A5049491814- Radiomics and Machine Learning in Medical Imaging
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- Lung Cancer Diagnosis and Treatment
- AI in cancer detection
- MRI in cancer diagnosis
- COVID-19 diagnosis using AI
- Cardiac Imaging and Diagnostics
- Head and Neck Cancer Studies
- Glioma Diagnosis and Treatment
- Prostate Cancer Diagnosis and Treatment
- Medical Imaging and Analysis
- Advanced Radiotherapy Techniques
- Prostate Cancer Treatment and Research
- Colorectal Cancer Screening and Detection
- Lung Cancer Treatments and Mutations
- Cardiac Structural Anomalies and Repair
- Sarcoma Diagnosis and Treatment
- ECG Monitoring and Analysis
- Colorectal Cancer Surgical Treatments
- Dental Radiography and Imaging
- Cardiac Valve Diseases and Treatments
- Thyroid Cancer Diagnosis and Treatment
- Cardiovascular Function and Risk Factors
- Hepatocellular Carcinoma Treatment and Prognosis
University Hospital of Geneva
2020-2025
Shaheed Rajaei Cardiovascular Medical and Research Center
2018-2025
Iran University of Medical Sciences
2020-2024
King's College London
2022
Kermanshah University of Medical Sciences
2018-2019
To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) lung/lesion radiomic features extracted from chest CT images. Overall, 152 were enrolled in this study protocol. These divided into 106 training/validation 46 test datasets (untouched during training), respectively. Radiomic the segmented lungs infectious lesions separately Clinical data,...
To investigate the impact of harmonization on performance CT, PET, and fused PET/CT radiomic features toward prediction mutations status, for epidermal growth factor receptor (EGFR) Kirsten rat sarcoma viral oncogene (KRAS) genes in non-small cell lung cancer (NSCLC) patients.Radiomic were extracted from tumors delineated wavelet images obtained 136 histologically proven NSCLC patients. Univariate multivariate predictive models developed using before after ComBat to predict EGFR KRAS...
Robust differentiation between infarcted and normal tissue is important for clinical diagnosis precision medicine. The aim of this work to investigate the radiomic features develop a machine learning algorithm myocardial infarction (MI) viable tissues/normal cases in left ventricular myocardium on non-contrast Cine Cardiac Magnetic Resonance (Cine-CMR) images.Seventy-two patients (52 with MI 20 healthy control patients) were enrolled study. MR imaging was performed 1.5 T MRI using following...
Objectives We evaluate the feasibility of treatment response prediction using MRI‐based pre‐, post‐, and delta‐radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT). Materials Methods This retrospective study included 53 LARC divided into a training set (Center#1, n = 36) external validation (Center#2, 17). T2‐weighted (T2W) MRI was acquired all patients, 2 weeks before 4 after nCRT. Ninety‐six radiomic features, including...
We aimed to analyze the prognostic power of CT-based radiomics models using data 14,339 COVID-19 patients.
We developed multi-modality radiomic models by integrating information extracted from18F-FDG PET and CT images using feature- image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 95 patients) were cycled as training testing datasets. Fusion approaches applied at levels, namely image-levels. For feature-level fusion, features individually concatenated. Alternatively, separately...
AimsDespite the promising results achieved by radiomics prognostic models for various clinical applications, multiple challenges still need to be addressed. The two main limitations of include information limitation owing single imaging modalities and selection optimum machine learning feature methods considered modality outcome. In this work, we applied several single-modality positron emission tomography (PET) computed (CT) multimodality PET/CT fusion identify best combinations different...
Abstract Background This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion predict outcomes in head neck cancer (HNC) patients. Methods A cohort 240 HNC patients five different centers was obtained The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), three algorithms (latent low-rank representation referred...
Abstract This study investigated the impact of ComBat harmonization on reproducibility radiomic features extracted from magnetic resonance images (MRI) acquired different scanners, using various data acquisition parameters and multiple image pre-processing techniques a dedicated MRI phantom. Four scanners were used to acquire an nonanatomic phantom as part TCIA RIDER database. In fast spin-echo inversion recovery (IR) sequences, several durations employed, including 50, 100, 250, 500, 750,...
Purpose Non–small cell lung cancer is the most common subtype of cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, studies reported in literature focused on information extracted from malignant lesions. This study aims explore relevance additional value healthy organs addition tumoral tissue ML algorithms. Patients Methods included PET/CT images 154 patients collected available online databases. The gross...
Purpose To assess the repeatability of radiomic features in magnetic resonance (MR) imaging glioblastoma (GBM) tumors with respect to test–retest, different image registration approaches and inhomogeneity bias field correction. Methods We analyzed MR images 17 GBM patients including T1‐ T2‐weighted (performed within same unit on two consecutive days). For segmentation, we used a comprehensive segmentation approach entire tumor, active area necrotic regions T1‐weighted images, edema (test...
Abstract A U-shaped contraction pattern was shown to be associated with a better Cardiac resynchronization therapy (CRT) response. The main goal of this study is automatically recognize left ventricular contractile patterns using machine learning algorithms trained on conventional quantitative features (ConQuaFea) and radiomic extracted from Gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). Among 98 patients standard resting GSPECT MPI included in...
This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas IDH-wild-type glioblastomas (GBMs). A cohort 57 treatment-naïve patients with (n = 23) and GBMs 34) underwent anatomical imaging on a 3T system standard parameters. Post-contrast T1-weighted T2-FLAIR were co-registered. semi-automatic segmentation approach was used generate regions interest (ROIs) different tissue components...
Abstract Purpose Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of brain with short overall survival (OS) time. We aim to assess potential radiomic features in predicting time-to-event OS patients GBM using machine learning (ML) algorithms. Materials and methods One hundred nineteen GBM, who had T1-weighted contrast-enhanced T2-FLAIR MRI sequences, along clinical data time, were enrolled. Image preprocessing included 64 bin discretization, Laplacian...
Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase is usually missing on public datasets and not standardized in clinic even same region language. This a barrier effective use available CECT images clinical research.
Overall Survival (OS) and Progression-Free (PFS) analyses are crucial metrics for evaluating the efficacy impact of treatment. This study evaluated role clinical biomarkers dosimetry parameters on survival outcomes patients undergoing
We present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347'259 2D slices) 190 (17 341 volumetric CT exams along with their corresponding manual of lungs lesions, respectively. All images were cropped, resized, the intensity values clipped normalized. A residual network non-square Dice loss function built upon TensorFlow...
This study is aimed at examining the synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET image radiomic features in non-small cell lung cancer (NSCLC) patients, investigating robustness performance differentiating NSCLC histopathology subtypes.An in-house developed thoracic phantom incorporating lesions with different sizes was used reconstruction settings, including various algorithms, number subsets iterations, full-width half-maximum post-reconstruction...