2020
DOI: 10.1186/s40644-019-0283-8
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A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

Abstract: Background: The difficulty of assessment of neoadjuvant chemotherapeutic response preoperatively may hinder personalized-medicine strategies that depend on the results from pathological examination. Methods: A total of 191 patients with high-grade osteosarcoma (HOS) were enrolled retrospectively from November 2013 to November 2017 and received neoadjuvant chemotherapy (NCT). A cutoff time of November 2016 was used to divide the training set and validation set. All patients underwent diagnostic CTs before and a… Show more

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Cited by 92 publications
(79 citation statements)
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“…We proposed Delta-radiomics signature and compared it with single-time-point radiomics signature at TP1. Interestingly, Delta-radiomics signature of LL approach showed higher AUC, which agrees with a recent paper ( 26 ). Although we did not find significant difference of AUC between them, the lower 95% confidence interval of AUC at TP1 is 0.51 in the test set, indicating an insufficient diagnosis efficiency.…”
Section: Discussionsupporting
confidence: 92%
See 1 more Smart Citation
“…We proposed Delta-radiomics signature and compared it with single-time-point radiomics signature at TP1. Interestingly, Delta-radiomics signature of LL approach showed higher AUC, which agrees with a recent paper ( 26 ). Although we did not find significant difference of AUC between them, the lower 95% confidence interval of AUC at TP1 is 0.51 in the test set, indicating an insufficient diagnosis efficiency.…”
Section: Discussionsupporting
confidence: 92%
“…Nevertheless, further evaluation needs to be carried out in translating such research into clinical practice because most literature in the field had a multi-localization/multi-type tumor cohort design. Deltaradiomics features (Delta-RFs) which capture therapy-induced changes in radiomics features are now being evaluated as a complement to Response Evaluation Criteria in Solid Tumor (RECIST) criteria for monitoring therapeutic response in several tumor types (25)(26)(27)(28)(29)(30)(31). Khorrami et al showed preliminary evidence for clinical use of Delta-radiomics calculated from contrast-enhanced CT images as predictive biomarkers of response to ICIs therapy in NSCLC (31).…”
Section: Introductionmentioning
confidence: 99%
“…Integration of multiple, diverse sources of data using different kind of fusion strategies either at a feature or at a model decision level is a current trend in predictive modeling. In particular, it is very common to add radiomic features to clinical variables that are predictors of the disease outcome in the form of nomograms [36][37][38], which can then be applied and tested within clinical cohorts.…”
Section: Training and Validating The Radiomics Modelmentioning
confidence: 99%
“…Feature Selection 3 (FS3) combines FS1 and FS2 [33,34]. After robust features are selected using test-retest and multiple segmentation, non-redundant features are selected using Pearson's correlation analysis with a threshold of 0.8.…”
Section: Feature Selectionmentioning
confidence: 99%
“…After robust features are selected using test-retest and multiple segmentation, non-redundant features are selected using Pearson's correlation analysis with a threshold of 0.8. FS1, FS2, and FS3 are commonly used as feature selection methods for prognostic studies based on radiomics [9,21,33,34]; therefore, we decided to adopt these feature selection methods in this study. MATLAB R2020a was used for all selection methods.…”
Section: Feature Selectionmentioning
confidence: 99%