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. 2019 Jan 7;19(1):6.
doi: 10.1186/s12883-018-1216-z.

Noninvasive molecular diagnosis of craniopharyngioma with MRI-based radiomics approach

Xi Chen  1 , Yusheng Tong  2 , Zhifeng Shi  2 , Hong Chen  3 , Zhong Yang  4 , Yuanyuan Wang  1 , Liang Chen  5 , Jinhua Yu  6
Affiliations

Noninvasive molecular diagnosis of craniopharyngioma with MRI-based radiomics approach

Xi Chen et al. BMC Neurol. .

Abstract

Background: Frequent somatic mutations of BRAF and CTNNB1 were identified in both histological subtypes of craniopharyngioma (adamantinomatous and papillary) which shed light on target therapy to cure this oncogenic disease. The aim of this study was to investigate the noninvasive MRI-based radiomics diagnosis to detect BRAF and CTNNB1 mutations in craniopharyngioma patients.

Methods: Forty-four patients pathologically diagnosed as adamantinomatous craniopharyngioma (ACP) or papillary craniopharyngioma (PCP) were retrospectively studied. High-throughput features were extracted from manually segmented tumors in MR images of each case. The modifications-robustness in region of interests and Random Forest-based feature selection methods were adopted to select the most significant features. Random forest classifier with 10-fold cross-validation was applied to build our radiomics model.

Results: Four features were selected to make pathological diagnosis between ACP and PCP with area under the receiver operating characteristic curve (AUC) of 0.89, accurancy (ACC) of 0.86, sensitivity (SENS) of 0.89 and specificity (SPEC) of 0.85. The other two features were applied to estimate BRAF V600E mutation with AUC of 0.91, ACC of 0.93, SENS of 0.83 and SPEC of 0.97. Accurate predication of CTNNB1 mutation by three selected features was realized with AUC of 0.93, ACC of 0.86, SENS of 0.86 and SPEC of 0.86.

Conclusions: We developed a reliable MRI-based radiomics approach to perform pathological and molecular diagnosis in craniopharyngioma patients with considerably accurate prediction, which could offer potential guidance for clinical decision-making.

Keywords: Craniopharyngioma; Machine learning; Molecular diagnosis; Non-invasiveness; Radiomics approach.

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Conflict of interest statement

Ethics approval and consent to participate

The present study was approved by the Ethics Committee of Hushan Hospital of Fudan University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee, and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. A written informed consent was obtained from all individual participants included in the study. For minor patients (< 16), the written consent form was obtained from the accompanying parents.

Consent for publication

A written consent form was obtained from all participants for potentially publishing their clinical data and images while protecting their personal information. For participants under the age of 16, we obtained the written consent form from their parents.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Tumor segmentation results of eight representative axial T1-MPRAGE MR images obtained obtained from one BRAF mutant case. In each image, the area surrounded by red line indicated the tumor
Fig. 2
Fig. 2
The violin plot of discriminative features. a Dissimilarity of LLL decomposition (feature A), kurtosis (feature B), root mean square (feature C) and compactness (feature D); b small zone emphasis of HHL decomposition (feature E) and short run low gray-level emphasis (feature F); c h-skewness of HLL decomposition (feature G), h-mean of HHH decomposition (feature H) and short run low gray-level emphasis (feature I). Mann-Whitney’ test was used to assess significance of difference and p value was put above the violin plot of each feature
Fig. 3
Fig. 3
ROC curves of prediction before and after feature selection based on main dataset. a Pathological subtypes ROC curve; b BRAF gene ROC curve; c CTNNB1 gene ROC curve
Fig. 4
Fig. 4
Three radiomics nomograms integrate four discriminative features in main dataset. a Feature A, feature B, feature C and feature D of pathological subtypes classification; b feature E and feature F of BRAF gene prediction; c feature G, feature H, and feature I of CTNNB1 gene estimation
Fig. 5
Fig. 5
ROC curves of estimation after feature selection based on main dataset and extensional dataset. a Pathological types ROC curve; b BRAF gene ROC curve; c CTNNB1 gene ROC curve
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