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Background Previous research has strongly supported the utility of spaced learning in enhancing memory, but its effectiveness in complex surgical procedures has largely been unexplored. The main objective of this study was to evaluate whether, in comparison to concentrated learning, spaced learning improves the short-term acquisition and long-term retention of cosmetic suturing skills as outcomes of surgical resident training courses.Methods This randomized controlled trial was conducted from February 2023 to June 2023. Surgical residents were recruited from a teaching hospital in Guangzhou, China. The participants were randomly assigned at a 1:1 ratio to either the spaced training group (40 min of training followed by a 20-minute break) or the concentrated training group (3 h of continuous training), in which they received one-on-one training for cosmetic suturing skills. The short-term acquisition and long-term retention outcomes were evaluated by three independent raters using an objective scoring scale to assess the participants’ cosmetic suturing skills before the training (pretraining test), within one hour after the training (posttraining test), and three months after the completion of the training (follow-up test). The score for each participant was calculated as the average of three independent scores.Results The study included 23 surgical residents, 12 in the spaced training group and 11 in the concentrated training group. The pretraining test revealed no significant difference between the groups. However, in the post-training test, the spaced training group achieved a significantly higher total score than did the concentrated training group (74.06 ± 5.87 vs. 63.43 ± 10.73, p = 0.0070). Specifically, the suture technique scores were 28.46 ± 1.78 and 22.85 ± 3.75, respectively, which were significantly different (p = 0.0002). During the long-term follow-up test, the spaced training group consistently outperformed the concentrated training group by having significantly higher total (75.60 ± 4.78 vs. 60.68 ± 10.40, p = 0.0001), suture quality (32.26 ± 4.01 vs. 26.23 ± 4.16, p = 0.0019), suture technique (28.68 ± 2.63 vs. 22.18 ± 3.94, p = 0.0001), and suturing time scores (14.67 ± 1.15 vs. 12.27 ± 6.07, p = 0.0460).Conclusions Incorporating the principles of spaced learning into the instructional process of obtaining cosmetic suture skills for surgical residents not only significantly enhances short-term skill improvement but also contributes to the long-term retention of training outcomes.

Summary: Background: Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer. Methods: From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively). Findings: A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2−, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2−, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882–0.908, 0.896–0.929 and 0.837–0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%–88.9%, sensitivities of 80.0%–86.3%, and specificities of 87.4%–91.5% in the external validation cohorts. Interpretation: Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer. Funding: This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.

Background: Previous studies revealed that patients with early-stage metaplastic breast cancer (MBC) underwent mastectomy more often than breast-conserving therapy (BCT) mainly due to the larger tumor size. This study was performed to compare the survival outcomes following BCT versus mastectomy for patients with early-stage MBC. Methods: Surveillance, Epidemiology, and End Results (SEER) database was used to identify women diagnosed with early-stage MBC (T1-3N0-3M0) between 2001 and 2016, who were treated with either BCT or mastectomy. We assessed overall survival (OS) and breast cancer-specific survival (BCSS) using the Kaplan-Meier method and hazard ratios using Cox proportional hazards models. Results: A total of 2412 MBC patients were identified, 881 (36.5%) of whom underwent BCT and 1531(63.5%) underwent mastectomy. The median follow-up time was 73 months. Most of patients had older age (≥50 years old), larger tumor size, higher American Joint Committee on Cancer (AJCC) stage and hormone receptor negativity. After adjustment for confounding variables, patients who underwent BCT had significantly improved OS (5-year OS: 84.3% vs 62.5%; 10-year OS: 73.0% vs 52.1%; adjusted HR = 0.76, 95%CI: 0.59–0.97, p = 0.028) and BCSS (5-year BCSS: 89.1% vs 70.8%; 10-year BCSS: 83.9% vs 67.5%; adjusted HR = 0.72, 95%CI: 0.53–0.96, p = 0.026) than those who underwent mastectomy, and this improvement remained significant for all T and N stages of MBC except for N2-3 stage. Conclusion: BCT conferred improved OS and BCSS compared with mastectomy for patients with early-stage MBC, and the improvement persisted in almost all of the subgroups of different T and N stages.

Background: Antiangiogenic therapy combined with chemotherapy could improve pathological complete response (pCR) for breast cancer. Apatinib is an oral tyrosine kinase inhibitor that selectively inhibits vascular endothelial growth factor receptor 2. We assessed the efficacy and safety of apatinib combined with standard neoadjuvant chemotherapy in patients with triple-negative breast cancer (TNBC). Materials and methods: This single-arm, phase II study enrolled patients aged 18–70 years with previously untreated stage IIA-IIIB TNBC. Patients received oral apatinib at a dose of 250 mg once daily and intravenously docetaxel every 3 weeks for four cycles, followed by epirubicin plus cyclophosphamide every 3 weeks for four cycles. The primary endpoint was the pCR rate in the breast and lymph nodes. Secondary endpoints included objective response rate, event-free survival (EFS), overall survival (OS), and safety. Results: In all, 31 patients were enrolled, and the median follow-up time was 22.9 months (range: 10.1–41.6 months). The pCRs in both breast and lymph nodes were achieved in 17 [54.8%; 95% confidence interval (CI): 36.0–72.7] of 31 patients. Objective responses were achieved in 29 patients (93.5%; 95% CI: 78.6–99.2), and disease control was achieved in 31 patients (100%; 95% CI: 88.8–100.0). The 2-year EFS and 2-year OS were 90.9% and 94.4%, respectively. The five most common treatment-related adverse events were fatigue (51%), hypertension (41%), anorexia (39%), hand–foot syndrome (35%), and diarrhea (32%). Few grade 3 or more adverse events were observed. Conclusion: The combination of apatinib with docetaxel followed by epirubicin plus cyclophosphamide showed excellent efficacy and manageable toxicities; and further randomized controlled phase III trials are warranted. Trial registration: This trial was registered with ClinicalTrials.gov (NCT03243838) on 5 August 2017.

Occult breast cancer, commonly presenting with axillary lymphadenopathy, is an extremely rare entity of breast cancer. Metastasis to the spleen as a single site is rarely seen and has been little reported in literature. Herein we described a case of a 60-year-old patient who presented with an asymptomatic solitary splenic mass 19 months after axillary lymph node dissection, regional radiotherapy, and systemic therapy. Laparoscopic splenectomy was performed, and histopathological examination confirmed metastasis from occult breast cancer. Then, the patient was administered with oral vinorelbine and dual-targeted treatment. With over 10 months of follow-up, there is no evidence of recurrence or metastasis of malignancy. To our knowledge, this study reports the first case of solitary splenic metastasis from occult breast cancer and highlights the importance of considering splenic metastasis as the only site of recurrence during follow-up of primary cancer, regardless of its rarity. If possible, splenectomy may be a therapeutic strategy.

Background: Recent studies have shown that homologous recombination deficiency (HRD) may be correlated with the pathological complete response (pCR) rate. This meta-analysis aimed to determine the predictive value of HRD for the pCR rate in patients with triple-negative breast cancer (TNBC) receiving platinum-based neoadjuvant chemotherapy (NCT). Methods: Published articles were searched in the PubMed, Embase, Medline, Web of Science, and Cochrane databases up to 1 June 2021, and studies reporting the pCR rate for HRD carriers on platinum-based NCT were selected. Odds ratios (ORs) with 95% confidence intervals (CIs) were determined for the pCR rate, clinical response rate, and Grade 3 or higher adverse events (AEs) using the random-effects model. Bias risk was evaluated using the Cochrane Collaboration tool (PROSPERO, registration number CRD42021249874). Results: Seven studies were eligible. The results showed that HRD carriers had higher pCR rates than non-HRD carriers across all treatment arms (OR = 3.84, 95% CI = [1.93, 7.64], p = 0.0001). Among HRD carriers, the pCR rate was higher in patients on platinum-based NCT than in those without platinum exposure (OR = 1.95, 95% CI = [1.17, 3.23], p = 0.01). We did not observe marked pCR improvements in non-HRD carriers. Among HRD carriers, the pCR rates in the mutant and wild-type breast cancer susceptibility gene (BRCA) groups did not differ significantly (OR = 2.00, 95% CI = [0.77, 5.23], p = 0.16), but HRD carriers with wild-type BRCA had a significant advantage over non-HRD carriers on platinum-based NCT (OR = 3.64, 95% CI = [1.83, 7.21], p = 0.0002). Conclusion: HRD is an effective predictor of increased pCR rates in platinum-based NCT, especially in wild-type BRCA patients. Adding platinum to NCT for non-HRD carriers can increase the incidence of AEs but may not improve the therapeutic effect.

Evidence has shown the roles of taVNS and TECS in improving depression but few studies have explored their synergistic effects on MDD. Therefore, the treatment responsivity and neurological effects of TECAS were investigated and compared to escitalopram, a commonly used medication for depression. Fifty patients with mild-to-moderate MDD (29 in the TECAS group and 21 in another) and 49 demographically matched healthy controls were recruited. After an eight-week treatment, the outcomes of TECAS and escitalopram were evaluated by the effective rate and reduction rate based on the Montgomery–Asberg Depression Rating Scale, Hamilton Depression Rating Scale, and Hamilton Anxiety Rating Scale. Altered brain networks were analyzed between pre- and post-treatment using independent component analysis. There was no significant difference in clinical scales between TECAS and escitalopram but these were significantly decreased after each treatment. Both treatments modulated connectivity of the default mode network (DMN), dorsal attention network (DAN), right frontoparietal network (RFPN), and primary visual network (PVN), and the decreased PVN–RFPN connectivity might be the common brain mechanism. However, there was increased DMN–RFPN and DMN–DAN connectivity after TECAS, while it decreased in escitalopram. In conclusion, TECAS could relieve symptoms of depression similarly to escitalopram but induces different changes in brain networks.

Background: Breast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies connecting germline mutations with possible risk of HER2-negative breast cancer, the main category of breast cancer, it remains challenging to obtain accurate risk assessment and to understand the potential underlying mechanisms. Methods: We developed a novel framework named Damage Assessment of Genomic Mutations (DAGM), which projects rare coding mutations and gene expressions into Activity Profiles of Signalling Pathways (APSPs). Findings: We characterized and validated DAGM framework at multiple levels. Based on an input of germline rare coding mutations, we obtained the corresponding APSP spectrum to calculate the APSP risk score, which was capable of distinguish HER2-negative from HER2-positive cases. These findings were validated using breast cancer data from TCGA (AUC = 0.7). DAGM revealed that HER2 signalling pathway was up-regulated in germline of HER2-negative patients, and those with high APSP risk scores had exhibited immune suppression. These findings were validated using RNA sequencing, phosphoproteome analysis, and CyTOF. Moreover, using germline mutations, DAGM could evaluate the risk for HER2-negative breast cancer, not only in women carrying BRCA1/2 mutations, but also in those without known disease-associated mutations. Interpretation: The DAGM can facilitate the screening of subjects at high risk of HER2-negative breast cancer for primary prevention. This study also provides new insights into the potential mechanisms of developing HER2-negative breast cancer. The DAGM has the potential to be applied in the prevention, diagnosis, and treatment of HER2-negative breast cancer. Funding: This work was supported by the National Key Research and Development Program of China (grant no. 2018YFC0910406 and 2018AAA0103302 to CZ); the National Natural Science Foundation of China (grant no. 81202076 and 82072939 to MY, 81871513 to KW); the Guangzhou Science and Technology Program key projects (grant no. 2014J2200007 to MY, 202002030236 to KW); the National Key R&D Program of China (grant no. 2017YFC1309100 to CL); Shenzhen Science and Technology Planning Project (grant no. JCYJ20170817095211560 574 to YN); and the Natural Science Foundation of Guangdong Province (grant no. 2017A030313882 to KW and S2013010012048 to MY); Hefei National Laboratory for Physical Sciences at the Microscale (grant no. KF2020009 to GN); and RGC General Research Fund (grant no. 17114519 to YQS).

ObjectiveInvasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) account for most breast cancers. However, the overall survival (OS) differences between ILC and IDC remain controversial. This study aimed to compare nonmetastatic ILC to IDC in terms of survival and prognostic factors for ILC.MethodsThis retrospective cohort study used data from the Surveillance, Epidemiology and End Results (SEER) Cancer Database (www.seer.cancer.gov). Women diagnosed with nonmetastatic ILC and IDC between 2006 and 2016 were included. A propensity score matching (PSM) method was used in our analysis to reduce baseline differences in clinicopathological characteristics and survival outcomes. Kaplan-Meier curves and log-rank test were used for survival analysis.ResultsCompared to IDC patients, ILC patients were diagnosed later in life with poorly differentiated and larger lesions, as well as increased expression of estrogen receptors (ERs) and/or progesterone receptors (PRs). A lower rate of radiation therapy and chemotherapy was observed in ILC. After PSM, ILC, and IDC patients exhibited similar OS (HR=1.017, p=0.409, 95% CI: 0.967–1.069). In subgroup analysis of HR-negative, AJCC stage III, N2/N3 stage patients, or those who received radiotherapy, ILC patients exhibited worse OS compared to IDC patients. Furthermore, multivariate analysis revealed a 47% survival benefit for IDC compared to ILC in HR-negative patients who received chemotherapy (HR=1.47, p=0.01, 95% CI: 1.09–1.97).ConclusionsOur results demonstrated that ILC and IDC patients had similar OS after PSM. However, ILC patients with high risk indicators had worse OS compared to IDC patients by subgroup analysis.

Objectives: Breast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients. Methods: We retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated. Results: Two radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343–0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774–0.9753) in the validation cohort. Conclusions: Our study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options.

Abstract Background To stage axillary lymph nodes in women with early-stage breast cancer, sentinel lymph node biopsy (SLNB), rather than axillary lymph node dissection (ALND), has been employed. Moreover, different tracer methods have various advantages and disadvantages. In recent years, carbon nanoparticle suspensions (CNSs) have been used as lymph node tracers during surgeries for thyroid cancer, gastric cancer, and colorectal cancer. The study retrospectively analyzed the feasibility and accuracy of CNS for sentinel lymph node (SLN) mapping in patients with early breast cancer. Methods This single-center, retrospective study included breast cancer patients who underwent SLNB from January 1, 2016, to December 31, 2017, in the Department of Breast Cancer, Guangdong General Hospital. All patients received standard SLNB surgery using a CNS tracer. Results A total of 332 cases were included in this study. The SLN identification rate was 99.1% (329/332), and the mean number of SLNs was 2.6 (range, 1–6). SLN metastasis was found in 62 (18.8%) cases, of which 90.3% were found to be macrometastases. The sensitivity of SLNB was 95.9% (47/49), with a specificity of 100% (42/42), a positive predictive value of 100% (47/47), a negative predictive value of 95.5% (42/44), and a false-negative rate of 4.1% (2/49). Conclusion The identification and predictive values of a CNS tracer for SLNB were satisfactory.