Texture Analysis of Standard Magnetic Resonance Images to Predict Response to Gamma Knife Radiosurgery in Vestibular Schwannomas

Speckter, Herwin; Santana, Jairo; Bido, Jose; Hernandez, Giancarlo; Rivera, Diones; Suazo, Luis; Valenzuela, Santiago; Oviedo, Jairo; Gonzalez, Cesar; Stoeter, P.

doi:10.1016/j.wneu.2019.08.193

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…Thirty three articles were excluded due to predicting only pathological features, e.g., grade (n = 16), or differentiating between tumor entities (n = 8) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Thirty four articles were excluded due to predicting only clinical parameters, e.g., tumor consistency (n = 7), response/treatment outcome (n = 12) or brain/bone invasion (n = 4) [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , …”

Section: Resultsmentioning

confidence: 99%

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusion: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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“…7 In addition, recent studies have established relationships between MRI texture parameters and the prediction of VS enlargement after radiosurgery. [8][9][10] Although these results are promising and exciting, the aforementioned texture analysis studies in VS all take place in the context of tumors that did not undergo surgical resection, and therefore, there has been no correlation between these texture parameters and underling tumor histology. In addition, given concerns surrounding potential variability in data acquisition for texture analysis, multiple studies have argued that there is a need to explore how the values of these parameters are affected by differences in image acquisition.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of VS, previous work has shown that a cystic or heterogenous appearance on MRI was associated with an increase in Antoni type B/mixed tissue 7 . In addition, recent studies have established relationships between MRI texture parameters and the prediction of VS enlargement after radiosurgery 8–10 . Although these results are promising and exciting, the aforementioned texture analysis studies in VS all take place in the context of tumors that did not undergo surgical resection, and therefore, there has been no correlation between these texture parameters and underling tumor histology.…”

Section: Introductionmentioning

confidence: 99%

MRI Texture Features are Associated with Vestibular Schwannoma Histology

et al. 2020

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Objectives/Hypothesis: To determine if commonly used radiomics features have an association with histological findings in vestibular schwannomas (VS).Study Design: Retrospective case-series. Methods: Patients were selected from an internal database of those who had a gadolinium-enhanced T1-weighted MRI scan captured prior to surgical resection of VS. Texture features from the presurgical magnetic resonance image (MRI) were extracted, and pathologists examined the resected tumors to assess for the presence of mucin, lymphocytes, necrosis, and hemosiderin and used a validated computational tool to determine cellularity. Sensitivity, specificity, and positive likelihood ratios were also computed for selected features using the Youden index to determine the optimal cut-off value.Results: A total of 45 patients were included. We found significant associations between multiple MRI texture features and the presence of mucin, lymphocytes, hemosiderin, and cellularity. No significant associations between MRI texture features and necrosis were identified. We were able to identify significant positive likelihood ratios using Youden index cut-off values for mucin (2.3; 95% CI 1.2-4.3), hemosiderin (1.5; 95% CI 1.04-2.1), lymphocytes (3.8; 95% CI 1.2-11.7), and necrosis (1.5; 95% CI 1.1-2.2).Conclusions: MRI texture features are associated with underlying histology in VS.

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“…[23][24][25][26] MRI is an imaging modality for patients who undergo SRS, and the multiple MR sequences that are used often comprise T1-weighted contrast-enhanced (T1W + C) and T2-weighted (T2W) imaging that are mainly acquired for treatment planning and SRS effect assessment. 27,28 This study aims to explore the feasibility of deep learning-based SRS lesion delineation framework for FL.…”

mentioning

confidence: 99%

“…Stereotactic radiosurgery (SRS) is one of the promising treatments for intracranial lesion, and several studies have proposed the deep learning‐based lesion segmentation methods to assist the physicians to delineate the lesions during dose planning and longitudinal follow‐up 23–26 . MRI is an imaging modality for patients who undergo SRS, and the multiple MR sequences that are used often comprise T1‐weighted contrast‐enhanced (T1W + C) and T2‐weighted (T2W) imaging that are mainly acquired for treatment planning and SRS effect assessment 27,28 . This study aims to explore the feasibility of deep learning‐based SRS lesion delineation framework for FL.…”

mentioning

confidence: 99%

Federated Learning: A Cross‐Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery

Lee,

Hong,

Lin

et al. 2023

Magnetic Resonance Imaging

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BackgroundDeep learning–based segmentation algorithms usually required large or multi‐institute data sets to improve the performance and ability of generalization. However, protecting patient privacy is a key concern in the multi‐institutional studies when conventional centralized learning (CL) is used.PurposeTo explores the feasibility of a proposed lesion delineation for stereotactic radiosurgery (SRS) scheme for federated learning (FL), which can solve decentralization and privacy protection concerns.Study TypeRetrospective.Subjects506 and 118 vestibular schwannoma patients aged 15–88 and 22–85 from two institutes, respectively; 1069 and 256 meningioma patients aged 12–91 and 23–85, respectively; 574 and 705 brain metastasis patients aged 26–92 and 28–89, respectively.Field Strength/Sequence5T, spin‐echo, and gradient‐echo.AssessmentThe proposed lesion delineation method was integrated into an FL framework, and CL models were established as the baseline. The effect of image standardization strategies was also explored. The dice coefficient was used to evaluate the segmentation between the predicted delineation and the ground truth, which was manual delineated by neurosurgeons and a neuroradiologist.Statistical TestsThe paired t‐test was applied to compare the mean for the evaluated dice scores (p < 0.05).ResultsFL performed the comparable mean dice coefficient to CL for the testing set of Taipei Veterans General Hospital regardless of standardization and parameter; for the Taichung Veterans General Hospital data, CL significantly (p < 0.05) outperformed FL while using bi‐parameter, but comparable results while using single‐parameter. For the non‐SRS data, FL achieved the comparable applicability to CL with mean dice 0.78 versus 0.78 (without standardization), and outperformed to the baseline models of two institutes.Data ConclusionThe proposed lesion delineation successfully implemented into an FL framework. The FL models were applicable on SRS data of each participating institute, and the FL exhibited comparable mean dice coefficient to CL on non‐SRS dataset. Standardization strategies would be recommended when FL is used.Level of Evidence4Technical EfficacyStage 1

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Texture Analysis of Standard Magnetic Resonance Images to Predict Response to Gamma Knife Radiosurgery in Vestibular Schwannomas

Cited by 14 publications

References 19 publications

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

MRI Texture Features are Associated with Vestibular Schwannoma Histology

Federated Learning: A Cross‐Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery

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