AIMC Topic: Tumor Burden
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Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment.
Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Oct 21, 2022
Accurate clinical target volume (CTV) delineation is important for head and neck intensity-modulated radiation therapy. However, delineation is time-consuming and susceptible to interobserver variability (IOV). Based on a manual contouring process co...
The efficacy of F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.
The British journal of radiology
Mar 28, 2022
OBJECTIVE: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumor...
Deep learning-based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images.
European radiology
Feb 15, 2022
OBJECTIVES: To demonstrate the effectiveness of automatic segmentation of diffuse large B-cell lymphoma (DLBCL) in 3D FDG-PET scans using a deep learning approach and validate its value in prognosis in an external validation cohort.
Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.
The British journal of radiology
Dec 15, 2021
OBJECTIVES: The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric a...
Deep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI.
Acta oncologica (Stockholm, Sweden)
Nov 16, 2021
BACKGROUND: Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time an...
PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning.
BMC bioinformatics
Nov 2, 2021
BACKGROUND: Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is pr...
Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.
The Lancet. Digital health
Oct 20, 2021
BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after r...
Humans
Diffusion Magnetic Resonance Imaging
Neural Networks, Computer
Prognosis
Retrospective Studies
Algorithms
Brain
Magnetic Resonance Imaging
Glioblastoma
Radiology
Disease Progression
Feasibility Studies
Brain Neoplasms
Deep Learning
Tumor Burden
Contrast Media
Middle Aged
Neoplasms
Germany
Gadolinium
Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.
Physics in medicine and biology
Aug 27, 2021
Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and nor...
Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.
Chest
Jun 4, 2021
BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules.
An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features.
Oral oncology
May 21, 2021
OBJECTIVES: We aimed to build a survival system by combining a highly-accurate machine learning (ML) model with explainable artificial intelligence (AI) techniques to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma (NPC...
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