ArticleQuantitative imaging in medicine and surgery2021
Prediction of neoadjuvant chemotherapy response in high-grade osteosarcoma: added value of non-tumorous bone radiomics using CT images.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics for predicting sensitivity to neoadjuvant chemotherapy in osteosarcoma: current status and advances.Oncology reviews · 2025Pooled it
- Evaluating sequence contributions to MRI radiomics for glioblastoma survival: single vs fusion models.Frontiers in oncology · 2025Article
- Exploring a multiparameter MRI-based radiomics approach to predict tumor proliferation status of serous ovarian carcinoma.Insights into imaging · 2024Article
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- Monitoring early responses to neoadjuvant chemotherapy and the factors affecting neoadjuvant chemotherapy responses in primary osteosarcoma.Quantitative imaging in medicine and surgery · 2023Article
- Development of a 3D-printed pelvic CT phantom combined with fresh pathological tissues of bone tumor.Quantitative imaging in medicine and surgery · 2022Article
- An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics.Insights into imaging · 2022Article
- Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma.Frontiers in oncology · 2022Article
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12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to determine the impact of including radiomics analysis of non-tumorous bone region of interest in improving the performance of pathological response prediction to chemotherapy in high-grade osteosarcomas (HOS), compared to radiomics analysis of tumor region alone.
methodsThis retrospective study included 157 patients diagnosed with HOS between November 2013 and November 2017 (age range, 5-44 years; mean age, 16.99 ±7.42 years), in which 69 and 88 patients were diagnosed as pathological good response (pGR) and non-pGR, respectively. Radiomics features were extracted from tumor and non-tumorous bone regions based on diagnostic CT images. Pathological response classifiers were developed and validated via leave-one-out cross validation (LOOCV) and independent validation methods by using the area under the receiver operating characteristic curve (AUC) value as the figure of merit.
resultsUsing the LOOCV, the classifiers combining features from tumor and non-tumorous regions showed better prediction performance than those from tumor region alone (AUC, 0.8207±0.0043
conclusionsRadiomics analysis of combined tumor and non-tumorous bone features showed improved performance of pathological response prediction to chemotherapy in HOS compared to that of tumor features alone. Moreover, the proposed classifier had the potential to predict pathological response to chemotherapy for HOS patients.
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