Assisted ◽  
Automatic Image Analysis ◽  
Histopathology Images ◽  
Image Enhancing ◽  
Assisted Diagnosis

<p>This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology.</p>

Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification

2014 ◽  
Vol 18 (5) ◽  
pp. 792-797 ◽  
Author(s):  
Samantha Denise F. Hilado ◽  
◽  
Laurence A. Gan Lim ◽  
Raouf N. G. Naguib ◽  
Elmer P. Dadios ◽  
...  
Keyword(s):  
Neural Networks ◽  
Colon Cancer ◽  
Death Rate ◽  
Computer Assisted ◽  
Histopathological Classification ◽  
High Death Rate ◽  
High Death ◽  
Histopathological Images ◽  
Assisted Diagnosis

Colon cancer is one type of cancer that has a high death rate, but early diagnosis can improve the chances of patient recovery. Computer-assisted diagnosis can aid in determining whether images are of healthy or cancerous tissues. This study aims to contribute to the automatic classification of microscopic colonic images by implementing a 2-D wavelet transform for feature extraction and neural networks for classification. The colonic histopathological images are assigned to either the normal, cancerous, or adenomatous polyp classes. The proposed algorithm is able to determine which of the three classes the images belong to at a 91.11% rate of accuracy.

Sign in / Sign up

Export Citation Format

Share Document