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Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy

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Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy. / Ibragimov, Bulat; Toesca, Diego A.S.; Chang, Daniel T.; Yuan, Yixuan; Koong, Albert C.; Xing, Lei; Vogelius, Ivan R.

I: Medical Physics, 2020.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Ibragimov, B, Toesca, DAS, Chang, DT, Yuan, Y, Koong, AC, Xing, L & Vogelius, IR 2020, 'Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy', Medical Physics. https://doi.org/10.1002/mp.14235

APA

Ibragimov, B., Toesca, D. A. S., Chang, D. T., Yuan, Y., Koong, A. C., Xing, L., & Vogelius, I. R. (2020). Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy. Medical Physics. https://doi.org/10.1002/mp.14235

Vancouver

Ibragimov B, Toesca DAS, Chang DT, Yuan Y, Koong AC, Xing L o.a. Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy. Medical Physics. 2020. https://doi.org/10.1002/mp.14235

Author

Ibragimov, Bulat ; Toesca, Diego A.S. ; Chang, Daniel T. ; Yuan, Yixuan ; Koong, Albert C. ; Xing, Lei ; Vogelius, Ivan R. / Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy. I: Medical Physics. 2020.

Bibtex

@article{63b1e00b7f984edb82abe92812983751,
title = "Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy",
abstract = "Purpose: Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50{\%} of patients with solid tumors. Radiation-induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during dose delivery. The existing RT planning solutions generally discard spatial dose distribution information and lose the ability to recognize radiosensitive regions of healthy organs potentially linked to toxicity manifestation. This study proposes a universal deep learning-based algorithm for recognitions of consistent dose patterns and generation of toxicity risk maps for the abdominal area. Methods: We investigated whether convolutional neural networks (CNNs) can automatically associate abdominal computed tomography (CT) images and RT dose plans with post-RT toxicities without being provided segmentation of abdominal organs. The CNNs were also applied to study RT plans, where doses at specific anatomical regions were reduced/increased, with the aim to pinpoint critical regions sparing of which significantly reduces toxicity risks. The obtained risk maps were computed for individual anatomical regions inside the liver and statistically compared to the existing clinical studies. Results: A database of 122 liver stereotactic body RT (SBRT) executed at Stanford Hospital from July 2004 and November 2015 was assembled. All patients treated for primary liver cancer, mainly hepatocellular carcinoma and cholangiocarcinoma, with complete follow-ups were extracted from the database. The SBRT treatment doses ranged from 26 to 50 Gy delivered in 1–5 fractions for primary liver cancer. The patients were followed up for 1–68 months depending on the survival time. The CNNs were trained to recognize acute and late grade 3+ biliary stricture/obstruction, hepatic failure or decompensation, hepatobiliary infection, liver function test (LFT) elevation or/and portal vein thrombosis, named for convenience hepatobiliary (HB) toxicities. The toxicity prediction accuracy was of 0.73 measured in terms of the area under the receiving operator characteristic curve. Significantly higher risk scores (P < 0.05) of HB toxicity manifestation were associated with irradiation for the hepatobiliary tract in comparison to the risk scores for liver segments I–VIII and portal vein. This observation is in strong agreement with anatomical and clinical expectations. Conclusion: In this work, we proposed and validated a universal deep learning-based solution for the identification of radiosensitive anatomical regions. Without any prior anatomical knowledge, CNNs automatically recognized the importance of hepatobiliary tract sparing during liver SBRT.",
keywords = "atlas, convolutional neural networks, deep learning, liver radiotherapy, toxicity, treatment outcome prediction",
author = "Bulat Ibragimov and Toesca, {Diego A.S.} and Chang, {Daniel T.} and Yixuan Yuan and Koong, {Albert C.} and Lei Xing and Vogelius, {Ivan R.}",
year = "2020",
doi = "10.1002/mp.14235",
language = "English",
journal = "Medical Physics",
issn = "0094-2405",
publisher = "John Wiley and Sons, Inc.",

}

RIS

TY - JOUR

T1 - Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy

AU - Ibragimov, Bulat

AU - Toesca, Diego A.S.

AU - Chang, Daniel T.

AU - Yuan, Yixuan

AU - Koong, Albert C.

AU - Xing, Lei

AU - Vogelius, Ivan R.

PY - 2020

Y1 - 2020

N2 - Purpose: Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation-induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during dose delivery. The existing RT planning solutions generally discard spatial dose distribution information and lose the ability to recognize radiosensitive regions of healthy organs potentially linked to toxicity manifestation. This study proposes a universal deep learning-based algorithm for recognitions of consistent dose patterns and generation of toxicity risk maps for the abdominal area. Methods: We investigated whether convolutional neural networks (CNNs) can automatically associate abdominal computed tomography (CT) images and RT dose plans with post-RT toxicities without being provided segmentation of abdominal organs. The CNNs were also applied to study RT plans, where doses at specific anatomical regions were reduced/increased, with the aim to pinpoint critical regions sparing of which significantly reduces toxicity risks. The obtained risk maps were computed for individual anatomical regions inside the liver and statistically compared to the existing clinical studies. Results: A database of 122 liver stereotactic body RT (SBRT) executed at Stanford Hospital from July 2004 and November 2015 was assembled. All patients treated for primary liver cancer, mainly hepatocellular carcinoma and cholangiocarcinoma, with complete follow-ups were extracted from the database. The SBRT treatment doses ranged from 26 to 50 Gy delivered in 1–5 fractions for primary liver cancer. The patients were followed up for 1–68 months depending on the survival time. The CNNs were trained to recognize acute and late grade 3+ biliary stricture/obstruction, hepatic failure or decompensation, hepatobiliary infection, liver function test (LFT) elevation or/and portal vein thrombosis, named for convenience hepatobiliary (HB) toxicities. The toxicity prediction accuracy was of 0.73 measured in terms of the area under the receiving operator characteristic curve. Significantly higher risk scores (P < 0.05) of HB toxicity manifestation were associated with irradiation for the hepatobiliary tract in comparison to the risk scores for liver segments I–VIII and portal vein. This observation is in strong agreement with anatomical and clinical expectations. Conclusion: In this work, we proposed and validated a universal deep learning-based solution for the identification of radiosensitive anatomical regions. Without any prior anatomical knowledge, CNNs automatically recognized the importance of hepatobiliary tract sparing during liver SBRT.

AB - Purpose: Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation-induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during dose delivery. The existing RT planning solutions generally discard spatial dose distribution information and lose the ability to recognize radiosensitive regions of healthy organs potentially linked to toxicity manifestation. This study proposes a universal deep learning-based algorithm for recognitions of consistent dose patterns and generation of toxicity risk maps for the abdominal area. Methods: We investigated whether convolutional neural networks (CNNs) can automatically associate abdominal computed tomography (CT) images and RT dose plans with post-RT toxicities without being provided segmentation of abdominal organs. The CNNs were also applied to study RT plans, where doses at specific anatomical regions were reduced/increased, with the aim to pinpoint critical regions sparing of which significantly reduces toxicity risks. The obtained risk maps were computed for individual anatomical regions inside the liver and statistically compared to the existing clinical studies. Results: A database of 122 liver stereotactic body RT (SBRT) executed at Stanford Hospital from July 2004 and November 2015 was assembled. All patients treated for primary liver cancer, mainly hepatocellular carcinoma and cholangiocarcinoma, with complete follow-ups were extracted from the database. The SBRT treatment doses ranged from 26 to 50 Gy delivered in 1–5 fractions for primary liver cancer. The patients were followed up for 1–68 months depending on the survival time. The CNNs were trained to recognize acute and late grade 3+ biliary stricture/obstruction, hepatic failure or decompensation, hepatobiliary infection, liver function test (LFT) elevation or/and portal vein thrombosis, named for convenience hepatobiliary (HB) toxicities. The toxicity prediction accuracy was of 0.73 measured in terms of the area under the receiving operator characteristic curve. Significantly higher risk scores (P < 0.05) of HB toxicity manifestation were associated with irradiation for the hepatobiliary tract in comparison to the risk scores for liver segments I–VIII and portal vein. This observation is in strong agreement with anatomical and clinical expectations. Conclusion: In this work, we proposed and validated a universal deep learning-based solution for the identification of radiosensitive anatomical regions. Without any prior anatomical knowledge, CNNs automatically recognized the importance of hepatobiliary tract sparing during liver SBRT.

KW - atlas

KW - convolutional neural networks

KW - deep learning

KW - liver radiotherapy

KW - toxicity

KW - treatment outcome prediction

UR - http://www.scopus.com/inward/record.url?scp=85085751520&partnerID=8YFLogxK

U2 - 10.1002/mp.14235

DO - 10.1002/mp.14235

M3 - Journal article

C2 - 32406531

AN - SCOPUS:85085751520

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

ER -

ID: 243524213