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Exploring Deep Learning for Estimating the Isoeffective Dose of FLASH Irradiation From Mouse Intestinal Histological Images.
Fu, Jie; Yang, Zi; Melemenidis, Stavros; Viswanathan, Vignesh; Dutt, Suparna; Manjappa, Rakesh; Lau, Brianna; Soto, Luis A; Ashraf, M Ramish; Skinner, Lawrie; Yu, Shu-Jung; Surucu, Murat; Casey, Kerriann M; Rankin, Erinn B; Graves, Edward; Lu, Weiguo; Loo, Billy W; Gu, Xuejun.
Afiliação
  • Fu J; Department of Radiation Oncology, University of Washington, Seattle, Washington; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Yang Z; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Melemenidis S; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Viswanathan V; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Dutt S; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Manjappa R; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Lau B; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Soto LA; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Ashraf MR; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Skinner L; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Yu SJ; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Surucu M; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Casey KM; Department of Comparative Medicine, Stanford University School of Medicine, Stanford, California.
  • Rankin EB; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Graves E; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Lu W; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Loo BW; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California. Electronic address: bwloo@stanford.edu.
  • Gu X; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California. Electronic address: xuejungu@stanford.edu.
Int J Radiat Oncol Biol Phys ; 119(3): 1001-1010, 2024 Jul 01.
Article em En | MEDLINE | ID: mdl-38171387
ABSTRACT

PURPOSE:

Ultrahigh-dose-rate (FLASH) irradiation has been reported to reduce normal tissue damage compared with conventional dose rate (CONV) irradiation without compromising tumor control. This proof-of-concept study aims to develop a deep learning (DL) approach to quantify the FLASH isoeffective dose (dose of CONV that would be required to produce the same effect as the given physical FLASH dose) with postirradiation mouse intestinal histology images. METHODS AND MATERIALS Eighty-four healthy C57BL/6J female mice underwent 16 MeV electron CONV (0.12 Gy/s; n = 41) or FLASH (200 Gy/s; n = 43) single fraction whole abdominal irradiation. Physical dose ranged from 12 to 16 Gy for FLASH and 11 to 15 Gy for CONV in 1 Gy increments. Four days after irradiation, 9 jejunum cross-sections from each mouse were hematoxylin and eosin stained and digitized for histological analysis. CONV data set was randomly split into training (n = 33) and testing (n = 8) data sets. ResNet101-based DL models were retrained using the CONV training data set to estimate the dose based on histological features. The classical manual crypt counting (CC) approach was implemented for model comparison. Cross-section-wise mean squared error was computed to evaluate the dose estimation accuracy of both approaches. The validated DL model was applied to the FLASH data set to map the physical FLASH dose into the isoeffective dose.

RESULTS:

The DL model achieved a cross-section-wise mean squared error of 0.20 Gy2 on the CONV testing data set compared with 0.40 Gy2 of the CC approach. Isoeffective doses estimated by the DL model for FLASH doses of 12, 13, 14, 15, and 16 Gy were 12.19 ± 0.46, 12.54 ± 0.37, 12.69 ± 0.26, 12.84 ± 0.26, and 13.03 ± 0.28 Gy, respectively.

CONCLUSIONS:

Our proposed DL model achieved accurate CONV dose estimation. The DL model results indicate that in the physical dose range of 13 to 16 Gy, the biologic dose response of small intestinal tissue to FLASH irradiation is represented by a lower isoeffective dose compared with the physical dose. Our DL approach can be a tool for studying isoeffective doses of other radiation dose modifying interventions.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Camundongos Endogâmicos C57BL Limite: Animals Idioma: En Revista: Int J Radiat Oncol Biol Phys Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Camundongos Endogâmicos C57BL Limite: Animals Idioma: En Revista: Int J Radiat Oncol Biol Phys Ano de publicação: 2024 Tipo de documento: Article