Fereshteh Yousefirizi

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 [ …

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Enhancing PRRT Outcome Prediction in Neuroendocrine Tumors: Aggregated Multi-Lesion PET Radiomics Incorporating Inter-Tumor Heterogeneity

Maziar Sabouri, Ghasem Hajianfar, Omid Gharibi, Alireza Rafiei Sardouei et autres

Introduction: Peptide Receptor Radionuclide Therapy (PRRT) with [177Lu]Lu-DOTA-TATE is effective in treating advanced Neuroendocrine Tumors (NETs), yet predicting individual response in this treatment remains a challenge due to inter-lesion heterogeneity. There is a lack of standardized, effective methods for using multi-lesion radiomics …

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Artificial Intelligence for Simplified Patient-centered Dosimetry in Radiopharmaceutical Therapies

Alejandro López-Montes, Fereshteh Yousefirizi, Yizhou Chen, Yazdan Salimi et autres

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PET Inter-Lesion Radiomics Aggregation for Enhanced PRRT Response Prediction in Neuroendocrine Tumors

M. Sabouri, Ghasem Hajianfar, Omid Gharibi, Alireza Rafiei Sardouei et autres

Peptide Receptor Radionuclide Therapy (PRRT) using [${ }^{177}$Lu]Lu-DOTA-TATE has significantly improved outcomes for patients with advanced neuroendocrine tumors (NETs), yet predicting therapeutic response remains challenging. This study investigates whether aggregating radiomic features of different lesions extracted from pre-treatment somatostatin receptor PET/CT scans …

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Seeing More, Treating Smarter

Pedro L. Esquinas, Fereshteh Yousefirizi, Ian Alberts, Nicolas A. Karakatsanis et autres

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Toward Integrated Clinical-Computational Nuclear Medicine

Faraz Farhadi, Shadi A. Esfahani, Fereshteh Yousefirizi, Monica Luo et autres

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Artificial intelligence for simplified patient-centered dosimetry in radiopharmaceutical therapies

Alejandro López-Montes, Fereshteh Yousefirizi, Yizhou Chen, Yazdan Salimi et autres

KEY WORDS: Artificial Intelligence (AI), Theranostics, Dosimetry, Radiopharmaceutical Therapy (RPT), Patient-friendly dosimetry KEY POINTS - The rapid evolution of radiopharmaceutical therapy (RPT) highlights the growing need for personalized and patient-centered dosimetry. - Artificial Intelligence (AI) offers solutions to the key limitations in …

Seeing More, Treating Smarter: Role of Long-Axial Field-of-View PET-CT in The Evolution of Theranostics

Pedro L. Esquinas, Fereshteh Yousefirizi, Ian L. Alberts, Nicolas A. Karakatsanis et autres

Long-axial field-of-view (LAFOV) PET/CT has the potential to redefine the role of molecular imaging in theranostics by making multiparametric whole-body (MPWB) imaging and predictive dosimetry more clinically feasible. Compared to conventional PET systems, LAFOV scanners provide dramatic gains in sensitivity and coverage, …

Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer

Fereshteh Yousefirizi, Ghasem Hajianfar, Maziar Sabouri, Caroline Holloway et autres

Background: Cervical cancer remains a major global health concern, with high recurrence rates in advanced stages. [18F]FDG PET/CT provides prognostic biomarkers such as SUV, MTV, and TLG, though these are not routinely integrated into clinical protocols. Radiomics offers quantitative analysis of tumor …

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Advancing Positron Emission Tomography Image Quantification

Fereshteh Yousefirizi, Movindu Dassanayake, Alejandro López, Andrew J. Reader et autres

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Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging

Movindu Dassanayake, Alejandro López, Andrew J. Reader, Gary Cook et autres

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Physician-in-the-Loop Active Learning in Radiology Artificial Intelligence Workflows: Opportunities, Challenges, and Future Directions

Monica Luo, Fereshteh Yousefirizi, Pouria Rouzrokh, Weina Jin et autres

Artificial intelligence (AI) is being explored for a growing range of applications in radiology, including image reconstruction, image segmentation, synthetic image generation, disease classification, worklist triage, and examination scheduling. However, training accurate AI models typically requires substantial amounts of expert-labeled data, which …

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