AIMC Topic: Lymphatic Metastasis
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Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.
BMC medical imaging
Apr 13, 2021
BACKGROUND: In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for auto...
Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts.
Scientific reports
Mar 19, 2021
Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's...
Preoperative prediction of regional lymph node metastasis of colorectal cancer based on F-FDG PET/CT and machine learning.
Annals of nuclear medicine
Mar 18, 2021
PURPOSE: To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on F-FDG PET/CT and radiomic features using machine-learning methods.
Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning.
Nature communications
Mar 12, 2021
N-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the ...
Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence.
Scientific reports
Mar 2, 2021
This study analyzed the prognostic significance of clinico-pathologic factors, including the number of metastatic lymph nodes (LNs) and lymph node ratio (LNR), in patients with papillary thyroid carcinoma (PTC), and attempted to construct a disease r...
Data-efficient and weakly supervised computational pathology on whole-slide images.
Nature biomedical engineering
Mar 1, 2021
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we...
Attention Guided Lymph Node Malignancy Prediction in Head and Neck Cancer.
International journal of radiation oncology, biology, physics
Feb 6, 2021
PURPOSE: Accurate lymph node (LN) malignancy classification is essential for treatment target identification in head and neck cancer (HNC) radiation therapy. Given the constraints imposed by relatively small sample sizes in real-world medical applica...
Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: A systematic review and meta-analysis.
Artificial intelligence in medicine
Feb 2, 2021
PURPOSE: Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for...
Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Jan 23, 2021
BACKGROUND: Traditional diagnosis methods for lymph node metastases are labor-intensive and time-consuming. As a result, diagnostic systems based on deep learning (DL) algorithms have become a hot topic. However, current research lacks testing with s...
A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer.
Breast cancer (Tokyo, Japan)
Jan 17, 2021
OBJECTIVE: The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasis in invasive ductal breast cancer (IDC) using F-18 fluorodeoxyglucose (FDG) positron emission tomog...
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