A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges

Wang, Haoru; Chen, Xin; He, Ling

doi:10.1007/s00247-023-05792-6

Cited by 5 publications

(2 citation statements)

References 59 publications

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“…However, the development of a simple and practical predictive model for chemotherapy response in HB is still a challenge. In recent years, radiomics can serve as a noninvasive tool to offer a comprehensive picture of tumor heterogeneity (Wang et al 2023a , b ). Radiomics features are widely used to predict the response to neoadjuvant chemotherapy in various types of solid tumors in both adults and children, including neuroblastoma and nephroblastoma.…”

Section: Introductionmentioning

confidence: 99%

“…Radiomics features are widely used to predict the response to neoadjuvant chemotherapy in various types of solid tumors in both adults and children, including neuroblastoma and nephroblastoma. Studies have shown promising results (Xu et al 2021 ; Wang et al 2021 ; Choudhery et al 2022 ; Wang et al 2023a , b ; Sharaby et al 2023 ). However, there are limited reports on the use of contrast-enhanced computed tomography (CECT) radiomics to predict the response of HB to neoadjuvant chemotherapy.…”

Section: Introductionmentioning

confidence: 99%

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Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics

Yang,

Wang,

et al. 2024

J Cancer Res Clin Oncol

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Objective To investigate the clinical value of contrast-enhanced computed tomography (CECT) radiomics for predicting the response of primary lesions to neoadjuvant chemotherapy in hepatoblastoma. Methods Clinical and CECT imaging data were retrospectively collected from 116 children with hepatoblastoma who received neoadjuvant chemotherapy. Tumor response was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST). Subsequently, they were randomly stratified into a training cohort and a test cohort in a 7:3 ratio. The clinical model was constructed using univariate and multivariate logistic regression, while the radiomics model was developed based on selected radiomics features employing the support vector machine algorithm. The combined clinical–radiomics model incorporated both clinical and radiomics features. Results The area under the curve (AUC) for the clinical, radiomics, and combined models was 0.704 (95% CI: 0.563–0.845), 0.830 (95% CI: 0.704–0.959), and 0.874 (95% CI: 0.768–0.981) in the training cohort, respectively. In the validation cohort, the combined model achieved the highest mean AUC of 0.830 (95% CI 0.616–0.999), with a sensitivity, specificity, accuracy, precision, and f1 score of 72.0%, 81.1%, 78.5%, 57.2%, and 63.5%, respectively. Conclusion CECT radiomics has the potential to predict primary lesion response to neoadjuvant chemotherapy in hepatoblastoma.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics

Yang,

Wang,

et al. 2024

J Cancer Res Clin Oncol

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A head-to-head comparison of computed tomography- and magnetic resonance imaging-based radiomics in assessing pediatric peripheral neuroblastic tumor cell behavior

Wang,

Chen,

et al. 2024

Abdom Radiol

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Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features

Wang,

He,

Chen

et al. 2024

Technol Cancer Res Treat

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Purpose To predict bone marrow metastasis in neuroblastoma using contrast-enhanced computed tomography (CECT) radiomics features and explainable machine learning. Methods This cohort study retrospectively included a total of 345 neuroblastoma patients who underwent testing for bone marrow metastatic status. Tumor lesions on CECT images were delineated by two radiologists, and 1409 radiomics features were extracted. Correlation analysis, Least Absolute Shrinkage and Selection Operator regression, and one-way analysis of variance were used to identify radiomics features associated with bone marrow metastasis. A predictive model for bone marrow metastasis was then developed using the support vector machine algorithm based on the selected radiomics features. The performance of the radiomics model was evaluated using the area under the curve (AUC), 95% confidence interval (CI), accuracy, sensitivity, and specificity. Results The radiomics model included 16 features, with a predominant focus on texture features (12/16, 75%). In the training set, the model demonstrated an AUC of 0.891 (95% CI: 0.848-0.933), an accuracy of 0.831 (95% CI: 0.829-0.832), a sensitivity of 0.893 (95% CI: 0.840-0.946), and a specificity of 0.757 (95% CI: 0.677-0.837). In the test set, the AUC, accuracy, sensitivity, and specificity were 0.807 (95% CI: 0.720-0.893), 0.767 (95% CI: 0.764-0.770), 0.696 (95% CI: 0.576-0.817), and 0.851 (95% CI: 0.749-0.953), respectively. Conclusion Radiomics features extracted from CECT images are associated with the presence of bone marrow metastasis in neuroblastoma, providing potential new imaging biomarkers for predicting bone marrow metastasis in this disease.

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A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges

Cited by 5 publications

References 59 publications

Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics

Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics

A head-to-head comparison of computed tomography- and magnetic resonance imaging-based radiomics in assessing pediatric peripheral neuroblastic tumor cell behavior

Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features

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