Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC

Fan, Ying; Wang, Xinti; Dong, Yue; Cui, Enuo; Wang, Huan; Sun, Xiaohui; Su, Juan; Luo, Yahong; Yu, Tao; Jiang, Xiran

doi:10.1007/s00330-023-09709-7

Cited by 11 publications

(3 citation statements)

References 63 publications

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“…The tissues surrounding tumors likewise contain a vast amount of heterogeneous information ( 45 ), especially vasogenic edema around intracranial tumors, which are sites of altered specific molecular, cellular, biological, and radiological information. Studies in other different classification tasks and our previous studies have shown that the region of the tumor combined with peritumoral edema will effectively improve the diagnostic performance of classification models ( 46 , 47 ). In this study, peritumoral edema was included in the region of interest together, aiming to maximize the accurate prediction of Ki-67 expression status.…”

Section: Discussionmentioning

confidence: 86%

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

Zhao,

Yang,

Bai

et al. 2024

Front. Med.

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ObjectivesTo investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL).Materials and methodsMRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction.ResultsThe mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision.ConclusionIn this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task.Clinical relevance statementKi-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

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Section: Discussionmentioning

confidence: 86%

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

Zhao,

Yang,

Bai

et al. 2024

Front. Med.

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“…The intensity values of the regions were normalized to 0–255 levels. For CNNs mean normalization is applied 16,17 …”

Section: Methodsmentioning

confidence: 99%

Differentiation of lung and breast cancer brain metastases: Comparison of texture analysis and deep convolutional neural networks

Gultekin,

Peker,

Oktay

et al. 2023

J of Clinical Ultrasound

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PurposeMetastases are the most common neoplasm in the adult brain. In order to initiate the treatment, an extensive diagnostic workup is usually required. Radiomics is a discipline aimed at transforming visual data in radiological images into reliable diagnostic information. We aimed to examine the capability of deep learning methods to classify the origin of metastatic lesions in brain MRIs and compare the deep Convolutional Neural Network (CNN) methods with image texture based features.MethodsOne hundred forty three patients with 157 metastatic brain tumors were included in the study. The statistical and texture based image features were extracted from metastatic tumors after manual segmentation process. Three powerful pre‐trained CNN architectures and the texture‐based features on both 2D and 3D tumor images were used to differentiate lung and breast metastases. Ten‐fold cross‐validation was used for evaluation. Accuracy, precision, recall, and area under curve (AUC) metrics were calculated to analyze the diagnostic performance.ResultsThe texture‐based image features on 3D volumes achieved better discrimination results than 2D image features. The overall performance of CNN architectures with 3D inputs was higher than the texture‐based features. Xception architecture, with 3D volumes as input, yielded the highest accuracy (0.85) while the AUC value was 0.84. The AUC values of VGG19 and the InceptionV3 architectures were 0.82 and 0.81, respectively.ConclusionCNNs achieved superior diagnostic performance in differentiating brain metastases from lung and breast malignancies than texture‐based image features. Differentiation using 3D volumes as input exhibited a higher success rate than 2D sagittal images.

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“…Chen's study demonstrated that MR imaging based radiomic analysis of BM in patients with primary lung cancer may be used to classify EGFR, ALK, and Frontiers in Pharmacology frontiersin.org KRAS mutation status and the AUC values based on cross validation was 0.912, 0.915, and 0.985, respectively (Chen et al, 2020b). Fan's foundings suggested that multiregional radiomics of BM for predicting EGFR mutations and response to EGFR-TKI and AUC were 0.889 and 0.808 in external validation cohort respectively (Fan et al, 2023a). The research of Fan showed preoperative MRI-based radiomics could assess T790M resistance mutation after EGFR-TKI treatment in NSCLC patients with BM with AUCs of 0.860 in the external validation sets (Fan et al, 2023b).…”

Section: Metastases Predictionmentioning

confidence: 98%

Application of radiomics in diagnosis and treatment of lung cancer

Pan,

Feng,

Liu

et al. 2023

Front. Pharmacol.

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Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks.

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Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC

Cited by 11 publications

References 63 publications

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

Differentiation of lung and breast cancer brain metastases: Comparison of texture analysis and deep convolutional neural networks

Application of radiomics in diagnosis and treatment of lung cancer

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