Sae Rom Hong1, Jin Hur2, Yong Wha Moon3, Kyunghwa Han4, Suyon Chang1, Jin Young Kim1, Dong Jin Im1, Young Joo Suh1, Yoo Jin Hong1, Hye-Jeong Lee1, Young Jin Kim1, Byoung Wook Choi1. 1. Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, 120-752, Seoul, Korea, Korea. 2. Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, 120-752, Seoul, Korea, Korea. Electronic address: khuhz@yuhs.ac. 3. Medical Oncology, Department of Internal Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Korea. 4. Yonsei Biomedical Research Institute, Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, 120-752, Seoul, Korea.
Abstract
PURPOSE: This study aimed to investigate whether the quantitative parameters of dual-energy computed tomography (DECT) can predict the effects of chemotherapy in advanced adenocarcinoma based on the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines. MATERIALS AND METHODS: A total of 90 patients (59 males, 31 females, age 61.4 ± 12.3 (23-85)) with unresectable lung adenocarcinoma (TNM stage IIIB or IV) who underwent DECT before chemotherapy were prospectively included in this study. By comparing baseline studies with the best response achieved during 1 st line chemotherapy, patients were divided into two groups according to RECIST (version 1.1) guidelines as follows; responders (CR or PR) and non-responders (SD or PD). Quantitative measurements were performed on baseline DECT, and a logistic regression model was used to evaluate predictive factors for a response to chemotherapy. RESULTS: Among 90 patients, 38 were categorized as responders, while 52 patients were non-responders. The mean iodine concentration measurements were significantly higher in responders compared with non-responders (1.81 ± 0.51 vs 1.33 ± 0.76 mg/ml, p < 0.001). On multivariate analysis, EGFR mutation (odds ratio (OR): 3.116, 95% confidential interval (CI):1.182-8.213, p = .019) and iodine concentration (OR: 1.112, 95% CI:1.034-1.196, p = .006) were found to be significant for predicting a treatment response. CONCLUSIONS: Dual-energy CT using a quantitative analytic method based on iodine concentration measurements can be used to predict the effects of chemotherapy in patients with advanced adenocarcinoma.
PURPOSE: This study aimed to investigate whether the quantitative parameters of dual-energy computed tomography (DECT) can predict the effects of chemotherapy in advanced adenocarcinoma based on the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines. MATERIALS AND METHODS: A total of 90 patients (59 males, 31 females, age 61.4 ± 12.3 (23-85)) with unresectable lung adenocarcinoma (TNM stage IIIB or IV) who underwent DECT before chemotherapy were prospectively included in this study. By comparing baseline studies with the best response achieved during 1 st line chemotherapy, patients were divided into two groups according to RECIST (version 1.1) guidelines as follows; responders (CR or PR) and non-responders (SD or PD). Quantitative measurements were performed on baseline DECT, and a logistic regression model was used to evaluate predictive factors for a response to chemotherapy. RESULTS: Among 90 patients, 38 were categorized as responders, while 52 patients were non-responders. The mean iodine concentration measurements were significantly higher in responders compared with non-responders (1.81 ± 0.51 vs 1.33 ± 0.76 mg/ml, p < 0.001). On multivariate analysis, EGFR mutation (odds ratio (OR): 3.116, 95% confidential interval (CI):1.182-8.213, p = .019) and iodine concentration (OR: 1.112, 95% CI:1.034-1.196, p = .006) were found to be significant for predicting a treatment response. CONCLUSIONS: Dual-energy CT using a quantitative analytic method based on iodine concentration measurements can be used to predict the effects of chemotherapy in patients with advanced adenocarcinoma.
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