Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

Yi-Yang Liu; Huan Zhang; Lan Wang; Shu-Shen Lin; Hao Lu; He-Jun Liang; Pan Liang; Jun Li; Pei-Jie Lv; Jian-Bo Gao

doi:10.3389/fonc.2021.740732

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

Front Oncol. 2021 Sep 15:11:740732. doi: 10.3389/fonc.2021.740732. eCollection 2021.

Authors

Yi-Yang Liu^{1

2}, Huan Zhang³, Lan Wang³, Shu-Shen Lin⁴, Hao Lu^{1

2}, He-Jun Liang⁵, Pan Liang^{1

2}, Jun Li¹, Pei-Jie Lv¹, Jian-Bo Gao^{1

2}

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
² Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China.
³ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ Department of DI CT Collaboration, Siemens Healthineers Ltd, Shanghai, China.
⁵ Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Abstract

Objective: To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC).

Methods: A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed.

Result: Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model.

Conclusion: The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.

Keywords: dual-energy CT; gastric cancer; radiomics; response prediction; systemic chemotherapy.