Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound

Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound

REVIEWED BY Robyn Boman, FASA | ASA SIG: Emerging Technology

REFERENCE | Authors: Hu HT, Wang W, Chen LD, Ruan SM, Chen SL, Li X, Lu MD, Xie XY & Kuang M

WHY THE STUDY WAS PERFORMED

The aim of this study was to assess if the interrater reliability between radiologists at different centres for the detection of malignant focal liver lesions could be improved. This study was based on combining contrast-enhanced ultrasound (CEUS) with artificial intelligence (AI).

HOW THE STUDY WAS PERFORMED

This study was a retrospective study. Patients were aged (P = 0.15), gender (P = 0.46) and lesion size (P = 0.25) matched between development and training and sets. Malignant lesions e.g. hepatocellular carcinoma and liver metastasis were biopsied. Benign lesions e.g. haemangiomas and focal nodular hyperplasia were identified by typical observations on CEUS and 12 months of follow-up demonstrating no change. Abscesses were confirmed by drainage of pus or a reduction in size following treatment with antibiotics. Indeterminate lesions were biopsied. Three hundred and sixty-three lesions were evaluated in 614,728 augmented images within the development set. A further 211 lesions were evaluated within 616 images as the testing set.

The radiologists involved in this study had an average of 4.75 years of experience in hepatic CEUS. Ultrasound machines utilised were the Acuson Sequoia 512 with a 4V1 transducer or Aplio 500 or Aplio XV with a 375 BT convex transducer. Images with one centimetre of perilesional clearance were acceptable. These lesions had to have no more than one-third of the image obscured by acoustic shadow. Five parts of a 152 Resnet architecture were used involving three convolutional layers to create an algorithm. Image augmentation was based on brightness, contrast, rotation and parallel shifting to recreate data diversity and the final algorithm.

Limitations in this study were the body mass index (BMI) tends to be lower in China, where this study was conducted. Therefore, the results may be biased and not transpose to a higher BMI. Only image data was incorporated into the training set. The lack of information on demographics and medical history may have assisted in increasing the identification of focal liver lesions. The training set was a relatively small data set, therefore future training sets with larger data sets may be superior. Additionally, the use of multicentre studies along with increased data may add to a higher performance for the AI model.

The influence of the AI-radiologist interaction on performance was assessed, focusing on AI’s potential to reduce interobserver heterogeneity.

WHAT THE STUDY FOUND

This study demonstrated a successful algorithm that decreased the reporting differences for radiologists between centres for the detection of malignant liver lesions. When radiologists reported with the assistance of AI, interobserver performance between four radiologists was comparable based on accuracy (91.0–92.9%, P = 0.904), sensitivity (97.0–99.4%, P = 0.360), and specificity (66.0–76.6%, P = 0.671).

RELEVANCE TO CLINICAL PRACTICE

This study demonstrated the value of combining CEUS with AI improving the interrater reliability for the detection by radiologists of malignant focal liver lesions. This result adds further credibility to the value of CEUS for the detection of focal liver lesions.