Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma

Yang, Pengfei; Xu, Lei; Cao, Zuozhen; Wan, Yidong; Xue, Yi; Jiang, Yangkang; Yen, Eric; Luo, Chen; Wang, Jing; Rong, Yi; Niu, Tianye

doi:10.1007/s11307-020-01507-7

Cited by 21 publications

(17 citation statements)

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“…Therefore, the consensus on the degree of radiomics reliability that has been achieved, or could be achievable in radiomics research could not be safely derived. Fourth, radiomics feature reliability has been suggested to be dependent on imaging modality, organ, disease, and other factors, which was also noticed in some included individual studies (72,89,91,103,104,158). But these dependencies could not be further generalized in this review.…”

Section: Discussionmentioning

confidence: 87%

“…Moreover, (semi-)automatic segmentation was frequently reported useful to further reduce the intra-/inter-observer radiomics feature variability induced by manual segmentation in the included studies in addition to its advantage in segmentation time, suggesting the future role of (semi-)automated segmentation in more reliable and cost-effective/efficient radiomics analysis (67,69,70,77,80,99,103,141,142). (18,36,44,57,134), post-processing and quantification (36,115,116,128,130,132), and segmentation (94,99,104,(142)(143)(144)(145). In different types of texture features, GLCM (gray-level co-occurrence matrix) features were observed to be more robust than other texture features in a few studies (18,44,94,116,128,143,145…”

Section: Comparable or Better Radiomics Feature Reliability Was Reported For (Semi-)automated Segmentation Than Manual Segmentation With mentioning

confidence: 99%

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Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review

Xue¹,

Yuan²,

Lo³

et al. 2021

Quant Imaging Med Surg

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Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, postprocessing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisitioninduced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review.More future researches on radiomics reliability are warranted.

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Section: Discussionmentioning

confidence: 87%

Section: Comparable or Better Radiomics Feature Reliability Was Reported For (Semi-)automated Segmentation Than Manual Segmentation With mentioning

confidence: 99%

Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review

Xue¹,

Yuan²,

Lo³

et al. 2021

Quant Imaging Med Surg

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“…The wavelet transform can decompose the image into low-frequency elements and/or high-frequency components at different scales, and the texture features obtained from the wavelet decomposition of the original data can signify different frequency ranges within the tumor volume [ 31 ]. Some studies have demonstrated that wavelet-based features are important in radiomics studies and can show promising capabilities in terms of tumor classification and prognosis [ 32 , 33 ]. Our study also indicates the value of wavelet features in predicting MKI status.…”

Section: Discussionmentioning

confidence: 99%

Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based 18F-FDG PET/CT Radiomics

et al. 2022

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Accurate differentiation of intermediate/high mitosis-karyorrhexis index (MKI) from low MKI is vital for the further management of neuroblastoma. The purpose of this research was to investigate the efficacy of 18F-FDG PET/CT–based radiomics features for the prediction of MKI status of pediatric neuroblastoma via machine learning. A total of 102 pediatric neuroblastoma patients were retrospectively enrolled and divided into training (68 patients) and validation sets (34 patients) in a 2:1 ratio. Clinical characteristics and radiomics features were extracted by XGBoost algorithm and were used to establish radiomics and clinical models for MKI status prediction. A combined model was developed, encompassing clinical characteristics and radiomics features and presented as a radiomics nomogram. The predictive performance of the models was evaluated by AUC and decision curve analysis. The radiomics model yielded AUC of 0.982 (95% CI: 0.916, 0.999) and 0.955 (95% CI: 0.823, 0.997) in the training and validation sets, respectively. The clinical model yielded AUC of 0.746 and 0.670 in the training and validation sets, respectively. The combined model demonstrated AUC of 0.988 (95% CI: 0.924, 1.000) and 0.951 (95% CI: 0.818, 0.996) in the training and validation sets, respectively. The radiomics features could non-invasively predict MKI status of pediatric neuroblastoma with high accuracy.

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“…To reduce the variation in different scanners, a two-step method of the image preprocessing was conducted before radiomics features extraction. Firstly, due to different pixel sizes and slice thicknesses of various CE-CT scanners, all the CT slices were resampled to 1 × 1 × 1 mm 3 using the bicubic interpolation [ 30 ]. Secondly, the images were normalized to 64 grey levels to compensate for the variation of CE-CT scanners.…”

Section: Methodsmentioning

confidence: 99%

Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography

Wan

Lou

et al. 2022

eClinicalMedicine

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Background The high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters. Methods Datasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk. Findings The developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set. Interpretation This study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies. Funding This work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

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Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma

Cited by 21 publications

References 50 publications

Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review

Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review

Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based 18F-FDG PET/CT Radiomics

Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography

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