Hamid Abdollahi

Cycle-dependent variation of tumor absorbed dose rates in 177 Lu-DOTATATE therapies

Ali Shabestani Monfared, Milad Peer-Firozjaei, Mohammad Reza Deevband, Mehrangiz Amiri et autres

Abstract Purpose. We aimed to evaluate the variability of tumor absorbed dose rate at 24 h post-injection across multiple cycles of 177 Lu-DOTATATE therapy. Using patient-specific Monte Carlo (MC) simulations, we quantified cycle-dependent variations in tumor absorbed dose rate and explicitly separated …

ir, ca, us (code pays fourni par la source)

Beyond the tumor: recurrence-prone radiomics for prognostication in negative PSMA PET/CT scans of prostate cancer

Fereshteh Yousefirizi, Sara Harsini, Mobin Mohebi, Ian Alberts et autres

Abstract Purpose. Prostate-specific membrane antigen (PSMA) PET/CT is routinely used to restage prostate cancer (PCa) in patients with biochemical recurrence (BCR), yet negative scans may still harbor subclinical disease. This study investigated whether radiomics features extracted from recurrence-prone organs on negative [ …

fr, ca, ir (code pays fourni par la source)

Development of a Radiomics-Based Machine Learning Model Utilizing CT-Guided Biopsy Images: Virtual Biopsy of Thoracic Cancers

Maryam Cheraghi, Mehran Malekshoar, Fatemeh Pakniyat, Seyed Mahmoud Reza Aghamiri et autres

ir, ca (code pays fourni par la source)

Predictive Dosimetry in PSMA-Targeted Radiopharmaceutical Therapies: A PBPK Modeling and Machine Learning Study

Hamid Abdollahi, JAMES FOWLER, Carlos Uribe, Arman Rahmim

ca (code pays fourni par la source)

Evaluating the robustness of dosiomics features over treatment planning parameters: a phantom-based study

M Rezaei, Abbas Haghparast, Hamid Abdollahi

Dosimetric biomarkers, in terms of dosiomics features, play a crucial role in modeling radiotherapy and should be analyze d for their robustness and stability. This study aims to investigate how these dosiomics features will change over variations in treatment planning parameters. Different …

ir, ca (code pays fourni par la source)

Quantitative and Computational Radiobiology for Precision Radiopharmaceutical Therapies

Tahir Yusufaly, Hamid Abdollahi, Babak Saboury, Arman Rahmim

us, ca (code pays fourni par la source)

Quantitative and Computational Radiobiology for Precision Radiopharmaceutical Therapies

Tahir Yusufaly, Hamid Abdollahi, Babak Saboury, Arman Rahmim

This article reviews the evolving field of radiobiology, emphasizing the need for advanced multiscale, mechanistic models to optimize radiopharmaceutical therapies (RPT). While the traditional linear-quadratic (LQ) model underpins external beam radiation therapy (EBRT), RPT's unique biological and spatial complexities demand new approaches. …

Effectiveness of Artificial Intelligence Models in Predicting Lung Cancer Recurrence: A Gene Biomarker-Driven Review

Niloufar Pourakbar, Alireza Motamedi, Mahta Pashapour, Mohammad Emad Sharifi et autres

BACKGROUND/OBJECTIVES: Lung cancer recurrence, particularly in NSCLC, remains a major challenge, with 30-70% of patients relapsing post-treatment. Traditional predictors like TNM staging and histopathology fail to account for tumor heterogeneity and immune dynamics. This review evaluates AI models integrating gene biomarkers (TP53, …

ir, ca (code pays fourni par la source)

Shifting the Spotlight to Low-Dose Rate Radiobiology in Radiopharmaceutical Therapies: Mathematical Modeling, Challenges, and Future Directions

Hamid Abdollahi, Babak Saboury, Tahir Yusufaly, Ian L. Alberts et autres

Radiopharmaceutical therapy (RPT) is an established treatment modality and is of increasing interest for different cancer types. A key unmet need, both in the wider adoption of RPT and in the improvement of outcomes with existing RPTs, is in treatment planning and …

ca, us (code pays fourni par la source)

Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy

Hossein Azadinejad, Mohammad Farhadi Rad, Ahmad Shariftabrizi, Arman Rahmim et autres

Radioimmunotherapy (RIT) is a novel cancer treatment that combines radiotherapy and immunotherapy to precisely target tumor antigens using monoclonal antibodies conjugated with radioactive isotopes. This approach offers personalized, systemic, and durable treatment, making it effective in cancers resistant to conventional therapies. Advances …

ir, us, ca (code pays fourni par la source)

Predicting prostate cancer radiotherapy complications: An integrated approach using radiomics, dosiomics, and machine learning

Elham Sadati, Bijan Hashemi, Seied Rabi Mahdavi, A R Nikoufar et autres

Background:We aimed to develop a robust prognostic model for assessing the risk of complications associated with radiotherapy in prostate cancer patients using radiomics and dosiomics feature and machine learning.Materials and Methods: A cohort of 60 patients undergoing pelvic radiation therapy was analyzed.The …

ir (code pays fourni par la source)

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis

Saeed Shafiee Hasanabadi, Seyed Mahmud Reza Aghamiri, Ahmad Ali Abin, Hamid Abdollahi et autres

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor …

ir, ca, ch, hu, nl, dk (code pays fourni par la source)