BackgroundTo 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.MethodsThis 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.ResultsA 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).ConclusionThe identification and predictive values of a CNS tracer for SLNB were satisfactory.

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.

Background Tumor location in the breast varies, with the highest frequency in the upper outer quadrant and lowest frequency in the lower inner quadrant. Nevertheless, tumors in the central and nipple portion (TCNP) are poorly studied types of breast cancer; therefore, we aimed to clarify the clinicopathological characteristics and prognostic features of TCNP. Methods Using the Surveillance, Epidemiology, and End Results database, we identifed 105,037 patients diagnosed with tumor in the breast peripheral quadrant (TBPQ) (n=97,046) or TCNP (n=7,991). The chi-squared test was used to compare categorical variables across TCNP and TBPQ. Cox proportional hazard models with hazard ratios were applied to estimate the factors associated with prognosis. Results The median follow-up was over 43 months. Compared with TBPQ, TCNP patients were signifcantly older (age ≥66 years: 40.4% vs 34.1%, P<0.001), with larger tumor sizes (>20 mm size: 46.9% vs 37.3%, P<0.001), higher proportions of TNM stage II–III (18.6% vs 9.9%, P<0.001), and more mastectomies (58.1% vs 37.8%, P<0.001). The breast cancer-specifc survival (BCSS)/overall survival (OS) rate was signifcantly worse for TCNP than for TBPQ. Multivariate Cox analysis showed a higher hazard ratios for TCNP over TBPQ (BCSS: hazard ratios =1.160, P=0.005, 95% CI: 1.046–1.287; OS: hazard ratios =1.301, P<0.001, 95% CI: 1.211–1.398). A subgroup analysis revealed inferior outcomes for TCNP in TNM stage II–III and breast subtype subgroup. Multivariate logistic regression indicated that TCNP was an independent contributing factor to LN metastasis. Conclusions TCNP was associated with older age, larger tumor size, higher TNM stage, and lymph node metastasis. Compared with TBPQ, TCNP had adverse impacts on BCSS and OS.