Corrigendum: Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: from static images to CEUS video analysis.

[This corrects the article DOI: 10.3389/fonc.2022.951973.].

Multimodal ultrasound features of breast cancers: correlation with molecular subtypes.

OBJECTIVES: To investigate whether multimodal intratumour and peritumour ultrasound features correlate with specific breast cancer molecular subtypes. METHODS: From March to November 2021, a total of 85 patients with histologically proven breast cancer underwent B-mode, real-time elastography (RTE), colour Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS). The time intensity curve (TIC) of CEUS was obtained, and the peak and time to peak (TTP) were analysed. Chi-square and binary logistic regression were used to analyse the connection between multimodal ultrasound imaging features and breast cancer molecular subtype. RESULTS: Among 85 breast cancers, the subtypes were as follows: luminal A (36 cases, 42.4%), luminal B (20 cases, 23.5%), human epidermal growth factor receptor-2 positive (HER2+) (16 cases, 18.8%), and triple negative breast cancer (TNBC) (13 cases, 15.3%). Binary logistic regression models showed that RTE (P < 0.001) and CDFI (P = 0.036) were associated with the luminal A cancer subtype (C-index: 0.741), RTE (P = 0.016) and the peak ratio between intratumour and corpus mamma (P = 0.036) were related to the luminal B cancer subtype (C-index: 0.788). The peak ratio between peritumour and intratumour (P = 0.039) was related to the HER2 + cancer subtype (C-index: 0.859), and CDFI (P = 0.002) was associated with the TNBC subtype (C-index: 0.847). CONCLUSIONS: Multimodal ultrasound features could be powerful predictors of specific breast cancer molecular subtypes. The intra- and peritumour CEUS features play assignable roles in separating luminal B and HER2 + breast cancer subtypes.

Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: From static images to CEUS video analysis.

Background: Continuous contrast-enhanced ultrasound (CEUS) video is a challenging direction for radiomics research. We aimed to evaluate machine learning (ML) approaches with radiomics combined with the XGBoost model and a convolutional neural network (CNN) for discriminating between benign and malignant lesions in CEUS videos with a duration of more than 1 min. Methods: We gathered breast CEUS videos of 109 benign and 81 malignant tumors from two centers. Radiomics combined with the XGBoost model and a CNN was used to classify the breast lesions on the CEUS videos. The lesions were manually segmented by one radiologist. Radiomics combined with the XGBoost model was conducted with a variety of data sampling methods. The CNN used pretrained 3D residual network (ResNet) models with 18, 34, 50, and 101 layers. The machine interpretations were compared with prospective interpretations by two radiologists. Breast biopsies or pathological examinations were used as the reference standard. Areas under the receiver operating curves (AUCs) were used to compare the diagnostic performance of the models. Results: The CNN model achieved the best AUC of 0.84 on the test cohort with the 3D-ResNet-50 model. The radiomics model obtained AUCs between 0.65 and 0.75. Radiologists 1 and 2 had AUCs of 0.75 and 0.70, respectively. Conclusions: The 3D-ResNet-50 model was superior to the radiomics combined with the XGBoost model in classifying enhanced lesions as benign or malignant on CEUS videos. The CNN model was superior to the radiologists, and the radiomics model performance was close to the performance of the radiologists.

Emergency embolization in the treatment of ruptured hepatocellular carcinoma following transcatheter arterial chemoembolization.

BACKGROUND/AIMS: Rupture of hepatocellular carcinoma (HCC) following transcatheter arterial chemoembolization (TACE) is a rare and life-threatening complication. The purpose of the present study was to evaluate the utility of emergency embolization for treatment of ruptured HCC following TACE. METHODOLOGY: Five patients developed rupture of tumor after TACE in 1005 patients of HCC from October 2007 to February 2010, and were studied retrospectively. Emergency selective arterial embolization was performed in all 5 patients because of intractable hemorrhage in the peritoneal cavity or around the liver that could not be controlled by conservative method. The mean hemoglobin level before embolization was 75.6 g/L +/- 20.7 (mean +/- standard deviation). RESULTS: Hepatic angiography did not revealed extravasation of contrast from the tumor in all 5 patients with rupture of HCC following TACE. After selective embolization of feeding arteries of the liver tumor, intraperitoneal bleeding from HCC was stopped immediately in all patients. The mean hemoglobin level after embolization in 48 hours was 102.6 g/L +/- 3.5. No severe complication related to emergency embolization was found after treatment. CONCLUSIONS: Emergency arterial embolization is effective for hemostasis of ruptured HCC following TACE in patients with hemodynamically unstable condition.