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 after chemotherapy. By quantifying the tumor regions on the CT images before and after NCT, 540 delta-radiomic features were calculated. The interclass correlation coefficients for segmentations of inter/intra-observers and feature pair-wise correlation coefficients (Pearson) were used for robust feature selection. A delta-radiomics signature was constructed using the lasso algorithm based on the training set. Radiomics signatures built from single-phase CT were constructed for comparison purpose. A radiomics nomogram was then developed from the multivariate logistic regression model by combining independent clinical factors and the delta-radiomics signature. The prediction performance was assessed using area under the ROC curve (AUC), calibration curves and decision curve analysis (DCA). Results:The delta-radiomics signature showed higher AUC than single-CT based radiomics signatures in both training and validation cohorts. The delta-radiomics signature, consisting of 8 selected features, showed significant differences between the pathologic good response (pGR) (necrosis fraction ≥90%) group and the non-pGR (necrosis fraction < 90%) group (P < 0.0001, in both training and validation sets). The delta-radiomics nomogram, which consisted of the delta-radiomics signature and new pulmonary metastasis during chemotherapy showed good calibration and great discrimination capacity with AUC 0.871 (95% CI, 0.804 to 0.923) in the training cohort, and 0.843 (95% CI, 0.718 to 0.927) in the validation cohort. The DCA confirmed the clinical utility of the radiomics model. Conclusion:The delta-radiomics nomogram incorporating the radiomics signature and clinical factors in this study could be used for individualized pathologic response evaluation after chemotherapy preoperatively and help tailor appropriate chemotherapy and further treatment plans.

Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer

“…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.…”

“…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).…”

How to develop a meaningful radiomic signature for clinical use in oncologic patients

“…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.…”

Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients

“…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.…”

“…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.…”