Hui Wu1, Xiaojian Chen2, Xin Yang3, Yalan Tao3, Yunfei Xia4, Xiaowu Deng4, Cheng Zheng5, Jared Robbins2, Christopher Schultz2, X Allen Li6. 1. The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China; Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin. 2. Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin. 3. Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin; The Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China. 4. The Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China. 5. Biostatistics, Joseph. J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin. 6. Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin. Electronic address: ali@mcw.edu.
Abstract
PURPOSE: To investigate radiation-induced changes of computed tomography (CT) textures in parotid glands (PG) to predict acute xerostomia during radiotherapy (RT) for head and neck cancer (HNC). METHODS AND MATERIALS: Daily or fraction kilovoltage CTs acquired using diagnostic CT scanners (eg, in-room CTs) during intensity-modulated RT for 59 HNC patients at 3 institutions were analyzed. The PG contours were generated on selected daily/fraction CTs. A series of histogram-based texture features, including the mean CT number (MCTN) in Hounsfield units, volume, standard deviation, skewness, kurtosis, and entropy for PGs were calculated for each fraction. Correlations between the changes of the texture features, radiation dose, and observed acute xerostomia were analyzed. A classifier model and the incurred CT-based xerostomia score (CTXS) were introduced to predict xerostomia based on combined changes of MCTN and volume of PGs. The t test and Spearman and Pearson correlation tests were used in the analyses. RESULTS: Substantial changes in various CT texture features of PGs were observed during RT delivery. The changes of PG MCTN or volume are not strongly correlated with the observed xerostomia grades if they are considered separately. The CTXS showed a significant correlation to the observed xerostomia grades (r = 0.71, P < .00001). The CTXS-based classifier can predict the xerostomia severity with a success rate ranging from 79% to 98%. The xerostomia severity at the end of treatment can be predicted based on the CTXS determined at the fifth week with a precision and sensitivity of 100%. CONCLUSION: Significant changes in the CT histogram features of the parotid glands were observed during RT of HNC. A practical method of using the changes of MCTN and volume of PGs is proposed to predict radiation-induced acute xerostomia, which may be used to help design adaptive treatment.
PURPOSE: To investigate radiation-induced changes of computed tomography (CT) textures in parotid glands (PG) to predict acute xerostomia during radiotherapy (RT) for head and neck cancer (HNC). METHODS AND MATERIALS: Daily or fraction kilovoltage CTs acquired using diagnostic CT scanners (eg, in-room CTs) during intensity-modulated RT for 59 HNC patients at 3 institutions were analyzed. The PG contours were generated on selected daily/fraction CTs. A series of histogram-based texture features, including the mean CT number (MCTN) in Hounsfield units, volume, standard deviation, skewness, kurtosis, and entropy for PGs were calculated for each fraction. Correlations between the changes of the texture features, radiation dose, and observed acute xerostomia were analyzed. A classifier model and the incurred CT-based xerostomia score (CTXS) were introduced to predict xerostomia based on combined changes of MCTN and volume of PGs. The t test and Spearman and Pearson correlation tests were used in the analyses. RESULTS: Substantial changes in various CT texture features of PGs were observed during RT delivery. The changes of PG MCTN or volume are not strongly correlated with the observed xerostomia grades if they are considered separately. The CTXS showed a significant correlation to the observed xerostomia grades (r = 0.71, P < .00001). The CTXS-based classifier can predict the xerostomia severity with a success rate ranging from 79% to 98%. The xerostomia severity at the end of treatment can be predicted based on the CTXS determined at the fifth week with a precision and sensitivity of 100%. CONCLUSION: Significant changes in the CT histogram features of the parotid glands were observed during RT of HNC. A practical method of using the changes of MCTN and volume of PGs is proposed to predict radiation-induced acute xerostomia, which may be used to help design adaptive treatment.
Authors: Mei Feng; Qingping Yin; Jing Ren; Fei Wu; Mei Lan; He Wang; Min Wang; Lu Li; Xiaojian Chen; Jinyi Lang Journal: Front Oncol Date: 2021-04-13 Impact factor: 6.244