Fang-Fang Yin

Overview:

Stereotactic radiosurgery, Stereotactic body radiation therapy, treatment planning optimization, knowledge guided radiation therapy, intensity-modulated radiation therapy, image-guided radiation therapy, oncological imaging and informatics

Positions:

Gustavo S. Montana Distinguished Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Professor in Radiation Oncology

Radiation Oncology
School of Medicine

Director of the Medical Physics Graduate Program at Duke Kunshan University

DKU Faculty
Duke Kunshan University

Professor of Medical Physics at Duke Kunshan University

DKU Faculty
Duke Kunshan University

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

B.S. 1982

Zhejiang University (China)

M.S. 1987

Bowling Green State University

Ph.D. 1992

The University of Chicago

Certificate In Therapeutic Radiologic Physics, Radiation Physics

American Board of Radiology

Grants:

Motion Management Using 4D-MRI for Liver Cancer in Radiation Therapy

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Co-Principal Investigator
Start Date
End Date

Digital tomosynthesis: a new paradigm for radiation treatment verification

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Robotic SPECT for Biological Imaging Onboard Radiation Therapy Machines

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Co-Principal Investigator
Start Date
End Date

Accurate, High Resolution 3D Dosimetry

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Collaborator
Start Date
End Date

A Limited-angle Intra-fractional Verification (LIVE) System for SBRT Treatments

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Co-Principal Investigator
Start Date
End Date

Publications:

Prior image-guided cone-beam computed tomography augmentation from under-sampled projections using a convolutional neural network

MLA Citation
Jiang, Zhuoran, et al. “Prior image-guided cone-beam computed tomography augmentation from under-sampled projections using a convolutional neural network (Accepted).” Quantitative Imaging in Medicine and Surgery, vol. 11, no. 12, AME Publishing Company, Dec. 2021, pp. 4767–80. Crossref, doi:10.21037/qims-21-114.
URI
https://scholars.duke.edu/individual/pub1493022
Source
crossref
Published In
Quantitative Imaging in Medicine and Surgery
Volume
11
Published Date
Start Page
4767
End Page
4780
DOI
10.21037/qims-21-114

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview

Authors
Sheng, Y; Zhang, J; Ge, Y; Li, X; Wang, W; Stephens, H; Yin, F-F; Wu, Q; Wu, QJ
MLA Citation
Sheng, Yang, et al. “Artificial intelligence applications in intensity modulated radiation treatment planning: an overview (Accepted).” Quantitative Imaging in Medicine and Surgery, vol. 11, no. 12, AME Publishing Company, Dec. 2021, pp. 4859–80. Crossref, doi:10.21037/qims-21-208.
URI
https://scholars.duke.edu/individual/pub1497760
Source
crossref
Published In
Quantitative Imaging in Medicine and Surgery
Volume
11
Published Date
Start Page
4859
End Page
4880
DOI
10.21037/qims-21-208

AAPM Task Group 198 Report: An implementation guide for TG 142 quality assurance of medical accelerators.

The charges on this task group (TG) were as follows: (a) provide specific procedural guidelines for performing the tests recommended in TG 142; (b) provide estimate of the range of time, appropriate personnel, and qualifications necessary to complete the tests in TG 142; and (c) provide sample daily, weekly, monthly, or annual quality assurance (QA) forms. Many of the guidelines in this report are drawn from the literature and are included in the references. When literature was not available, specific test methods reflect the experiences of the TG members (e.g., a test method for door interlock is self-evident with no literature necessary). In other cases, the technology is so new that no literature for test methods was available. Given broad clinical adaptation of volumetric modulated arc therapy (VMAT), which is not a specific topic of TG 142, several tests and criteria specific to VMAT were added.
Authors
Hanley, J; Dresser, S; Simon, W; Flynn, R; Klein, EE; Letourneau, D; Liu, C; Yin, F-F; Arjomandy, B; Ma, L; Aguirre, F; Jones, J; Bayouth, J; Holmes, T
MLA Citation
Hanley, Joseph, et al. “AAPM Task Group 198 Report: An implementation guide for TG 142 quality assurance of medical accelerators.Med Phys, vol. 48, no. 10, Oct. 2021, pp. e830–85. Pubmed, doi:10.1002/mp.14992.
URI
https://scholars.duke.edu/individual/pub1483450
PMID
34036590
Source
pubmed
Published In
Med Phys
Volume
48
Published Date
Start Page
e830
End Page
e885
DOI
10.1002/mp.14992

A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials.

BACKGROUND AND PURPOSE: The efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient-specific anatomy. This study examined these two techniques for dose-volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge-based planning (KBP) technique and another first principle (FP) technique. METHODS: This analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi-institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure. RESULTS: For the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re-optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ-at-risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy. CONCLUSIONS: Both methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles.
Authors
Geng, H; Giaddui, T; Cheng, C; Zhong, H; Ryu, S; Liao, Z; Yin, F-F; Gillin, M; Mohan, R; Xiao, Y
MLA Citation
Geng, Huaizhi, et al. “A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials.J Appl Clin Med Phys, vol. 22, no. 10, Oct. 2021, pp. 329–37. Pubmed, doi:10.1002/acm2.13401.
URI
https://scholars.duke.edu/individual/pub1496685
PMID
34432946
Source
pubmed
Published In
Journal of Applied Clinical Medical Physics
Volume
22
Published Date
Start Page
329
End Page
337
DOI
10.1002/acm2.13401

Radiomics: a primer on high-throughput image phenotyping.

Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
Authors
Lafata, KJ; Wang, Y; Konkel, B; Yin, F-F; Bashir, MR
MLA Citation
Lafata, Kyle J., et al. “Radiomics: a primer on high-throughput image phenotyping.Abdom Radiol (Ny), Aug. 2021. Pubmed, doi:10.1007/s00261-021-03254-x.
URI
https://scholars.duke.edu/individual/pub1494970
PMID
34435228
Source
pubmed
Published In
Abdom Radiol (Ny)
Published Date
DOI
10.1007/s00261-021-03254-x

Research Areas:

Bioinformatics
Medical physics