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Background: Triple-negative breast cancer (TNBC) is the most fatal breast cancer subtype, which often shows aggressive progression, a high potential to metastasize, and resistance to chemotherapy. Comprehensive genomic profiling using next-generation sequencing (NGS) has been expected to identify gene alterations that are targetable by drugs. However, the significance of these genomic alterations in the cancer biology of TNBC patients has not yet been fully understood due to the lack of accurate clinical outcome data to compare with the genomic data. The aim of this study was to clarify the clinical impact of genomic profiling data, including copy number alterations (CNAs), in TNBC by comparing comprehensive genomic data with clinical outcomes. Methods: A total of 47 patients diagnosed with stage I-III TNBC (from the cohort reported in JCO Precis Oncol. 2018;2:PO.17.00211) were enrolled in this study. The genomic profiling of 435 known cancer genes by NGS with clinical outcomes were analyzed. Overall survival (OS) was evaluated for its association to gene alterations and distinctively CNAs. The cut-off values of CNA for OS were determined from the receiver operating characteristic curve using the Youden index for area under the curve (AUC). Kaplan-Meier plots and log-rank tests of OS were applied for each group. Univariate and multivariate analyses for OS were performed using a Cox proportional-hazards model to obtain the hazard ratio (HR) and 95% confidence intervals. Results: Utilizing NGS-based genomic profiling, at least one alteration was found in 82 of the 435 cancer-associated genes, and a total of 162 alterations were found in the 47 patients. Among the 82 genes with alterations, the presence or absence of TP53 and PTEN alterations was significantly associated with OS of TNBC patients; patients with TP53 alterations (n = 31) showed significantly shorter OS than those without TP53 alterations (n = 16, p = 0.023), and patients with PTEN alterations (n = 9) showed significantly shorter OS than those without PTEN alterations (n = 38, p = 0.023). The cut-off value of CNA for OS was set at 25 (AUC, 0.788; sensitivity, 0.727; specificity, 0.900). Interestingly, CNA-high patients (n = 20) showed significantly shorter OS than CNA-low patients (n = 27, p = 0.014). Univariate analysis revealed that TP53 alterations and CNAs were significant prognostic factors for OS (HR, 8.81; p = 0.008; and HR, 8.00; p = 0.014, respectively). Finally, multivariate analysis using background clinical data revealed that CNA was an independent prognostic factor for OS in TNBC patients (HR, 7.15; p = 0.044). Conclusion: Our data suggest that CNA is an independent prognostic marker in TNBC, and that can be estimated from comprehensive genomic profiling data by NGS. Further investigation is needed to clarify the mechanisms of how CNAs are associated with this lethal disease. Citation Format: Masayuki Nagahashi, Chie Toshikawa, YiWei Ling, Tetsu Hayashida, Yuko Kitagawa, Manabu Futamura, Takashi Kuwayama, Seigo Nakamura, Hideko Yamauchi, Teruo Yamauchi, Koji Kaneko, Chizuko Kanbayashi, Nobuaki Sato, Junko Tsuchida, Kazuki Moro, Masato Nakajima, Yoshifumi Shimada, Hiroshi Ichikawa, Stephen Lyle, Yasuo Miyoshi, Kazuaki Takabe, Shujiro Okuda, Toshifumi Wakai. Copy number alteration is an independent prognostic biomarker in triple-negative breast cancer patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-11-28.

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) has become widespread worldwide, the problem of inter-observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a novel artificial intelligence (AI) system in which AI can distinguish whether a static image obtained using a breast ultrasound represents BI-RADS3 or lower, or BI-RADS4a or higher, to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4,028 images containing 5,014 lesions and a test dataset consisting of 3,166 images containing 3,656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 90.0%, and 88.5%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Then, we conducted a trial to introduce the system for use in clinical practice. Physicians reviewed the images and determined whether they were BI-RADS3 or lower, or BI-RADS4a or higher. Next, the classification was performed again for the same images concerning the AI diagnosis. At this time, the initial judgment was allowed to be overturned. We checked whether there was any difference in the diagnostic accuracy, sensitivity, and specificity before and after reviewing to the AI diagnosis. Reviews by 24 physicians were evaluated: 4 Japanese Breast Cancer Society breast specialists, 5 non-specialists and physicians with experience treating more than 40 cases of breast cancer, and 15 non-specialists and physicians with no experience treating more than 40 cases of breast cancer. The average rate of accuracy before confirming the AI diagnosis increased to 73.1% after confirming the AI diagnosis (p=0.00548), compared to 69.3% on average before the AI diagnosis. Compared to practice experience, the accuracy increased from an average of 77.1% to 79.6% for the 9 physicians who were breast specialists or who had treated 40 or more cases of breast cancer. For the 15 physicians with less than 40 breast cancer cases, the average rate of accuracy increased from 64.7% to 69.2%. Furthermore, sensitivity increased significantly to an average of 99.7% after reviewing of the AI diagnosis from an average of 88.8% prior to reviewing the AI-diagnosis.(p< 0.01). Specificity increased from an average of 62.4% to 63.8% (p=0.433) after reviewing AI diagnosis. We showed that our AI system, when applied to clinical practice and used by physicians, contributes to the improvement of diagnostic accuracy. Our results indicated that our AI diagnostic system was sufficiently accurate to be used in the clinical practice. Citation Format: Erina Odani, Tetsu Hayashida, masayuki kikuchi, Aiko Nagayama, tomoko seki, maiko takahashi, Akiko Matsumoto, Takeshi Murata, Rurina Watanuki, Takamichi Yokoe, Ayako Nakashoji, Hinako Maeda, Tatsuya Onishi, Sota Asaga, Takashi Hojo, Hiromitsu Jinno, Keiichi Sotome, Akira Matsui, Akihiko Suto, Shigeru Imoto, Yuko Kitagawa. Establishment of the breast ultrasound support system using deep-learning system [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-06.

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) has become widespread worldwide, the problem of inter-observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a system in which artificial intelligence (AI) can distinguish whether a static image obtained using a breast ultrasound represents BI-RADS3 or lower or BI-RADS4a or higher to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4028 images containing 5014 lesions and a test dataset consisting of 3166 images containing 3656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 91.2%, and 90.7%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p 0.001). Although deep-learning methods to categorize benign and malignant tumors using breast ultrasound have been extensively reported, our work represents the first attempt to establish an AI system to classify BI-RADS3 or lower and BI-RADS4a or higher successfully, providing important implications for clinical actions. These results suggest that the AI diagnostic system is sufficient to proceed to the next stage of clinical application.

Background Anthracyclines are commonly used for the treatment of solid tumors and hematological malignancies because of their inevitable dose-dependent cardiotoxic effects. The study aimed to assess the feasibility of applying a biomarker-based surveillance strategy according to European guidelines for the early prediction of anthracycline-induced cardiotoxicity in patients with breast cancer.Methods From April 2018 to December 2021, 45 women with breast cancer (53.9 ± 11.0 years) treated with anthracycline-based regimens were evaluated for 1 year. We measured and analyzed high-sensitivity cardiac troponin T (hsTnT), B-type natriuretic peptide (BNP), global longitudinal strain (GLS), and left ventricular ejection fraction (LVEF) at baseline, and 3 and 6 months after the initiation of anthracycline-based chemotherapy. Cardiotoxicity was defined as a reduction in LVEF > 10% compared with baseline to LVEF 15% from baseline.Results After initiating anthracycline treatment, hsTnT levels peaked at 3 months, whereas BNP levels were the highest at 6 months. Cardiotoxicity was detected in 18 (20.0%) patients (one patient with LVEF decline), where GLS declined over time yet became significant only after 6 months. Elevated hsTnT at 3 months were significantly associated with a GLS decline at 6 months (P Conclusions Among patients with early-stage breast cancer, an increase in hsTnT at 3 months after initiation of the anthracycline-based regimen predicted a subsequent decline in GLS with high sensitivity and moderate specificity.

Due to the increasing complexity of cancer treatment, ensuring safety and maintaining the quality of life during treatment are important issues. Patient-reported outcomes (PROs) in oncology are essential for assessing patient symptoms. A feasibility study was undertaken on breast cancer patients by building a PRO data collection system based on LINE, one of the most popular social network service applications in Japan. In this study, one or more predefined PRO questions for each breast cancer patient's clinical situation were sent to the patient's LINE application daily. The patient selected a predefined answer by tapping the screen, but no free-text answers were allowed. Seventy-three patients were enrolled. The median observation period was 435 days (84-656 days), and the total number of PROs collected was 16,417, with a mean of 224.9 reports per patient. Patients on adjuvant endocrine therapy were notified of 2.5 questions per week, and the median number of responses per week and response rate were 2.387 (1.687-11.627) and 95.5%, respectively. Analyzing the results by age group, the number of responses from those aged 60 and above was equal to or higher than that of the younger age group. It was also possible to track each patient's PROs accurately. These results suggested that the design of the system, based on an application used daily, instead of using specifically prepared applications for collecting electronic PROs, was the reason for the favorable acceptance from patients and the satisfactory response rate from all age groups, including the elderly.

Supplementary Methods from The Integrin αvβ3-5 Ligand MFG-E8 Is a p63/p73 Target Gene in Triple-Negative Breast Cancers but Exhibits Suppressive Functions in ER+ and erbB2+ Breast Cancers

Supplementary Figure 2 from The Integrin αvβ3-5 Ligand MFG-E8 Is a p63/p73 Target Gene in Triple-Negative Breast Cancers but Exhibits Suppressive Functions in ER+ and erbB2+ Breast Cancers

Supplementary Figure Legends 1-2 from The Integrin αvβ3-5 Ligand MFG-E8 Is a p63/p73 Target Gene in Triple-Negative Breast Cancers but Exhibits Suppressive Functions in ER+ and erbB2+ Breast Cancers

Supplementary Figure 1 from The Integrin αvβ3-5 Ligand MFG-E8 Is a p63/p73 Target Gene in Triple-Negative Breast Cancers but Exhibits Suppressive Functions in ER+ and erbB2+ Breast Cancers

Purpose Male breast cancer (MBC) is a rare cancer accounting for only 1% of all male cancers and is, therefore, poorly studied. We aimed to characterize the subtypes of MBC in Japanese patients based on genetic profiling, the presence of tumor-infiltrating cells, and the expression of immunohistochemical markers. Methods This retrospective study included 103 patients with MBC diagnosed between January 2009 and December 2019 at various hospitals in Japan. Clinicopathological patient characteristics were obtained from medical records, and formalin-fixed paraffin-embedded tissue specimens were analyzed for histological markers, mutations of 126 genes, BRCA1 methylation, and stromal tumor-infiltrating lymphocytes. Results The median patient age was 71 (range 31–92) years. T1-stage tumors were the most frequent (47.6%), and most were node negative (77.7%). The majority of tumors were positive for estrogen receptor (98.1%), progesterone receptor (95.1%), and androgen receptor (96.1%), and BRCA2 was the most frequently mutated gene (12.6%). The most common treatment was surgery (99.0%), either total mastectomy (91.1%) or partial mastectomy (7.0%). Survival analysis showed a 5-year recurrence-free survival rate of 64.4% (95% confidence interval [CI] 46.7–88.8) and a 5-year overall survival rate of 54.3% (95% CI 24.1–100.0). Conclusion Japanese MBC is characterized by a high rate of hormonal receptor positivity and BRCA2 somatic mutation. Due to the observed clinicopathological differences in MBC between the Western countries and Japan, further prospective studies are needed to evaluate the most suitable treatment strategies.

In the medical field, surgical videos can be used to introduce surgical skills. Medical students and residents watch the videos to study the surgical skills and increase learning speed by compensating for the lack of experience in surgical rooms due to limited opportunity to join in surgery. To record egocentric surgical videos by a wearable camera is a solution to record surgical skills of a surgeon in detail. However, most egocentric surgical videos are of quite long duration. For example, in the case of tumor removal in breast surgery, a video recording time often reaches 2[Formula: see text]h. With that length, it is time consuming to see important scenes in the video, particularly because many surgical videos include nonessential scenes such as sterilization and preparation of tools. For extracting specific scenes from a long video, we can apply scene estimation by machine learning. Furthermore, it is important to know where the surgeon is looking to observe the area of the incision in detail. In particular, it is vital to be able to zoom in on key elements, allowing viewers to see the incision area and the fine details of the necessary surgical skills. In this study, we aimed to highlight incision scenes from egocentric surgical videos in the spatiotemporal domain by utilizing two neural networks for the temporal and spatial highlights. For the temporal highlights, we designed a neural network that estimates the incision scenes by learning gaze speed, hand movements, number of hands, and background movements in egocentric surgical videos. For the spatial highlights, in order to estimate the important area to zoom in, we designed a neural network that learns the surgeon’s gaze on natural features of surgical scenes to form a probability map as a representation of the estimated gaze area. The estimated gaze area was also used to calculate the appropriate zoom-in position and zoom-in ratio. To control the highlighted parameters in accord with user preferences, we also made a user interface that allows for the selection of playback speed gain and zoom ratio gain. For the evaluation, we verified the performance of the networks by a quantitative assessment and conducted a user study with medical doctors by showing an actual surgical video to obtain a qualitative assessment on the proposed system.

e12576 Background: From the results of ACOSOG Z0011, IBCSG23-01 and AMAROS trials, axilla surgery in node-positive breast cancer (BC) tends to be less invasive with sentinel node biopsy (SNB) followed by adjuvant therapy and regional node irradiation (RNI). However, optimized axilla treatment including SNB without RNI is still debated. The Japanese Society for Sentinel Node Navigation Surgery conducted a multi-institutional prospective cohort study to compare SNB with SNB followed by axillary lymph node dissection (ALND) in cases with positive-sentinel lymph nodes (SLN)(UMIN No. 000011782, Jpn J Clin Oncol, p.876-9, 2014). Methods: Female BC patients with cT1-3N0-1M0 were eligible. When 1 to 3 positive micrometastases or macrometastases in SLN were confirmed by histological or molecular diagnosis, SNB alone or additional ALND had been decided by physician’s discretion. Primary chemotherapy before or after SNB was acceptable for registration. Lymph node sampling was also allowed in the SNB group. Cases with bilateral BC, isolated tumor cells only in SLN, past history of invasive cancer within 5 years at the registration were ineligible. The primary endpoint was the 5-year recurrence rate of regional node (RN) in the SNB group. The secondary endpoint was overall survival (OS). We planned to collect 240 patients to reject that the 5-year recurrence rate of RN was more than 10% assuming the rate 5%. To compare the SNB group and ALND group, the propensity score matching (PSM) was performed. Matching variables were initial treatment, metastatic size and numbers of SLN, clinical stage, age, body mass index, menopausal status, family history, past history of invasive cancer, breast surgery. Results: Eight-hundred eighty cases had been registered between 2013 and 2016. In the 871 eligible cases, 308 cases were the SNB group. At the median follow-up of 6.3 years, 5-year recurrence rate of RN was 2.7% [95% confidence interval, 1.4% to 5.4%] and 5-year OS was 97.6% [94.9% to 98.8%]. After PSM, 209 cases were matched in the SNB and ALND group. Among them, 343 cases (82%) received operation at initial treatment. Partial and total mastectomy was performed in 225 (54%) and 193 cases (46%), respectively. One-positive SLN was recorded in 366 cases (88%), 2 in 48 (11%) and 3 in 4 (1%). Macrometastases and micrometastases in SLN were diagnosed in 271 (65%) and 147 cases (35%), respectively. Three-hundred seventy-six cases (90%) belonged to luminal-like subtype. RNI was underwent in 42 cases (20%) of the SNB group and 13 cases (6%) of the ALND group. Five-year recurrence rate of RN was 2.1% [0.8% to 5.5%] and 2.0% [0.8% to 5.3%] for the SNB and ALND group, respectively. Conclusions: Our series suggests that RNI is not necessary for regional control in cases with 1 to 3 positive SLN. In conclusion, SNB alone is acceptable in cases with fewer metastatic SLN. Clinical trial information: UMIN No. 000011782.