nach oben

30.04.2024 | Research

Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases

verfasst von: Mustafa M. Basree, Chengnan Li, Hyemin Um, Anthony H. Bui, Manlu Liu, Azam Ahmed, Pallavi Tiwari, Alan B. McMillan, Andrew M. Baschnagel

Erschienen in: Journal of Neuro-Oncology

Einloggen, um Zugang zu erhalten

Abstract

Objective

Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differentiate RN from recurrence in patients with brain metastases treated with SRS.

Methods

Patients with brain metastases treated with SRS who developed either RN or tumor reccurence were retrospectively identified. Image preprocessing and radiomic feature extraction were performed using ANTsPy and PyRadiomics, yielding 105 features from MRI T1-weighted post-contrast (T1c), T2, and fluid-attenuated inversion recovery (FLAIR) images. Univariate analysis assessed significance of individual features. Multivariable analysis employed various classifiers on features identified as most discriminative through feature selection. ML models were evaluated through cross-validation, selecting the best model based on area under the receiver operating characteristic (ROC) curve (AUC). Specificity, sensitivity, and F1 score were computed.

Results

Sixty-six lesions from 55 patients were identified. On univariate analysis, 27 features from the T1c sequence were statistically significant, while no features were significant from the T2 or FLAIR sequences. For clinical variables, only immunotherapy use after SRS was significant. Multivariable analysis of features from the T1c sequence yielded an AUC of 76.2% (standard deviation [SD] ± 12.7%), with specificity and sensitivity of 75.5% (± 13.4%) and 62.3% (± 19.6%) in differentiating radionecrosis from recurrence.

Conclusions

Radiomics with ML may assist the diagnostic ability of distinguishing RN from tumor recurrence after SRS. Further work is needed to validate this in a larger multi-institutional cohort and prospectively evaluate it’s utility in patient care.

Nur mit Berechtigung zugänglich

Ocaña-Tienda B, Pérez-Beteta J, Villanueva-García JD et al (2023) A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data. Sci Data 10:208. https://doi.org/10.1038/s41597-023-02123-0CrossRefPubMedPubMedCentral

Wang XY, Rosen MN, Chehade R et al (2022) Analysis of Rates of Brain Metastases and Association With Breast Cancer Subtypes in Ontario. Canada JAMA Netw Open 5:e2225424. https://doi.org/10.1001/jamanetworkopen.2022.25424CrossRefPubMed

Cagney DN, Martin AM, Catalano PJ et al (2017) Incidence and prognosis of patients with brain metastases at diagnosis of systemic malignancy: a population-based study. Neuro Oncol 19:1511–1521. https://doi.org/10.1093/neuonc/nox077CrossRefPubMedPubMedCentral

Gondi V, Bauman G, Bradfield L et al (2022) Radiation Therapy for Brain Metastases: An ASTRO Clinical Practice Guideline. Pract Radiat Oncol 12:265–282. https://doi.org/10.1016/j.prro.2022.02.003CrossRefPubMed

Vogelbaum MA, Brown PD, Messersmith H et al (2022) Treatment for Brain Metastases: ASCO-SNO-ASTRO Guideline. JCO 40:492–516. https://doi.org/10.1200/JCO.21.02314CrossRef

Miller JA, Bennett EE, Xiao R et al (2016) Association Between Radiation Necrosis and Tumor Biology After Stereotactic Radiosurgery for Brain Metastasis. Int J Rad Oncol Biol Phy 96:1060–1069. https://doi.org/10.1016/j.ijrobp.2016.08.039CrossRef

Sneed PK, Mendez J, Vemer-van Den Hoek JGM, et al (2015) Adverse radiation effect after stereotactic radiosurgery for brain metastases: incidence, time course, and risk factors. JNS 1s23:373–386. https://doi.org/10.3171/2014.10.JNS141610

Kohutek ZA, Yamada Y, Chan TA et al (2015) Long-term risk of radionecrosis and imaging changes after stereotactic radiosurgery for brain metastases. J Neurooncol 125:149–156. https://doi.org/10.1007/s11060-015-1881-3CrossRefPubMedPubMedCentral

Siddiqui ZA, Squires BS, Johnson MD et al (2020) Predictors of radiation necrosis in long-term survivors after Gamma Knife stereotactic radiosurgery for brain metastases. Neuro-Oncology Practice 7:400–408. https://doi.org/10.1093/nop/npz067CrossRefPubMed

10.

Lawrence YR, Li XA, el Naqa I et al (2010) Radiation dose-volume effects in the brain. Int J Radiat Oncol Biol Phys 76:S20-27. https://doi.org/10.1016/j.ijrobp.2009.02.091CrossRefPubMedPubMedCentral

11.

Belka C, Budach W, Kortmann RD, Bamberg M (2001) Radiation induced CNS toxicity–molecular and cellular mechanisms. Br J Cancer 85:1233–1239. https://doi.org/10.1054/bjoc.2001.2100CrossRefPubMedPubMedCentral

12.

Lohmann P, Franceschi E, Vollmuth P et al (2022) Radiomics in neuro-oncological clinical trials. Lancet Digit Health 4:e841–e849. https://doi.org/10.1016/S2589-7500(22)00144-3CrossRefPubMed

13.

Zhou M, Scott J, Chaudhury B et al (2018) Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am J Neuroradiol 39:208–216. https://doi.org/10.3174/ajnr.A5391CrossRefPubMedPubMedCentral

14.

Rizzo S, Botta F, Raimondi S et al (2018) Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2:36. https://doi.org/10.1186/s41747-018-0068-zCrossRefPubMedPubMedCentral

15.

Kickingereder P, Isensee F, Tursunova I et al (2019) Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 20:728–740. https://doi.org/10.1016/S1470-2045(19)30098-1CrossRefPubMed

16.

Peng L, Parekh V, Huang P et al (2018) Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics. Int J Rad Oncol Biol Phys 102:1236–1243. https://doi.org/10.1016/j.ijrobp.2018.05.041CrossRef

17.

Chen X, Parekh VS, Peng L, et al (2021) Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery. Neuro-Oncology Advances 3:vdab150. https://doi.org/10.1093/noajnl/vdab150

18.

Salari E, Elsamaloty H, Ray A et al (2023) Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning. Am J Clin Oncol 46:486–495. https://doi.org/10.1097/COC.0000000000001036CrossRefPubMedPubMedCentral

19.

Choi Y, Jang J, Kim B, Ahn K-J (2023) Pretreatment MR-based radiomics in patients with glioblastoma: A systematic review and meta-analysis of prognostic endpoints. Eur J Radiol 168:111130. https://doi.org/10.1016/j.ejrad.2023.111130CrossRefPubMed

20.

Hu X, Wong KK, Young GS et al (2011) Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 33:296–305. https://doi.org/10.1002/jmri.22432CrossRefPubMedPubMedCentral

21.

Beig N, Bera K, Prasanna P et al (2020) Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma. Clin Cancer Res 26:1866–1876. https://doi.org/10.1158/1078-0432.CCR-19-2556CrossRefPubMedPubMedCentral

22.

Beig N, Singh S, Bera K et al (2021) Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in glioblastoma. Neuro Oncol 23:251–263. https://doi.org/10.1093/neuonc/noaa231CrossRefPubMed

23.

Okimoto N, Yasaka K, Fujita N et al (2024) Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography. Neuroradiology 66:63–71. https://doi.org/10.1007/s00234-023-03251-5CrossRefPubMed

24.

Nowakowski A, Lahijanian Z, Panet-Raymond V, et al (2022) Radiomics as an emerging tool in the management of brain metastases. Neuro-Oncology Advances 4:vdac141. https://doi.org/10.1093/noajnl/vdac141

25.

Hettal L, Stefani A, Salleron J et al (2020) Radiomics Method for the Differential Diagnosis of Radionecrosis Versus Progression after Fractionated Stereotactic Body Radiotherapy for Brain Oligometastasis. Radiat Res 193:471. https://doi.org/10.1667/RR15517.1CrossRefPubMed

26.

Larroza A, Moratal D, Paredes-Sánchez A et al (2015) Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. Magn Reson Imaging 42:1362–1368. https://doi.org/10.1002/jmri.24913CrossRef

27.

Leeman JE, Clump DA, Flickinger JC et al (2013) Extent of perilesional edema differentiates radionecrosis from tumor recurrence following stereotactic radiosurgery for brain metastases. Neuro Oncol 15:1732–1738. https://doi.org/10.1093/neuonc/not130CrossRefPubMedPubMedCentral

28.

Musunuru HB, Witt JS, Yadav P et al (2019) Impact of adjuvant fractionated stereotactic radiotherapy dose on local control of brain metastases. J Neurooncol 145:385–390. https://doi.org/10.1007/s11060-019-03308-7CrossRefPubMed

29.

Enright TL, Witt JS, Burr AR et al (2021) Combined Immunotherapy and Stereotactic Radiotherapy Improves Neurologic Outcomes in Patients with Non-small-cell Lung Cancer Brain Metastases. Clin Lung Cancer 22:110–119. https://doi.org/10.1016/j.cllc.2020.10.014CrossRefPubMed

30.

Anderson BM, Wahid KA, Brock KK (2021) Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays. Pract Radiat Oncol 11:226–229. https://doi.org/10.1016/j.prro.2021.02.003CrossRefPubMedPubMedCentral

31.

Fonov V, Evans AC, Botteron K et al (2011) Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54:313–327. https://doi.org/10.1016/j.neuroimage.2010.07.033CrossRefPubMed

32.

Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: Improved N3 Bias Correction. IEEE Trans Med Imaging 29:1310–1320. https://doi.org/10.1109/TMI.2010.2046908CrossRefPubMedPubMedCentral

33.

Nyul LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 19:143–150. https://doi.org/10.1109/42.836373CrossRefPubMed

34.

ANTsX. Advanced Normalization Tools (ANTs). GitHub. https://github.com/ANTsX/ANTs. Accessed 1 Nov 2023

35.

van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res 77:e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339CrossRefPubMedPubMedCentral

36.

Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Machine Intell 27:1226–1238. https://doi.org/10.1109/TPAMI.2005.159CrossRef

37.

Demircioğlu A (2021) Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics. Insights Imaging 12:172. https://doi.org/10.1186/s13244-021-01115-1CrossRefPubMedPubMedCentral

38.

Zhang Z, Yang J, Ho A et al (2018) A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 28:2255–2263. https://doi.org/10.1007/s00330-017-5154-8CrossRefPubMed

39.

Feng A, Yuan P, Huang T et al (2022) Distinguishing Tumor Recurrence From Radiation Necrosis in Treated Glioblastoma Using Multiparametric MRI. Acad Radiol 29:1320–1331. https://doi.org/10.1016/j.acra.2021.11.008CrossRefPubMed

40.

Moradmand H, Aghamiri SMR, Ghaderi R (2020) Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma. J Appl Clin Med Phys 21:179–190. https://doi.org/10.1002/acm2.12795CrossRefPubMed

41.

Bologna M, Corino V, Mainardi L (2019) Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain. Med Phys 46:5116–5123. https://doi.org/10.1002/mp.13834CrossRefPubMed

42.

Carré A, Klausner G, Edjlali M et al (2020) Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics. Sci Rep 10:12340. https://doi.org/10.1038/s41598-020-69298-zCrossRefPubMedPubMedCentral

Titel: Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases
verfasst von: Mustafa M. Basree
Chengnan Li
Hyemin Um
Anthony H. Bui
Manlu Liu
Azam Ahmed
Pallavi Tiwari
Alan B. McMillan
Andrew M. Baschnagel
Publikationsdatum: 30.04.2024
Verlag: Springer US
Erschienen in: Journal of Neuro-Oncology
Print ISSN: 0167-594X
Elektronische ISSN: 1573-7373
DOI: https://doi.org/10.1007/s11060-024-04669-4

Leitlinien kompakt für die Neurologie

Mit medbee Pocketcards sicher entscheiden.

^{Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag}

Kostenlos registrieren

Neu im Fachgebiet Neurologie

Hirnblutung unter DOAK und VKA ähnlich bedrohlich

17.05.2024 Direkte orale Antikoagulanzien Nachrichten

Kommt es zu einer nichttraumatischen Hirnblutung, spielt es keine große Rolle, ob die Betroffenen zuvor direkt wirksame orale Antikoagulanzien oder Marcumar bekommen haben: Die Prognose ist ähnlich schlecht.

Thrombektomie auch bei großen Infarkten von Vorteil

16.05.2024 Ischämischer Schlaganfall Nachrichten

Auch ein sehr ausgedehnter ischämischer Schlaganfall scheint an sich kein Grund zu sein, von einer mechanischen Thrombektomie abzusehen. Dafür spricht die LASTE-Studie, an der Patienten und Patientinnen mit einem ASPECTS von maximal 5 beteiligt waren.

Schwindelursache: Massagepistole lässt Otholiten tanzen

14.05.2024 Benigner Lagerungsschwindel Nachrichten

Wenn jüngere Menschen über ständig rezidivierenden Lagerungsschwindel klagen, könnte eine Massagepistole der Auslöser sein. In JAMA Otolaryngology warnt ein Team vor der Anwendung hochpotenter Geräte im Bereich des Nackens.

Schützt Olivenöl vor dem Tod durch Demenz?

10.05.2024 Morbus Alzheimer Nachrichten

Konsumieren Menschen täglich 7 Gramm Olivenöl, ist ihr Risiko, an einer Demenz zu sterben, um mehr als ein Viertel reduziert – und dies weitgehend unabhängig von ihrer sonstigen Ernährung. Dafür sprechen Auswertungen zweier großer US-Studien.

Update Neurologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen