Correlation Analysis of Pathological Features and Axillary Lymph Node Metastasis in Patients with Invasive Breast Cancer

Incidence and risk factors of lymph node metastasis in breast cancer patients without preoperative chemoradiotherapy and neoadjuvant therapy: analysis of SEER data

Background

Breast cancer (BC) is the leading cause of death in the female reproductive system, often linked to lymph node involvement, indicating poor prognosis. This study investigated lymph node metastasis incidence and risk factors in M0 stage BC patients who hadn't received preoperative chemoradiotherapy or neoadjuvant therapy. We explored the influence of various factors on lymph node metastasis.

Methods

We conducted a retrospective analysis using Surveillance, Epidemiology, and End Results data from BC patients diagnosed between 2010 and 2015. Binary logistic regression and propensity score matching (PSM) assessed significant factors in BC patients without preoperative treatment. We developed predictive nomograms and evaluated model performance using the concordance index, calibration curve, area under the curve, and decision curve analysis.

Results

Among 256,504 eligible BC patients, 25.57% had lymph node metastasis. Multivariate logistic regression revealed associations between lymph node metastasis and younger age, African-American ethnicity, central/nipple location, lobular carcinoma, human epidermal growth factor receptor 2 (HER2)-positive status, grade III classification, and T3 stage. PSM confirmed these findings. Interactions were identified between age, race, primary site, histology, breast subtype, grade, and T stage, all influencing lymph node metastasis.

Conclusions

This retrospective study identified lymph node metastasis in female BC patients with distinct clinicopathological characteristics who received no preoperative treatment. We constructed valuable nomograms, revealing that: (I) young age (<35 years), African-American race, central/nipple location, infiltrating duct carcinoma, HER2 positivity, high histological grade (grade III), and larger tumor size are risk factors for regional lymph node metastasis; (II) lymph node metastasis may not solely represent the invasive nature of triple-negative BC; (III) patients with different BC subtypes in T1c-T2 stages may benefit from individualized neoadjuvant treatment strategies.

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images

Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography images, and to evaluate how DL radiomics can be used to classify SLNM in breast cancer. Clinical and ultrasound data of 317 patients diagnosed with breast cancer at the Second Affiliated Hospital of Nanchang University were collected from January 2018 to December 2021 and randomly divided into training and internal validation cohorts at a ratio of 7:3. An external validation cohort comprising data from Nanchang Third Hospital with 42 patients collected. Three DL models, namely DL-grayscale, DL-CDFI, and DL-elastography, were proposed to predict SLNM by analyzing grayscale ultrasound, CDFI, and elastography images. Three DL models were compared and evaluated to assess diagnostic performance based on the area under the curve (AUC). The AUCs of the DL-grayscale were 0.855 and 0.788 in the internal and external validation cohorts, respectively. For the DL-CDFI model, the AUCs were 0.761 and 0.728, respectively. The diagnostic performance of DL-elastography was superior to that of the DL-grayscale and DL-CDFI. The AUC of the DL-elastography model was 0.879 in the internal validation cohort, with a classification accuracy of 86.13%, sensitivity of 91.60%, and specificity of 82.79%. The generalization capability of DL-elastography remained high in the external cohort, with an AUC of 0.876, and an accuracy of 85.00%. DL radiomics can be used to classify SLNM in breast cancer using ultrasound images. The proposed DL-elastography model based on elastography images achieved the best diagnostic performance and holds good potential for the management of patients with SLNM.

Association between the apparent diffusion coefficient and the preoperative chemotherapy response of patients with locally advanced gastric cancer

Background

The apparent diffusion coefficient is a parameter measured by magnetic resonance imaging (MRI). Studies in breast cancer and osteosarcoma have shown that the apparent diffusion coefficient has a good correlation with the efficacy of neoadjuvant chemotherapy. However, to date, no studies have evaluated the association between the apparent diffusion coefficient and the preoperative chemotherapy response of patients with locally advanced gastric cancer.

Methods

The data of 143 patients with locally advanced gastric cancer admitted to Zhejiang Medical and Health Group Quzhou Hospital (Zhejiang Quhua Hospital) from January 2018 to January 2019 were retrospectively collected. All the patients underwent preoperative chemotherapy and dynamic enhanced MRI to analyze the correlation between the apparent diffusion coefficient and preoperative chemotherapy response.

Results

Compared to the control group, the apparent diffusion coefficient of the objective remission group was significantly increased [(1.16±0.26) ×10^-3 vs. (0.95±0.26) ×10^-3 mm²/s, P<0.001]; the rate of the apparent diffusion coefficient >1.095×10^-3 mm²/s was significantly increased (61.29% vs. 30.00%, P<0.001). The apparent diffusion coefficient was valuable in predicting objective remission after preoperative chemotherapy in patients with locally advanced gastric cancer, the area under the curve (AUC) was 0.708 [95% confidence interval (CI): 0.621-0.796, P<0.001], the best diagnostic cut-off value was 1.095×10^-3 mm²/s, and the sensitivity and specificity were 0.613 and 0.700, respectively. The multivariate logistics regression analysis showed that the apparent diffusion coefficient of >1.095×10^-3 mm²/s was associated with the objective response of patients with locally advanced gastric cancer after preoperative chemotherapy [P=0.004, relative risk =3.135 (95% CI: 1.452-6.768)]. The apparent diffusion coefficient was valuable in predicting the non-recurrence of locally advanced gastric cancer patients, and the AUC was 0.647 (95% CI: 0.557-0.738, P=0.003). The apparent diffusion coefficient was also valuable in predicting the postoperative survival of patients with locally advanced gastric cancer, and the AUC was 0.630 (95% CI: 0.537-0.723, P=0.007).

Conclusions

The elevated apparent diffusion coefficient was associated with objective remission of the preoperative chemotherapy response and prognosis of patients with locally advanced gastric cancer.