Breast Lesions Research Articles

Effectiveness of ultrasound-guided vacuum-assisted excision for treating benign breast lesions.

Ultrasound‑guided vacuum-assisted excision (UGVAE) and breast biopsy are widely used for the diagnosis and treatment of both benign and suspicious breast lesions. In this retrospective study, we aimed to determine the safety of UGVAE for benign breast lesions and provide guidance for clinical practice. We analyzed clinical and pathological data of female patients who had undergone UGVAE between January 2015 and December 2017 at our institution. All breast lesions were categorized according to the Breast Imaging Reporting and Data System (BI-RADS) before performing UGVAE. In our study cohort, UGVAE was used to resect 10,378 breast lesions from 5789 patients, and selected clinical and histopathological data were analyzed. The most common adverse events were postoperative bleeding (0.24%) and skin hypersensitivity (0.67%). The residual lesion rate was 2.27%. Fibroadenomas accounted for most of the benign lesions (7932 of 10,193; 77.82%). Breast cancer was diagnosed in 150 lesions from 128 patients. Multivariable binary logistic regression analyses showed that older age (odds ratio [OR] = 2.034, 95% confidence interval [CI]: 1.668-2.480, p < 0.001), higher BI-RADS category (OR = 9.514, 95% CI: 6.790-13.332, p < 0.001), and larger legion size (OR = 1.048, 95% CI: 1.019-1.077, p = 0.001) were associated with an increased likelihood of breast cancer. Ninety-six patients with breast cancer had undergone follow-up treatment, achieving a 3-year disease-free survival rate of 97.2% and a 3-year overall survival rate of 100%. UGVAE is a safe and effective means of removing benign breast lesions, causing minimal postoperative trauma and fewer complications compared with open surgery. Moreover, UGVAE had little impact on the follow-up treatment and survival of patients diagnosed with breast cancer.

Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.

This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI). A total of 273 benign lesions (benign group) and 274 malignant lesions (malignant group) were collected and randomly divided into a training set (246 benign and 245 malignant lesions) and a testing set (28 benign and 28 malignant lesions) in a 9:1 ratio. An additional 53 lesions from 53 patients were designated as the validation set. Five models-VGG16, VGG19, DenseNet201, ResNet50, and MobileNetV2-were evaluated. Model performance was assessed using accuracy (Ac) in the training and testing sets, and precision (Pr), recall (Rc), F1 score (F1), and area under the receiver operating characteristic curve (AUC) in the validation set. The accuracy of VGG19 on the test set (0.96) is higher than that of VGG16 (0.91), DenseNet201 (0.91), ResNet50 (0.67), and MobileNetV2 (0.88). For the validation set, VGG19 achieved higher performance metrics (Pr 0.75, Rc 0.76, F1 0.73, AUC 0.76) compared to the other models, specifically VGG16 (Pr 0.73, Rc 0.75, F1 0.70, AUC 0.73), DenseNet201 (Pr 0.71, Rc 0.74, F1 0.69, AUC 0.71), ResNet50 (Pr 0.65, Rc 0.68, F1 0.60, AUC 0.65), and MobileNetV2 (Pr 0.73, Rc 0.75, F1 0.71, AUC 0.73). S4 model achieved higher performance metrics (Pr 0.89, Rc 0.88, F1 0.87, AUC 0.89) compared to the other four fine-tuned models, specifically S1 (Pr 0.75, Rc 0.76, F1 0.74, AUC 0.75), S2 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77), S3 (Pr 0.76, Rc 0.76, F1 0.73, AUC 0.75), and S5 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77). Additionally, S4 model showed the lowest loss value in the testing set. Notably, the AUC of S4 for BI-RADS 3 was 0.90 and for BI-RADS 4 was 0.86, both significantly higher than the 0.65 AUC for BI-RADS 5. The S4 model we propose has demonstrated superior performance in predicting the likelihood of malignancy in DCE-BMRI, making it a promising candidate for clinical application in patients with breast diseases. However, further validation is essential, highlighting the need for additional data to confirm its efficacy.

Diagnostic Accuracy of Spectral Doppler Indices and Sonoelastography in Predicting Malignancy in Breast Imaging Reporting and Database System 3 Breast Lesions With Histopathology as the Reference Standard

Diagnostic Accuracy of Spectral Doppler Indices and Sonoelastography in Predicting Malignancy in Breast Imaging Reporting and Database System 3 Breast Lesions With Histopathology as the Reference Standard

Image Features and Diagnostic Value of Contrast-Enhanced Ultrasound for Ductal Carcinoma In Situ of the Breast: Preliminary Findings.

To explore the image features and the diagnostic value of contrast-enhanced ultrasound (CEUS) for ductal carcinoma in situ (DCIS) of the breast. A total of 96 female patients with a solitary and histologically proven DCIS were analyzed retrospectively, and 100 female cases of invasive ductal carcinoma (IDC) lesions were used as the control group. The Breast Imaging Reporting and Data System (BI-RADS) category of breast lesions was assessed according to conventional ultrasound features. The DCIS lesions were classified into mass type and non-mass type. The CEUS characteristics of these breast lesions were retrospectively analyzed qualitatively and quantitatively. The final gold standard was biopsy or surgery with histo-pathological examination. Comparing the ultrasound images of DCIS with that of IDC, there were significant differences in echo pattern, calcification morphology, and calcification distribution (p < .05 for all). There was a significant difference between DCIS and IDC in enhancement intensity, perfusion defects, peripheral high enhancement, intratumoral vessels, and arrival time (AT) (p < .05 for all). In the logistic multivariate regression analysis, two indicators linked with DCIS were recognized: perfusion defects (p = .002) and peripheral high enhancement (p < .001). In forecasting DCIS, the logistic regression equation resulted in an AUC of 0.689, a specificity of 0.720, and a sensitivity of 0.563. CEUS showed differences in enhancement characteristics between DCIS and IDC, with perfusion defects and peripheral high enhancement being associated with DCIS.

Primary osteosarcoma of the breast during lactation: a case report and literature review

Primary osteosarcoma of the breast (POB) is an aggressive and exceedingly rare tumor, and cases with onset during lactation are extremely rare. The exact mechanism of POB development remains unclear. They may originate from totipotent mesenchymal cells in the breast stroma or may be the result of neoplastic transformation of prior breast lesions. Here, we present the case of a 40-year-old Chinese woman who was found with a palpable tumor measuring approximately 3 cm in diameter in her right breast while breastfeeding 4 months post-partum. The lactating woman was misdiagnosed with lactational mastitis during her initial visit to a community hospital. However, due to negative bacterial cultures and ineffective anti-infective treatment, later on the patient was taken to a more advanced hospital where a tissue biopsy was taken. The superior hospital considered that it might be a malignant tumor and performed local excision of the breast mass, leading to a final pathological diagnosis of POB. This is the first reported case of primary osteosarcoma during breastfeeding. We hope that this case report will improve readers’ understanding of the diagnosis and differential diagnosis of POB, especially for patients with atypical clinical symptoms and imaging findings, which should not be taken lightly.

A Rare Presentation of Multiple Fibroadenomas: A Case Report of 8 Concurrent Lesions in a Single Breast

A Rare Presentation of Multiple Fibroadenomas: A Case Report of 8 Concurrent Lesions in a Single Breast

Accuracy of imaging of BI-RADS 4 subcategorizations in breast lesion diagnosis: Radiologic-pathologic correlation.

To correlate breast imaging-reporting and data system (BI-RADS) category 4 lesions with histopathology results to assess the accuracy of subcategorization. A retrospective study was carried out from September 2021 to June 2022. A total of 247 breast lesions were reviewed categorized as BI-RADS 4 using ultrasound (US) and digital mammography. Feature analysis of the lesions were obtained using BI-RADS terminology and assigned to subcategories (4A, 4B, and 4C). Pathological analysis was carried out on tissue obtained through US-guided core biopsy. A p-value of <0.05 was considered significant. Of the 247 lesions, 135 were categorized as subcategory 4A, 68 as 4B, and 44 as 4C. Overall, 41 (16.6%) had malignant lesions, while 206 (83.4%) had benign lesions. The mean age of the patients with benign versus malignant lesions was (43.18±14.02 vs. 51.24±14.15 years; p<0.001). Mean size of benign versus malignant lesions was (1.93±1.65 vs. 3.82±3.89cm; p<0.001). Findings were compared with histopathology, and the positive predictive value fell within the reference range for subcategories 4C (>70%). High reliability was observed between the 2 readers, with a weighted Cohen's Kappa value of 0.79 (0.73-0.85). Significant disagreements in the assignment of features on radiological lesion characterization were observed between the 2 readers regarding lesion density, shape, echo pattern, vascularity, and borders. The results of this study contribute to the existing body of knowledge, emphasizing the need for standardized guidelines for the characterization of BI-RADS 4 subcategories and improved diagnostic accuracy in the management of breast lesions.

Pseudoangiomatous stromal hyperplasia causing bilateral galactomastia with short-term recurrence after surgery: a case report.

Pseudoangiomatous stromal hyperplasia (PASH) is a rare, benign breast lesion characterized by collagen proliferation, often identified as an incidental microscopic finding. Clinically, it may present as a palpable, well-defined breast mass or, in rare instances, as a diffuse, bilateral process leading to significant breast enlargement. We herein report a case of extensive diffuse PASH with short-term recurrence following lesion resection at another hospital. Because of the severity of symptoms, the patient underwent bilateral mastectomy at our facility. This article reviews the clinical, radiologic, and pathologic features of PASH and emphasizes the importance of an accurate preoperative imaging diagnosis for guiding appropriate treatment.

The Value of Second-look Ultrasound and Mammography for Assessment and Biopsy of MRI-detected Breast Lesions

Rationale and ObjectivesSuspicious lesions detected in multiparametric breast MRI can be further analyzed with second-look ultrasound (SLUS) and/or mammography. This study aims to assess the value of second-look imaging in selecting the appropriate biopsy method for different lesion characteristics. Materials and MethodsBetween January 2021 and December 2023, 212 women underwent contrast-enhanced multiparametric breast MRI at 3.0 Tesla. A total of 241 suspicious lesions (108 malignancies, 44.8%) were further assessed with SLUS and second-look mammography. Subsequent image-guided biopsy of each lesion was performed using the most suitable modality. Size-dependent lesion detection rates in SLUS and mammography were compared by means of the McNemar test. ResultsLesions referred to MRI-guided biopsy were predominantly ≤ 10 mm in size (52.8%). SLUS displayed higher detection rates than mammography for mass lesions (55.6% [95% confidence interval 46.4–64.4%] versus 16.7% [10.6–24.3%]; p<0.001) with a particularly high sensitivity for malignant mass lesions > 10 mm (88.5 % [69.9–97.6%]). In contrast, the detection rate for malignant non-mass lesions was lower in SLUS than in second-look mammography (22.0% [11.5–36.0%] versus 38.0% [24.7–52.8%]; p<0.001). The malignancy rates in ultrasound-, mammography-, and MRI-guided biopsies were 53.7%, 55.2%, and 35.0%, respectively. ConclusionSLUS is an excellent tool for further assessment and biopsy of suspicious mass lesions > 10 mm without associated calcifications. In contrast, supplemental ultrasound is of limited value in the evaluation and biopsy guidance of suspicious non-mass lesions compared to second-look mammography.

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.

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. 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. 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. 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. Breast conservation surgery, Localisation, Non-palpable, Margins, Image intensifier.

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.

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. 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. 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. 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. Breast conservation surgery, Localisation, Non-palpable, Margins, Image intensifier.

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.

Myoid Hamartoma of the Breast With HMGA2 Rearrangement and Associated In-Situ and Invasive Carcinoma: Case Report and Review of Literature.

Myoid hamartoma of the breast is an uncommon benign breast neoplasm. We describe an unusual example of an in-situ and invasive carcinoma arising in a myoid hamartoma. We also describe the unique molecular findings in the myoid hamartoma and review the pertinent literature. In radiology studies, the mammogram showed increased density/attenuation, and the ultrasound revealed a change in the overall echogenicity with hyperechoic and anechoic areas. The stromal compartment contained smooth muscle bundles and adipose tissue on microscopic examination. The stromal cells showed HMGA2 gene rearrangement by fluorescence in-situ hybridization analysis and overexpression of HMGA2 by immunohistochemistry. The breast myoid hamartoma is likely underpinned by HMGA2 gene rearrangement and HMGA2 protein overexpression, which can be used as an ancillary test to diagnose this rare breast lesion. Although most myoid hamartomas have a benign clinical course, the likelihood of malignant transformation should always be considered.

Role of Image-Guided Biopsy in Nonpalpable Breast Lesions: A Study in the Sub-Himalayan Region of North India

Background Nonpalpable breast lesions pose a challenge in their early diagnosis. Image-guided biopsy is preferred in these cases so that a pathological diagnosis of breast carcinoma is reached timely for a better prognosis as the disease has an increased chance of successful outcome with early identification and treatment. Objective The study aims at evaluating the role of stereotactic core needle biopsy (CNB) and percutaneous ultrasound-guided core needle biopsy (US-CNB) in diagnosing suspicious nonpalpable breast lesions. Methods Our study included 35 patients with nonpalpable breast lesions and having a Breast Imaging Reporting and Data System (BI-RADS) risk assessment category IV or V on mammography or sonography. These 35 lesions were subjected to percutaneous image-guided (stereotactic or US) biopsy for histopathological analysis. Results Out of a total of 35 cases, 17 were pathologically malignant (48.6%), with the most common subtype being invasive ductal carcinoma (82.3%). Twenty-nine cases underwent US-CNB, 16 (55.1%) of which were malignant and 13 (44.8%) were benign on histopathological evaluation (HPE). The remaining six cases, which on mammography showed no mass but suspicious malignant calcification only, were subjected to stereotactic CNB, out of which one (16.6%) was malignant and five (83.3%) were benign on HPE. Hence, the lesions visible on sonography were more likely to be malignant. Conclusion Sonography and mammography play a complimentary role in detecting breast carcinoma. Percutaneous biopsy under image guidance can be used as an accurate diagnostic alternative to open surgical excisional biopsy to avoid diagnostic delay.

Ultrasound-Guided Versus Wire-Guided Breast-Conserving Surgery for Non-palpable Breast Lesions: A Retrospective Review

Ultrasound-Guided Versus Wire-Guided Breast-Conserving Surgery for Non-palpable Breast Lesions: A Retrospective Review

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

BackgroundBreast 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.MethodsTransfer 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.ResultsNetworks 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.ConclusionIn 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 statementTransfer 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 PointsProperly 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.Graphical

Breast cancer imaging-clinical experience with two-dimensional-shear wave elastography: A retrospective study.

Breast cancer morbidity has been increasing worldwide, but treatments are improving. The therapeutic response depends on the stage at which the disease is diagnosed. Therefore, early diagnosis has never been more essential for successful treatment and a reduction in mortality rates. Radiology plays a pivotal role in cancer detection, and advances in ultrasound (US) palpation have shown promising results for breast cancer imaging. The addition of two-dimensional-shear wave elastography (2D-SWE) US in the routine breast imaging exam can increase early cancer detection and promote better surveillance. To evaluate the clinical applications of 2D-SWE US in breast cancer detection and its combination with other imaging modalities. The 200 consecutive female patients aged 50-80 were examined to evaluate palpable breast lesions. All patients underwent mammography, bright mode (B-mode) US, and 2D-SWE followed by US-guided biopsy in two consecutive sessions. Combining B-mode and shear wave US imaging with X-ray mammography revealed 100% of the suspicious lesions, resulting in greater sensitivity, specificity, and negative predictive value. The result improves compared to either B-mode or 2D-SWE alone (P = 0.02). Combining 2D-SWE with conventional US and X-ray techniques improves the chance of early cancer detection. Including 2D-SWE in regular breast imaging routines can reduce the need for biopsies and improve the chance of early cancer detection and survivability with the proper line of therapy.

Breast Lesion Detection for Ultrasound Images Using MaskFormer.

This study evaluates the performance of the MaskFormer model for segmenting and classifying breast lesions using ultrasound images, addressing ultrasound's limitations. Ultrasound used for breast cancer detection faces challenges like low image contrast and difficulty in the detection of small or multiple lesions, further complicated by variability based on operator skill. Initial experiments with U-Net and other CNN-based models revealed constraints, such as early plateauing in model loss, which indicated suboptimal learning and performance. In contrast, MaskFormer demonstrated continuous improvement, achieving higher precision in breast lesion segmentation and significantly reducing both false positives and false negatives. Comparative analysis showed MaskFormer's superior performance, with the highest precision and recall rates for malignant lesions and an overall mean average precision (mAP) of 0.943. The model's ability to detect a diverse range of breast lesions, including those potentially missed by the human eye, especially by less experienced practitioners, underscores its potential. These findings suggest that integrating AI models like MaskFormer could greatly enhance ultrasound performance for breast cancer detection, providing reliable, operator-independent image analysis and potentially improving patient outcomes on a global scale.

Non-Palpable Breast Cancer: A Targeting Challenge–Comparison of Radio-Guided vs. Wire-Guided Localization Techniques

Background: The incidence of non-palpable breast cancer is increasing due to widespread screening and neo-adjuvant therapies. Among the available tumor localization techniques, radio-guided occult lesion localization (ROLL) has largely replaced wire-guided localization (WGL). The aim of this study was to compare the ROLL and WGL techniques in terms of the effectiveness of isotopic marking of axillary sentinel lymph nodes and to assess patient perspectives along with surgeon and radiologist preferences. Methods: A single-center, prospective, randomized study enrolled 110 patients with non-palpable breast lesions (56 ROLL, 54 WGL). Breast type, tumor volume, location, histological and radiological features, and localization/surgical duration were evaluated in the context of sentinel lymph node marking using isotope (technetium-99m-labeled human serum albumin) and blue dye. Statistical analysis was performed with significance set at p &lt; 0.05 and strong significance at p &lt; 0.01. Results: A single-center, prospective, randomized study enrolled 110 patients with non-palpable breast lesions (56 ROLL, 54 WGL). Breast type, tumor volume, location, histological and radiological features, and localization/surgical duration were evaluated in the context of sentinel lymph node marking using isotope (technetium-99m-labeled human serum albumin) and blue dye. Statistical analysis was performed with significance set at p &lt; 0.05 and strong significance at p &lt; 0.01. Conclusions: While ROLL provided advantages in terms of patient comfort and logistical simplicity, WGL was superior for axillary sentinel lymph node marking, particularly in inner quadrant tumors, suggesting that WGL may be preferred in these cases.

Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.

The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of themammogram based CADx schemes.However, digital mammograms are grayscale images, while deep learning models are typically optimized using the natural images containing three channels. Thus, it is needed to convert the grayscale mammograms into three channel images for the input of deep transfer models. This study aims to developa novel pseudo color image generation methodwhich utilizesthe mass contour information to enhance the classification performance.Accordingly, a total of 830 breast cancer cases were retrospectively collected, which contains 310 benign and 520 malignant cases, respectively. For each case, a total of four regions of interest (ROI) are collected from the grayscale images captured for both the CC and MLO views of the two breasts. Meanwhile, a total of seven pseudo color image sets are generated as the input of the deep learning models, which arecreated through a combination of the original grayscale image, a histogram equalized image, a bilaterally filtered image, and a segmented mass. Accordingly, the output features from four identical pre-trained deep learning models are concatenated and then processed by a support vector machine-based classifier to generate the final benign/malignant labels. The performance of each image set was evaluated and compared. The results demonstrate that the pseudo color sets containing the manually segmented mass performed significantly better than all other pseudo color sets, which achieved an AUC (area under the ROC curve) up to 0.889 ± 0.012 and an overall accuracy up to 0.816 ± 0.020, respectively. At the same time, the performance improvement is also dependent on the accuracy of the mass segmentation. The results of this study support our hypothesis that adding accurately segmented mass contours can provide complementary information, thereby enhancing the performance of the deep transfer model in classifying suspicious breast lesions.