Publications by authors named "S Setayeshi"
Background: Respiratory motion is a challenge for accurate radiotherapy that may be mitigated by real-time tracking. Commercial tracking systems utilize a hybrid external-internal correlation model (ECM), integrating continuous external breathing monitoring with sparse X-ray imaging of the internal tumor position.
Purpose: This study investigates the feasibility of using the next generation reservoir computing (NG-RC) model as a hybrid ECM to transform measured external motions into estimated 3D internal motions.
Article Synopsis
- The study looks at how to estimate the movement of liver tumors during radiation therapy by tracking both external breathing motion and sparse internal imaging.
- It tested four different models to see how accurately they could predict internal tumor motion based on external movements, measuring their performance with root-mean-square error (RMSE).
- The results showed that while the augmented quadratic model (ECM3) had the best overall fitting accuracy, the simpler augmented linear model (ECM2) with frequent updates was more effective for estimating motion accurately, especially in a specific directional movement.
Background: Intraoperative Irradiation Therapy (IORT) refers to the delivery of radiation during surgery and needs the computed- thickness of the target as one of the most significant factors.
Objective: This paper aimed to compute target thickness and design a radiation pattern distributing the irradiation uniformly throughout the target.
Material And Methods: The Monte Carlo code was used to simulate the experimental setup in this simulation study.
Article Synopsis
- - This study focused on analyzing the presence and condition of the HPV-16 virus in various cervical lesions, including non-cancerous, precancerous, and cancerous tissues.
- - Researchers used quantitative real-time PCR to measure the viral loads of two HPV-16 genes (E2 and E6) in 132 cervical samples, finding significant differences in these loads across the lesion types.
- - The results indicated that the E2 gene viral load is a strong biomarker for differentiating lesion types, with E2 and E6 showing high specificity and sensitivity in identifying cancerous samples.
Cancer, as identified by the World Health Organization, stands as the second leading cause of death globally. Its intricate nature makes it challenging to study solely based on biological knowledge, often leading to expensive research endeavors. While tremendous strides have been made in understanding cancer, gaps remain, especially in predicting tumor behavior across various stages.
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