Radiomics in Radiation Oncology for Gynecological Malignancies: a Review of Literature

Overview

Journal Br J Radiol

Publisher Oxford University Press

Specialty Radiology

Date 2021 Apr 21

PMID 33882246

Citations 4

Authors

Morgan Michalet

David Azria

Marion Tardieu

Hichem Tibermacine

Stephanie Nougaret

Affiliations

Soon will be listed here.

Abstract

Radiomics is the extraction of a significant number of quantitative imaging features with the aim of detecting information in correlation with useful clinical outcomes. Features are extracted, after delineation of an area of interest, from a single or a combined set of imaging modalities (including X-ray, US, CT, PET/CT and MRI). Given the high dimensionality, the analytical process requires the use of artificial intelligence algorithms. Firstly developed for diagnostic performance in radiology, it has now been translated to radiation oncology mainly to predict tumor response and patient outcome but other applications have been developed such as dose painting, prediction of side-effects, and quality assurance. In gynecological cancers, most studies have focused on outcomes of cervical cancers after chemoradiation. This review highlights the role of this new tool for the radiation oncologists with particular focus on female GU oncology.

Citing Articles

Robustness of radiomics features on 0.35 T magnetic resonance imaging for magnetic resonance-guided radiotherapy.

Michalet M, Valenzuela G, Debuire P, Riou O, Azria D, Nougaret S Phys Imaging Radiat Oncol. 2024; 31:100613.

PMID: 39140002 PMC: 11320460. DOI: 10.1016/j.phro.2024.100613.

Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions.

OSullivan N, Kelly M Curr Oncol. 2023; 30(5):4936-4945.

PMID: 37232830 PMC: 10217419. DOI: 10.3390/curroncol30050372.

An Integrated Clinical-MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer.

Li X, Marcus D, Russell J, Aboagye E, Ellis L, Sheeka A J Magn Reson Imaging. 2022; 57(6):1922-1933.

PMID: 36484309 PMC: 10947322. DOI: 10.1002/jmri.28544.

female genitourinary oncology special feature: introductory editorial.

Nougaret S, Vargas H, Sala E Br J Radiol. 2021; 94(1125):20219003.

PMID: 34415200 PMC: 9327764. DOI: 10.1259/bjr.20219003.

References

Sheikh K, Lee S, Cheng Z, Lakshminarayanan P, Peng L, Han P . Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands. Radiat Oncol. 2019; 14(1):131. PMC: 6664784. DOI: 10.1186/s13014-019-1339-4. View

Zhang Y, Oikonomou A, Wong A, Haider M, Khalvati F . Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer. Sci Rep. 2017; 7:46349. PMC: 5394465. DOI: 10.1038/srep46349. View

Shiradkar R, Podder T, Algohary A, Viswanath S, Ellis R, Madabhushi A . Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI. Radiat Oncol. 2016; 11(1):148. PMC: 5103611. DOI: 10.1186/s13014-016-0718-3. View

Giardino A, Gupta S, Olson E, Sepulveda K, Lenchik L, Ivanidze J . Role of Imaging in the Era of Precision Medicine. Acad Radiol. 2017; 24(5):639-649. DOI: 10.1016/j.acra.2016.11.021. View

Zwanenburg A, Vallieres M, Abdalah M, Aerts H, Andrearczyk V, Apte A . The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020; 295(2):328-338. PMC: 7193906. DOI: 10.1148/radiol.2020191145. View

Yu Y, Zhang R, Dong R, Hu Q, Yu T, Liu F . Feasibility of an ADC-based radiomics model for predicting pelvic lymph node metastases in patients with stage IB-IIA cervical squamous cell carcinoma. Br J Radiol. 2019; 92(1097):20180986. PMC: 6580913. DOI: 10.1259/bjr.20180986. View

Steyerberg E, Vickers A, Cook N, Gerds T, Gonen M, Obuchowski N . Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2009; 21(1):128-38. PMC: 3575184. DOI: 10.1097/EDE.0b013e3181c30fb2. View

Glowa C, Karger C, Brons S, Zhao D, Mason R, Huber P . Carbon ion radiotherapy decreases the impact of tumor heterogeneity on radiation response in experimental prostate tumors. Cancer Lett. 2016; 378(2):97-103. PMC: 7747017. DOI: 10.1016/j.canlet.2016.05.013. View

Parmar C, Grossmann P, Bussink J, Lambin P, Aerts H . Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep. 2015; 5:13087. PMC: 4538374. DOI: 10.1038/srep13087. View

10.

van Dijk L, Noordzij W, Brouwer C, Boellaard R, Burgerhof J, Langendijk J . F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia. Radiother Oncol. 2017; 126(1):89-95. DOI: 10.1016/j.radonc.2017.08.024. View

11.

Dou T, Coroller T, van Griethuysen J, Mak R, Aerts H . Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC. PLoS One. 2018; 13(11):e0206108. PMC: 6214508. DOI: 10.1371/journal.pone.0206108. View

12.

Nyflot M, Thammasorn P, Wootton L, Ford E, Chaovalitwongse W . Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks. Med Phys. 2018; 46(2):456-464. DOI: 10.1002/mp.13338. View

13.

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

14.

Thomeer M, Vandecaveye V, Braun L, Mayer F, Franckena-Schouten M, de Boer P . Evaluation of T2-W MR imaging and diffusion-weighted imaging for the early post-treatment local response assessment of patients treated conservatively for cervical cancer: a multicentre study. Eur Radiol. 2018; 29(1):309-318. PMC: 6291430. DOI: 10.1007/s00330-018-5510-3. View

15.

Lue K, Wu Y, Liu S, Hsieh T, Chuang K, Lin H . Prognostic Value of Pretreatment Radiomic Features of 18F-FDG PET in Patients With Hodgkin Lymphoma. Clin Nucl Med. 2019; 44(10):e559-e565. DOI: 10.1097/RLU.0000000000002732. View

16.

Moran A, Daly M, Yip S, Yamamoto T . Radiomics-based Assessment of Radiation-induced Lung Injury After Stereotactic Body Radiotherapy. Clin Lung Cancer. 2017; 18(6):e425-e431. DOI: 10.1016/j.cllc.2017.05.014. View

17.

Hou L, Zhou W, Ren J, Du X, Xin L, Zhao X . Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer. Front Oncol. 2020; 10:1393. PMC: 7468409. DOI: 10.3389/fonc.2020.01393. View

18.

Jalaguier-Coudray A, Villard-Mahjoub R, Delouche A, Delarbre B, Lambaudie E, Houvenaeghel G . Value of Dynamic Contrast-enhanced and Diffusion-weighted MR Imaging in the Detection of Pathologic Complete Response in Cervical Cancer after Neoadjuvant Therapy: A Retrospective Observational Study. Radiology. 2017; 284(2):432-442. DOI: 10.1148/radiol.2017161299. View

19.

Ibragimov B, Xing L . Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017; 44(2):547-557. PMC: 5383420. DOI: 10.1002/mp.12045. View

20.

De Bernardi E, Buda A, Guerra L, Vicini D, Elisei F, Landoni C . Radiomics of the primary tumour as a tool to improve F-FDG-PET sensitivity in detecting nodal metastases in endometrial cancer. EJNMMI Res. 2018; 8(1):86. PMC: 6104464. DOI: 10.1186/s13550-018-0441-1. View