Share：
Share this content in WeChat
X
[Chinese][PDF]6284
• 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.

[1]
LOUIS D N, PERRY A, WESSELING P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[2]
BRAT D J, VERHAAK R G, ALDAPE K D, et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas[J]. N Engl J Med, 2015, 372(26): 2481-2498. DOI: 10.1056/NEJMoa1402121.
[3]
FAMILIARI P, LAPOLLA P, PICOTTI V, et al. Role of 1p/19q codeletion in diffuse low-grade glioma tumour prognosis[J]. Anticancer Res, 2023, 43(6): 2659-2670. DOI: 10.21873/anticanres.16432.
[4]
HOU Z, ZHANG K, LIU X, et al. Molecular subtype impacts surgical resection in low-grade gliomas: A Chinese Glioma Genome Atlas database analysis[J]. Cancer Lett, 2021, 522: 14-21. DOI: 10.1016/j.canlet.2021.09.008.
[5]
JIANG T, NAM D H, RAM Z, et al. Clinical practice guidelines for the management of adult diffuse gliomas[J]. Cancer Lett, 2021, 499: 60-72. DOI: 10.1016/j.canlet.2020.10.050.
[6]
BALL M K, KOLLMEYER T M, PRASKA C E, et al. Frequency of false-positive FISH 1p/19q codeletion in adult diffuse astrocytic gliomas[J/OL]. Neurooncol Adv, 20202(1): vdaa109 [2023-12-28]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654379/pdf/vdaa109.pdf. DOI: 10.1093/noajnl/vdaa109.
[7]
PATEL S H, BATCHALA P P, MRACHEK E K S, et al. MRI and CT identify isocitrate dehydrogenase (IDH)-mutant lower-grade gliomas misclassified to 1p/19q codeletion status with fluorescence in situ hybridization[J]. Radiology, 2020, 294(1): 160-167. DOI: 10.1148/radiol.2019191140.
[8]
ZHAO K, SUN G, WANG Q, et al. The diagnostic value of conventional MRI and CT features in the Identification of the IDH1-mutant and 1p/19q co-deletion in WHO grade ii gliomas[J/OL]. Acad Radiol, 2021, 28(7): e189-e198 [2023-12-28]. https://www.academicradiology.org/article/S1076-6332(20)30149-5/abstract. DOI: 10.1016/j.acra.2020.03.008.
[9]
LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[10]
CHOI Y S, BAE S, CHANG J H, et al. Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics[J]. Neuro Oncol, 2021, 23(2): 304-313. DOI: 10.1093/neuonc/noaa177.
[11]
VAN DER VOORT S R, INCEKARA F, WIJNENGA M M J, et al. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning[J]. Neuro Oncol, 2023, 25(2): 279-289. DOI: 10.1093/neuonc/noac166.
[12]
NISHIKAWA T, OHKA F, AOKI K, et al. Easy-to-use machine learning system for the prediction of IDH mutation and 1p/19q codeletion using MRI images of adult-type diffuse gliomas[J]. Brain Tumor Pathol, 2023, 40(2): 85-92. DOI: 10.1007/s10014-023-00459-4.
[13]
VAN DER VOORT S R, INCEKARA F, WIJNENGA M M J, et al. Predicting the 1p/19q codeletion status of presumed low-grade glioma with an externally validated machine learning algorithm[J]. Clin Cancer Res, 2019, 25(24): 7455-7462. DOI: 10.1158/1078-0432.Ccr-19-1127.
[14]
HAN Y, XIE Z, ZANG Y, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas[J]. J Neurooncol, 2018, 140(2): 297-306. DOI: 10.1007/s11060-018-2953-y.
[15]
FAN Z, SUN Z, FANG S, et al. Preoperative radiomics analysis of 1p/19q status in WHO grade II gliomas[J/OL] . Front Oncol, 2021, 11: 616740 [2024-3-27]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290517/pdf/fonc-11-616740.pdf. DOI: 10.3389/fonc.2021.616740.
[16]
CASALE R, LAVROVA E, SANDULEANU S, et al. Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients[J/OL]. Eur J Radiol, 2021, 139: 109678 [2023-12-28]. https://www.ejradiology.com/action/showPdf?pii=S0720-048X%2821%2900158-3. DOI: 10.1016/j.ejrad.2021.109678.
[17]
YAN J, ZHANG B, ZHANG S, et al. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients[J/OL]. NPJ Precis Oncol, 2021, 5(1): 72 [2024-3-27]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8313682/pdf/41698_2021_Article_205.pdf. DOI: 10.1038/s41698-021-00205-z.
[18]
VAN GRIETHUYSEN J J M, FEDOROV A, PARMAR C, et al. Computational Radiomics System to Decode the Radiographic Phenotype[J/OL]. Cancer Res, 2017, 77(21): e104-e107 [2024-3-27]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5672828/pdf/nihms894056.pdf. DOI: 10.1158/0008-5472.Can-17-0339.
[19]
WU C C, JAIN R, RADMANESH A, et al. Predicting genotype and survival in glioma using standard clinical MR imaging apparent diffusion coefficient images: A pilot study from the cancer genome atlas[J]. AJNR Am J Neuroradiol, 2018, 39(10): 1814-1820. DOI: 10.3174/ajnr.A5794.
[20]
KIM M, JUNG S Y, PARK J E, et al. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma[J]. Eur Radiol, 2020, 30(4): 2142-2151. DOI: 10.1007/s00330-019-06548-3.
[21]
SOLIMAN R K, ESSA A A, ELHAKEEM A A S, et al. Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue[J]. Diagn Interv Imaging, 2021, 102(5): 287-295. DOI: 10.1016/j.diii.2020.12.001.
[22]
THUST S C, MAYNARD J A, BENENATI M, et al. Regional and volumetric parameters for diffusion-weighted WHO grade II and III glioma genotyping: A method comparison[J]. AJNR Am J Neuroradiol, 2021, 42(3): 441-447. DOI: 10.3174/ajnr.A6965.
[23]
JOHNSON D R, DIEHN F E, GIANNINI C, et al. Genetically defined oligodendroglioma is characterized by indistinct tumor borders at MRI[J]. AJNR Am J Neuroradiol, 2017, 38(4): 678-684. DOI: 10.3174/ajnr.A5070.
[24]
MA X, CHENG K, CHENG G, et al. Apparent diffusion coefficient as imaging biomarker for identifying IDH mutation, 1p19q codeletion, and MGMT promoter methylation status in patients with glioma[J]. J Magn Reson Imaging, 2023, 58(3): 732-738. DOI: 10.1002/jmri.28589.
[25]
MAYNARD J, OKUCHI S, WASTLING S, et al. World Health Organization grade II/III glioma molecular status: Prediction by MRI morphologic features and apparent diffusion coefficient[J]. Radiology, 2020, 296(1): 111-121. DOI: 10.1148/radiol.2020191832.
[26]
YIP S S, AERTS H J. Applications and limitations of radiomics[J/OL]. Phys Med Biol, 2016, 61(13): R150-166 [2023-12-28]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927328/pdf/nihms795218.pdf. DOI: 10.1088/0031-9155/61/13/r150.
[27]
KOCAK B, DURMAZ E S, ATES E, et al. Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status[J]. Eur Radiol, 2020, 30(2): 877-886. DOI: 10.1007/s00330-019-06492-2.
[28]
KHA Q H, LE V H, HUNG T N K, et al. Development and validation of an efficient MRI radiomics signature for improving the predictive performance of 1p/19q co-deletion in lower-grade gliomas[J/OL]. Cancers (Basel), 2021, 13(21): 5398 [2023-12-28]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582370/pdf/cancers-13-05398.pdf. DOI: 10.3390/cancers13215398.
[29]
LI Y, AMMARI S, LAWRANCE L, et al. Radiomics-based method for predicting the glioma subtype as defined by tumor grade, IDH mutation, and 1p/19q codeletion[J/OL]. Cancers (Basel), 2022, 14(7): 1778 [2023-12-28]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8997070/pdf/cancers-14-01778.pdf. DOI: 10.3390/cancers14071778.
[30]
BAHRAMI N, HARTMAN S J, CHANG Y H, et al. Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics[J]. J Neurooncol, 2018, 139(3): 633-642. DOI: 10.1007/s11060-018-2908-3.
[31]
SHBOUL Z A, CHEN J, M IFTEKHARUDDIN K. Prediction of molecular mutations in diffuse low-grade gliomas using MR imaging features[J/OL]. Sci Rep, 2020, 10(1): 3711 [2023-12-28]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7048831/pdf/41598_2020_Article_60550.pdf. DOI: 10.1038/s41598-020-60550-0.
[32]
ZWANENBURG A, VALLIÈRES M, ABDALAH M A, et al. The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2): 328-338. DOI: 10.1148/radiol.2020191145.
[33]
HE J, REN J, NIU G, et al. Multiparametric MR radiomics in brain glioma: models comparation to predict biomarker status[J/OL]. BMC Med Imaging, 2022, 22(1): 137 [2023-12-28]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354364/pdf/12880_2022_Article_865.pdf. DOI: 10.1186/s12880-022-00865-8.

PREV Research of Ki-67 expression in glioma by multimodal multi-parameter magnetic resonance imaging
NEXT The value of multi-sequence MRI-based radiomics in differential diagnosis of meningioma
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn