Breast cancer is the most common malignancy in women. Clinical models such as Oncotype DX recurrence score (RS) and Clinical Treatment Score post–5 years (CTS5) model for survival prediction are crucial for clinical practice. However, it remains unclear whether CTS5 or RS would be a more powerful clinical model for recurrence risk evaluation. Therefore, we conducted the present study to compare the performance of CTS5 risk model and RS on different recurrence evaluation. And we further integrated the two models into a novel nomogram to improve the power for prognosis prediction. Female patients with invasive hormone receptor positive breast cancer in the Surveillance, Epidemiology, and End Results Program (SEER) database with RS data available were included. The clinicopathological data were directly extracted from SEER database. Participants were divided into three subsets according to recurrence timing (60 months) for model evaluation. Survival receiver operating characteristic curve and C-index were calculated to evaluate discrimination. Calibration curve were used to visual inspection for calibration. Model comparison was assessed by net reclassification index (NRI) method. Nomogram prognostic model was developed with the combination of CTS5 score and RS and also included other critical clinicopathological parameters. In total, 64044 breast cancer patients were enrolled for analysis. The number of patients with survival 60 months (late recurrence subset) were 64044, 36878 and 15926, respectively. For model discrimination, CTS5 model was superior to RS for overall survival (OS) prediction (likelihood ratio test P < 0 0.001). RS model showed better performance for breast cancer specific survival (BCSS) in late recurrence subsets and worse performance in early and intermediate recurrence subsets than CTS5 (likelihood ratio test P < 0 0.001). For calibration, CTS5 model was superior to RS model for OS, which overestimated the recurrence risk in low-risk subgroup. Both models overestimated the risk for BCSS. In either early/intermediate/late recurrence patient subsets, there was no significant difference in NRI between two models in terms of both BCSS and OS, indicating the two models had comparable prognostic value. The nomogram which combined these two models largely improved the discrimination and calibration power (C-index 0.70–0.72). Our study proved the CTS5 risk model had comparable prognostic value as RS in HR + breast cancer patients. And the novel nomogram model had better discrimination and calibration than both CTS5 and RS, and future large-scale clinical trials are warranted for further validation.

The residency matching process has become increasingly competitive across surgical subspecialties, with "interview hoarding" exacerbating resource inefficiencies and systemic inefficiencies in candidate selection. Traditional reliance on non-standardized metrics-particularly USMLE Step 1 scores, institutional prestige, and geographic connections-has further compounded equity concerns in the matching ecosystem. Preference signaling mechanisms have emerged as a standardized solution to enhance both matching efficiency and fairness, though current evidence remains limited by single-specialty studies and insufficient examination of long-term match outcomes. Studies on the preference signaling mechanism in surgical subspecialties published after 2020 were retrieved from PubMed, Embase, and CENTRAL databases. Eleven high-quality retrospective cohort studies involving 10,448 applicants were included. Meta-analysis was performed to compare interview and match rates between signal and non-signal groups, with subgroup analyses assessing the impacts of specialty, data source, and other factors. Totally, eleven studies with 10,488 patients were included for meta-analyses. The interview rate in the signal group was 9.30 times higher than that in the non-signal group (95% CI 7.72-10.89), though substantial heterogeneity was observed across specialties (I² = 96.5%). Orthopedics demonstrated the strongest signaling effect (OR = 18.05), while general surgery showed the weakest (OR = 4.53). Studies based on program directors' data revealed a larger effect size (OR = 34.74). Pooled analysis of four studies showed the match rate in the signal group was 6.76 times higher than that in the non-signal group (95% CI 2.88-10.65), with moderate heterogeneity (I² = 65%). This meta-analysis confirms that the preference signaling mechanism significantly enhances interview rates and match rates in resident matching, demonstrating dual value in optimizing efficiency and promoting equity across surgical specialties. By providing cross-specialty evidence for system optimization, this study advances the preference signaling system toward greater efficiency and fairness in the residency matching process.

Introduction: The US plays a crucial role in screening Asian women for breast disease. ABUS offers several advantages over traditional HHUS, including quicker examination, objectivity, and the ability to store and reconstruct images. This study marks the first large-scale opportunistic screening of ABUS in the population.Methods: Between January 1, 2016, and December 31, 2019, 10,537 women aged 35–75 years from nine districts were randomly assigned to either HHUS or ABUS groups. Diagnostic methods were quantified, and comparisons were made using the Chi-square test.Results: The screening groups consisted of 5,445 participants for HHUS and 4,936 for ABUS. The HHUS and ABUS groups identified 90 carcinomas and 292 benign lesions or 71 carcinomas and 178 benign lesions, respectively. SE), SP, AC, PPV, and NPV for HHUS were 51.11%, 93.84%, 93.13%, 12.23%, and 99.13%, respectively, while for ABUS, they were 66.20%, 93.77%, 93.38%, 13.43%, and 98.98%. The area under the curve (AUC) values for HHUS and ABUS were 0.72 (95% CI: 0.67–0.78) and 0.86 (95% CI: 0.82–0.91), respectively, indicating superior diagnostic performance of ABUS over HHUS (Delong test p < 0.05).Discussion: ABUS is user-friendly, requires minimal training, reduces reliance on examiner experience, and demonstrates potential for superior sensitivity compared to HHUS in breast cancer screening.

This meta-analysis evaluates the diagnostic and staging accuracy of [18F]F-FES PET/CT, its ability to detect estrogen receptor (ER) positivity, and its effectiveness in predicting response to endocrine therapy in ER-positive (ER+) breast cancer. A systematic search of PubMed, Embase (OVID), and Web of Science databases was conducted for studies published between 2013 and June 2024. Studies involving ER + breast cancer patients who underwent [18F]F-FES PET/CT were included. We analyzed the diagnostic accuracy, ER detection capability, and predictive ability for endocrine therapy response. Out of 189 studies initially identified, 21 met the inclusion criteria. For diagnosis and staging compared to [18F]F-FDG PET (10 studies), [18F]F-FES PET/CT demonstrated a sensitivity of 0.75 (95% CI: 0.62–0.85) and a false positive rate (FPR) of 0.27 (95% CI: 0.09–0.50). For ER detection (7 studies), sensitivity was 0.86 (95% CI: 0.71–0.94) with an FPR of 0.45 (95% CI: 0.19–0.73). For predicting response to endocrine therapy (12 studies), [18F]F-FES PET/CT showed a sensitivity of 0.79 (95% CI: 0.62–0.89) and an FPR of 0.58 (95% CI: 0.42–0.72). [18F]F-FES PET/CT is valuable for diagnosing and staging ER + breast cancer, assessing ER status, and predicting response to endocrine therapy. Its implementation can improve treatment planning and patient outcomes in breast cancer management.

Breast cancer is the most common cancer among women worldwide. To evaluate the real-world effectiveness of Neo-adjuvant chemotherapy (NACT) and adjuvant chemotherapy (ACT), we conducted a retrospective study comparing their impact on breast cancer prognosis. We retrieved data from the SEER database. We applied propensity score matching (PSM) to minimize confounding factors. Breast-cancer specific survival (BCSS) and overall survival (OS) were compared between the two groups. We also developed a nomogram prognostic model using clinicopathological parameters to predict survival rates. We identified 46,447 eligible patients in the SEER database. After PSM, 9,025 patients were included in each group, with 11,762 patients receiving NACT and 34,685 undergoing ACT before PSM. In the PSM cohort, the 5-year BCSS and OS were 88.3% (95% CI: 87.6–89.0%) and 85.7% (95% CI: 84.9–86.5%) for the NACT group, respectively. Notably, patients in the ACT group showed significantly better survival prognosis compared to those in the NACT group. We also developed nomograms to predict the 3-year, 5-year, and 8-year OS and BCSS rates for patients receiving NACT or ACT. Breast cancer patients who undergo surgery followed by ACT have a more favorable prognosis compared to those receiving NACT before surgery.

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