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Abstract Background Oncotype DX(ODX) is a 21-gene breast cancer recurrence score(RS) assay that aids in decision-making for chemotherapy in early-stage hormone receptor-positive(HR+)breast cancer. We developed a prediction tool using machine learning for high- or low-risk ODX criteria (i.e., RS < 11 for low-risk; RS > 25 for high-risk). Methods We performed a retrospective review of 301 breast cancer patients who underwent surgery between April 2011 and July 2017 and then an ODX test at Samsung Medical Center in Seoul, Korea. Among them, 208 cases were defined as the modeling group and 76 cases were defined as the validation group. We built a supervised machine learning classification model using the Azure ML platform. Results For the high RS group, accuracy was 0.903 through Two-class Decision Jungle method in test set. For the low RS group, the accuracy was 0.726 when the Two-class Neural Network method was applied. The AUC of the ROC curve was 0.917 in the high RS group and 0.744 in the low RS group in test set. In addition, we conducted an internal validation using 76 patients who underwent ODX testing between January 2017 and July 2017. The accuracy of validation was 0.880 in the high RS group and 0.790 in the low RS group. Conclusion We developed a predictive model using machine learning that could represent a useful and easy-to-access tool for the selection of high ODX RS patients. After additional evaluation with large data and external validation, worldwide use of our model could be expected. [ABSTRACT FROM AUTHOR]

Summary Background A survey of breast surgeons was conducted to evaluate changes in clinical practice regarding sentinel node biopsy (SNB) among clinically node-negative patients after neoadjuvant chemotherapy for breast cancer with axillary node metastasis. Methods We conducted two surveys among 252 members of the Korean Breast Cancer Society. The questionnaire comprised a case presentation and two associated questions. The case outlined a woman diagnosed with right breast cancer; core needle biopsy had confirmed invasive ductal carcinoma and tumor size was 4 cm on imaging examination. Fine needle aspiration examination for axillary lymph node enlargement showed metastatic carcinoma. The patient underwent neoadjuvant chemotherapy and tumor size was decreased by 2 cm; axillary lymph node enlargement was not observed on palpitation or imaging examination. Finally, the patient underwent breast conserving surgery. Survey recipients were asked: 1. Would you perform SNB in this patient? 2. If you perform SNB and no axillary node metastasis is seen, would you perform additional axillary lymph node dissection (ALND)? Results The response rate was 28.2% (71/252) and 15.1% (38/252) in 2013 and 2017, respectively. For the first question, the SNB to ALND ratio increased significantly from 54% versus 46% in 2013 to 92% versus 8% in 2017 (p < 0.001). The proportion of surgeons performing no additional ALND versus additional ALND increased from 38% versus 54% in 2013 to 53% versus 37% in 2017, but did not reach statistical significance (p = 0.1). Conclusions These data show that application of SNB among clinically node-negative patients after neoadjuvant chemotherapy for breast cancer with node metastasis has increased among surgeons in Korea. [ABSTRACT FROM AUTHOR]

Background: Improvements in mobile telecommunication technologies have enabled clinicians to collect patient-reported outcome (PRO) data more frequently, but there is as yet limited evidence regarding the frequency with which PRO data can be collected via smartphone applications (apps) in breast cancer patients receiving chemotherapy. Objective: The primary objective of this study was to determine the feasibility of an app for sleep disturbance-related data collection from breast cancer patients receiving chemotherapy. A secondary objective was to identify the variables associated with better compliance in order to identify the optimal subgroups to include in future studies of smartphone-based interventions. Methods: Between March 2013 and July 2013, patients who planned to receive neoadjuvant chemotherapy for breast cancer at Asan Medical Center who had access to a smartphone app were enrolled just before the start of their chemotherapy and asked to self-report their sleep patterns, anxiety severity, and mood status via a smartphone app on a daily basis during the 90-day study period. Push notifications were sent to participants daily at 9 am and 7 pm. Data regarding the patients' demographics, interval from enrollment to first self-report, baseline Beck's Depression Inventory (BDI) score, and health-related quality of life score (as assessed using the EuroQol Five Dimensional [EQ5D-3L] questionnaire) were collected to ascertain the factors associated with compliance with the self-reporting process. Results: A total of 30 participants (mean age 45 years, SD 6; range 35-65 years) were analyzed in this study. In total, 2700 daily push notifications were sent to these 30 participants over the 90-day study period via their smartphones, resulting in the collection of 1215 self-reporting sleep-disturbance data items (overall compliance rate=45.0%, 1215/2700). The median value of individual patient-level reporting rates was 41.1% (range 6.7-95.6%). The longitudinal day-level compliance curve fell to 50.0% at day 34 and reached a nadir of 13.3% at day 90. The cumulative longitudinal compliance curve exhibited a steady decrease by about 50% at day 70 and continued to fall to 45% on day 90. Women without any form of employment exhibited the higher compliance rate. There was no association between any of the other patient characteristics (ie, demographics, and BDI and EQ5D-3L scores) and compliance. The mean individual patient-level reporting rate was higher for the subgroup with a 1-day lag time, defined as starting to self-report on the day immediately after enrollment, than for those with a lag of 2 or more days (51.6%, SD 24.0 and 29.6%, SD 25.3, respectively; P=.03). Conclusions: The 90-day longitudinal collection of daily self-reporting sleep-disturbance data via a smartphone app was found to be feasible. Further research should focus on how to sustain compliance with this self-reporting for a longer time and select subpopulations with higher rates of compliance for mobile health care. [ABSTRACT FROM AUTHOR]