Jiyeong Lee1, Chan Kyo Kim2,3,4, Sung Yoon Park1. 1. Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. 2. Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. chankyokim@skku.edu. 3. Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. chankyokim@skku.edu. 4. Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. chankyokim@skku.edu.
Abstract
OBJECTIVE: To investigate the value of apparent diffusion coefficient (ADC) histogram analysis in predicting pelvic lymph node (LN) metastasis in patients with cervical cancer undergoing surgery. MATERIALS AND METHODS: A total of 162 cervical cancer patients who underwent radical abdominal hysterectomy with pelvic LN dissection performed with pelvic 3 T-MRI including diffusion-weighted imaging were enrolled in this study. The ADC histogram variables (minimum, mean, median, 97.5th percentile [ADC97.5], and maximum) of the tumors were developed using in-house software. For predicting pelvic LN metastasis, clinical and imaging variables were evaluated using logistic regression and receiver-operating characteristic (ROC) analyses. RESULTS: Pelvic LN metastasis was identified histopathologically in 50 patients (30.9%). In patients with LN metastasis, all ADC histogram variables were significantly different from those without LN metastasis (all p < 0.01). Univariate analysis demonstrated that long- and short-axis diameter of LN, MRI T-stage, squamous cell carcinoma antigen, tumor size, and the ADC97.5 were significantly associated with pelvic LN metastasis (all p < 0.05). However, multivariate analysis demonstrated that the ADC97.5 was the only independent predictor of pelvic LN metastasis (odds ratio, 0.996; p = 0.001). The area under the ROC curve of ADC97.5 was 0.782, which was the greatest among all variables. Interobserver agreement of all ADC histogram variables was fair to good. DISCUSSION: The ADC97.5 from histogram analysis may be a useful marker for the prediction of pelvic LN metastasis in patients with cervical cancer.
OBJECTIVE: To investigate the value of apparent diffusion coefficient (ADC) histogram analysis in predicting pelvic lymph node (LN) metastasis in patients with cervical cancer undergoing surgery. MATERIALS AND METHODS: A total of 162 cervical cancerpatients who underwent radical abdominal hysterectomy with pelvic LN dissection performed with pelvic 3 T-MRI including diffusion-weighted imaging were enrolled in this study. The ADC histogram variables (minimum, mean, median, 97.5th percentile [ADC97.5], and maximum) of the tumors were developed using in-house software. For predicting pelvic LN metastasis, clinical and imaging variables were evaluated using logistic regression and receiver-operating characteristic (ROC) analyses. RESULTS: Pelvic LN metastasis was identified histopathologically in 50 patients (30.9%). In patients with LN metastasis, all ADC histogram variables were significantly different from those without LN metastasis (all p < 0.01). Univariate analysis demonstrated that long- and short-axis diameter of LN, MRI T-stage, squamous cell carcinoma antigen, tumor size, and the ADC97.5 were significantly associated with pelvic LN metastasis (all p < 0.05). However, multivariate analysis demonstrated that the ADC97.5 was the only independent predictor of pelvic LN metastasis (odds ratio, 0.996; p = 0.001). The area under the ROC curve of ADC97.5 was 0.782, which was the greatest among all variables. Interobserver agreement of all ADC histogram variables was fair to good. DISCUSSION: The ADC97.5 from histogram analysis may be a useful marker for the prediction of pelvic LN metastasis in patients with cervical cancer.
Entities:
Keywords:
Cervical cancer; Diffusion-weighted MRI; Lymphatic metastasis; Magnetic resonance imaging
Authors: Lindsey A Torre; Farhad Islami; Rebecca L Siegel; Elizabeth M Ward; Ahmedin Jemal Journal: Cancer Epidemiol Biomarkers Prev Date: 2017-02-21 Impact factor: 4.254