AIMC Topic: Neuroendocrine Tumors

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Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study.

International journal of computer assisted radiology and surgery Sep 26, 2019
PURPOSE: The World Health Organization (WHO) grading system of pancreatic neuroendocrine tumor (PNET) plays an important role in the clinical decision. The rarity of PNET often negatively affects the radiological application of deep learning algorith...
Neoplasm Grading World Health Organization Neuroendocrine Tumors Deep Learning Contrast Media Magnetic Resonance Imaging Pancreatic Neoplasms Humans Neural Networks, Computer Algorithms Female Male Middle Aged
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Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network.

Neuroendocrinology Sep 13, 2019
INTRODUCTION: The pathological grading of pancreatic neuroendocrine neoplasms (pNENs) is an independent predictor of survival and indicator for treatment. Deep learning (DL) with a convolutional neural network (CNN) may improve the preoperative predi...
Humans Aged Adult Male Middle Aged Neural Networks, Computer Machine Learning Retrospective Studies Female Neuroendocrine Tumors Deep Learning Tomography, Spiral Computed Pancreatic Neoplasms Neoplasm Grading Image Interpretation, Computer-Assisted
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Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images.

IEEE transactions on bio-medical engineering Feb 22, 2019
OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeli...
Neuroendocrine Tumors Ki-67 Antigen Microscopy Image Interpretation, Computer-Assisted Cell Nucleus Immunohistochemistry Algorithms Humans Databases, Factual Pancreatic Neoplasms Neural Networks, Computer Supervised Machine Learning
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Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning.

PloS one Apr 12, 2018
The World Health Organization (WHO) has clear guidelines regarding the use of Ki67 index in defining the proliferative rate and assigning grade for pancreatic neuroendocrine tumor (NET). WHO mandates the quantification of Ki67 index by counting at le...
Machine Learning Image Processing, Computer-Assisted Humans Pancreatic Neoplasms Neuroendocrine Tumors Molecular Imaging Ki-67 Antigen
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Efficient and robust cell detection: A structured regression approach.

Medical image analysis Jul 26, 2017
Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and l...
Humans Cytological Techniques Neural Networks, Computer Algorithms Breast Neoplasms Bone Marrow Cells Female Neuroendocrine Tumors Uterine Cervical Neoplasms Reproducibility of Results Diagnosis, Computer-Assisted Sensitivity and Specificity Deep Learning
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Impact of chromogranin A, differentiation, and mitoses in nonfunctional pancreatic neuroendocrine tumors ≤ 2 cm.

The Journal of surgical research Dec 29, 2016
BACKGROUND: Small pancreatic neuroendocrine tumors (PNETs) are a unique subset of pancreatic neoplasms. Chromogranin A (CgA) levels, mitotic rate, and histologic differentiation are often used to characterize PNET behavior. This study evaluates the i...
Adult Female Chromogranin A Humans Mitosis Neoplasm Staging Databases, Factual Biomarkers, Tumor Neuroendocrine Tumors Survival Analysis Prognosis Multivariate Analysis Retrospective Studies Propensity Score Male Middle Aged Adolescent Logistic Models Pancreatic Neoplasms Cell Differentiation Aged Aged, 80 and over Young Adult
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Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Nov 18, 2015
Robust and accurate nuclei localization in microscopy image can provide crucial clues for accurate computer-aid diagnosis. In this paper, we propose a convolutional neural network (CNN) based hough voting method to localize nucleus centroids with hea...
Politics Humans Diagnosis, Computer-Assisted Microscopy Neural Networks, Computer Cell Nucleus Algorithms Neuroendocrine Tumors Reproducibility of Results Sensitivity and Specificity
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Fast Cell Segmentation Using Scalable Sparse Manifold Learning and Affine Transform-approximated Active Contour.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Nov 18, 2015
Efficient and effective cell segmentation of neuroendocrine tumor (NET) in whole slide scanned images is a difficult task due to a large number of cells. The weak or misleading cell boundaries also present significant challenges. In this paper, we pr...
Cell Separation Neuroendocrine Tumors Reproducibility of Results Sensitivity and Specificity Efficiency Pancreatic Neoplasms Humans Machine Learning Algorithms
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Uncertainty quantification for deep learning-based metastatic lesion segmentation on whole body PET/CT.

Physics in medicine and biology May 23, 2025
Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be ensured. Several uncerta...
Neoplasm Metastasis Monte Carlo Method Neuroendocrine Tumors Uncertainty Whole Body Imaging Humans Deep Learning Image Processing, Computer-Assisted Positron Emission Tomography Computed Tomography
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Selection of neuroendocrine markers in diagnostic workup of neuroendocrine neoplasms: The real-world data and machine learning model algorithms.

Cancer cytopathology May 1, 2025
BACKGROUND: Accurate diagnosis of neuroendocrine neoplasms (NENs) is challenging, especially in poorly differentiated neuroendocrine carcinomas (NECs). This study was aimed to search the best or best combination of neuroendocrine markers in the diagn...
Chromogranins Synaptophysin Repressor Proteins Neuroendocrine Tumors Middle Aged Machine Learning ROC Curve Algorithms Carcinoma, Neuroendocrine Female CD56 Antigen Aged Humans Neural Networks, Computer Adult Sensitivity and Specificity Biomarkers, Tumor Male
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