Differential diagnosis of hepatocellular carcinoma and hepatic hemangioma based on maximum wavelet-coefficient statistics: Novel radiomics features from plain CT. Issue 5 (September 2022)

• Record Type: Journal Article
• Authors: Qiu, Jia-Jun; Yin, Jin; Ji, Lin; Lu, Chun-Yan; Li, Kang; Zhang, Yong-Gang; Lin, Yi-Xin
• Is Part Of: Information processing & management. Volume 59:Issue 5(2022)
• Publication Date: 2022-09
• Subjects: Medical image processing -- Radiomics -- Computer vision -- Wavelet representation -- Liver tumor -- Feature engineering; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.103046
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: A novel radiomics technique to differentially diagnose HCC and hepatic hemangioma. Extracting features from plain CT to represent histopathological characteristics. Achieved state-of-the-art performance in radiomics diagnoses of HCC and hemangioma. Exploration of the associations between the features and histopathological characteristics. The features can highlight histopathological differences and enhanced diagnosability. Abstract: In computed tomography (CT)-based diagnoses of liver tumors, contrast-enhanced CT may cause renal toxicity and allergic reactions. Regular health examinations prefer plain CT, but subsequent diagnoses significantly depend on subjective experience. Radiomics provides a quantitative, objective, and noninvasive way for diagnosing liver tumors. This study aimed to use plain CT-based radiomics to diagnose hepatocellular (HCC, malignant) and hemangioma (HH, benign) liver tumors. Inspired by the knowledge that HCC and HH exhibit different histopathological characteristics, we developed a novel feature extraction technique (referred to as maximum wavelet-coefficient statistics, MWCS) to highlight the differences in histopathological characteristics by reorganizing and expressing the patterns of wavelet-coefficients that represent local changes. We attempted multiple feature selection algorithms and various machine learning approaches to train classification models and tested these models on an independent test cohort. Experimental results showed that the classification models based on the proposed MWCS-COM (using a statistical method of co-occurrence matrix in MWCS) feature set exhibited performance superior to those based on traditional feature sets. Furthermore, the linear support vector machine (SVM) model achieved state-of-the-art performance in the classification experiments with a test area under receiver operator characteristic curve (AUC) of 0.8734 (95% confidence interval, 0.8666–0.8802). This result indicated that the MWCS-COM features are highly advantageous to the differential diagnosis of HCC and HH from plain CT images. We also explored the potential associations between MWCS-COM features and histopathological characteristics and observed that the MWCS-COM features could potentially enhance radiologists' diagnostic ability.