Professional Education

  • Internship, North Oaks Medical Center, Transitional Year (2026)
  • MD, University of Queensland, Medical Doctorate (2024)
  • BS, University of California Irvine, Biological Sciences (2017)

All Publications

  • Radioembolization (90Y) achieves higher response rates and reduces progression risk compared with DEB-TACE in hepatocellular carcinoma. Hepatology communications Núñez, K., Hasani, N., Cronan, J., Gimenez, J., Cohen, A., Sandow, T., Thevenot, P. 2026; 10 (5)

    Abstract

    Drug-eluting bead transarterial chemoembolization (DEB-TACE) and yttrium-90 (90Y) radioembolization are approved therapies to treat hepatocellular carcinoma (HCC). Several randomized controlled trials and propensity score-matched studies (PSM) have been conducted to compare these 2 treatments; many utilized 90Y standard dosimetry (<200 Gy), which produced inferior outcomes compared with modern-day 90Y personalized dosimetry, which yields tumor doses exceeding 205 Gy.This study utilized PSM between DEB-TACE and 90Y with personalized dosimetry to compare treatment and patient outcomes in Barcelona Clinic Liver Cancer (BCLC) A-B HCC.This retrospective study included 258 patients with unresectable BCLC A-B stage HCC treated with DEB-TACE or 90Y as the initial treatment approach from 2015 to 2024. PSM was performed (90Y:DEB-TACE), matching for tumor burden and alpha-fetoprotein levels at diagnosis. The primary endpoint was target response rate with secondary endpoints of overall response, target retreatment rate (TTR), target and overall time-to-progression (TTP), and overall survival (OS).Overall, 90Y achieved significantly higher target complete (CR) and objective response (OR) rates compared with DEB-TACE (71% vs. 33% and 88% vs. 58%), respectively. In multifocal disease, target CR rates were higher following 90Y (68% vs. 13%). 90Y also yielded a longer duration of CR with a 1-year target retreatment rate of 12% compared with 40% with DEB-TACE. This translated into a longer target TPP (p=0.030) with 90Y, although overall TPP and OS were similar between treatment modalities. In multifocal disease, 90Y generated superior response rates as well as target (p=0.007) and overall TTP (p=0.015).90Y with personalized dosimetry achieved higher response rates and extended the duration of complete responses compared with DEB-TACE. 90Y was also more effective at treating multifocal disease.

    View details for DOI 10.1097/HC9.0000000000000935

    View details for PubMedID 42008775

    View details for PubMedCentralID PMC13090074

  • Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma. Seminars in nuclear medicine Veziroglu, E. M., Farhadi, F., Hasani, N., Nikpanah, M., Roschewski, M., Summers, R. M., Saboury, B. 2023; 53 (3): 426-448

    Abstract

    Our review shows that AI-based analysis of lymphoma whole-body FDG-PET/CT can inform all phases of clinical management including staging, prognostication, treatment planning, and treatment response evaluation. We highlight advancements in the role of neural networks for performing automated image segmentation to calculate PET-based imaging biomarkers such as the total metabolic tumor volume (TMTV). AI-based image segmentation methods are at levels where they can be semi-automatically implemented with minimal human inputs and nearing the level of a second-opinion radiologist. Advances in automated segmentation methods are particularly apparent in the discrimination of lymphomatous vs non-lymphomatous FDG-avid regions, which carries through to automated staging. Automated TMTV calculators, in addition to automated calculation of measures such as Dmax are informing robust models of progression-free survival which can then feed into improved treatment planning.

    View details for DOI 10.1053/j.semnuclmed.2022.11.003

    View details for PubMedID 36870800

  • Trustworthy Artificial Intelligence in Medical Imaging. PET clinics Hasani, N., Morris, M. A., Rhamim, A., Summers, R. M., Jones, E., Siegel, E., Saboury, B. 2022; 17 (1): 1-12

    Abstract

    Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.

    View details for DOI 10.1016/j.cpet.2021.09.007

    View details for PubMedID 34809860

    View details for PubMedCentralID PMC8785402

  • Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities. PET clinics Hasani, N., Farhadi, F., Morris, M. A., Nikpanah, M., Rhamim, A., Xu, Y., Pariser, A., Collins, M. T., Summers, R. M., Jones, E., Siegel, E., Saboury, B. 2022; 17 (1): 13-29

    Abstract

    Almost 1 in 10 individuals can suffer from one of many rare diseases (RDs). The average time to diagnosis for an RD patient is as high as 7 years. Artificial intelligence (AI)-based positron emission tomography (PET), if implemented appropriately, has tremendous potential to advance the diagnosis of RDs. Patient advocacy groups must be active stakeholders in the AI ecosystem if we are to avoid potential issues related to the implementation of AI into health care. AI medical devices must not only be RD-aware at each stage of their conceptualization and life cycle but also should be trained on diverse and augmented datasets representative of the end-user population including RDs. Inability to do so leads to potential harm and unsustainable deployment of AI-based medical devices (AIMDs) into clinical practice.

    View details for DOI 10.1016/j.cpet.2021.09.009

    View details for PubMedID 34809862

    View details for PubMedCentralID PMC8764708

  • Artificial Intelligence in Lymphoma PET Imaging: A Scoping Review (Current Trends and Future Directions) PET CLINICS Hasani, N., Paravastu, S. S., Farhadi, F., Yousefirizi, F., Morris, M. A., Rahmim, A., Roschewski, M., Summers, R. M., Saboury, B. 2022; 17 (1): 145-174

    Abstract

    Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.

    View details for DOI 10.1016/j.cpet.2021.09.006

    View details for Web of Science ID 000721407300014

    View details for PubMedID 34809864

    View details for PubMedCentralID PMC8735853