Dorsa Shirini

Dr. Dorsa Shirini


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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29 January 2022 in Pharmaceutics
Verified
Journal Article
Open access
PDF file
MAMozhgan Aghajanzadeh
Mozhgan Aghajanzadeh
MZMostafa Zamani-Roudbaraki
Mostafa Zamani-Roudbaraki
FKFereshteh Rajabi Kouchi
Fereshteh Rajabi Kouchi
JEJoshua Eixenberger
Joshua Eixenberger
DSDorsa Shirini
Dorsa Shirini
FSFarhad Shirini
Farhad Shirini
This review provides a summary of recent progress in the development of different nano-platforms for the efficient synergistic effect between photodynamic therapy and chemotherapy. In particular, this review focuses on various methods in which photosensitizers and chemotherapeutic agents are co-delivered to the targeted tumor site. In many cases, the photosensitizers act as drug carriers, but this review, also covers different types of appropriate nanocarriers that aid in the delivery of photosensitizers to the tumor site. These nanocarriers include transition metal, silica and graphene-based materials, liposomes, dendrimers, polymers, metal–organic frameworks, nano emulsions, and biologically derived nanocarriers. Many studies have demonstrated various benefits from using these nanocarriers including enhanced water solubility, stability, longer circulation times, and higher accumulation of therapeutic agents/photosensitizers at tumor sites. This review also describes novel approaches from different research groups that utilize various targeting strategies to increase treatment efficacy through simultaneous photodynamic therapy and chemotherapy.

Verified
Journal Article
Open access
PDF file
JMJeremy McGale
Jeremy McGale
SKSakshi Khurana
Sakshi Khurana
AHAlice Huang
Alice Huang
TRTina Roa
Tina Roa
RYRandy Yeh
Randy Yeh
RSRomain David Seban
Romain David Seban
HER2 (Human Epidermal Growth Factor Receptor 2)-positive breast cancer is characterized by amplification of the HER2 gene and is associated with more aggressive tumor growth, increased risk of metastasis, and poorer prognosis when compared to other subtypes of breast cancer. HER2 expression is therefore a critical tumor feature that can be used to diagnose and treat breast cancer. Moving forward, advances in HER2 in vivo imaging, involving the use of techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), may allow for a greater role for HER2 status in guiding the management of breast cancer patients. This will apply both to patients who are HER2-positive and those who have limited-to-minimal immunohistochemical HER2 expression (HER2-low), with imaging ultimately helping clinicians determine the size and location of tumors. Additionally, PET and SPECT could help evaluate effectiveness of HER2-targeted therapies, such as trastuzumab or pertuzumab for HER2-positive cancers, and specially modified antibody drug conjugates (ADC), such as trastuzumab-deruxtecan, for HER2-low variants. This review will explore the current and future role of HER2 imaging in personalizing the care of patients diagnosed with breast cancer.

20 November 2023 in Diagnostics
Verified
Journal Article
Open access
PDF file
HHHayley Higgins
Hayley Higgins
ANAbanoub Nakhla
Abanoub Nakhla
ALAndrew Lotfalla
Andrew Lotfalla
DKDavid Khalil
David Khalil
PDParth Doshi
Parth Doshi
Laurent DercleLaurent Dercle
Laurent Dercle
Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma.