ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images.

Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widely used for screening as a non-invasive and effective method for breast examination. In this study, we used the Mendeley and BUSI datasets, comprising 250 images (100 benign, 150 malignant) and 780 images (133 normal, 487 benign, 210 malignant), respectively. The datasets were split into 80% for training and 20% for validation. The accurate measurement and characterization of different breast tumors play a crucial role in guiding clinical decision-making. The area and shape of the different breast tumors detected are critical for clinicians to make accurate diagnostic decisions. In this study, a deep learning method for mass segmentation in breast ultrasound images is proposed, which uses densely connected U-net with attention gates (AGs) as well as channel attention modules and scale attention modules for accurate breast tumor segmentation.The densely connected network is employed in the encoding stage to enhance the network's feature extraction capabilities. Three attention modules are integrated in the decoding stage to better capture the most relevant features. After validation on the Mendeley and BUSI datasets, the experimental results demonstrate that our method achieves a Dice Similarity Coefficient (DSC) of 0.8764 and 0.8313, respectively, outperforming other deep learning approaches. The source code is located at github.com/zhanghaoCV/plos-one.

Preoperative Ultrasound-Guided Marking, Mammogram, and Peroperative Use of Image Intensifier: A Cost-Effective Technique in Clipped Non-Palpable Breast Cancer Lesions to Achieve Adequate Surgical Margins.

OBJECTIVE: To measure the effectiveness of localisation and removal of impalpable target lesions without compromising patient safety in a resource-limited setup using preoperative ultrasound and mammography with peroperative use of C-arm image intensifier. STUDY DESIGN: Descriptive study. Place and Duration of the Study: Department of Breast Surgery, Ittefaq Hospital (Trust), Lahore, Pakistan, from 25th October 2011 to 17th February 2023. METHODOLOGY: All the breast cancer patients who achieved complete clinical response after neoadjuvant systemic treatment and underwent breast conservation surgery during the study period were included. Tumour / clip localisation was done using preoperative ultrasound or image-guided marking, a 2-view mammogram in all cases and the use of an image intensifier to confirm the presence of clips in the excised specimen. The primary outcome was the accurate localisation and removal of the index lesion, while the secondary outcome included the reoperation rate for positive margins and early local recurrence. RESULTS: Data from 144 patients were reviewed. Successful localisation was done in all the patients; only one patient had a positive margin for ductal carcinoma-in situ (DCIS), achieving a 99.3% clear margin rate. Local recurrence within two years after primary operation was seen in one patient only.  Conclusion: By a combined approach of preoperative ultrasound-guided marking, a 2-view mammogram, and the use of image intensifier, successful localisation of an impalpable breast lesion is possible without compromising oncological and aesthetic principles. KEY WORDS: Breast conservation surgery, Localisation, Non-palpable, Margins, Image intensifier.

Enhancing ultrasonic attenuation images through multi-frequency coupling with total nuclear variation.

Quantitative ultrasound is a non-invasive image modality that numerically characterizes tissues for medical diagnosis using acoustical parameters, such as the attenuation coefficient slope. A previous study introduced the total variation spectral log difference (TVSLD) method, which denoises spectral log ratios on a single-channel basis without inter-channel coupling. Therefore, this work proposes a multi-frequency joint framework by coupling information across frequency channels exploiting structural similarities among the spectral ratios to increase the quality of the attenuation images. A modification based on the total nuclear variation (TNV) was considered. Metrics were compared to the TVSLD method with simulated and experimental phantoms and two samples of fibroadenoma in vivo breast tissue. The TNV demonstrated superior performance, yielding enhanced attenuation coefficient slope maps with fewer artifacts at boundaries and a stable error. In terms of the contrast-to-noise ratio enhancement, the TNV approach obtained an average percentage improvement of 34% in simulation, 38% in the experimental phantom, and 89% in two in vivo breast tissue samples compared to TVSLD, showing potential to enhance visual clarity and depiction of attenuation images.

Nomogram based on multimodal ultrasound features for evaluating breast nonmass lesions: a single center study.

BACKGROUND: It is challenging to correctly identify and diagnose breast nonmass lesions. This study aimed to explore the multimodal ultrasound features associated with malignant breast nonmass lesions (NMLs), and evaluate their combined diagnostic performance. METHODS: This retrospective analysis was conducted on 573 breast NMLs, including 309 were benign and 264 were malignant, their multimodal ultrasound features (B-mode, color Doppler and strain elastography) were assessed by two experienced radiologists. Univariate and multivariate logistic regression analysises were used to explore multimodal ultrasound features associated with malignancy, and a nomogram was developed. Diagnostic performance and clinical utility were evaluated and validated by the receiver operating characteristic (ROC) curve, calibration curve and decision curve in the training and validation cohorts. RESULTS: Multimodal ultrasound features including linear (odds ratio [OR] = 4.69) or segmental distribution (OR = 7.67), posterior shadowing (OR = 3.14), calcification (OR = 7.40), hypovascularity (OR = 0.38), elasticity scored 4 (OR = 7.00) and 5 (OR = 15.77) were independent factors associated with malignant breast NMLs. The nomogram based on these features exhibited diagnostic performance in the training and validation cohorts were comparable to that of experienced radiologists, with superior specificity (89.4%, 89.5% vs. 81.2%) and positive predictive value (PPV) (89.2%, 90.4% vs. 82.4%). The nomogram also demonstrated good calibration in both training and validation cohorts (all P > 0.05). Decision curve analysis indicated that interventions guided by the nomogram would be beneficial across a wide range of threshold probabilities (0.05-1 in the training cohort and 0.05-0.93 in the validation cohort). CONCLUSIONS: The combined use of linear or segmental distribution, posterior shadowing, calcification, hypervascularity and high elasticity score, displayed as a nomogram, demonstrated satisfied diagnostic performance for malignant breast NMLs, which may contribute to the imaging interpretation and clinical management of tumors.

Knowledge related to breast cancer screening programs by physicians in Brazil.

BACKGROUND: Antonini et al. evaluated gynecologists', obstetricians', and family and community physicians knowledge of breast cancer screening and their adherence to recommendations defined by the BI-RADS™ system. The study demonstrated that inadequate training resulted in insufficient screening and failure to follow the protocols recommended by the BIRADS™ system. BACKGROUND: ■ Variability in screening protocols: only 42.8% of gynecologists and obstetricians follow the 40-74 years protocol, while 76.6% of family physicians follow the 50-69 years protocol. BACKGROUND: ■ High rate of incorrect BIRADS. interpretation: there were 46.3% incorrect responses among gynecologists and obstetricians and 77.9% among family physicians, highlighting significant knowledge gaps. BACKGROUND: ■ Misconception about breast ultrasound: 39.1% of gynecologists and obstetricians and 20.3% of family physicians incorrectly consider ultrasound as a screening method. BACKGROUND: ■ Impact of inadequate training: inadequate training leads to improper screening practices that do not align with the BIRADS. recommended guidelines. OBJECTIVE: To evaluate the knowledge and practices of gynecologists, obstetricians, and family and community physicians in Brazil regarding breast cancer screening, mammographic findings defined by the BIRADS™ system, and their approach to suspected clinical lesions. METHODS: This was an observational, cross-sectional, descriptive study conducted using an online research questionnaire distributed via email to 9,000 gynecologists and obstetricians and 5,600 family and community and preventive medicine doctors actively practicing in Brazil. RESULTS: Among gynecologists and obstetricians, 42.8% follow the 40-74 years screening, 33.5% follow the 50-69 years screening, and 23.6% do not follow any specific protocol. Among the family and community physicians, 76.6% follow the 50-69 years screening protocol, and 23.4% do not follow any specific protocol. When we evaluated the responses regarding the behaviors of each BIRADS™ classification, 46.3% of responses were wrong among gynecologists and obstetricians, and 77.9% were wrong among community and preventive medicine doctors, exhibiting a significant difference. The role of breast ultrasound in screening was evaluated; 39.1% of gynecologists and obstetricians and 20.3% of community and preventive medicine doctors consider it as a screening method. Among gynecologists and obstetricians who do not follow any screening protocol, 94.7% consider ultrasound as a screening method. Among community and preventive medicine doctors, only 26.5% of physicians who follow the 50-69 years screening method consider it as a screening method. CONCLUSION: Inadequate training results in gynecologists and obstetricians, and family and community physicians performing inadequate screening and not following the recommended practices outlined in the BIRADS™ system.

Contrast-Enhanced Ultrasound-Based Radiomics for the Prediction of Axillary Lymph Nodes Status in Breast Cancer.

BACKGROUND: Breast cancer is the leading cause of cancer-related deaths in the female population. Axillary lymph nodes (ALN) are a group of the most common metastatic sites of breast cancer. Timely assessment of ALN status is of paramount clinical importance for medical decision making. AIMS: To utilize contrast-enhanced ultrasound (CEUS)-based radiomics models for noninvasive pretreatment prediction of ALN status. METHODS AND RESULTS: Clinical data and pretreatment CEUS images of primary breast tumors were retrospectively studied to build radiomics signatures for pretreatment prediction of nodal status between May 2015 and July 2021. The cases were divided into the training cohorts and test cohorts in a 9:1 ratio. The mRMR approach and stepwise forward logistic regression technique were used for feature selection, followed by the multivariate logistic regression technique for building radiomics signatures in the training cohort. The confusion matrix and receiver operating characteristic (ROC) analysis were used for accessing the prediction efficacy of the radiomics models. The radiomics models, which consist of six features, achieved predictive accuracy with the area under the ROC curve (AUC) of 0.713 in the test set for predicting lymph node metastasis. CONCLUSION: The CEUS-based radiomics is promising to be developed as a reliable noninvasive tool for predicting ALN status.

Diagnosis of Benign and Malignant Breast Nodules by Conventional Ultrasound in Combination with S-Detect Technology and Elastic Imaging.

OBJECTIVE: To determine the diagnostic value of conventional ultrasound combined with S-Detect and elastic imaging technology in differentiating between benign and malignant breast nodules. STUDY DESIGN: Observational study. Place and Duration of the Study: Department of Ultrasound Imaging, Yichang Central People's Hospital, Yichang, China, from October 2019 to October 2022. METHODOLOGY: The study included all breast nodules diagnosed using ultrasound, with patients undergoing conventional ultrasound for BI-RADS classification, elasticity score, and S-Detect examination. Benign and malignant breast nodules were classified according to the three tests and their combinations. The diagnostic sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under curve (AUV) of those alone and combinations were calculated and compared. RESULTS: Of the three methods, BI-RADS, elasticity score, and S-Detect, BI-RADS had the highest accuracy (89.29%), elasticity score had the highest specificity (96.20%), and S-Detect had the highest sensitivity (93.92%). The accuracy of combined groups were higher than that of the single group. When combined with elasticity score, the AUC of the new BI-RADS increased from 0.882 to 0.917 (p <0.001); and combined with S-Detect, the AUC of the new BI-RADS increased from 0.882 to 0.927 (p <0.001). CONCLUSION: The combination of conventional ultrasound BI-RADS classification with elasticity score or S-Detect technology has a higher diagnostic efficacy for breast nodules, which can improve breast cancer detection and provide valuable diagnostic evidence for clinical practice. KEY WORDS: S-Detect technology, Ultrasound elastic imaging, Elasticity scoring, Elasticity strain ratio value, Breast tumour.

Interpretable diagnosis of breast lesions in ultrasound imaging using deep multi-stage reasoning.

Objective.Ultrasound is the primary screening test for breast cancer. However, providing an interpretable auxiliary diagnosis of breast lesions is a challenging task. This study aims to develop an interpretable auxiliary diagnostic method to enhance usability in human-machine collaborative diagnosis.Approach.To address this issue, this study proposes the deep multi-stage reasoning method (DMSRM), which provides individual and overall breast imaging-reporting and data system (BI-RADS) assessment categories for breast lesions. In the first stage of the DMSRM, the individual BI-RADS assessment network (IBRANet) is designed to capture lesion features from breast ultrasound images. IBRANet performs individual BI-RADS assessments of breast lesions using ultrasound images, focusing on specific features such as margin, contour, echogenicity, calcification, and vascularity. In the second stage, evidence reasoning (ER) is employed to achieve uncertain information fusion and reach an overall BI-RADS assessment of the breast lesions.Main results.To evaluate the performance of DMSRM at each stage, two test sets are utilized: the first for individual BI-RADS assessment, containing 4322 ultrasound images; the second for overall BI-RADS assessment, containing 175 sets of ultrasound image pairs. In the individual BI-RADS assessment of margin, contour, echogenicity, calcification, and vascularity, IBRANet achieves accuracies of 0.9491, 0.9466, 0.9293, 0.9234, and 0.9625, respectively. In the overall BI-RADS assessment of lesions, the ER achieves an accuracy of 0.8502. Compared to independent diagnosis, the human-machine collaborative diagnosis results of three radiologists show increases in positive predictive value by 0.0158, 0.0427, and 0.0401, in sensitivity by 0.0400, 0.0600 and 0.0434, and in area under the curve by 0.0344, 0.0468, and 0.0255.Significance.This study proposes a DMSRM that enhances the transparency of the diagnostic reasoning process. Results indicate that DMSRM exhibits robust BI-RADS assessment capabilities and provides an interpretable reasoning process that better suits clinical needs.

Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?

BACKGROUND: Breast cancer (BC) is the most common malignancy in women and the second cause of cancer death. In recent years, there has been a strong development in artificial intelligence (AI) applications in medical imaging for several tasks. Our aim was to evaluate the potential of transfer learning with convolutional neural networks (CNNs) in discriminating suspicious breast lesions on ultrasound images. METHODS: Transfer learning performances of five different CNNs (Inception V3, Xception, Densenet121, VGG 16, and ResNet50) were evaluated on a public and on an institutional dataset (526 and 392 images, respectively), customizing the top layers for the specific task. Institutional images were contoured by an expert radiologist and processed to feed the CNNs for training and testing. Postimaging biopsies were used as a reference standard for classification. The area under the receiver operating curve (AUROC) was used to assess diagnostic performance. RESULTS: Networks performed very well on the public dataset (AUROC 0.938-0.996). The direct generalization to the institutional dataset resulted in lower performances (max AUROC 0.676); however, when tested on BI-RADS 3 and BI-RADS 5 only, results were improved (max AUROC 0.792). Good results were achieved on the institutional dataset (AUROC 0.759-0.818) and, when selecting a threshold of 2% for classification, a sensitivity of 0.983 was obtained for three of five CNNs, with the potential to spare biopsy in 15.3%-18.6% of patients. CONCLUSION: In conclusion, transfer learning with CNNs may achieve high sensitivity and might be used as a support tool in managing suspicious breast lesions on ultrasound images. RELEVANCE STATEMENT: Transfer learning is a powerful technique to exploit the performances of well-trained CNNs for image classification. In a clinical scenario, it might be useful for the management of suspicious breast lesions on breast ultrasound, potentially sparing biopsy in a non-negligible number of patients. KEY POINTS: Properly trained CNNs with transfer learning are highly effective in differentiating benign and malignant lesions on breast ultrasound. Setting clinical thresholds increased sensitivity. CNNs might be useful as support tools in managing suspicious lesions on breast ultrasound.

Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour.

This study addresses computer-aided breast cancer diagnosis through a hybrid framework for breast tumor segmentation in ultrasound images. The core of the three-stage method is based on the autoencoder convolutional neural network. In the first stage, we prepare a hybrid pseudo-color image through multiple instances of fuzzy connectedness analysis with a novel distance-adapted fuzzy affinity. We produce different weight combinations to determine connectivity maps driven by particular image specifics. After the hybrid image is processed by the deep network, we adjust the segmentation outcome with the Chan-Vese active contour model. We find the idea of incorporating fuzzy connectedness into the input data preparation for deep-learning image analysis our main contribution to the study. The method is trained and validated using a combined dataset of 993 breast ultrasound images from three public collections frequently used in recent studies on breast tumor segmentation. The experiments address essential settings and hyperparameters of the method, e.g., the network architecture, input image size, and active contour setup. The tumor segmentation reaches a median Dice index of 0.86 (mean at 0.79) over the combined database. We refer our results to the most recent state-of-the-art from 2022-2023 using the same datasets, finding our model comparable in segmentation performance.

Ultrasound Radiomics for the Prediction of Breast Cancers with HER2-Zero, -Low, and -Positive Status: A Dual-Center Study.

PURPOSE: To assess whether gray-scale ultrasound (US) based radiomic features can help distinguish HER2 expressions (ie, HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer. MATERIALS AND METHODS: This retrospective study encompassed female breast cancer patients who underwent US examinations at two distinct centers from February 2021 to July 2023. Tumor segmentation and radiomic feature extraction were performed on grayscale US images. Decision Tree analysis was employed to simultaneously evaluate feature importance, and the Least Absolute Shrinkage and Selection Operator technique was utilized for feature selection to construct the radiomic signature. The Area Under the Curve (AUC) of the Receiver Operating Characteristic curve was employed to assess the performance of the radiomic features. Multivariate logistic regression was used to identify independent predictors for distinguishing HER2 expression in the dataset. RESULTS: The training set comprised 292 patients from Center 1 (median, 51 years; interquartile range [IQR]: 45-61), while the external validation set included 131 patients from Center 2 (median, 51 years; IQR: 45-62). In the external validation dataset, the radiomic features achieved AUC of 0.76 for distinguishing between HER2-low and positive tumors versus HER2-zero tumors. The AUC for differentiating HER2-low (1+) from HER2-zero tumors was 0.74, and for distinguishing HER2-low (2+) from HER2-zero tumors, the AUC was 0.77. In the multivariate analysis assessing HER2-low and HER2-positive versus HER2-zero tumors, internal echoes (P = .029) and margins (P < .001) emerged as independent predictive factors. CONCLUSION: The radiomic signature and tumor descriptors from gray-scale US may predict distinct HER2 expressions of breast cancers with therapeutic implications.

Imaging Surveillance After Breast-Conserving Surgery for Cancer With Acellular Dermal Matrix Reconstruction.

OBJECTIVE: The aim of this study was to investigate postoperative imaging findings of patients who underwent breast-conserving surgery for cancer and reconstruction with MegaDerm® (sheet-type and pellet-type), analyzing false positives and recurrences, using multi-modality images. MATERIALS AND METHODS: This study included 201 women (age range: 28-81 years, mean age ± standard deviation: 53.2 ± 8.6 years) who underwent breast-conserving surgery and immediate reconstruction with MegaDerm®. Post-surgery, each patient underwent at least one mammography (MG), ultrasonography (US), and MRI, totaling 713 MG, 1063 US, and 607 MRI examinations. Postoperative images were reviewed separately for the two types of MegaDerm®, and suspicious imaging findings (false positives and recurrences) were analyzed, with a particular focus on the findings in direct contact with MegaDerm®. RESULTS: MegaDerm® appeared as a circumscribed mass with homogeneous iso- or high density on MG, posterior shadowing on US, and no enhancement on MRI. Calcification was more common and increased in size in sheet-type MegaDerm®, while pellet-type often exhibited irregular margins. Nine out of 17 false positives had suspicious findings in direct contact with MegaDerm®, and six out of nine recurrences showed similar findings. Common suspicious findings included calcifications, asymmetries, and MegaDerm® irregularities on MG; masses and MegaDerm® irregularities on US; and enhancing masses and MegaDerm® irregularities with enhancement on MRI. Notably, MegaDerm® irregularity with calcification was observed on MG and US in only one recurrence case. In 44.4% (4/9) of false-positives in direct contact with MegaDerm®, suspicious findings showed no change or resolution on follow-up. CONCLUSION: Suspicious imaging findings in direct contact with MegaDerm® may be associated with false positives or recurrences. Therefore, it is essential to recognize these characteristic findings and review the patient's history of MegaDerm® insertion when in doubt.

Probing clarity: AI-generated simplified breast imaging reports for enhanced patient comprehension powered by ChatGPT-4o.

BACKGROUND: To assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o. METHODS: A radiologist with 20 years' experience selected 21 anonymized breast radiology reports, 7 mammography, 7 breast ultrasound, and 7 breast magnetic resonance imaging (MRI), categorized according to breast imaging reporting and data system (BI-RADS). These reports underwent simplification by prompting ChatGPT-4o with "Explain this medical report to a patient using simple language". Five breast radiologists assessed the quality of these simplified reports for factual accuracy, completeness, and potential harm with a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Another breast radiologist evaluated the text comprehension of five non-healthcare personnel readers using a 5-point Likert scale from 1 (excellent) to 5 (poor). Descriptive statistics, Cronbach's α, and the Kruskal-Wallis test were used. RESULTS: Mammography, ultrasound, and MRI showed high factual accuracy (median 2) and completeness (median 2) across radiologists, with low potential harm scores (median 5); no significant group differences (p ≥ 0.780), and high internal consistency (α > 0.80) were observed. Non-healthcare readers showed high comprehension (median 2 for mammography and MRI and 1 for ultrasound); no significant group differences across modalities (p = 0.368), and high internal consistency (α > 0.85) were observed. BI-RADS 0, 1, and 2 reports were accurately explained, while BI-RADS 3-6 reports were challenging. CONCLUSION: The model demonstrated reliability and clarity, offering promise for patients with diverse backgrounds. LLMs like ChatGPT-4o could simplify breast radiology reports, aid in communication, and enhance patient care. RELEVANCE STATEMENT: Simplified breast radiology reports generated by ChatGPT-4o show potential in enhancing communication with patients, improving comprehension across varying educational backgrounds, and contributing to patient-centered care in radiology practice. KEY POINTS: AI simplifies complex breast imaging reports, enhancing patient understanding. Simplified reports from AI maintain accuracy, improving patient comprehension significantly. Implementing AI reports enhances patient engagement and communication in breast imaging.

Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions.

Accurate and unbiased classification of breast lesions is pivotal for early diagnosis and treatment, and a deep learning approach can effectively represent and utilize the digital content of images for more precise medical image analysis. Breast ultrasound imaging is useful for detecting and distinguishing benign masses from malignant masses. Based on the different ways in which benign and malignant tumors affect neighboring tissues, i.e., the pattern of growth and border irregularities, the penetration degree of the adjacent tissue, and tissue-level changes, we investigated the relationship between breast cancer imaging features and the roles of inter- and extra-lesional tissues and their impact on refining the performance of deep learning classification. The novelty of the proposed approach lies in considering the features extracted from the tissue inside the tumor (by performing an erosion operation) and from the lesion and surrounding tissue (by performing a dilation operation) for classification. This study uses these new features and three pre-trained deep neuronal networks to address the challenge of breast lesion classification in ultrasound images. To improve the classification accuracy and interpretability of the model, the proposed model leverages transfer learning to accelerate the training process. Three modern pre-trained CNN architectures (MobileNetV2, VGG16, and EfficientNetB7) are used for transfer learning and fine-tuning for optimization. There are concerns related to the neuronal networks producing erroneous outputs in the presence of noisy images, variations in input data, or adversarial attacks; thus, the proposed system uses the BUS-BRA database (two classes/benign and malignant) for training and testing and the unseen BUSI database (two classes/benign and malignant) for testing. Extensive experiments have recorded accuracy and AUC as performance parameters. The results indicate that the proposed system outperforms the existing breast cancer detection algorithms reported in the literature. AUC values of 1.00 are calculated for VGG16 and EfficientNet-B7 in the dilation cases. The proposed approach will facilitate this challenging and time-consuming classification task.

Discrimination Model Construction for Non-Lactational Mastitis and Breast Cancer Based on Imaging Features.

Aims/Background The clinical presentation of non-lactational mastitis (NLM) shares similarities with some symptoms and examination results of breast cancer (BC), which can lead to misdiagnosis or delayed treatment. Current studies on breast lesions mostly focus on the diagnostic performance of a single imaging technique. This study aims to construct a discrimination diagnostic model for NLM and BC based on such imaging features as ultrasound and magnetic resonance imaging (MRI) and to validate the application value of the model, assisting clinicians in improving disease diagnosis and refining medical decisions. Methods This study is a retrospective analysis. Clinical data of 108 patients suspected of NLM based on imaging diagnosis, admitted to The First Affiliated Hospital with Nanjing Medical University between May 2018 and August 2023, were collected. Among them, 94 cases were pathologically confirmed as NLM and 14 cases as BC. Univariate and multivariate logistic regression analyses were performed on the patients' clinical data, ultrasound features, and MRI features to select the risk factors for discriminating NLM and BC, and construct a discrimination model. The discrimination performance of the model was analyzed with the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve. Results In the NLM group, there were 24 cases of granulomatous lobular mastitis (25.53%) and 70 cases of plasma cell mastitis (74.47%). In the BC group, there were 2 cases of infiltrating ductal carcinoma, 2 cases of atypical hyperplasia, 3 cases of papillary carcinoma, and 7 cases of ductal carcinoma in situ. Age, internal blood flow, calcification, edge, enhancement characteristics, apparent diffusion coefficient (ADC) values, and time-intensity curve (TIC) type were independent factors for differentiating NLM and BC (p < 0.05). The ROC analysis showed that the area under the curve of the model for discriminating NLM and BC was 0.920. The DCA results showed that the model had high net benefits for discriminating NLM and BC. The calibration curve analysis showed that the model had good consistency with the actual diagnosis of NLM and BC, with a chi-square value of 4.545 and a p-value of 0.155 according to the Hosmer-Lemeshow test. Conclusion Age, internal blood flow, calcification, edge, enhancement characteristics, ADC, and TIC curve types are important factors in distinguishing NLM and BC, and the model based on the above characteristics to distinguish NLM and BC has a high net benefit in distinguishing the two.

Deep learning Radiomics Based on Two-Dimensional Ultrasound for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer.

We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. We enrolled 155 patients with pathologically confirmed breast cancer who underwent NAC. The patients were randomly divided into the training set and the validation set in the ratio of 7:3. The deep learning and radiomics features of pre-treatment ultrasound images were extracted, and the random forest recursive elimination algorithm and the least absolute shrinkage and selection operator were used for feature screening and DL-Score and Rad-Score construction. According to multifactorial logistic regression, independent clinical predictors, DL-Score, and Rad-Score were selected to construct the comprehensive prediction model DLRC. The performance of the model was evaluated in terms of its predictive effect, and clinical practicability. Compared to the clinical, radiomics (Rad-Score), and deep learning (DL-Score) models, the DLRC accurately predicted the pCR status, with an area under the curve (AUC) of 0.937 (95%CI: 0.895-0.970) in the training set and 0.914 (95%CI: 0.838-0.973) in the validation set. Moreover, decision curve analysis confirmed that the DLRC had the highest clinical value among all models. The comprehensive model DLRC based on ultrasound radiomics, deep learning, and clinical features can effectively and accurately predict the pCR status of breast cancer after NAC, which is conducive to assisting clinical personalized diagnosis and treatment plan.

Development and Assessment of a Predictive Model for Ki-67 Expression Using Ultrasound Indicators and Non-Morphological Magnetic Resonance Imaging Parameters Before Breast Cancer Therapy.

To formulate a predictive model for assessing Ki-67 expression in breast cancer by integrating pre-treatment ultrasound features with non-morphological magnetic resonance imaging (MRI) parameters, encompassing functional and hemodynamic indicators. A retrospective study was conducted on 167 patients. All patients underwent a breast mass biopsy for histopathological and Ki-67 analysis prior to neoadjuvant chemotherapy (NAC) treatment. Additionally, all patients underwent ultrasonography and MRI examinations prior to the biopsy. The recorded variables were Ki-67, apparent diffusion coefficient (ADC) values, Max Slope, time to peak (TTP), signal enhancement ratio (SER), early enhancement rate (EER), time-signal intensity curve (TIC), tumor maximum diameter, tumor margins and boundaries, aspect ratio, microcalcification, color Doppler flow imaging grading, resistance index (RI), and axillary lymph node metastasis. Statistical analysis was performed using the R software package. Normally distributed continuous data are presented as mean ± standard deviation (SD), skewed continuous data as median, and categorical variables as frequency or percentage. The dataset was randomly divided into a modeling group and a validation group following a 7:3 ratio, employing a predetermined random seed. The selection of variables was conducted using the random forest algorithm. Specifically, in the initial analysis, we trained a random forest model using all available variables. By evaluating the Gini importance scores of each variable, we identified those that contributed the most to predicting Ki-67 expression. The predictive model for Ki-67 expression was constructed using selected variables: Maximum Diameter, ADC value, SER value, Max Slope value, TTP value, and EER value. Within the validation group, the evaluation metrics demonstrated an Area under the curve of 0.961 with a 95% confidence interval ranging from 0.865 to 0.995. The model achieved a kappa score of 1.00, precision of 0.949, recall of 1, an F1 score of 0.974, sensitivity of 100%, specificity of 85.71%, a positive predictive value of 94.87%, and a negative predictive value of 100%. The combination of non-morphological MRI parameters and pre-treatment ultrasound features in a breast cancer prediction model powered by RF machine learning demonstrated favorable clinical outcomes and improved diagnostic performance.

Characterization of Indeterminate Breast Lesions Based on Pressure Estimates by Noninvasive 3D Contrast-Enhanced Ultrasound.

OBJECTIVE: To assess the ability of the pressure gradient between breast lesions and adjacent normal tissue estimated by 3D subharmonic-aided pressure estimation (SHAPE) to characterize indeterminate breast lesions. METHODS: This prospective study enrolled patients scheduled for ultrasound-guided needle biopsies of a breast lesion. Before the biopsy, 3D SHAPE data were collected from the breast lesion during the infusion of an ultrasound contrast agent (Definity) as well as after clearance of the agent. Direct, invasive pressure measurements in the lesion and adjacent normal tissue were then obtained using an intracompartmental pressure monitoring system (C2DX) before tissue sampling as part of the biopsy procedure. The mean SHAPE gradient and invasive measurement gradient between the lesion and adjacent normal tissue were compared to the biopsy results. The SHAPE gradients were also compared to the invasive pressure gradients. RESULTS: There were 8 malignant and 13 benign lesions studied. The SHAPE gradients and invasive pressure gradients were significantly different between the benign and malignant lesions (2.86 ± 3.24 vs. -0.03 ± 1.72 a.u.; p = 0.03 and 9.9 ± 8.5 vs. 20.9 ± 8.0 mmHg; p = 0.008, respectively). The area under the curves, specificities, and sensitivities for detecting malignancy by SHAPE gradients and invasive pressure gradients were 0.79 and 0.88, 77% and 92%, and 88% and 50%, respectively. A weak negative correlation was found between the SHAPE and invasive pressure gradients (r = -0.2). CONCLUSION: The pressure gradient between a breast lesion and adjacent normal tissue estimated by 3D SHAPE shows potential for characterizing indeterminate breast lesions.

Deep learning-assisted distinguishing breast phyllodes tumours from fibroadenomas based on ultrasound images: a diagnostic study.

OBJECTIVES: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumours (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences. METHODS: We retrospectively collected 1180 ultrasound images from 539 patients (247 PTs and 292 FAs). Five DL network models with different structures were trained and validated using nodule regions annotated by radiologists on breast ultrasound images. DL models were trained using the methods of transfer learning and 3-fold cross-validation. The model demonstrated the best evaluation index in the 3-fold cross-validation was selected for comparison with radiologists' diagnostic decisions. Two-round reader studies were conducted to investigate the value of DL model in assisting 6 radiologists with different levels of experience. RESULTS: Upon testing, Xception model demonstrated the best diagnostic performance (area under the receiver-operating characteristic curve: 0.87; 95% CI, 0.81-0.92), outperforming all radiologists (all P < .05). Additionally, the DL model enhanced the diagnostic performance of radiologists. Accuracy demonstrated improvements of 4%, 4%, and 3% for senior, intermediate, and junior radiologists, respectively. CONCLUSIONS: The DL models showed superior predictive abilities compared to experienced radiologists in distinguishing breast PTs from FAs. Utilizing the model led to improved efficiency and diagnostic performance for radiologists with different levels of experience (6-25 years of work). ADVANCES IN KNOWLEDGE: We developed and validated a DL model based on the largest available dataset to assist in diagnosing PTs. This model has the potential to allow radiologists to discriminate 2 types of breast tumours which are challenging to identify with precision and accuracy, and subsequently to make more informed decisions about surgical plans.

Correlation of preoperative sonographic staging and postoperative histopathologic staging in patients with invasive breast cancer.

PURPOSE: To assess the accuracy of preoperative sonographic staging in patients with primary invasive breast cancer. METHODS: We retrospectively analyzed a prospectively kept service database of patients with newly diagnosed, unifocal, cT1-3, invasive breast cancer. All patients were diagnosed at a single center institution between January 2013 and December 2021. Clinical T stage was assessed preoperatively by ultrasound and correlated with the definite postoperative pathologic T stage. Demographics, clinical and pathological characteristics were collected. Factors influencing accuracy, over- and underdiagnosis of sonographic staging were analyzed with multivariable regression analysis. RESULTS: A total of 2478 patients were included in the analysis. Median patients' age was 65 years. 1577 patients (63.6%) had clinical T1 stage, 864 (34.9%) T2 and 37 (1.5%) T3 stage. The overall accuracy of sonography and histology was 76.5% (n = 1896), overestimation was observed in 9.1% (n = 225) of all cases, while underestimation occurred in 14.4% (n = 357) of all cases. Accuracy increased when clinical tumor stage cT was higher (OR 1.23; 95% CI 1.10-1.38, p ≤ 0.001). The highest accuracy was seen for patients with T2 stage (82.8%). The accuracy was lower in Luminal B tumors compared to Luminal A tumors (OR 0.71; 95% CI 0.59-0.87, p ≤ 0.001). We could not find any association between sonographic accuracy in HER2 positive patients, and demographic characteristics, or tumor-related factors. CONCLUSION: Our unicentric study showed a high accuracy of sonography in predicting T stage, especially for tumors with clinical T2 stage. Tumor stage and biological tumor factors do affect the accuracy of sonographic staging.