Open Access

ARTICLE

Development of an Ultrasonic Nomogram for Preoperative Prediction of Castleman Disease Pathological Type

Xinfang Wang¹, Lianqing Hong², Xi Wu³, Jia He³, Ting Wang^3,4,*, Hongbo Li⁵, Shaoling Liu⁶

1 Ultrasound Department of Nanjing Integrated Traditional Chinese and Western Medicine Hospital Affiliated with Nanjing University of Chinese Medicine, Nanjing, 210014, China.
2 Pathology Department of Nanjing Integrated Traditional Chinese and Western Medicine Hospital Affiliated with Nanjing University of Chinese Medicine, Nanjing, 210014, China.
3 Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.
4 Vanderbilt University Institute of Imaging Science, Nashville, TN, 37232, USA.
5 Ultrasound Department of Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
6 Ultrasound Department of Shandong Provincial Medical Imaging Research Institute, Jinan, 250021, China.

* Corresponding Author: Ting Wang. Email: .

Computers, Materials & Continua 2019, 61(1), 141-154. https://doi.org/10.32604/cmc.2019.06030

Abstract

An ultrasonic nomogram was developed for preoperative prediction of Castleman disease (CD) pathological type (hyaline vascular (HV) or plasma cell (PC) variant) to improve the understanding and diagnostic accuracy of ultrasound for this disease. Fifty cases of CD confirmed by pathology were gathered from January 2012 to October 2018 from three hospitals. A grayscale ultrasound image of each patient was collected and processed. First, the region of interest of each gray ultrasound image was manually segmented using a process that was guided and calibrated by radiologists who have been engaged in imaging diagnosis for more than 5 years. In addition, the clinical characteristics and other ultrasonic features extracted from the color Doppler and spectral Doppler ultrasound images were also selected. Second, the chi-square test was used to select and reduce features. Third, a naïve Bayesian model was used as a classifier. Last, clinical cases with gray ultrasound image datasets from the hospital were used to test the performance of our proposed method. Among these patients, 31 patients (18 patients with HV and 13 patients with PC) were used to build a training set for the predictive model and 19 (11 patients with HV and 8 patients with PC) were used for the test set. From the set, 584 high-throughput and quantitative image features, such as mass shape size, intensity, texture characteristics, and wavelet characteristics, were extracted, and then 152 images features were selected. Comparing the radiomics classification results with the pathological results, the accuracy rate, sensitivity, and specificity were 84.2%, 90.1%, and 87.5%, respectively. The experimental results show that radiomics was valuable for the differentiation of CD pathological type.

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Keywords

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