Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions.

Background: The aim of this study was to develop a conventional ultrasound (US) features-based nomogram for the prediction of malignant nonmasslike (NML) breast lesions. Methods: Consecutive cases of adult females diagnosed with NML breast lesions via US screening in our center from June 1st, 2017, to April 17th, 2020, were retrospectively enrolled. Candidate variables included age, clinical symptoms, and the image features obtained from the conventional US. Nomograms were developed based on the results of the multiple logistic regression analysis via R language. One thousand bootstraps were used for internal validation. The area under the curve (AUC) and the bias-corrected concordance index (C-index) were calculated. Decision curve analysis (DCA) was also performed for further comparison between the nomogram and the Breast Imaging Reporting and Data System (BI-RADS). The study has not yet been registered. Results: A total of 229 patients were included in the study after exclusion and follow-up. The overall malignant rate of NML breast lesions was 31.0%. Age, clinical symptoms, echo pattern, calcification, orientation, and Adler's classification were selected to generate the nomogram according to the results of the multivariable logistic regression analysis. The bias-corrected C-index and the AUC of our nomogram were 0.790 and 0.828, respectively. The DCA showed that our model had larger net benefits in a range from 0.2 to 0.7 when compared with the BI-RADS. Conclusions: We developed a prediction model using a combination of age, clinical symptoms, echo pattern, calcification, orientation, and Adler's classification for malignant NML breast lesion prediction that yielded adequate discrimination and calibration.

Application of Convolution Neural Network Algorithm Based on Multicenter ABUS Images in Breast Lesion Detection.

Objective: This study aimed to evaluate a convolution neural network algorithm for breast lesion detection with multi-center ABUS image data developed based on ABUS image and Yolo v5. Methods: A total of 741 cases with 2,538 volume data of ABUS examinations were analyzed, which were recruited from 7 hospitals between October 2016 and December 2020. A total of 452 volume data of 413 cases were used as internal validation data, and 2,086 volume data from 328 cases were used as external validation data. There were 1,178 breast lesions in 413 patients (161 malignant and 1,017 benign) and 1,936 lesions in 328 patients (57 malignant and 1,879 benign). The efficiency and accuracy of the algorithm were analyzed in detecting lesions with different allowable false positive values and lesion sizes, and the differences were compared and analyzed, which included the various indicators in internal validation and external validation data. Results: The study found that the algorithm had high sensitivity for all categories of lesions, even when using internal or external validation data. The overall detection rate of the algorithm was as high as 78.1 and 71.2% in the internal and external validation sets, respectively. The algorithm could detect more lesions with increasing nodule size (87.4% in ≥10 mm lesions but less than 50% in <10 mm). The detection rate of BI-RADS 4/5 lesions was higher than that of BI-RADS 3 or 2 (96.5% vs 79.7% vs 74.7% internal, 95.8% vs 74.7% vs 88.4% external). Furthermore, the detection performance was better for malignant nodules than benign (98.1% vs 74.9% internal, 98.2% vs 70.4% external). Conclusions: This algorithm showed good detection efficiency in the internal and external validation sets, especially for category 4/5 lesions and malignant lesions. However, there are still some deficiencies in detecting category 2 and 3 lesions and lesions smaller than 10 mm.

Evaluation of liver parenchyma stiffness in patients with liver tumours: optimal strategy for shear wave elastography.

OBJECTIVES: To determine the methodology of non-invasive test for evaluation of liver stiffness (LS) with tumours using two-dimensional (2D) shear wave elastography (SWE). METHODS: One hundred and twenty-seven patients with liver tumours underwent 2D-SWE before surgery to measure liver and spleen stiffness (SS). Two-dimensional SWE values were obtained in the liver at 0-1 cm, 1-2 cm and >2 cm from the tumour edge (PLS-1, PLS-2 and RLS, respectively). The influence of tumour-associated factors was evaluated. The area under the receiver operating characteristic curve (AUC) for each value was analysed to diagnose cirrhosis. RESULTS: PLS-1 was higher than PLS-2, which was even higher than RLS (p < 0.001). The AUCs of PLS-1, PLS-2, RLS and SS for diagnosing cirrhosis were 0.760, 0.833, 0.940 and 0.676, with the specificity of 75.7%, 67.6%, 90.3% and 77.4%, respectively. Tumour sizes, locations or types showed no apparent influence on 2D-SWE values except for RLS, which was higher in patients with primary hepatic carcinomas (p < 0.05). CONCLUSIONS: LS with tumours is best measured at >2 cm away from the tumour edge. SS measurement could be used as an alternative to LS measurement in the event of no available liver for detection. KEY POINTS: • Tumour-associated factors impact background liver stiffness assessment. • Background liver stiffness is best measured at >2 cm from tumour edge. • Spleen stiffness can be an alternative to assess background liver stiffness.

Ultrasound elastography as an imaging biomarker for detection of early tumor response to chemotherapy in a murine breast cancer model: a feasibility study.

OBJECTIVE: This study investigated the feasibility of using strain elastography (SE) and real time shear wave elastography (RT-SWE) to evaluate early tumor response to cytotoxic chemotherapy in a murine xenograft breast cancer tumor model. METHODS: MCF-7 breast cancer-bearing nude mice were treated with either cisplatin 2 mg kg-1 plus paclitaxel 10 mg kg-1 (treatment group) or sterile saline (control group) once daily for 5 days. The tumor elasticity was measured by SE or RT-SWE before and after therapy. Tumor cell density was assessed by hematoxylin and eosin staining, and the ratio of collagen fibers in the tumor was evaluated by Van Gieson staining. The correlation between tumor elasticity, as determined by SE and SWE, as well as the pathological tumor responses were analyzed. RESULTS: Chemotherapy significantly attenuated tumor growth compared to the control treatment (p < 0.05). Chemotherapy also significantly increased tumor stiffness (p < 0.05) and significantly decreased (p < 0.05) tumor cell density compared with the control. Moreover, chemotherapy significantly increased the ratio of collagen fibers (p < 0.05). Tumor stiffness was positively correlated with the ratio of collagen fibers but negatively correlated with tumor cell density. CONCLUSION: The study suggests that ultrasound elastography by SE and SWE is a feasible tool for assessing early responses of breast cancer to chemotherapy in our murine xenograft model. Advances in knowledge: This study showed that the tumor elasticity determined by ultrasound elastography could be a feasible imaging biomarker for assessing very early therapeutic responses to chemotherapy.