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BACKGROUND: The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this study, the authors propose the use of radiomic technology and three-dimensional (3D) visualization technology to develop an unsupervised learning model for predicting axillary lymph node metastasis in patients with breast cancer (BC), aiming to provide a new method for clinical axillary lymph node assessment in patients with this disease. METHODS: In this study, we retrospectively analyzed the data of 350 patients with invasive BC who underwent lung-enhanced computed tomography (CT) and axillary lymph node dissection surgery at the Department of Breast Surgery of the Second Xiangya Hospital of Central South University. The authors used 3D visualization technology to create a 3D atlas of ALNs and identified the region of interest for the lymph nodes. Radiomic features were subsequently extracted and selected, and a prediction model for ALNs was constructed using the K-means unsupervised algorithm. To validate the model, the authors prospectively collected data from 128 BC patients who were clinically evaluated as negative at our center. RESULTS: Using 3D visualization technology, we extracted and selected a total of 36 CT radiomics features. The unsupervised learning model categorized 1737 unlabeled lymph nodes into two groups, and the analysis of the radiomic features between these groups indicated potential differences in lymph node status. Further validation with 1397 labeled lymph nodes demonstrated that the model had good predictive ability for axillary lymph node status, with an area under the curve of 0.847 (0.825-0.869). Additionally, the model's excellent predictive performance was confirmed in the 128 axillary clinical assessment negative cohort (cN0) and the 350 clinical assessment positive (cN+) cohort, for which the correct classification rates were 86.72 and 87.43%, respectively, which were significantly greater than those of clinical assessment methods. CONCLUSIONS: The authors created an unsupervised learning model that accurately predicts the status of ALNs. This approach offers a novel solution for the precise assessment of ALNs in patients with BC.

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Background: The frequency of nipple-sparing mastectomy (NSM) surgery is presently increasing. Nonetheless, there is a paucity of long-term prognosis data on NSM. This study compared the long-standing prognosis of NSM in relation to breast-conserving surgery (BCS). Methods: Population-level data for 438,588 female breast cancer patients treated with NSM or BCS and postoperative radiation from 2000 to 2018 were identified in the Surveillance, Epidemiology, and End Results (SEER) database; 321 patients from the Second Xiangya Hospital of Central South University were also included. Propensity score matching (PSM) was performed to reduce the influence of selection bias and confounding variables to make valid comparisons. The Kaplan-Meier analysis, log-rank test, and Cox regression were applied to analyze the data. Results: There were no significant differences in long-term survival rates between patients who underwent NSM and those who underwent BCS+radiotherapy (BCS+RT), as indicated by the lack of significant differences in overall survival (OS) (p = 0.566) and breast cancer-specific survival (BCSS) (p = 0.431). Cox regression indicated that NSM and BCS+RT had comparable prognostic values (p = 0.286) after adjusting for other clinicopathological characteristics. For OS and BCSS, subgroup analysis showed that the majority of patients achieved an analogous prognosis whether they underwent NSM or BCS. The groups had comparable recurrence-free survival (RFS), with no significant difference found (p = 0.873). Conclusions: This study offers valuable insights into the long-term safety and comparative effectiveness of NSM and BCS in the treatment of breast cancer. These findings can assist clinicians in making informed decisions on a case-by-case basis.

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BACKGROUND: Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development. METHODS: This study included 488 patients diagnosed with PTC by ultrasound-guided fine-needle aspiration biopsy, collected clinicopathological data, analyzed the correlation between CLNM and clinicopathological features using univariate analysis and binary logistic regression, and constructed prediction models. RESULTS: Binary logistic regression analysis showed that age, maximum diameter of thyroid nodules, capsular invasion, and BRAF V600E gene mutation were independent risk factors for CLNM, and statistically significant indicators were included to construct a nomogram prediction model, which had an area under the curve (AUC) of 0.778. A convolutional neural network (CNN) prediction model built with an artificial intelligence (AI) deep learning algorithm achieved AUCs of 0.89 in the training set and 0.78 in the test set, which indicated a high prediction efficacy for CLNM. In addition, the prediction models were validated in the subclinical metastasis and clinical metastasis groups with high sensitivity and specificity, suggesting the broad applicability of the models. Furthermore, CNN prediction models were constructed for patients with nodule diameters less than 1 cm. The AUCs in the training set and test set were 0.87 and 0.76, respectively, indicating high prediction efficacy. CONCLUSIONS: The deep learning-based multifeature integration prediction model provides a reference for the clinical diagnosis and treatment of PTC.

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Background: External beam radiotherapy (EBRT), an adjuvant to breast-conserving surgery (BCS), requires a long treatment period, is costly, and is associated with numerous complications. Large sample studies with long follow-up periods are lacking regarding whether intraoperative radiotherapy (IORT), an emerging radiotherapy modality, can replace EBRT for patients with T1-2 early stage breast cancer without lymph node metastasis treated with BCS. Methods: We identified 270,842 patients with T1-2N0M0 breast cancer from 2000 to 2018 in the Surveillance, Epidemiology, and End Results (SEER) database. A total of 10,992 patients were matched by propensity score matching (PSM). According to the radiotherapy method, the patients were divided into the IORT and EBRT groups. Overall survival (OS) and breast cancer-specific survival (BCSS) rates were analyzed and compared between the IORT and EBRT groups by Kaplan-Meier analysis. Bilateral P < 0.05 was considered to indicate significance. Results: After PSM, the survival analysis showed no significant differences in OS or BCSS rates between the IORT and EBRT groups. In the subgroup analysis, the IORT population diagnosed from 2010 to 2013 (HRs = 0.675, 95% CI 0.467-0.976, P = 0.037) or with T2 stage (HRs = 0.449, 95% CI 0.261-0.772, P = 0.004) had better OS rates, but in the overall population, the OS and BCSS rates were better in patients with T1 stage than in patients with T2 stage (P < 0.0001), and the proportion of chemotherapy was significantly higher in T2 stage than in T1 stage. Patients who had EBRT with unknown estrogen receptor had better OS rates (HRs = 3.392, 95% CI 1.368-8.407, P = 0.008). In addition, the IORT group had better BCSS rates for married (HRs = 0.403, 95% CI 0.184-0.881, P = 0.023), grade III (HRs = 0.405, 95% CI 0.173-0.952, P = 0.038), and chemotherapy-receiving (HRs = 0.327, 95% CI 0.116-0.917, P = 0.034) patients with breast cancer compared to the EBRT group. Conclusion: Intraoperative radiotherapy results of non-inferior OS and BCSS rates, compared to those of EBRT, in patients with early stage breast cancer without lymph node metastasis treated with BCS, and IORT may provide substantial benefits to patients as an effective alternative to standard treatment. This finding provides new insights into radiotherapy strategies for early stage breast cancer.

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The status of axillary lymph node metastases determines the treatment and overall survival of breast cancer (BC) patients. Three-dimensional (3D) assessment methods have advantages for spatial localization and are more responsive to morphological changes in lymph nodes than two-dimensional (2D) assessment methods, and we speculate that methods developed using 3D reconstruction systems have high diagnostic efficacy. This exploratory study included 43 patients with histologically confirmed BC diagnosed at Second Xiangya Hospital of Central South University between July 2017 and August 2020, all of whom underwent preoperative CT scans. Patients were divided into a training cohort to train the model and a validation cohort to validate the model. A 3D axillary lymph node atlas was constructed on a 3D reconstruction system to create various methods of assessing lymph node metastases for a comparison of diagnostic efficacy. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic values of these methods. A total of 43 patients (mean [SD] age, 47 [10] years) met the eligibility criteria and completed 3D reconstruction. An axillary lymph node atlas was established, and a correlation between lymph node sphericity and lymph node metastasis was revealed. By continuously fitting the size and characteristics of axillary lymph nodes on the 3D reconstruction system, formulas and models were established to determine the presence or absence of lymph node metastasis, and the 3D method had better sensitivity for axillary lymph node assessment than the 2D method, with a statistically significant difference in the correct classification rate. The combined diagnostic method was superior to a single diagnostic method, with a 92.3% correct classification rate for the 3D method combined with ultrasound. In addition, in patients who received neoadjuvant chemotherapy (NAC), the correct classification rate of the 3D method (72.7%) was significantly higher than that of ultrasound (45.5%) and CT (54.5%). By establishing an axillary lymph node atlas, the sphericity formula and model developed with the 3D reconstruction system achieve a high correct classification rate when combined with ultrasound or CT and can also be applied to patients receiving NAC.

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BACKGROUND: Whether tumor mutation burden (TMB) correlated with improved survival outcomes or promotion of immunotherapies remained controversy in various malignancies. We aimed to explore the prognostic value of TMB and the relationship between TMB and immune infiltration in human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC). METHODS: We downloaded somatic mutation data and clinical information for 216 HER2+ BC patients from the The Cancer Genome Atlas (TCGA) and cBioPortal databases. Patients were divided into high- and low-TMB groups through TMB calculation. Cox regression analysis was used to establish an immune- and mutant-related risk model based on 5-hub genes. The relationship between 5-hub genes mutants and the level of immune infiltration, as well as the relationship between the risk model and the immune microenvironment were analyzed by "TIMER" database. RESULTS: TMB was negatively correlated with overall survival (OS) and disease-free survival (DFS), and high TMB may inhibit immune infiltration in HER2+ BC. Furthermore, risk score classified effectively patients into low- and high-risk groups in training and validation cohorts. The infiltration of CD4+ T cells and NK cells and the levels of immune checkpoint pathway genes were lower in the high-risk group, which indicated a poor prognosis. CONCLUSIONS: Higher TMB correlated with poor survival outcomes and might inhibit the immune infiltrates in HER2+ BC. The 5-hub TMB-related signature conferred lower immune cells infiltration which deserved further validation.

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The current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1-2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR-/HER2-) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1-2 BC patients, particularly given that it can be used to adjust surgical options in the future.

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