Clinical cancer research : an official journal of the American Association for Cancer Research
30975664
PURPOSE: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC).
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
31151585
Biological tumour volume (GTV) delineation on F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptake and volume of the GTV. Automatic segmentation algorithms applied to F-FDG PET (PET-AS) imaging hav...
BACKGROUND: To explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal carcinoma (NPC) using the method of deep learning.
BACKGROUND: Images from magnetic resonance imaging (MRI) are crucial unstructured data for prognostic evaluation in nasopharyngeal carcinoma (NPC). We developed and validated a prognostic system based on the MRI features and clinical data of locoregi...
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
34817635
BACKGROUND: Deep learning methods have great potential to predict treatment response. The objective of this study was to evaluate and validate the predictive performance of the computed tomography (CT)-based model using deep learning features for ide...
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...
Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This mult...
OBJECTIVES: To extract intratumoral, peritumoral, and integrated intratumoral-peritumoral CT radiomic features, develop multi-source radiomic models using various machine learning algorithms to identify the optimal model, and integrate clinical facto...