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Start Over Author "LIN MU" Journal journal of applied clinical medical physics
15 results on '"LIN MU"'

1. Beyond conventional bounds: Surpassing system limits for stereotactic ablative (SAbR) lung radiotherapy using CBCT‐based adaptive planning system

Author

Gonzalez, Yesenia, primary, Visak, Justin, additional, Overman, Luke, additional, Liao, Chien‐Yi, additional, Yen, Allen, additional, Zhuang, Tingliang, additional, Cai, Bin, additional, Godley, Andrew, additional, Zhang, Yuanyuan, additional, Timmerman, Robert, additional, Iyengar, Puneeth, additional, Westover, Kenneth, additional, Parsons, David, additional, and Lin, Mu‐Han, additional

Published
2024
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3. Evaluating machine learning enhanced intelligent‐optimization‐engine (IOE) performance for ethos head‐and‐neck (HN) plan generation

Author

Visak, Justin, primary, Inam, Enobong, additional, Meng, Boyu, additional, Wang, Siqiu, additional, Parsons, David, additional, Nyugen, Dan, additional, Zhang, Tingliang, additional, Moon, Dominic, additional, Avkshtol, Vladimir, additional, Jiang, Steve, additional, Sher, David, additional, and Lin, Mu‐Han, additional

Published
2023
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6. On the feasibility of improved target coverage without compromising organs at risk using online adaptive stereotactic partial breast irradiation (A‐SPBI)

Author

Montalvo, Steven K., primary, Kim, Nathan, additional, Nwachukwu, Chika, additional, Alluri, Prasanna, additional, Parsons, David, additional, Lin, Mu‐Han, additional, Cai, Bin, additional, Zhuang, Tingliang, additional, Hrycushko, Brian, additional, Chen, Liyuan, additional, Timmerman, Robert, additional, and Rahimi, Asal, additional

Published
2022
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8. On the feasibility of improved target coverage without compromising organs at risk using online adaptive stereotactic partial breast irradiation (A‐SPBI).

Author

Montalvo, Steven K., Kim, Nathan, Nwachukwu, Chika, Alluri, Prasanna, Parsons, David, Lin, Mu‐Han, Cai, Bin, Zhuang, Tingliang, Hrycushko, Brian, Chen, Liyuan, Timmerman, Robert, and Rahimi, Asal

Subjects
STEREOTAXIC techniques, IRRADIATION, ADAPTIVE radiation, FRACTIONS, SCHEDULING
Abstract

Purpose: Describe an early‐adopting institution's experience with online adaptive radiation for stereotactic partial breast irradiation. Methods and materials: Retrospective review of 22 women treated between May 2021 and March 2022 with adaptive stereotactic partial breast irradiation. A total of 106 of 110 fractions were evaluated for dosimetric changes in target coverage and organ‐at‐risk (OAR) dose. Patient set up with stereotactic wooden frame and adapted per fraction. Treatment and planning times were collected prospectively by radiation therapists. Results: Scheduled PTV30 Gy was <95% in 72.1% and <90% in 38.5% of fractions, and both PTV and CTV coverage were improved significantly after adaption, and 83.7% of fractions were delivered as adapted per physician choice. There was no difference in OAR coverage. Average adaptive treatment planning took 15 min and average time‐on‐couch was 34.4 min. Conclusions: Adaptive stereotactic breast irradiation resulted in improved target coverage with equivalent dosing to OARs in an efficient and tolerated treatment time. Improved target coverage allowed for decreased PTV margins compared to prior trial protocols that may improve acute and late toxicities. [ABSTRACT FROM AUTHOR]

Published
2023
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9. Extensive upfront validation and testing are needed prior to the clinical implementation of AI‐based auto‐segmentation tools.

Author

Roper, Justin, Lin, Mu‐Han, and Rong, Yi

Subjects
ARTIFICIAL intelligence, DEEP learning, THRESHOLDING algorithms, SPINE, TECHNOLOGICAL innovations
Abstract

Extensive upfront validation and testing are needed prior to the clinical implementation of AI-based auto-segmentation tools Keywords: AI model prevalidation; Artificial Intelligence; Automatic segmentation; Model performance and QA EN AI model prevalidation Artificial Intelligence Automatic segmentation Model performance and QA 1 6 6 01/27/23 20230101 NES 230101 INTRODUCTION Artificial intelligence (AI) has emerged as a promising approach for automatic contouring in radiation therapy, compared to previous attempts, that is, image value thresholding, Atlas-based, and so forth. Amongst those early adopters of AI tools, the consensus is lacking regarding the required prior validation tests of AI auto-segmentation models before releasing them for clinical use. REBUTTAL For the proposition: Justin Roper, PhD Dr. Mu-Han Lin is an exceptional colleague who has real-world experience with AI at an institution broadly recognized as a leader in AI research. [Extracted from the article]

Published
2023
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