AIMC Topic: Tumor Burden

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PAIP 2019: Liver cancer segmentation challenge.

Medical image analysis Oct 8, 2020
Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater ac...
Artificial Intelligence Algorithms Liver Neoplasms Image Processing, Computer-Assisted Tumor Burden Humans
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2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma.

Scientific reports Sep 24, 2020
For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy, we explored the capabilities of different deep learning approaches for predicting loco-regional tu...
Chemoradiotherapy Head and Neck Neoplasms Follow-Up Studies Neoplasm Recurrence, Local Tumor Burden Female Aged Aged, 80 and over Adult Middle Aged Image Processing, Computer-Assisted Squamous Cell Carcinoma of Head and Neck Survival Rate Male Prospective Studies Prognosis Retrospective Studies Tomography, X-Ray Computed Humans Neural Networks, Computer
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A Comparative Analysis of Two Machine Learning-Based Diagnostic Patterns with Thyroid Imaging Reporting and Data System for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate.

Thyroid : official journal of the American Thyroid Association Sep 9, 2020
The risk stratification system of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for thyroid nodules is affected by low diagnostic specificity. Machine learning (ML) methods can optimize the diagnostic perf...
Ultrasonography Thyroid Nodule Predictive Value of Tests Tumor Burden Image Interpretation, Computer-Assisted Middle Aged China Adolescent Thyroid Neoplasms Biopsy, Fine-Needle Unnecessary Procedures Elasticity Imaging Techniques Retrospective Studies Humans Clinical Decision-Making Machine Learning Female Young Adult Reproducibility of Results Adult Aged Male Aged, 80 and over
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Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs.

JAMA network open Sep 1, 2020
IMPORTANCE: The improvement of pulmonary nodule detection, which is a challenging task when using chest radiographs, may help to elevate the role of chest radiographs for the diagnosis of lung cancer.
Humans Artificial Intelligence Algorithms Female Middle Aged Lung Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Reproducibility of Results Aged Sensitivity and Specificity Male Radiography, Thoracic Radiographic Image Enhancement Early Detection of Cancer Predictive Value of Tests Radiologists Tumor Burden
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Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs.

Journal of the American College of Radiology : JACR Jul 25, 2020
BACKGROUND: Tumor response to therapy is often assessed by measuring change in liver lesion size between consecutive MRIs. However, these evaluations are both tedious and time-consuming for clinical radiologists.
Artificial Intelligence Magnetic Resonance Imaging Deep Learning Tumor Burden Liver Neoplasms Humans Neural Networks, Computer
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Deep-Learning F-FDG Uptake Classification Enables Total Metabolic Tumor Volume Estimation in Diffuse Large B-Cell Lymphoma.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine Jun 12, 2020
Total metabolic tumor volume (TMTV), calculated from F-FDG PET/CT baseline studies, is a prognostic factor in diffuse large B-cell lymphoma (DLBCL) whose measurement requires the segmentation of all malignant foci throughout the body. No consensus cu...
Tumor Burden Fluorodeoxyglucose F18 Cohort Studies Humans Image Processing, Computer-Assisted Biological Transport Positron-Emission Tomography Adult Lymphoma, Large B-Cell, Diffuse Software Male Retrospective Studies Middle Aged Female Deep Learning Aged
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Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma.

European radiology Jun 10, 2020
OBJECTIVE: To assess the utility of deep learning analysis using F-fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET/CT) to predict disease-free survival (DFS) in patients with oral cavity squamous cell carcinoma (OCSCC).
Retrospective Studies Female Reproducibility of Results Aged Sensitivity and Specificity Male Middle Aged Aged, 80 and over Adult Humans Algorithms Disease-Free Survival Squamous Cell Carcinoma of Head and Neck Diagnosis, Computer-Assisted Tumor Burden Positron Emission Tomography Computed Tomography Predictive Value of Tests Fluorodeoxyglucose F18 Deep Learning Neoplasm Staging Glycolysis Mouth Neoplasms Kaplan-Meier Estimate Treatment Outcome
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Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning.

Radiation oncology (London, England) May 6, 2020
BACKGROUND: Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resectio...
Tumor Burden Radiotherapy, Adjuvant Radiotherapy, Image-Guided Magnetic Resonance Imaging Glioblastoma Radiographic Image Interpretation, Computer-Assisted Neurosurgical Procedures Brain Humans Brain Neoplasms Deep Learning
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Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.

European journal of nuclear medicine and molecular imaging Apr 20, 2020
PURPOSE: In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based...
Liver Humans Tumor Burden Neural Networks, Computer Observer Variation Deep Learning Image Processing, Computer-Assisted
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Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering.

Computers in biology and medicine Apr 10, 2020
BACKGROUND: Cancer typically exhibits genotypic and phenotypic heterogeneity, which can have prognostic significance and influence therapy response. Computed Tomography (CT)-based radiomic approaches calculate quantitative features of tumour heteroge...
Tomography, X-Ray Computed Image Processing, Computer-Assisted Fuzzy Logic Cluster Analysis Algorithms Tumor Burden
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