Tomoyuki Fujioka

Objective: Neoadjuvant chemotherapy (NAC) has increased the number of patients achieving pathological complete response, making primary tumors and axillary lymph nodes difficult to identify on imaging. Accurate localization is therefore important for surgery, and breast markers play a key role. The aim of this study was to test recently developed volumetric breast markers to ascertain if they provide improved visibility, with sonograph. Materials and Methods: Three configurations of the UltraCor Twirl Breast Tissue Marker (BD) were evaluated: (1) Ring, a conventional planar configuration; (2) Curls, a novel volumetric configuration; and (3) Clover, a novel volumetric configuration. Markers were placed into a chicken breast phantom under sonographic guidance. Marker visibility was evaluated based on imaging completed with sonography, mammography, and magnetic resonance imaging (MRI) by experienced breast radiologists. Results: All breast markers were successfully deployed. Curls showed the highest visibility with sonography, while Clover was also clearly visualized. All markers were well visualized on mammography. On MRI, visibility increased with marker size, with Curls showing the highest visibility. Conclusion: Volumetric breast markers demonstrated good visibility across all imaging techniques, especially sonography. Curls showed the highest visibility, while Clover may be suitable for small lesions or axillary lymph nodes.

Background and Clinical Significance: Breast hemangioma is an extremely rare benign vascular tumor of the breast. Its imaging findings are nonspecific, and differentiation from malignant tumors such as encapsulated papillary carcinoma, mucinous carcinoma or angiosarcoma is often difficult. We report a case of breast hemangioma that showed slow growth over an 11-year period. Case Presentation: A woman in her 50s presented with a well-defined 11 mm mass in the upper outer quadrant of the left breast detected by ultrasonography. A core needle biopsy revealed a benign lesion, and follow-up was recommended. Eleven years later, the mass had increased to 27 mm. Magnetic resonance imaging showed high signal intensity on T2-weighted images and a fast-plateau enhancement pattern extending from the periphery to the center. Although malignancy was suspected, vacuum-assisted biopsy revealed a hemangioma. Conclusions: Breast hemangioma can show slow enlargement over a long period. Recognition of a characteristic peripheral-to-central enhancement pattern may aid in distinguishing this benign vascular lesion from malignant tumors.

Background and Objectives: Surgical smoke generated by energy devices during surgery contains hazardous substances and poses health risks to staff in the operating room. Exposure to surgical smoke must be reduced to minimize the risk of health hazards. Many studies have evaluated surgical smoke qualitatively, but few have performed quantitative assessment. The aim of this study was to quantify the number of particles generated during various breast surgery procedures. Materials and Methods: In this prospective, randomized study, breast surgeries performed at Tokyo Medical and Dental University Hospital (the present Institute of Science Tokyo Hospital) between December 2022 and August 2023 were randomly assigned to two groups: the electrosurgical device group and the electrosurgical device with smoke evacuator group. The number of particles generated by energy devices during surgery was measured using a particle counter. Results: Surgical smoke was generated in all procedures. The number of measured particles was significantly less in the electrosurgical device with smoke evacuator group than in the electrosurgical device group during all procedures (all p < 0.001). Conclusions: All breast surgery procedures produced a significant amount of surgical smoke, which was effectively reduced by using an electrosurgical device with a smoke evacuator. These findings support the routine use of smoke evacuators in breast surgery to reduce occupational exposure to hazardous particles. Implementation of such devices could improve operating room safety and may inform future guidelines and institutional policies regarding surgical smoke management.

Purpose: The purpose of this study is to evaluate the diagnostic accuracy of an artificial intelligence (AI)-based Computer-Aided Diagnosis (CADx) system for breast ultrasound, compare its performance with radiologists, and assess the effect of AI-assisted diagnosis. This study aims to investigate the system’s ability to differentiate between benign and malignant breast masses among Japanese patients. Materials and Methods: This retrospective study included 171 breast mass ultrasound images (92 benign, 79 malignant). The AI system, BU-CAD™, provided Breast Imaging Reporting and Data System (BI-RADS) categorization, which was compared with the performance of three radiologists. Diagnostic accuracy, sensitivity, specificity, and area under the curve (AUC) were analyzed. Radiologists’ diagnostic performance with and without AI assistance was also compared, and their reading time was measured using a stopwatch. Results: The AI system demonstrated a sensitivity of 91.1%, specificity of 92.4%, and an AUC of 0.948. It showed comparable diagnostic performance to Radiologist 1, with 10 years of experience in breast imaging (0.948 vs. 0.950; p = 0.893), and superior performance to Radiologist 2 (7 years of experience, 0.948 vs. 0.881; p = 0.015) and Radiologist 3 (3 years of experience, 0.948 vs. 0.832; p = 0.001). When comparing diagnostic performance with and without AI, the use of AI significantly improved the AUC for Radiologists 2 and 3 (p = 0.001 and 0.005, respectively). However, there was no significant difference for Radiologist 1 (p = 0.139). In terms of diagnosis time, the use of AI reduced the reading time for all radiologists. Although there was no significant difference in diagnostic performance between AI and Radiologist 1, the use of AI substantially decreased the diagnosis time for Radiologist 1 as well. Conclusion: The AI system significantly improved diagnostic efficiency and accuracy, particularly for junior radiologists, highlighting its potential clinical utility in breast ultrasound diagnostics.

Although several studies have been conducted on artificial intelligence (AI) use in mammography (MG), there is still a paucity of research on the diagnosis of metachronous bilateral breast cancer (BC), which is typically more challenging to diagnose. This study aimed to determine whether AI could enhance BC detection, achieving earlier or more accurate diagnoses than radiologists in cases of metachronous contralateral BC. We included patients who underwent unilateral BC surgery and subsequently developed contralateral BC. This retrospective study evaluated the AI-supported MG diagnostic system called FxMammo™. We evaluated the capability of FxMammo™ (FathomX Pte Ltd., Singapore) to diagnose BC more accurately or earlier than radiologists’ assessments. This evaluation was supplemented by reviewing MG readings made by radiologists. Out of 1101 patients who underwent surgery, 10 who had initially undergone a partial mastectomy and later developed contralateral BC were analyzed. The AI system identified malignancies in six cases (60%), while radiologists identified five cases (50%). Notably, two cases (20%) were diagnosed solely by the AI system. Additionally, for these cases, the AI system had identified malignancies a year before the conventional diagnosis. This study highlights the AI system’s effectiveness in diagnosing metachronous contralateral BC via MG. In some cases, the AI system consistently diagnosed cancer earlier than radiological assessments.

Purpose: To compare different biopsy systems with different-sized needles by determining the weight of the tissue cores, which is one of the important factors for precise pathological diagnoses, and to provide a rationale for choosing the appropriate breast biopsy system with the appropriate needle for breast cancer biopsy. Methods: Six different vacuum-assisted biopsy (VAB) systems and one core needle biopsy (CNB) system with different-sized needles in different modes were compared, representing 15 total combinations. Tissue cores were obtained from a chicken breast phantom, which is a common substitute for human breast tissue. Five cores were taken for each combination and weighed. Results: The CNB combination provided significantly lighter tissue cores compared with the VAB combinations with the same-size (14-G) needle (P < 0.01). The combinations using the thickest needle obtained the heaviest among all systems (P < 0.02). The untethered battery-free VAB system yielded the lightest specimen among the VAB systems with the same-sized (12-G) needle (P < 0.04). The percent coefficient of variation (%CV) of the core weights obtained using VAB without a basket was significantly smaller compared with the core weights obtained using VAB with a basket (P < 0.01). Conclusion: VAB systems can yield larger tissue cores compared with CNB systems. The size of the tissue cores varies even with the same-sized needle among different VAB systems. When performing a breast tissue biopsy, it is important to consider not only CNB versus VAB but also what specific device to use with which needle size.

Background: Although several studies have been conducted on the use of Artificial Intelligence (AI) in mammography (MG), research focusing on the diagnosis of metachronous bilateral breast cancer (BC), which is typically more challenging to diagnose, remains limited. This study assessed whether AI could enhance the detection of BC, achieving earlier or more accurate diagnoses than radiologists in cases of metachronous contralateral BC. Methods: The study included patients who underwent unilateral BC surgery and subsequently developed contralateral BC during follow-up. We evaluated the capability of the AI-supported MG diagnostic system FxMammo™ (FathomX Pte Ltd, Singapore) to diagnose BC more accurately or earlier than radiologists' assessments. This evaluation was supplemented by reviewing MG readings by radiologists. Results: Out of 1,101 patients who underwent surgery, 10 patients who had initially received a partial mastectomy and later developed contralateral BC were analyzed. The AI system identified malignancies in 6 cases (60%), while radiologists identified 5 cases (50%). Notably, two cases (20%) were diagnosed solely by the AI system. Additionally, for these cases, the AI system had identified malignancies a year prior to the conventional diagnosis. Conclusions: This study highlights the AI system's effectiveness in diagnosing metachronous contralateral BC via MG. In some cases, the AI system consistently diagnosed cancer at earlier than radiological assessments.