Delta radiomics to track radiation response in lung tumors receiving stereotactic magnetic resonance-guided radiotherapy

Yining Zha; Zezhong Ye; Anna Zapaishchykova; John He; Shu-Hui Hsu; Jonathan E. Leeman; Kelly J. Fitzgerald; David E. Kozono; Raymond H. Mak; Hugo J.W.L. Aerts; Benjamin H. Kann

Physics and Imaging in Radiation Oncology (Jul 2024)

Delta radiomics to track radiation response in lung tumors receiving stereotactic magnetic resonance-guided radiotherapy

Yining Zha,
Zezhong Ye,
Anna Zapaishchykova,
John He,
Shu-Hui Hsu,
Jonathan E. Leeman,
Kelly J. Fitzgerald,
David E. Kozono,
Raymond H. Mak,
Hugo J.W.L. Aerts,
Benjamin H. Kann

Affiliations

Yining Zha: Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Zezhong Ye: Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Anna Zapaishchykova: Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
John He: Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Shu-Hui Hsu: Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Jonathan E. Leeman: Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Kelly J. Fitzgerald: Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
David E. Kozono: Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Raymond H. Mak: Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Hugo J.W.L. Aerts: Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands; Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
Benjamin H. Kann: Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; Corresponding authors at: Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.

Journal volume & issue: Vol. 31
p. 100626

Abstract

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Background and purpose: Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. However, the heterogeneous response to radiation at the tumor level poses challenges. Currently, standardized dosage regimens lack adaptation based on individual patient or tumor characteristics. Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes. Materials and methods: A retrospective study of 47 MR-guided lung SBRT treatments for 39 patients was conducted. Radiomic features were extracted using Pyradiomics, and stability was evaluated temporally and spatially. Delta radiomics were correlated with radiation dose delivery and assessed for associations with tumor control and survival with Cox regressions. Results: Among 107 features, 49 demonstrated temporal stability, and 57 showed spatial stability. Fifteen stable and non-collinear features were analyzed. Median Skewness and surface to volume ratio decreased with radiation dose fraction delivery, while coarseness and 90th percentile values increased. Skewness had the largest relative median absolute changes (22 %–45 %) per fraction from baseline and was associated with locoregional failure (p = 0.012) by analysis of covariance. Skewness, Elongation, and Flatness were significantly associated with local recurrence-free survival, while tumor diameter and volume were not. Conclusions: Our study establishes the feasibility and stability of delta radiomics analysis for MR-guided lung SBRT. Findings suggest that MR delta radiomics can capture short-term radiographic manifestations of the intra-tumoral radiation effect.

Published in Physics and Imaging in Radiation Oncology

ISSN: 2405-6316 (Online)
Publisher: Elsevier
Country of publisher: Ireland
LCC subjects: Medicine: Medicine (General): Medical physics. Medical radiology. Nuclear medicine; Medicine: Internal medicine: Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Website: https://www.journals.elsevier.com/physics-and-imaging-in-radiation-oncology/

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