RESUMEN
OBJECTIVES: We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. METHODS: We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. RESULTS: The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. CONCLUSION: The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. CLINICAL RELEVANCE STATEMENT: The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. KEY POINTS: ⢠We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. ⢠Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. ⢠The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI.
RESUMEN
BACKGROUND: Nestin has been revealed to promote tumorigenesis, progression, metastasis, and angiogenesis of breast cancer. Although the prognostic and clinicopathological impact of nestin expression on breast cancer patients has been assessed in several independent studies, their results remained conflicting. Therefore, we performed this meta-analysis to elucidate the prognostic and clinicopathological association of nestin expression with breast cancer. METHODS: A comprehensive literature search was performed in the electronic databases PubMed, EMBASE, Web of Science, the Cochrane Library, China National Knowledge Infrastructure (CNKI), and the Wangfang Data. The statistical analysis was conducted using Stata 15.0 and Review Manager 5.3. RESULTS: A total of 15 studies with 6066 breast cancer patients were included in this meta-analysis. Pooled results indicated that positive expression of nestin was significantly associated with reduced breast cancer-specific survival (BCSS, univariate analysis, HR = 2.11, 95% CI [1.79, 2.49], P < 0.00001; multivariate analysis, HR = 1.30, 95% CI [1.06, 1.60], P = 0.01), worse overall survival (OS, univariate analysis, HR = 1.88, 95% CI [1.31, 2.71], P = 0.0007; multivariate analysis, HR = 1.89, 95% CI [1.34, 2.67], P = 0.0003) and poorer recurrence-free survival (univariate analysis, HR = 2.60, 95% CI [1.52, 4.46], P = 0.0005), but not with distant metastasis-free survival in univariate analysis (P > 0.05). In addition, increased nestin expression was correlated with younger age, higher tumor grade, larger tumor size, positive blood vessel invasion and high vascular proliferation index, but not with lymph node metastasis or lymph vessel invasion. Nestin was preferentially expressed in invasive ductal carcinoma, triple-negative breast cancer and basal-like subtypes. Nestin expression was inversely associated with the expression of ER and PR, but not with HER-2. Conversely, nestin expression was positively correlated with the expression of basal-like markers CK5, P-cadherin and EGFR. Moreover, nestin expression was strongly associated with the presence of five basal-like profiles (BLP1-5). CONCLUSIONS: This meta-analysis revealed the prognostic value and clinicopathological significance of nestin expression in breast cancer. Nestin is an independent prognostic factor for worse BCSS and OS of breast cancer patients. Nestin is also a valuable biomarker for unfavorable clinicopathological features and tumor angiogenesis of breast cancer. Therefore, nestin is a promising therapeutic target for malignant breast cancer, especially for TNBC and basal-like phenotype.
