Exploration of an adaptive proton therapy strategy using CBCT with the concept of digital twins

Chih-Wei Chang; Zhen Tian; Richard L J Qiu; H Scott McGinnis; Duncan Henry Bohannon; Pretesh Patel; Yinan Wang; David S Yu; Sagar A Patel; Jun Zhou; Xiaofeng Yang

doi:10.1088/1361-6560/ada684

Exploration of an adaptive proton therapy strategy using CBCT with the concept of digital twins

Phys Med Biol. 2025 Jan 6. doi: 10.1088/1361-6560/ada684. Online ahead of print.

Authors

Chih-Wei Chang¹, Zhen Tian², Richard L J Qiu³, H Scott McGinnis⁴, Duncan Henry Bohannon⁵, Pretesh Patel⁶, Yinan Wang⁷, David S Yu⁵, Sagar A Patel⁸, Jun Zhou⁹, Xiaofeng Yang¹⁰

Affiliations

¹ Department of Radiology Oncology, Emory University, 1365 Clifton RD NE, Atlanta, Georgia, 30322-1007, UNITED STATES.
² Department of Radiation and Cellular Oncology, University of Chicago Pritzker School of Medicine, 924 E 57th St #104, Chicago, Illinois, 60637-1470, UNITED STATES.
³ Department of Radiology and Sciences Imaging Department of Radiology Oncology, Emory University, 1365 Clifton Rd NE Building C, Atlanta, Georgia, 30322, UNITED STATES.
⁴ Emory University School of Medicine, 1365 Clifton Rd NE Building C, Atlanta, Georgia, 30322, UNITED STATES.
⁵ Radiation Oncology, Emory University School of Medicine, 1365 Clifton Rd NE Building C, Atlanta, Georgia, 30303-3073, UNITED STATES.
⁶ Department of Radiation Oncology and Winship Cancer Institute, Emory University, 1365 Clifton Rd NE Building C, Atlanta, Georgia, 30322, UNITED STATES.
⁷ Radiation Oncology, Emory Univeristy, 1365 Clifton Rd NE Building C, Atlanta, Georgia, 30322, UNITED STATES.
⁸ Emory University, 1365 E Clifton Rd NE Building C, Atlanta, Georgia, 30322-1007, UNITED STATES.
⁹ Department of Radiology and Sciences Imaging Department of Radiology Oncology, Emory University School of Medicine, 1365 Clifton Rd NE Building C, Atlanta, Georgia, 30303-3073, UNITED STATES.
¹⁰ Department of Radiology Oncology, Emory University, Clifton Rd, Atlanta, Georgia, 30322-1007, UNITED STATES.

PMID: 39761649
DOI: 10.1088/1361-6560/ada684

Abstract

This study aims to develop a digital twin (DT) framework to achieve adaptive proton prostate stereotactic body radiation therapy (SBRT) with fast treatment plan selection and patient-specific clinical target volume (CTV) setup uncertainty. Prostate SBRT has emerged as a leading option for external beam radiotherapy due to its effectiveness and reduced treatment duration. However, interfractional anatomy variations can impact treatment outcomes. This study seeks to address these uncertainties using DT concept to improve treatment quality.Approach: A retrospective study on two-fraction prostate proton SBRT was conducted, involving a cohort of 10 randomly selected patient cases from an institutional database (n=43). DT-based treatment plans were developed using patient-specific CTV setup uncertainty, determined through machine learning predictions. Plans were optimized using pre-treatment CT and corrected cone-beam CT (cCBCT). The cCBCT was corrected for CT numbers and artifacts, and plan evaluation was performed using cCBCT to account for actual patient anatomy. The ProKnow scoring system was adapted to determine the optimal treatment plans.Main Results: Average CTV D98 values for original clinical and DT-based plans across 10 patients were 99.0% and 98.8%, with hot spots measuring 106.0% and 105.1%. Regarding bladder, clinical plans yielded average bladder neck V100 values of 29.6% and bladder V20.8Gy values of 12.0cc, whereas DT-based plans showed better sparing of bladder neck with values of 14.0% and 9.5cc. Clinical and DT-based plans resulted in comparable rectum dose statistics due to SpaceOAR. Compared to clinical plans, the proposed DT-based plans improved dosimetry quality, improving plan scores ranging from 2.0 to 15.5.Significance: Our study presented a pioneering approach that leverages DT technology to enhance adaptive proton SBRT, potentially revolutionizing prostate radiotherapy to offer personalized treatment solutions using fast adaptive treatment plan selections and patient-specific setup uncertainty. This research contributes to the ongoing efforts to achieve personalized prostate radiotherapy.&#xD.

Keywords: CBCT; Digital twins; adaptive proton therapy; machine learning.

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