Clinical Significance of Ultrasound Elastography and Fibrotic Focus and Their Association in Breast Cancer

AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis

Breast cancer is the most prevalent cancer among women worldwide. B-mode ultrasound (US) is essential for early detection, offering high sensitivity and specificity without radiation exposure. This study introduces a semi-automatic method to streamline breast US report generation, aiming to reduce the burden on radiologists. Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists' annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. Key modules in our method include image classification using visual features (ICVF), type classification via deep learning (TCDL), and automatic report structuring and compilation (ARSC). Experiments showed that the proposed method reduced the average report generation time to 3.8 min compared to manual processes, even when using relatively low-spec hardware. Generated reports perfectly matched ground truth reports for suspicious masses without a single failure on our evaluation datasets. Additionally, the deep-learning-based algorithm, utilizing DenseNet-121 as its core model, achieved an overall accuracy of 0.865, precision of 0.868, recall of 0.847, F1-score of 0.856, and area under the receiver operating characteristics of 0.92 in classifying tissue stiffness in breast US shear-wave elastography (SWE-mode) images. These improvements not only streamline the report generation process but also allow radiologists to dedicate more time and focus on patient care, ultimately enhancing clinical outcomes and patient satisfaction.

Integrative analysis identifies molecular features of fibroblast and the significance of fibrosis on neoadjuvant chemotherapy response in breast cancer

BACKGROUND

The molecular features of fibroblasts and the role of fibrosis in neoadjuvant chemotherapy (NAC) response and breast cancer (BRCA) prognosis remain unclear. Therefore, this study aimed to investigate the impact of interstitial fibrosis on the response and prognosis of patients with BRCA undergoing NAC treatment.

MATERIALS AND METHODS

The molecular characteristics of pathologic complete response (pCR) and non-pCR (npCR) in patients with BRCA were analyzed using multiomics analysis. A clinical cohort was collected to investigate the predictive value of fibrosis in patients with BRCA.

RESULTS

Fibrosis-related signaling pathways were significantly upregulated in patients with npCR. npCR may be associated with distinct and highly active fibroblast subtypes. Patients with high fibrosis had lower pCR rates. The fibrosis-dependent nomogram for pCR showed efficient predictive ability [training set: area under the curve [AUC]=0.871, validation set: AUC=0.792]. Patients with low fibrosis had a significantly better prognosis than those with high fibrosis, and those with a high fibrotic focus index had significantly shorter overall and recurrence-free survival. Therefore, fibrosis can be used to predict pCR. Our findings provide a basis for decision-making in the treatment of BRCA.

CONCLUSIONS

npCR is associated with a distinct and highly active fibroblast subtype. Furthermore, patients with high fibrosis have lower pCR rates and shorter long-term survival. Therefore, fibrosis can predict pCR. A nomogram that includes fibrosis can provide a basis for decision-making in the treatment of BRCA.

Imaging findings of breast leukaemia: a case series

Aims/Background Breast leukaemia (BL) is a rare breast malignancy that is treated differently from other malignant conditions. However, it is easily confused with other conditions; therefore, how to accurately diagnose is crucial. We retrospectively analysed the imaging findings of 13 patients to provide a diagnostic reference. Methods From January 2015 to April 2023, 13 patients with BL confirmed by biopsy who underwent imaging in Peking University People's hospital were retrospectively analysed. The imaging findings obtained via ultrasound (US), mammography (MMG), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) were analysed, and the detection rates of these methods for diagnosing BL were compared. Results Twenty-nine lesions were detected in the 13 patients. These patients presented with palpable masses or breast swelling several months after treatment for leukaemia, mainly involving the bilateral breasts. Ultrasonography was performed for 13 patients, and all lesions were detected. Most of the identified masses were hypoechoic and had indistinct boundaries, irregular shapes, no enhancement of the posterior echo, and no abundant blood flow. MMG was performed for five patients, revealing breast masses, architectural distortion, and no abnormalities. MRI was performed for four patients, and all lesions were detected; most of the lesions were hypointense on T1-weighted imaging and hyperintense on T2-weighted imaging and diffusion-weighted imaging, with a decreased apparent diffusion coefficient and inhomogeneous enhancement. The enhancement curves were mostly inflow patterns. PET/CT was performed for four patients; two patients had hypermetabolism, and the other two had no obvious radioactive uptake. Conclusion Compared to MMG and PET/CT, US and MRI have higher detection rates. Furthermore, compared to MRI, US is inexpensive, convenient and efficient; therefore, it should be the first choice for diagnosing BL.

Strain Elastography Fat-to-Lesion Index Is Associated with Mammography BI-RADS Grading, Biopsy, and Molecular Phenotype in Breast Cancer

Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique for BC diagnosis. This study assessed strain elastography (SE) and the fat-to-lesion (F/L) index for BC diagnosis. This prospective study included 216 women who underwent SE, ultrasound, mammography, and breast biopsy (108 malignant, 108 benign). Three expert radiologists performed imaging and biopsies. Mean F/L index was 3.70 ± 2.57 for benign biopsies and 18.10 ± 17.01 for malignant. We developed two predictive models: a logistic regression model with AUC 0.893, 79.63% sensitivity, 87.62% specificity, 86.9% positive predictive value (+PV), and 80.7% negative predictive value (-PV); and a neural network with AUC 0.902, 80.56% sensitivity, 88.57% specificity, 87.9% +PV, and 81.6% -PV. The optimal Youden F/L index cutoff was >5.76, with 84.26% sensitivity and specificity. The F/L index positively correlated with BI-RADS (Spearman's r = 0.073, p < 0.001) and differed among molecular subtypes (Kruskal-Wallis, p = 0.002). SE complements mammography for BC diagnosis. With adequate predictive capacity, SE is fast, minimally invasive, and useful when mammography is contraindicated.