The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma

被引:2
作者
Chen, Xin [1 ]
Wang, Haoru [1 ]
Xia, Yuwei [2 ]
Shi, Feng [2 ]
He, Ling [1 ]
Liu, Enmei [3 ]
机构
[1] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol,Childrens Hosp,Chongqing Key Lab Child, Minist Educ Key Lab Child Dev & Disorders, Chongqing 400014, Peoples R China
[2] Shanghai United Imaging Intelligence Co Ltd, Shanghai 200030, Peoples R China
[3] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Resp Med,Childrens Hosp,Chongqing Key Lab Chi, Minist Educ Key Lab Child Dev & Disorders, Chongqing 400014, Peoples R China
关键词
Neuroblastoma; Computed tomography; Radiomics; Mitosis karyorrhexis index; PATHOLOGY CLASSIFICATION;
D O I
10.1007/s12672-024-01067-0
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Objective Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma.Materials and methods 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status.Results The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively.Conclusions CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.
引用
收藏
页数:13
相关论文
共 37 条
[1]   Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology [J].
Ak, M. ;
Toll, S. A. ;
Hein, K. Z. ;
Colen, R. R. ;
Khatua, S. .
AMERICAN JOURNAL OF NEURORADIOLOGY, 2022, 43 (06) :792-801
[2]   [18F]-FDG PET and MRI radiomic signatures to predict the risk and the location of tumor recurrence after re-irradiation in head and neck cancer [J].
Beddok, Arnaud ;
Orlhac, Fanny ;
Calugaru, Valentin ;
Champion, Laurence ;
Eddine, Catherine Ala ;
Nioche, Christophe ;
Crehange, Gilles ;
Buvat, Irene .
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2023, 50 (02) :559-571
[3]   MYCN amplification and International Neuroblastoma Risk Group stratification on fine-needle aspiration biopsy and their correlation to survival in neuroblastoma [J].
Bhardwaj, Neha ;
Rohilla, Manish ;
Trehan, Amita ;
Bansal, Deepak ;
Kakkar, Nandita ;
Srinivasan, Radhika .
JOURNAL OF CLINICAL PATHOLOGY, 2023, 76 (09) :599-605
[4]   Mitosis-Karyorrhexis Index evaluation by digital image visual analysis for application of International Neuroblastoma Pathology Classification in FNA biopsy [J].
Bhardwaj, Neha ;
Rohilla, Manish ;
Trehan, Amita ;
Bansal, Deepak ;
Kakkar, Nandita ;
Srinivasan, Radhika .
CANCER CYTOPATHOLOGY, 2022, 130 (02) :128-135
[5]   CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma [J].
Chen, Xin ;
Wang, Haoru ;
Huang, Kaiping ;
Liu, Huan ;
Ding, Hao ;
Zhang, Li ;
Zhang, Ting ;
Yu, Wenqing ;
He, Ling .
FRONTIERS IN ONCOLOGY, 2021, 11
[6]   Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities [J].
Depeursinge, Adrien ;
Foncubierta-Rodriguez, Antonio ;
De Ville, Dimitri Van ;
Mueller, Henning .
MEDICAL IMAGE ANALYSIS, 2014, 18 (01) :176-196
[7]   Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer [J].
Du, Getao ;
Zeng, Yun ;
Chen, Dan ;
Zhan, Wenhua ;
Zhan, Yonghua .
JAPANESE JOURNAL OF RADIOLOGY, 2023, 41 (03) :245-257
[8]   Gray-level discretization impacts reproducible MRI radiomics texture features [J].
Duron, Loic ;
Balvay, Daniel ;
Perre, Saskia Vande ;
Bouchouicha, Afef ;
Savatovsky, Julien ;
Sadik, Jean-Claude ;
Thomassin-Naggara, Isabelle ;
Fournier, Laure ;
Lecler, Augustin .
PLOS ONE, 2019, 14 (03)
[9]   Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based 18F-FDG PET/CT Radiomics [J].
Feng, Lijuan ;
Qian, Luodan ;
Yang, Shen ;
Ren, Qinghua ;
Zhang, Shuxin ;
Qin, Hong ;
Wang, Wei ;
Wang, Chao ;
Zhang, Hui ;
Yang, Jigang .
DIAGNOSTICS, 2022, 12 (02)
[10]   PROLIFERATION AND APOPTOSIS IN NEUROBLASTOMA - SUBDIVIDING THE MITOSIS-KARYORRHEXIS INDEX [J].
GESTBLOM, C ;
HOEHNER, JC ;
PAHLMAN, S .
EUROPEAN JOURNAL OF CANCER, 1995, 31A (04) :458-463