AIMC Topic: Neuroendocrine Tumors

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Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.

Cancer medicine
BACKGROUND: The study aims to evaluate the performance of three advanced machine learning algorithms and a traditional Cox proportional hazard (CoxPH) model in predicting the overall survival (OS) of patients with pancreatic neuroendocrine neoplasms ...

Implementation of robot-assisted curative resection for rare anorectal tumours on the basis of individualised treatment.

The international journal of medical robotics + computer assisted surgery : MRCAS
PURPOSE: To evaluate the validity of robot-assisted curative operation for rare anorectal tumours, characterised by biological heterogeneity and anatomical complexity.

Exploration of machine learning techniques to examine the journey to neuroendocrine tumor diagnosis with real-world data.

Future oncology (London, England)
Machine learning reveals pathways to neuroendocrine tumor (NET) diagnosis. Patients with NET and age-/gender-matched non-NET controls were retrospectively selected from MarketScan claims. Predictors (e.g., procedures, symptoms, conditions for which...

Imaging and liquid biopsy in the prediction and evaluation of response to PRRT in neuroendocrine tumors: implications for patient management.

European journal of nuclear medicine and molecular imaging
PURPOSE: The aim of this narrative review is to give an overview on current and emerging imaging methods and liquid biopsy for prediction and evaluation of response to PRRT. Current limitations and new perspectives, including artificial intelligence,...

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.

Scientific reports
The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the ...

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
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...

Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network.

Neuroendocrinology
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...

Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images.

IEEE transactions on bio-medical engineering
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...

Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning.

PloS one
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...