"Après Mois, Le Déluge": Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era

Overview

Journal Front Oncol

Specialty Oncology

Date 2019 Oct 22

PMID 31632914

Citations 9

Authors

Kendall J Kiser

Benjamin D Smith

Jihong Wang

Clifton D Fuller

Affiliations

Soon will be listed here.

Abstract

Magnetic resonance imaging provides a sea of quantitative and semi-quantitative data. While radiation oncologists already navigate a pool of clinical (semantic) and imaging data, the tide will swell with the advent of hybrid MRI/linear accelerator devices and increasing interest in MRI-guided radiotherapy (MRIgRT), including adaptive MRIgRT. The variety of MR sequences (of greater complexity than the single parameter Hounsfield unit of CT scanning routinely used in radiotherapy), the workflow of adaptive fractionation, and the sheer quantity of daily images acquired are challenges for scaling this technology. Biomedical informatics, which is the science of information in biomedicine, can provide helpful insights for this looming transition. Funneling MRIgRT data into clinically meaningful information streams requires committing to the flow of inter-institutional data accessibility and interoperability initiatives, standardizing MRIgRT dosimetry methods, streamlining MR linear accelerator workflow, and standardizing MRI acquisition and post-processing. This review will attempt to conceptually ford these topics using clinical informatics approaches as a theoretical bridge.

Citing Articles

Institutionalisation of convergent medical innovation: an empirical study of the MRI-guided linear accelerator in the Netherlands and the United States.

Hehakaya C, Moors E Innovation (North Syd). 2025; 27(1):74-95.

PMID: 39935856 PMC: 11809769. DOI: 10.1080/14479338.2023.2213212.

Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge.

Wahid K, Dede C, El-Habashy D, Kamel S, Rooney M, Khamis Y ArXiv. 2024; .

PMID: 39650598 PMC: 11623708.

Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study.

Boldrini L, Chiloiro G, Cusumano D, Yadav P, Yu G, Romano A Radiol Med. 2024; 129(4):615-622.

PMID: 38512616 DOI: 10.1007/s11547-024-01761-7.

Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy.

Wahid K, Xu J, El-Habashy D, Khamis Y, Abobakr M, McDonald B Front Oncol. 2022; 12:975902.

PMID: 36425548 PMC: 9679225. DOI: 10.3389/fonc.2022.975902.

Implementation of Magnetic Resonance Imaging-Guided Radiation Therapy in Routine Care: Opportunities and Challenges in the United States.

Hehakaya C, Sharma A, van der Voort Van Zijp J, Grobbee D, Verkooijen H, Izaguirre E Adv Radiat Oncol. 2022; 7(5):100953.

PMID: 35651662 PMC: 9149022. DOI: 10.1016/j.adro.2022.100953.

References

Bi W, Hosny A, Schabath M, Giger M, Birkbak N, Mehrtash A . Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019; 69(2):127-157. PMC: 6403009. DOI: 10.3322/caac.21552. View

Stanley J, Dunscombe P, Lau H, Burns P, Lim G, Liu H . The effect of contouring variability on dosimetric parameters for brain metastases treated with stereotactic radiosurgery. Int J Radiat Oncol Biol Phys. 2013; 87(5):924-31. DOI: 10.1016/j.ijrobp.2013.09.013. View

Salembier C, Villeirs G, De Bari B, Hoskin P, Pieters B, van Vulpen M . ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer. Radiother Oncol. 2018; 127(1):49-61. DOI: 10.1016/j.radonc.2018.01.014. View

Rajkomar A, Dean J, Kohane I . Machine Learning in Medicine. N Engl J Med. 2019; 380(14):1347-1358. DOI: 10.1056/NEJMra1814259. View

Wang X, Li L, Hu C, Qiu J, Xu Z, Feng Y . A comparative study of three CT and MRI registration algorithms in nasopharyngeal carcinoma. J Appl Clin Med Phys. 2009; 10(2):3-10. PMC: 5720458. DOI: 10.1120/jacmp.v10i2.2906. View

Hinton G . Deep Learning-A Technology With the Potential to Transform Health Care. JAMA. 2018; 320(11):1101-1102. DOI: 10.1001/jama.2018.11100. View

Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L . Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett. 2017; 403:21-27. DOI: 10.1016/j.canlet.2017.06.004. View

Rajkomar A, Hardt M, Howell M, Corrado G, Chin M . Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med. 2018; 169(12):866-872. PMC: 6594166. DOI: 10.7326/M18-1990. View

Lim-Reinders S, Keller B, Al-Ward S, Sahgal A, Kim A . Online Adaptive Radiation Therapy. Int J Radiat Oncol Biol Phys. 2017; 99(4):994-1003. DOI: 10.1016/j.ijrobp.2017.04.023. View

10.

Bernstam E, Smith J, Johnson T . What is biomedical informatics?. J Biomed Inform. 2009; 43(1):104-10. PMC: 2814957. DOI: 10.1016/j.jbi.2009.08.006. View

11.

Li Y, Jiang X, Wang S, Xiong H, Ohno-Machado L . VERTIcal Grid lOgistic regression (VERTIGO). J Am Med Inform Assoc. 2015; 23(3):570-9. PMC: 4901373. DOI: 10.1093/jamia/ocv146. View

12.

Persson G, Nygaard D, Hollensen C, Munck Af Rosenschold P, Mouritsen L, Due A . Interobserver delineation variation in lung tumour stereotactic body radiotherapy. Br J Radiol. 2012; 85(1017):e654-60. PMC: 3487081. DOI: 10.1259/bjr/76424694. View

13.

Yang X, Wu N, Cheng G, Zhou Z, Yu D, Beitler J . Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy. Int J Radiat Oncol Biol Phys. 2014; 90(5):1225-33. PMC: 4362545. DOI: 10.1016/j.ijrobp.2014.08.350. View

14.

Henke L, Kashani R, Robinson C, Curcuru A, DeWees T, Bradley J . Phase I trial of stereotactic MR-guided online adaptive radiation therapy (SMART) for the treatment of oligometastatic or unresectable primary malignancies of the abdomen. Radiother Oncol. 2017; 126(3):519-526. DOI: 10.1016/j.radonc.2017.11.032. View

15.

Chandarana H, Wang H, Tijssen R, Das I . Emerging role of MRI in radiation therapy. J Magn Reson Imaging. 2018; 48(6):1468-1478. PMC: 6986460. DOI: 10.1002/jmri.26271. View

16.

Heerkens H, van Vulpen M, Erickson B, Reerink O, Intven M, van den Berg C . MRI guided stereotactic radiotherapy for locally advanced pancreatic cancer. Br J Radiol. 2018; 91(1091):20170563. PMC: 6475956. DOI: 10.1259/bjr.20170563. View

17.

Olberg S, Green O, Cai B, Yang D, Rodriguez V, Zhang H . Optimization of treatment planning workflow and tumor coverage during daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT) of pancreatic cancer. Radiat Oncol. 2018; 13(1):51. PMC: 5866525. DOI: 10.1186/s13014-018-1000-7. View

18.

Law M, Liu B, Chan L . Informatics in radiology: DICOM-RT-based electronic patient record information system for radiation therapy. Radiographics. 2009; 29(4):961-72. DOI: 10.1148/rg.294085073. View

19.

Bayouth J, Low D, Zaidi H . MRI-linac systems will replace conventional IGRT systems within 15 years. Med Phys. 2019; 46(9):3753-3756. DOI: 10.1002/mp.13657. View

20.

Fortunati V, Verhaart R, Angeloni F, van der Lugt A, Niessen W, Veenland J . Feasibility of multimodal deformable registration for head and neck tumor treatment planning. Int J Radiat Oncol Biol Phys. 2014; 90(1):85-93. DOI: 10.1016/j.ijrobp.2014.05.027. View