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• Clinical Article •
ADC radiomics model in predicting 1p/19q molecular features of lower-grade gliomas
WANG Hanwei  ZENG Linlan  ZHAO Mimi  LI Xuan  XIE Huan  TIAN Jing  SUN Jie  CHEN Lizhao  WANG Shunan 

Cite this article as WANG H W, ZENG L L, ZHAO M M, et al. ADC radiomics model in predicting 1p/19q molecular features of lower-grade gliomas[J]. Chin J Magn Reson Imaging, 2024, 15(5): 41-46, 54. DOI:10.12015/issn.1674-8034.2024.05.008.


[Abstract] Objective To establish and validate a radiomics model to predict 1p/19q molecular feature of adult intracranial lower-grade gliomas (LGG) based on preoperative magnetic resonance apparent diffusion coefficient (ADC) map.Materials and Methods A total of 146 adult intracranial LGG (WHO grade 2-3) patients confirmed by postoperative pathology in our hospital from January 2017 to December 2021 with complete magnetic resonance data were retrospectively analyzed, including 68 cases with 1p/19q co-deleted (1p/19q-Codel) and 78 cases with 1p/19q non-codeleted (1p/19q-Noncodel). A completely random method was used to divide the training and validation sets in a 7:3 ratio. Image segmentation was performed independently by a radiologist using ITK-SNAP software, and 30 patient images were then segmented between radiologists to evaluate the stability of the extracted features. The volume of interest (VOI) was defined as the abnormal area in FLAIR, excluding obvious cystic and necrosis. The VOI extracted from the FLAIR image was copied to the registered ADC map, and then the radiomics features were extracted using Python software, and the features with good stability were retained for Z-score standardization. Pearson or Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) analysis were used for feature selection. The radiomics score (Rad-score) model was built using the selected radiomics features. The performance of the Rad-score model was evaluated using receiver operating characteristic (ROC) curves and validated within validation sets.Results One hundred and forty six LGG patients were randomly divided into the training set (n=102) and the validation set (n=44) in a 7∶3 ratio. There was no statistical difference in clinical features between the two sets (P>0.05). Fifteen non-zero coefficient features were selected by intra-rater and inter-rater correlation coefficients, Pearson or Spearman correlation analysis and LASSO analysis, and the Rad-score model was constructed. There were significant differences in Rad-score between the 1p/19q-Codel and the 1p/19q-Noncodel in both the training and validation sets (P<0.001). At the same time, the Rad-score model showed good predictive performance in both the training and validation sets, with an area under the curve (AUC) value of 0.896 in the training set, the accuracy was 85.29%, the sensitivity was 87.72% and the specificity was 82.22%. The AUC value of the validation set was 0.778, the accuracy was 77.27%, the sensitivity was 71.43% and the specificity was 82.61%.Conclusions The radiomics model based on the preoperative ADC map can noninvasively predict the 1p/19q molecular features in adult intracranial LGG.
[Keywords] lower-grade gliomas；radiomics；magnetic resonance imaging；apparent diffusion coefficient；1p/19q；molecular typing

WANG Hanwei1, 2   ZENG Linlan1, 2   ZHAO Mimi1, 2   LI Xuan1, 2   XIE Huan1, 2   TIAN Jing1, 2   SUN Jie1, 2   CHEN Lizhao3   WANG Shunan1, 2*  

1 Department of Radiology, Daping Hospital, Army Military Medical University, Chongqing 400042, China

2 Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Chongqing 400042, China

3 Department of Neurosurgery, Daping Hospital, Army Military Medical University, Chongqing 400042, China

Corresponding author: WANG S N, E-mail: wangshunan@tmmu.edu.cn

Conflicts of interest   None.

Received  2023-12-18
Accepted  2024-04-30
DOI: 10.12015/issn.1674-8034.2024.05.008
Cite this article as WANG H W, ZENG L L, ZHAO M M, et al. ADC radiomics model in predicting 1p/19q molecular features of lower-grade gliomas[J]. Chin J Magn Reson Imaging, 2024, 15(5): 41-46, 54. DOI:10.12015/issn.1674-8034.2024.05.008.

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