AIMC Topic: Abdominal Neoplasms

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Towards real-time respiratory motion prediction based on long short-term memory neural networks.

Physics in medicine and biology Apr 10, 2019
Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion into treatments. To precisely account for the patient's respiratory motions and predict the respiratory signals, a generalized model for predictions of d...
Radiotherapy Planning, Computer-Assisted Respiration Abdominal Neoplasms Neural Networks, Computer Thoracic Neoplasms Humans Motion
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Change in liver, spleen and bone marrow magnetic resonance imaging signal intensity over time in children with solid abdominal tumors.

Pediatric radiology Dec 19, 2017
BACKGROUND: Reticuloendothelial system MRI signal hypointensity is common in pediatric oncology patients with solid abdominal tumors.
Blood Component Transfusion Ganglioneuroblastoma Hepatoblastoma Neuroblastoma Abdominal Neoplasms Bone Marrow Wilms Tumor Liver Neoplasms Female Kidney Neoplasms Magnetic Resonance Imaging Male Spleen Humans Child, Preschool Retrospective Studies
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Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution.

Physics in medicine and biology Nov 23, 2016
The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challeng...
Tomography, X-Ray Computed Liver Neoplasms Imaging, Three-Dimensional Automation Abdominal Neoplasms Neural Networks, Computer Algorithms Databases, Factual Humans
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Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Jul 1, 2025
PURPOSES: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets.
Radiotherapy Planning, Computer-Assisted Kidney Neoplasms Neuroblastoma Abdominal Neoplasms Child, Preschool Child Organs at Risk Deep Learning Adolescent Female Male Humans Infant Tomography, X-Ray Computed
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Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective.

Journal of computer assisted tomography May 13, 2025
The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imagin...
Diagnostic Imaging Image Interpretation, Computer-Assisted Abdominal Neoplasms Humans Machine Learning Abdomen
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Enhancing Oncological Surveillance Through Large Language Model-Assisted Analysis: A Comparative Study of GPT-4 and Gemini in Evaluating Oncological Issues From Serial Abdominal CT Scan Reports.

Academic radiology May 1, 2025
RATIONALE AND OBJECTIVES: We aimed to compare the capabilities of two leading large language models (LLMs), GPT-4 and Gemini, in analyzing serial radiology reports, to highlight oncological issues that require further clinical attention.
Humans Tomography, X-Ray Computed Radiography, Abdominal Male Middle Aged Aged Natural Language Processing Adult Radiology Information Systems Sensitivity and Specificity Abdominal Neoplasms Female Large Language Models Reproducibility of Results
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Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging.

Abdominal radiology (New York) Jun 1, 2019
Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image an...
Image Interpretation, Computer-Assisted Medical Oncology Abdominal Neoplasms Biomarkers, Tumor Radiology Humans Machine Learning Precision Medicine
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Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports.

Journal of digital imaging Apr 1, 2018
A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in th...
Algorithms Tomography, X-Ray Computed Cross-Sectional Studies Artificial Intelligence Machine Learning Pelvis Pelvic Neoplasms Medical Records Abdomen Magnetic Resonance Imaging Natural Language Processing Humans Abdominal Neoplasms
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