Publications by authors named "Sina Mansoorian"
Prognostic significance of pretreatment PET parameters in inoperable, node-positive NSCLC patients with poor prognostic factors undergoing hypofractionated radiotherapy: a single-institution retrospective study.
EJNMMI Rep
October 2024
Article Synopsis
- Node-positive non-small cell lung cancer (NSCLC) presents treatment challenges for patients unsuitable for concurrent chemoradiotherapy, leading to a study on the prognostic value of pretreatment PET parameters in high-risk patients receiving hypofractionated radiotherapy.
- A retrospective analysis of 42 patients from a single institution revealed median progression-free survival (PFS) of 11.5 months and overall survival (OS) of 24.3 months, with variables like SUVmax and ECOG performance status significantly predicting these outcomes.
- Multivariable analysis highlighted SUVmax as a key predictor for PFS and ECOG performance status for OS, with patients in the high total metabolic tumor volume (tMTV) group experiencing significantly poorer
Purpose: The aim of this study was to investigate a first-site-metastasis pattern (FSMP) in unresectable stage III NSCLC after concurrent chemoradiotherapy (cCRT) with or without immune checkpoint inhibition (ICI).
Methods: We defined three patient subgroups according to the year of initial multimodal treatment: A (2011-2014), B (2015-2017) and C (2018-2020). Different treatment-related parameters were analyzed.
We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation.
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- The study explores the use of deep learning for autodelineation of head and neck lymph nodes to improve radiotherapy planning, highlighting the lack of existing public resources for this purpose.
- A trained nnU-net model was evaluated using a cohort of CT images, with clinical experts comparing its segmentation against manually drawn contours.
- Results showed no significant difference in quality ratings between deep learning and expert contours, but the inclusion of a CT slice plane adjustment improved the ratings for the deep learning segments.
To investigate the occurrence of pseudoprogression/transient enlargement in meningiomas after stereotactic radiotherapy (RT) and to evaluate recently proposed volumetric RANO meningioma criteria for response assessment in the context of RT. Sixty-nine meningiomas (benign: 90%, atypical: 10%) received stereotactic RT from January 2005-May 2018. A total of 468 MRI studies were segmented longitudinally during a median follow-up of 42.
View Article and Find Full Text PDFObjective: To assess the change in inpatient radiotherapy related to COVID-19 lockdown measures during the first wave of the pandemic in 2020.
Methods: We included cases hospitalized between January 1 and August 31, 2018-2020, with a primary ICD-10 diagnosis of C00-C13, C32 (head and neck cancer, HNC) and C53 (cervical cancer, CC). Data collection was conducted within the Medical Informatics Initiative.
Background: Traditional clinical target volume (CTV) definition for pelvic radiotherapy in prostate cancer consists of large volumes being treated with homogeneous doses without fully utilizing information on the probability of microscopic involvement to guide target volume design and prescription dose distribution.
Methods: We analyzed patterns of nodal involvement in 75 patients that received RT for pelvic and paraaortic lymph node metastases (LNs) from prostate cancer in regard to the new NRG-CTV recommendation. Non-rigid registration-based LN mapping and weighted three-dimensional kernel density estimation were used to visualize the average probability distribution for nodal metastases.
Purpose: To share our experiences in implementing a dedicated magnetic resonance (MR) scanner for radiotherapy (RT) treatment planning using a novel coil setup for brain imaging in treatment position as well as to present developed core protocols with sequences specifically tuned for brain and prostate RT treatment planning.
Materials And Methods: Our novel setup consists of two large 18-channel flexible coils and a specifically designed wooden mask holder mounted on a flat tabletop overlay, which allows patients to be measured in treatment position with mask immobilization. The signal-to-noise ratio (SNR) of this setup was compared to the vendor-provided flexible coil RT setup and the standard setup for diagnostic radiology.