A5101678831- Radiomics and Machine Learning in Medical Imaging
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- COVID-19 diagnosis using AI
- Advanced Radiotherapy Techniques
- Head and Neck Cancer Studies
- AI in cancer detection
- Lung Cancer Diagnosis and Treatment
- Pain Mechanisms and Treatments
- Hepatocellular Carcinoma Treatment and Prognosis
- Genetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
- Radiation Dose and Imaging
- Salivary Gland Disorders and Functions
- Pomegranate: compositions and health benefits
- Oral Health Pathology and Treatment
- MRI in cancer diagnosis
- Reproductive Biology and Fertility
- Prenatal Screening and Diagnostics
- Oral microbiology and periodontitis research
- Digital Radiography and Breast Imaging
- Nuclear Receptors and Signaling
- Acute Kidney Injury Research
- Paraoxonase enzyme and polymorphisms
- Glutathione Transferases and Polymorphisms
- Neuroscience and Neuropharmacology Research
University Hospital of Geneva
2021-2025
Ahvaz Jundishapur University of Medical Sciences
2020-2025
Shiraz University of Medical Sciences
2021-2024
Universal Scientific Education and Research Network
2024
Shahid Beheshti University of Medical Sciences
2011-2024
Khorramshahr University of Marine Science and Technology
2024
Geneva College
2021-2023
Baqiyatallah University of Medical Sciences
2022
Yazd University
2020
Zabol University of Medical Sciences
2019
We aimed to analyze the prognostic power of CT-based radiomics models using data 14,339 COVID-19 patients.
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 Purpose Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical practice, MIRD formalisms are widely employed. However, with rapid advancement deep learning (DL) algorithms, there has been an increasing interest in leveraging calculation speed automation capabilities different tasks. We aimed to develop a hybrid transformer-based model that incorporates multiple voxel S -value (MSV) approach voxel-level [ 177...
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...
Abstract Background Despite the prevalence of chest CT in clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed assess additional dose associated with overscanning and develop an automated deep learning-assisted scan range selection technique reduce patients. Results A significant (31 ± 24) mm was observed clinical setting for over 95% cases. The average Dice coefficient lung segmentation 0.96 0.97 anterior–posterior (AP)...
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 aimed to improve patient positioning accuracy by relying on a CT localizer and deep neural network optimize image quality radiation dose.We included 5754 chest axial anterior-posterior (AP) images from two different centers, C1 C2. After pre-processing, were split into training (80%) test (20%) datasets. A was trained generate 3D the AP localizer. The geometric centerlines of bodies indicated creating bounding box predicted images. distance between body centerline, estimated...
Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant diseases during early stages. However, procedure time-consuming and requires vigour experience. Moreover, interpretation WBS scans stages disorders might be challenging because patterns often reflect normal appearance that prone to subjective interpretation. To simplify gruelling, subjective, prone-to-error task interpreting scans, we developed deep learning (DL) models automate two major...
Abstract Purpose We evaluate the role of radiomics, dosiomics, and dose-volume constraints (DVCs) in predicting response hepatocellular carcinoma to selective internal radiation therapy with 90 Y glass microspheres. Methods 99m Tc-macroagregated albumin ( Tc-MAA) SPECT/CT images 17 patients were included. Tumor responses at three months evaluated using modified evaluation criteria solid tumors categorized as responders or non-responders. Dosimetry was conducted local deposition method (Dose)...
Extracting water equivalent diameter (DW), as a good descriptor of patient size, from the CT localizer before spiral scan not only minimizes truncation errors due to limited field-of-view but also enables prior size-specific dose estimation well protocol optimization. This study proposed unified methodology measure shape, and attenuation parameters 2D anterior-posterior image using deep learning algorithms without need for labor-intensive vendor-specific calibration procedures.3D chest...
Selective internal radiation therapy with 90Y radioembolization aims to selectively irradiate liver tumours by administering radioactive microspheres under the theragnostic assumption that pre-therapy injection of 99mTc labelled macroaggregated albumin (99mTc-MAA) provides an estimation biodistribution, which is not always case. Due growing interest in dosimetry for personalized radionuclide therapy, a robust relationship between delivered and pre-treatment absorbed doses required. In this...
Abstract Purpose To derive and validate an effective radiomics-based model for differentiation of COVID-19 pneumonia from other lung diseases using a very large cohort patients. Methods We collected 19 private 5 public datasets, accumulating to 26,307 individual patient images (15,148 COVID-19; 9,657 with e.g. non-COVID-19 pneumonia, cancer, pulmonary embolism; 1502 normal cases). Images were automatically segmented validated deep learning (DL) the results carefully reviewed. first cropped...
The importance of chromosomal abnormalities in etiology premature ovarian failure (POF) is well known but many cases, POF still remains idiopathic. We investigated the frequency and type aberrations Iranian women diagnosed with idiopathic POF. Standard cytogenetic analysis was carried out a total 179 patients. Karyotype these patients revealed that 161 (89.95%) had normal female karyotype 18 (10.05%) abnormal karyotypes. karyotypes included sex reverse determining region Y (SRY) negative...
Abstract To derive and validate an effective machine learning radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private five public datasets of chest CT images, accumulating 26 307 images (15 148 COVID‐19; 9657 including non‐COVID‐19 pneumonia, cancer, pulmonary embolism; 1502 normal cases). We tested 96 learning‐based models by cross‐combining four feature selectors (FSs) eight...
Abstract Background Notwithstanding the encouraging results of previous studies reporting on efficiency deep learning (DL) in COVID‐19 prognostication, clinical adoption developed methodology still needs to be improved. To overcome this limitation, we set out predict prognosis a large multi‐institutional cohort patients with using DL‐based model. Purpose This study aimed evaluate performance privacy‐preserving federated (DPFL) predicting outcomes chest CT images. Methods After applying...
Enhanced chromosomal radiosensitivity is a feature of many cancer predisposition conditions, indicative the important role alterations in carcinogenesis. In this study cytokinesis-blocked micronucleous assay was used to compare blood lymphocytes obtained from Iranian breast or esophageal patients (n = 50, n 16; respectively) with that control individuals 40). For each sample, one thousand binucleate were analyzed before and after vitro exposure 3 Gy gamma rays. The radiation-induced...