Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery

Huang, Ching Yuang; Lee, Cheng‐Chia; Yang, Huai Che; Lin, Chung Jung; Wu, Hsiu Mei; Chung, Wen‐Hsin; Shiau, Cheng Ying; Guo, Wan Yuo; Pan, David Hung Chi; Peng, Syu‐Jyun

doi:10.1007/s11060-019-03343-4

Cited by 43 publications

(50 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Goodman et al. reported that the pattern of tumor images seen on the Gd-contrast enhanced T1-weighted MR images is valuable for predicting the response of a tumor to radiosurgery ( 20 ). The current study used radiomics features extracted from radiotherapy planning MRI (Gd-contrast enhanced T1-weighted) to predict the local response (LR) by a machine learning (ML) method with a neural network (NN) classifier.…”

Section: Discussionmentioning

confidence: 99%

“…Goodman et al. classified the lesion characteristics into homogeneous, heterogeneous, or ring-enhancing by the pattern of enhancement ( 20 ). The uniform enhancement of the entire lesion was defined as homogeneous.…”

Section: Methodsmentioning

confidence: 99%

“…showed that important prognostic factors for complete remission were the small volume and no necrosis ( 25 ). Based on the well-accepted knowledge ( 20 , 25 ), we assigned the predicted response of the homogeneous tumors to the response group (group I) and tumors with heterogeneous or ring enhancement to the non-response group (group II). We compared the visual evaluation method and the ML method using the data in the model building dataset (115 tumors of 42 patients).…”

Section: Methodsmentioning

confidence: 99%

“…Other studies have combined radiomics with genomics to associate radiomics features with gene mutations that are clinically proven to predict therapy response ( 19 ). A recent study reported that radiomics features could potentially be used as surrogate biomarkers for predicting tumor prognosis following Gamma Knife radiosurgery (GKRS) ( 20 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting the Local Response of Metastatic Brain Tumor to Gamma Knife Radiosurgery by Radiomics With a Machine Learning Method

et al. 2021

View full text Add to dashboard Cite

PurposeThe current study proposed a model to predict the response of brain metastases (BMs) treated by Gamma knife radiosurgery (GKRS) using a machine learning (ML) method with radiomics features. The model can be used as a decision tool by clinicians for the most desirable treatment outcome.Methods and MaterialUsing MR image data taken by a FLASH (3D fast, low-angle shot) scanning protocol with gadolinium (Gd) contrast-enhanced T1-weighting, the local response (LR) of 157 metastatic brain tumors was categorized into two groups (Group I: responder and Group II: non-responder). We performed a radiomics analysis of those tumors, resulting in more than 700 features. To build a machine learning model, first, we used the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of radiomics features to the minimum number of features useful for the prediction. Then, a prediction model was constructed by using a neural network (NN) classifier with 10 hidden layers and rectified linear unit activation. The training model was evaluated with five-fold cross-validation. For the final evaluation, the NN model was applied to a set of data not used for model creation. The accuracy and sensitivity and the area under the receiver operating characteristic curve (AUC) of the prediction model of LR were analyzed. The performance of the ML model was compared with a visual evaluation method, for which the LR of tumors was predicted by examining the image enhancement pattern of the tumor on MR images.ResultsBy the LASSO analysis of the training data, we found seven radiomics features useful for the classification. The accuracy and sensitivity of the visual evaluation method were 44 and 54%. On the other hand, the accuracy and sensitivity of the proposed NN model were 78 and 87%, and the AUC was 0.87.ConclusionsThe proposed NN model using the radiomics features can help physicians to gain a more realistic expectation of the treatment outcome than the traditional method.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the Local Response of Metastatic Brain Tumor to Gamma Knife Radiosurgery by Radiomics With a Machine Learning Method

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Guidelines from the American Congress of Neurological Surgeons recommend SRS for patients with multiple brain metastases, and even for more than four lesions as long as their cumulative volume is under 7 mL [6]. Huang et al [7] reported that several radiomic features including shape flatness, skewness, Gray Level Co-occurrence Matrix (GLCM) cluster shade and GLCM correlation could predict the response of metastatic brain tumors to Gamma Knife radiosurgery in a group of lung cancer patients. Whether the local response to SRS was influenced by the patients' general medical condition and concomitant target or immunotherapies was uncertain.…”

mentioning

confidence: 99%

Editorial: advances in neuro-oncology and clinical treatment—from ASNO 2019

Chen

Wei

2020

J Neurooncol

View full text Add to dashboard Cite

show abstract

A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects

Ghaderi,

Mohammadi,

Mohammadi

et al. 2024

J of Medical Radiation Sci

View full text Add to dashboard Cite

IntroductionBrain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC.MethodsThis systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies.ResultsSixty‐five studies were included. Standard MRI sequences such as T1‐weighted (T1w), T2‐weighted (T2w) and fluid‐attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion‐weighted imaging (PWI), diffusion‐weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival.ConclusionAdvanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.

show abstract

Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery

Cited by 43 publications

References 46 publications

Predicting the Local Response of Metastatic Brain Tumor to Gamma Knife Radiosurgery by Radiomics With a Machine Learning Method

Predicting the Local Response of Metastatic Brain Tumor to Gamma Knife Radiosurgery by Radiomics With a Machine Learning Method

Editorial: advances in neuro-oncology and clinical treatment—from ASNO 2019

A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects

Contact Info

Product

Resources

About