AIMC Topic: Head and Neck Neoplasms
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Clinical evaluation of deep learning and atlas-based auto-segmentation for organs at risk delineation.
Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Dec 6, 2023
Manual delineation of organs at risk and clinical target volumes is essential in radiotherapy planning. Atlas-based auto-segmentation (ABAS) algorithms have become available and been shown to provide accurate contouring for various anatomical sites. ...
Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study.
Ultraschall in der Medizin (Stuttgart, Germany : 1980)
Dec 5, 2023
PURPOSE: To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of metastatic cervical lymphadenopathy (CLA).
Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.
Computer methods and programs in biomedicine
Nov 22, 2023
BACKGROUND AND OBJECTIVE: Recently, deep learning (DL) algorithms showed to be promising in predicting outcomes such as distant metastasis-free survival (DMFS) and overall survival (OS) using pre-treatment imaging in head and neck cancer. Gross Tumor...
Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck.
Magma (New York, N.Y.)
Nov 21, 2023
OBJECTIVES: To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).
Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma.
Academic radiology
Nov 18, 2023
RATIONALE AND OBJECTIVES: To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers, and transcriptomics data for predicting the response to induction chemotherapy (IC) in patients wi...
HeNeCOn: An ontology for integrative research in Head and Neck cancer.
International journal of medical informatics
Nov 8, 2023
BACKGROUND: Head and Neck Cancer (HNC) has a high incidence and prevalence in the worldwide population. The broad terminology associated with these diseases and their multimodality treatments generates large amounts of heterogeneous clinical data, wh...
Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.
Biomedical engineering online
Nov 1, 2023
PURPOSE: The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and...
Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization.
Scientific reports
Oct 24, 2023
In the past decade, there has been a sharp increase in publications describing applications of convolutional neural networks (CNNs) in medical image analysis. However, recent reviews have warned of the lack of reproducibility of most such studies, wh...
Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis.
Oral radiology
Oct 19, 2023
PURPOSE: This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data.
Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy.
Medical physics
Oct 13, 2023
BACKGROUND: Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have re...
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