Clinical breast cancer最新文献

Background: Breast cancer is a prevalent malignancy in women, with mastectomy as the main surgery. Common postmastectomy complications are seroma (15%-81%), infections (2.9%-3.8%), and flap necrosis (10%-18%), severely impacting quality of life and costs. However, there's a lack of standardized flap care protocols and limited staff knowledge.

Objectives: This study aims to apply best evidence for flap management post-mastectomy to standardize practices, reduce complications, and enhance patient's quality of life.

Methods: This project followed JBI PACES and GRiP principles, implementing evidence-based practices in a Chinese tertiary hospital between January and May 2023. It entailed evidence identification, integration into clinical context, protocol development, baseline audits, barrier/enabler analysis. The study compared pre- and post-evidence implementation rates of flap complications, healthcare staff's knowledge/skill scores on mastectomy flap management, and audit indicator adherence by both staff and patients.

Results: After evidence application, flap ischemia/necrosis rates dropped from 8.57% to 5.56% (P < .001), wound infection rates after surgery reduced from 5.71% to 2.78% (P < .001), and seroma rates decreased from 17.14% to 2.78% (P < .001). Healthcare staff's knowledge and skill scores for flap management following mastectomy increased from 50.67 ±18.32 preimplementation to 98.33 ± 4.01 (t = -13.90, P < .001). Audit criterion compliance rates increased from 8.57% to 94.29% to between 91.67% and 100%, with statistically significant differences in all 15 criteria (P < .001).

Conclusions: Evidence-based management of flaps after mastectomy improves healthcare staff's knowledge and skills, enhances nursing quality, effectively reduces flap complications in patients, and boosts their quality of life.

Purpose: A subpopulation of women with ductal carcinoma in situ (DCIS) remains at risk for in-breast recurrence (IBR) following breast-conserving surgery (BCS) and radiation therapy (RT). The NSABP B-43 trial evaluated the role of concurrent RT and trastuzumab in patients with HER2-positive DCIS but did not reach the prespecified endpoint. We hypothesized that a 7-gene biosignature (DCISionRT) with its Residual Risk subtype (RRt) could identify 2 groups of HER2(3+) patients with significantly different IBR risks after BCS plus RT.

Patients and methods: All patients with HER2(3+) DCIS (n = 178) treated with BCS plus RT were selected from a combined multinational patient cohort. Treatment decisions were neither randomized nor strictly rules-based. Biosignature testing was performed on all patients and stratified with previously defined groups: (1) Combined Low Risk group (DS ≤ 2.8) and Elevated Risk group (DS > 2.8) without RRt or (2) Residual Risk subtype. Kaplan-Meier analysis was used to compute IBR curves.

Results: Sixty-three percent of HER2(3+) patients (113/178) were classified into the Residual Risk subtype. These patients had significantly higher 10-year rates of IBR compared to the nonresidual risk group (16.2% vs. 1.6%, P = .01). The Residual Risk subtype had more nuclear grade 3 disease (87% vs. 63%, P < .001), but age, size, and grade were not associated with IBR rate (P = NS) on univariate and multivariable analysis. Only the Residual Risk group was associated with IBR (P = .05) in multivariate analysis.

Conclusion: The 7-gene biosignature with RRt identified a subset of HER2(3+) patients with greater IBR rates following BCS and RT beyond traditional clinical and pathologic features. Consideration of therapies to reduce these elevated IBR rates should be evaluated, including the incorporation of HER2-targeted therapy.

Purpose: Mucinous breast carcinoma (MBC) tends to be misdiagnosed as fibroadenomas (FA) due to its benign imaging characteristics. We aimed to develop a deep learning (DL) model to differentiate MBC and FA based on ultrasound (US) images. The model could contribute to the diagnosis of MBC for radiologists.

Methods: In this retrospective study, 884 eligible patients (700 FA patients and 184 MBC patients) with 2257 US images were enrolled. The images were randomly divided into a training set (n = 1805 images) and a test set (n = 452 images) in a ratio of 8:2. First, we used the training set to establish DL model, DL+ age-cutoff model and DL+ age-tree model. Then, we compared the diagnostic performance of three models to get the optimal model. Finally, we evaluated the diagnostic performance of radiologists (4 junior and 4 senior radiologists) with and without the assistance of the optimal model in the test set.

Results: The DL+ age-tree model yielded higher areas under the receiver operating characteristic curve (AUC) than DL model and DL+ age-cutoff model (0.945 vs. 0.835, P < .001; 0.945 vs. 0.931, P < .001, respectively). With the assistance of DL+ age-tree model, both junior and senior radiologists' AUC had significant improvement (0.746-0.818, P = .010, 0.827-0.860, P = .005, respectively).

Conclusions: The DL+ age-tree model based on US images and age showed excellent performance in the differentiation of MBC and FA. Moreover, it can effectively improve the performance of radiologists with different degrees of experience that may contribute to reducing the misdiagnosis of MBC.