Forskning
Udskriv Udskriv
Switch language
Region Hovedstaden - en del af Københavns Universitetshospital
Udgivet

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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

DOI

  1. Individualized estimates of overall survival in radiation therapy plan optimization - A concept study

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Modeling tumor control probability for spatially inhomogeneous risk of failure based on clinical outcome data

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Technical note: DoseMapper - A validated GUI based exact numerical modelling method of shielding in PET/CT facilities

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. A patch-based pseudo-CT approach for MRI-only radiotherapy in the pelvis

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. Outcome in patients with isolated regional recurrence after primary radiotherapy for head and neck cancer

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Biological optimization for mediastinal lymphoma radiotherapy - a preliminary study

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Harnessing data science to advance radiation oncology

    Publikation: Bidrag til tidsskriftReviewForskningpeer review

  • Bulat Ibragimov
  • Diego A S Toesca
  • Daniel T Chang
  • Yixuan Yuan
  • Albert C Koong
  • Lei Xing
  • Ivan R Vogelius
Vis graf over relationer

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.

OriginalsprogEngelsk
TidsskriftMedical Physics
Vol/bind47
Udgave nummer8
Sider (fra-til)3721-3731
Antal sider11
ISSN0094-2405
DOI
StatusUdgivet - aug. 2020

Bibliografisk note

© 2020 American Association of Physicists in Medicine.

ID: 60922267