A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program

A new prediction nomogram of non-sentinel lymph node metastasis in cT1-2 breast cancer patients with positive sentinel lymph nodes

We aimed to analyze the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis for cT1-2 breast cancer patients with positivity after sentinel lymph node biopsy (SLNB). A total of 830 breast cancer patients who underwent surgery between 2016 and 2021 at multi-center were included in the retrospective analysis. Patients were divided into training (n = 410), internal validation (n = 298), and external validation cohorts (n = 122) based on periods and centers. A nomogram-based prediction model for the risk of NSLN metastasis was constructed by incorporating independent predictors of NSLN metastasis identified through univariate and multivariate logistic regression analyses in the training cohort and then validated by validation cohorts. The multivariate logistic regression analysis revealed that the number of positive sentinel lymph nodes (SLNs) (P < 0.001), the proportion of positive SLNs (P = 0.029), lymph-vascular invasion (P = 0.029), perineural invasion (P = 0.023), and estrogen receptor (ER) status (P = 0.034) were independent risk factors for NSLN metastasis. The area under the receiver operating characteristics curve (AUC) value of this model was 0.730 (95% CI 0.676-0.785) for the training, 0.701 (95% CI 0.630-0.773) for internal validation, and 0.813 (95% CI 0.734-0.891) for external validation cohorts. Decision curve analysis also showed that the model could be effectively applied in clinical practice. The proposed nomogram estimated the likelihood of positive NSLNs and assisted the surgeon in deciding whether to perform further axillary lymph node dissection (ALND) and avoid non-essential ALND as well as postoperative complications.

Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review)

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.

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.

Identifying clinicopathological risk factors for regional lymph node metastasis in Chinese patients with T1 breast cancer: a population-based study

Objectives

To analyze clinicopathological risk factors and regular pattern of regional lymph node metastasis (LNM) in Chinese patients with T1 breast cancer and the effect on overall survival (OS) and disease-free survival (DFS).

Materials and methods

Between 1999 and 2020, breast cancer patients meeting inclusion criteria of unilateral, no distant metastatic site, and T1 invasive ductal carcinoma were reviewed. Clinical pathology characteristics were retrieved from medical records. Survival analysis was performed using Kaplan-Meier methods and an adjusted Cox proportional hazards model.

Results

We enrolled 11,407 eligible patients as a discovery cohort to explore risk factors for LNM and 3484 patients with stage T1N0 as a survival analysis cohort to identify the effect of those risk factors on OS and DFS. Compared with patients with N- status, patients with N+ status had a younger age, larger tumor size, higher Ki67 level, higher grade, higher HR+ and HER2+ percentages, and higher luminal B and HER2-positive subtype percentages. Logistic regression indicated that age was a protective factor and tumor size/higher grade/HR+ and HER2+ risk factors for LNM. Compared with limited LNM (N1) patients, extensive LNM (N2/3) patients had larger tumor sizes, higher Ki67 levels, higher grades, higher HR- and HER2+ percentages, and lower luminal A subtype percentages. Logistic regression indicated that HR+ was a protective factor and tumor size/higher grade/HER2+ risk factors for extensive LNM. Kaplan-Meier analysis indicated that grade was a predictor of both OS and DFS; HR was a predictor of OS but not DFS. Multivariate survival analysis using the Cox regression model demonstrated age and Ki67 level to be predictors of OS and grade and HER2 status of DFS in stage T1N0 patients.

Conclusion

In T1 breast cancer patients, there were several differences between N- and N+ patients, limited LNM and extensive LNM patients. Besides, HR+ plays a dual role in regional LNM. In patients without LNM, age and Ki67 level are predictors of OS, and grade and HER2 are predictors of DFS.

Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning

The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190 patients from four medical centers in Hebei Province. Integrating clinicopathological data and image features build multi-modal and multi-instance (MMMI) deep learning model to obtain the final prediction. For predicting with or without lymph node metastasis, the AUC was 0.770, 0.709, 0.809 based on the clinicopathological features, WSI and MMMI, respectively. For predicting four classification of lymph node status (no metastasis, isolated tumor cells (ITCs), micrometastasis, and macrometastasis), the prediction based on clinicopathological features, WSI and MMMI were compared. The AUC for no metastasis was 0.770, 0.709, 0.809, respectively; ITCs were 0.619, 0.531, 0.634, respectively; micrometastasis were 0.636, 0.617, 0.691, respectively; and macrometastasis were 0.748, 0.691, 0.758, respectively. The MMMI model achieved the highest prediction accuracy. For prediction of different molecular types of breast cancer, MMMI demonstrated a better prediction accuracy for any type of lymph node status, especially in the molecular type of triple negative breast cancer (TNBC). In the external validation sets, MMMI also showed better prediction accuracy in the four classification, with AUC of 0.725, 0.757, 0.525, and 0.708, respectively. Finally, we developed a breast cancer lymph node metastasis prediction model based on a MMMI model. Through all cases tests, the results showed that the overall prediction ability was high.

Development and Validation of a Nomogram for Predicting Axillary Lymph Node Metastasis in Breast Cancer

BACKGROUND

Axillary lymph node (ALN) status is a key prognosis indicator for breast cancer patients. To develop an effective tool for predicting axillary lymph node metastasis in breast cancer, a nomogram was established based on mRNA expression data and clinicopathological characteristics.

MATERIALS AND METHODS

A 1062 breast cancer patients with mRNA data and clinical information were obtained from The Cancer Genome Atlas (TCGA). We first analyzed the differentially expression genes (DEGs) between ALN positive and ALN negative patients. Then, logistic regression, least absolute shrinkage and selection operator (Lasso) regression, and backward stepwise regression were performed to select candidate mRNA biomarkers. The mRNA signature was constructed by the mRNA biomarkers and corresponding Lasso coefficients. The key clinical factors were obtained by Wilcoxon-Mann-Whitney U test or Pearson's χ² test. Finally, the nomogram for predicting axillary lymph node metastasis was developed and evaluated by concordance index (C-index), calibration curve, decision curve analysis (DCA), and receptor operating characteristic (ROC) curve. Furthermore, the nomogram was externally validated using Gene Expression Omnibus (GEO) dataset.

RESULTS

The nomogram for predicting ALN metastasis yielded a C-index of 0.728 (95% CI: 0.698-0.758) and an AUC of 0.728 (95% CI: 0.697-0.758) in the TCGA cohort. In the independent validation cohort, the C-index and AUC of the nomogram were up to 0.825 (95% CI: 0.695-0.955) and 0.810 (95% CI: 0.666-0.953), respectively.

CONCLUSION

This nomogram could predict the risk of axillary lymph node metastasis in breast cancer and may provide a reference for clinicians to design individualized axillary lymph node management strategies.

Implications for surveillance for breast cancer patients based on the internally and externally validated BRENDA-metastatic recurrence score

PURPOSE

Although the incidence of distant relapse is decreasing, 20-30% of patients with early breast cancer die of metastasis. The aim of this study is to characterize patients with metastasis-free survival(MFS) less than 5 years, to analyze the most probable site of metastases according to the internally and externally validated BRENDA-score. The BRENDA-score is a combination of the biological subtype and clinical staging.

METHOD

3832 patients with primary diagnosis of breast cancer and either distant metastatic recurrence within 5 years or MFS ≥ 5 years were assigned to this study. Patients were classified for metastatic recurrence according to the BRENDA-score. 1765 patients were in a validation set. Statistical methods were Kaplan-Meier curves, Cox regression analysis, Exhausted CHAID, likelihood-ratio tests and the Nearest Neighbor Estimation method.

RESULTS

There was a significant(p < 0.001) difference between the Kaplan-Meier MFS-functions of M0-patients stratified by BRENDA-score. The BRENDA score outperforms intrinsic subtypes and the Nottingham prognostic score. It fits the original data and the validation set equally well (p = 0.179).There was a significant(p < 0.001) difference between mean BRENDA-Index for patients with MFS < 5y(21.0 ± 9.0) and patients with MFS ≥ 5y(mean BRENDA-Index 11.7 ± 8.2). 55.6% of the very high risk patients(BRENDA-Index ≥ 27) had metastases within 5 years. The most likely primary metastatic site was bone(30%) followed by liver(19%) and lung(18%). The discriminatory ability(areas under the time dependent ROC curve) of the BRENDA score is good to acceptable for the first 5 years. In the very low/low risk (intermediate, high/very high) risk group 50% of all metastases were diagnosed within 26 months. Guideline adherence had a highly significant influence on outcome independent of the risk group.

CONCLUSION

The evaluation showed that the BRENDA-Score is a robust predictive tool for breast cancer recurrence and site of metastases in the first five years after diagnosis. It outperforms intrinsic subtypes and the Nottingham prognostic score. The BRENDA-score could be a tool for a risk orientated and targeted follow up.

Impact of oral statin therapy on clinical outcomes in patients with cT1 breast cancer

PURPOSE

A previous meta-analysis examining the relationship between statin use and breast cancer reported that the inhibitory effect of statins on breast cancer may be more pronounced in early-stage cases. In this study, we aimed to investigate the effects of hyperlipidemia treatment at the time of breast cancer diagnosis and to examine its correlation with metastasis to axillary lymph nodes among patients with so-called cT1 breast cancer whose primary lesion was 2 cm or less and was pathologically evaluated by sentinel lymph node biopsy or axillary lymph node dissection. We also investigated the effects of hyperlipidemic drugs on the prognosis of patients with early-stage breast cancer.

METHODS

After excluding cases that did not meet the criteria, we analyzed data from 719 patients who were diagnosed with breast cancer, with a primary lesion of 2 cm or less identified by preoperative imaging, and who underwent surgery without preoperative chemotherapy.

RESULTS

Regarding hyperlipidemia drugs, no correlation was found between statin use and lymph node metastasis (p = 0.226), although a correlation was found between lipophilic statin use and lymph node metastasis (p = 0.042). Also, the disease-free survival periods were prolonged following treatment of hyperlipidemia (p = 0.047, hazard ratio: 0.399) and statin administration (p = 0.028, hazard ratio: 0.328).

CONCLUSION

In cT1 breast cancer, the results suggest that oral statin therapy may contribute to favorable outcomes.

Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound

Purpose

The aim of this study was to develop a radiomics nomogram based on grayscale ultrasound (US) for preoperatively predicting Lymphovascular invasion (LVI) in patients with pathologically confirmed T1 (pT1) breast invasive ductal carcinoma (IDC).

Methods

One hundred and ninety-two patients with pT1 IDC between September 2020 and August 2022 were analyzed retrospectively. Study population was randomly divided in a 7: 3 ratio into a training dataset of 134 patients (37 patients with LVI-positive) and a validation dataset of 58 patients (19 patients with LVI-positive). Clinical information and conventional US (CUS) features (called clinic_CUS features) were recorded and evaluated to predict LVI. In the training dataset, independent predictors of clinic_CUS features were obtained by univariate and multivariate logistic regression analyses and incorporated into a clinic_CUS prediction model. In addition, radiomics features were extracted from the grayscale US images, and the radiomics score (Radscore) was constructed after radiomics feature selection. Subsequent multivariate logistic regression analysis was also performed for Radscore and the independent predictors of clinic_CUS features, and a radiomics nomogram was developed. The performance of the nomogram model was evaluated via its discrimination, calibration, and clinical usefulness.

Results

The US reported axillary lymph node metastasis (LNM) (US_LNM) status and tumor margin were determined as independent risk factors, which were combined for the construction of clinic_CUS prediction model for LVI in pT1 IDC. Moreover, tumor margin, US_LNM status and Radscore were independent predictors, incorporated as the radiomics nomogram model, which achieved a superior discrimination to the clinic_CUS model in the training dataset (AUC: 0.849 vs. 0.747; P < 0.001) and validation dataset (AUC: 0.854 vs. 0.713; P = 0.001). Calibration curve for the radiomic nomogram showed good concordance between predicted and actual probability. Furthermore, decision curve analysis (DCA) confirmed that the radiomics nomogram had higher clinical net benefit than the clinic_CUS model.

Conclusion

The US-based radiomics nomogram, incorporating tumor margin, US_LNM status and Radscore, showed a satisfactory preoperative prediction of LVI in pT1 IDC patients.

Nomogram models for stratified prediction of axillary lymph node metastasis in breast cancer patients (cN0)

Objectives

To determine the predictors of axillary lymph node metastasis (ALNM), two nomogram models were constructed to accurately predict the status of axillary lymph nodes (ALNs), mainly high nodal tumour burden (HNTB, > 2 positive lymph nodes), low nodal tumour burden (LNTB, 1-2 positive lymph nodes) and negative ALNM (N0). Accordingly, more appropriate treatment strategies for breast cancer patients without clinical ALNM (cN0) could be selected.

Methods

From 2010 to 2015, a total of 6314 patients with invasive breast cancer (cN0) were diagnosed in the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and internal validation groups at a ratio of 3:1. As the external validation group, data from 503 breast cancer patients (cN0) who underwent axillary lymph node dissection (ALND) at the Second Affiliated Hospital of Chongqing Medical University between January 2011 and December 2020 were collected. The predictive factors determined by univariate and multivariate logistic regression analyses were used to construct the nomograms. Receiver operating characteristic (ROC) curves and calibration plots were used to assess the prediction models' discrimination and calibration.

Results

Univariate analysis and multivariate logistic regression analyses showed that tumour size, primary site, molecular subtype and grade were independent predictors of both ALNM and HNTB. Moreover, histologic type and age were independent predictors of ALNM and HNTB, respectively. Integrating these independent predictors, two nomograms were successfully developed to accurately predict the status of ALN. For nomogram 1 (prediction of ALNM), the areas under the receiver operating characteristic (ROC) curve in the training, internal validation and external validation groups were 0.715, 0.688 and 0.876, respectively. For nomogram 2 (prediction of HNTB), the areas under the ROC curve in the training, internal validation and external validation groups were 0.842, 0.823 and 0.862. The above results showed a satisfactory performance.

Conclusion

We established two nomogram models to predict the status of ALNs (N0, 1-2 positive ALNs or >2 positive ALNs) for breast cancer patients (cN0). They were well verified in further internal and external groups. The nomograms can help doctors make more accurate treatment plans, and avoid unnecessary surgical trauma.

Breast Cancer-Delivered Exosomal miRNA as Liquid Biopsy Biomarkers for Metastasis Prediction: A Focus on Translational Research with Clinical Applicability

Metastasis represents the most important cause of breast cancer-associated mortality. Even for early diagnosed stages, the risk of metastasis is significantly high and predicts a grim outcome for the patient. Nowadays, efforts are made for identifying blood-based biomarkers that could reliably distinguish patients with highly metastatic cancers in order to ensure a closer follow-up and a more personalized therapeutic method. Exosomes are nano vesicles secreted by cancer cells that can transport miRNAs, proteins, and other molecules and deliver them to recipient cells all over the body. Through this transfer, cancer cells modulate their microenvironment and facilitate the formation of the pre-metastatic niche, leading to sustained progression. Exosomal miRNAs have been extensively studied due to their promising potential as prognosis biomarkers for metastatic breast cancer. In this review, we tried to depict an overview of the existing literature regarding exosomal miRNAs that are already validated as potential biomarkers, and which could be immediately available for the clinic. Moreover, in the last section, we highlighted several miRNAs that have proven their function in preclinical studies and could be considered for clinical validation. Considering the lack of standard methods for evaluating exosomal miRNA, we also discussed the challenges and the technical aspects underlying this issue.

Evaluation of different imaging modalities for axillary lymph node staging in breast cancer patients to provide a personalized and optimized therapy algorithm

PURPOSE

The reliable detection of tumor-infiltrated axillary lymph nodes for breast cancer [BC] patients plays a decisive role in further therapy. We aimed to find out whether cross-sectional imaging techniques could improve sensitivity for pretherapeutic axillary staging in nodal-positive BC patients compared to conventional imaging such as mammography and sonography.

METHODS

Data for breast cancer patients with tumor-infiltrated axillary lymph nodes having received surgery between 2014 and 2020 were included in this study. All examinations (sonography, mammography, computed tomography [CT] and magnetic resonance imaging [MRI]) were interpreted by board-certified specialists in radiology. The sensitivity of different imaging modalities was calculated, and binary logistic regression analyses were performed to detect variables influencing the detection of positive lymph nodes.

RESULTS

All included 382 breast cancer patients had received conventional imaging, while 52.61% of the patients had received cross-sectional imaging. The sensitivity of the combination of all imaging modalities was 68.89%. The combination of MRI and CT showed 63.83% and the combination of sonography and mammography showed 36.11% sensitivity.

CONCLUSION

We could demonstrate that cross-sectional imaging can improve the sensitivity of the detection of tumor-infiltrated axillary lymph nodes in breast cancer patients. Only the safe detection of these lymph nodes at the time of diagnosis enables the evaluation of the response to neoadjuvant therapy, thereby allowing access to prognosis and improving new post-neoadjuvant therapies.

Survival of BRCA1/BRCA2-associated pT1 breast cancer patients, a cohort study

Purpose

Intensive screening in BRCA1/2 mutation carriers aims to improve breast cancer (BC) prognosis. Our aim is to clarify the prognostic impact of tumor size in BRCA mutation carriers with a pT1 BC, which is currently unclear. We are especially interested in differences between pT1a, pT1b, and pT1c regarding the prognosis of node-negative breast cancer, the effect of chemotherapy, and the prevalence of lymph node involvement.

Methods

For this study, BRCA1/2-associated BC patients were selected from a nationwide cohort. Primary outcomes were 10-year overall survival (OS) per pT1a-b-c group and the effect of chemotherapy on prognosis of node-negative BC, using Kaplan–Meier and Cox models. Finally, we evaluated lymph node involvement per pT1a-b-c group.

Results

963 women with pT1 BRCA1/2-associated BC diagnosed between 1990 and 2017 were included, of which 679 had pN0 BC. After a median follow-up of 10.5 years, 10-year OS in patients without chemotherapy was 77.1% in pT1cN0 and lower than for pT1aN0 (91.4%, p = 0.119) and pT1bN0 (90.8%, p = 0.024). OS was better with than without chemotherapy for pT1cN0 (91.6% vs. 77.1%, p = 0.001; hazard ratio (HR) 0.56, 95% confidence interval (CI): 0.21–1.48). Lymph node involvement was 24.9% in pT1c, 18.8% in pT1b, and 8.6% in pT1a.

Conclusion

Smaller tumor size is associated with better OS and less lymph node involvement in pT1 BRCA1/2-associated BC patients. The results suggest that early detection in BRCA1/2 mutation carriers of pT1a/b BC may reduce mortality and the need for systemic therapy.

Supplementary Information

The online version of this article contains supplementary material available (10.1007/s10549-022-06608-1.

Low RUFY3 expression level is associated with lymph node metastasis in older women with invasive breast cancer

PURPOSE

Sentinel lymph node biopsy is omitted in older women (≥ 70 years old) with clinical lymph node (LN)-negative hormone receptor-positive breast cancer as it does not influence adjuvant treatment decision-making. However, older women are heterogeneous in frailty while the chance of recurrence increase with improving longevity. Therefore, a biomarker that identifies LN metastasis may facilitate treatment decision-making. RUFY3 is associated with cancer progression. We evaluated RUFY3 expression level as a biomarker for LN-positive breast cancer in older women.

METHODS

Clinical and transcriptomic data of breast cancer patients were obtained from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC, n = 1903) and The Cancer Genome Atlas (TCGA, n = 1046) Pan-cancer study cohorts.

RESULTS

A total of 510 (METABRIC) and 211 (TCGA) older women were identified. LN-positive breast cancer, which represented 51.4% (METABRIC) and 48.4% (TCGA), demonstrated worse disease-free, disease-specific, and overall survival. RUFY3 levels were significantly lower in LN-positive tumors regardless of age. The area under the curve for the receiver operator characteristic (AUC-ROC) curves showed RUFY3-predicted LN metastasis. Low RUFY3 enriched oxidative phosphorylation, DNA repair, MYC targets, unfolded protein response, and mtorc1 signaling gene sets, was associated with T helper type 1 cell infiltration, and with intratumor heterogeneity and fraction altered. Low RUFY3 expression was associated with LN-positive breast cancer and with worse disease-specific survival among older women.

CONCLUSION

Older women with breast cancers who had low expression level of RUFY3 were more frequently diagnosed with LN-positive tumors, which translated into worse prognosis.

Incidence, prognosis and nomograms of breast cancer with bone metastases at initial diagnosis: a large population-based study

BACKGROUND

Bone is the most common metastatic site for breast cancer, and patients' condition will deteriorate when it occurs.

METHODS

We performed a retrospective analysis on 6482 breast cancer patients with bone metastases (BCBM), who were selected from the Surveillance, Epidemiology, and End Result (SEER) 18 registry database. The optimal age cut-points were generated by using the X-tile software. By using Cox regression, we selected independent prognostic factors from 21 variables, and plotted a visual nomogram to predict the probability of surviving to the median survival time. We also diagrammed a competing risk nomogram on the basis of competitive risk model.

RESULTS

Compared with other three common metastatic sites, the incidence of bone metastasis was the highest for patients with breast cancer. The incidence of BCBM peaked around the age of 60, and a large majority of patients were between the ages of 50 and 70. The survival rate decreased with age, and the median survival time was about 19 months. Factors of age, race, marital status, grade, human epidermal growth factor receptor-2 (HER2) receptor, hormone receptor, concurrent brain metastasis, concurrent liver metastasis, concurrent lung metastasis, surgery and chemotherapy are strongly related to the prognosis of patients with BCBM. It was revealed that the C-index of the nomogram was 0.72 and the calibration curves showed good agreement between the nomogram prediction and actual observation.

CONCLUSION

Our practical nomograms provide a visual and user-friendly tool in the risk evaluation and prognostic prediction for breast cancer patients with bone metastases.

Identifying Clinicopathological Risk Factors of the Regional Lymph Node Metastasis in Patients with T1-2 Mucinous Breast Cancer: A Population-Based Study

Background

Pure mucinous breast cancer (PMBC) has a better prognosis than other types of invasive breast cancer. However, regional lymph node metastasis (LNM) might reverse this outcome. We aim to determine the independent predictive factors for regional LNM and further develop a nomogram model for clinical practice.

Method

Data of PMBC patients from the Surveillance, Epidemiology, and End Results (SEER) program between Jan 2010 and Dec 2015 were retrospectively reviewed. Univariate and multivariate logistic regression analyses were used to determine the risk factors for LNM in T_1-2 MBC. The nomogram was constructed and further evaluated by an internal validation cohort. The receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were performed to evaluate the accuracy of this model.

Result

Five variables, including age, race, tumor size, grade, and breast subtype, were identified to be significantly associated with regional LNM in female patients with T_1-2 PMBC. A nomogram was successfully established with a favorable concordance index (C-index) of 0.780, supported by an internal validation cohort with a C-index of 0.767.

Conclusion

A nomogram for predicting regional LNM in female patients with T_1-2 PMBC was successfully established and validated via an internal cohort. This visualized model would assist surgeons to make appropriate clinical decisions in the management of primary PMBC, especially in terms of whether axillary lymph node dissection (ALND) is warranted.

Predictive Nomogram and Risk Factors for Lymph Node Metastasis in Bladder Cancer

Lymph node metastasis (LNM) is an important prognostic factor for bladder cancer (BCA) and determines the treatment strategy. This study aimed to determine related clinicopathological factors of LNM and analyze the prognosis of BCA. A total of 10,653 eligible patients with BCA were randomly divided into training or verification sets using the 2004-2015 data of the Surveillance, Epidemiology, and End Results database. To identify prognostic factors for the overall survival of BCA, we utilized the Cox proportional hazard model. Independent risk factors for LNM were evaluated via logistic regression analysis. T-stage, tumor grade, patient age and tumor size were identified as independent risk factors for LNM and were used to develop the LNM nomogram. The Kaplan-Meier method and competitive risk analyses were applied to establish the influence of lymph node status on BCA prognosis. The accuracy of LNM nomogram was evaluated in the training and verification sets. The areas under the receiver operating characteristic curve (AUC) showed an effective predictive accuracy of the nomogram in both the training (AUC: 0.690) and verification (AUC: 0.704) sets. In addition, the calibration curve indicated good consistency between the prediction of deviation correction and the ideal reference line. The decision curve analysis showed that the nomogram had a high clinical application value. In conclusion, our nomogram displayed high accuracy and reliability in predicting LNM. This could assist the selection of the optimal treatment for patients.

MicroRNA signatures associated with lymph node metastasis in intramucosal gastric cancer

Although a certain proportion of intramucosal carcinomas (IMCs) of the stomach does metastasize, the majority of patients are currently treated with endoscopic resection without lymph node dissection, and this potentially veils any existing metastasis and may put some patients in danger. In this regard, biological markers from the resected IMC that can predict metastasis are warranted. Here, we discovered unique miRNA expression profiles that consist of 21 distinct miRNAs that are specifically upregulated (miR-628-5p, miR-1587, miR-3175, miR-3620-5p, miR-4459, miR-4505, miR-4507, miR-4720-5p, miR-4742-5p, and miR-6779-5p) or downregulated (miR-106b-3p, miR-125a-5p, miR-151b, miR-181d-5p, miR-486-5p, miR-500a-3p, miR-502-3p, miR-1231, miR-3609, and miR-6831-5p) in metastatic (M)-IMC compared to nonmetastatic (N)-IMC, or nonneoplastic gastric mucosa. Intriguingly, most of these selected miRNAs showed stepwise increased or decreased expression from nonneoplastic tissue to N-IMC to M-IMC. This suggests that common oncogenic mechanisms are gradually intensified during the metastatic process. Using a machine-learning algorithm, we demonstrated that such miRNA signatures could distinguish M-IMC from N-IMC. Gene ontology and pathway analysis revealed that TGF-β signaling was enriched from upregulated miRNAs, whereas E2F targets, apoptosis-related, hypoxia-related, and PI3K/AKT/mTOR signaling pathways, were enriched from downregulated miRNAs. Immunohistochemical staining of samples from multiple institutions indicated that PI3K/AKT/mTOR pathway components, MAPK1, phospho-p44/42 MAPK, and pS6 were highly expressed and the expression of SMAD7, a TGF-β pathway component, was decreased in M-IMC, which could aid in distinguishing M-IMC from N-IMC. The miRNA signature discovered in this study is a valuable biological marker for identifying metastatic potential of IMCs, and provides novel insights regarding the metastatic progression of IMC.

Cancer-Specific Survival Outcome in Early-Stage Young Breast Cancer: Evidence From the SEER Database Analysis

BACKGROUND

Young women with breast cancer are determined to present poorer survival compare with elderly patients. Therefore, identifying the clinical prognostic factors in young women with early-stage (T_1-2N_0-1M₀) breast cancer is pivotal for surgeons to make better postoperative management.

METHODS

The clinicopathological characteristics of female patients with early-stage breast cancer from the Surveillance, Epidemiology, and End Results program between Jan 2010 and Dec 2015 were retrospectively reviewed and analyzed. Univariate and multivariate Cox regression analyses were used to determine the potential risk factors of cancer-specific survival in young women with early-stage breast cancer. The nomogram was constructed and further evaluated by an internal validation cohort. The Kaplan-Meier survival curves were used to estimate cancer-specific survival probability and the cumulative incidence.

RESULTS

Six variables including race, tumor location, grade, regional lymph node status, tumor subtype, and size were identified to be significantly associated with the prognosis of young women with early-stage breast cancer during the postoperative follow-up. A nomogram for predicting the 3-, 5- year cancer-specific survival probability in this subpopulation group was established with a favorable concordance index of 0.783, supported by an internal validation cohort with the AUC of 0.722 and 0.696 in 3-, 5- year cancer-specific survival probability, respectively.

CONCLUSIONS

The first predictive nomogram containing favorable discrimination is successfully established and validated for predicting the 3-, 5- year cancer-specific survival probability in young women with early-stage breast cancer during the postoperative follow-up. This model would help clinicians to make accurate treatment decisions in different clinical risk population.

A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis

BACKGROUND

Distant metastasis (DM) is relatively rare in superficial gastric cancer (SGC), especially in patients without lymph node metastasis. This study aimed to explore the main clinical risk factors for DM in patients with superficial gastric cancer-no lymph node metastasis (SGC-NLNM) and the prognostic factors for patients with DM.

METHODS

Records of patients with SGC-NLNM between 2004 and 2015 were collected from the public Surveillance, Epidemiology, and End Results (SEER) database. Both univariate and multivariate logistic regressions were performed to analyze the clinical risk factors for DM. The Kaplan-Meier method and Cox regression model were used to identify prognostic factors for patients with DM. A nomogram was built based on multivariate logistic regression and evaluated by the C-index, the calibration, and the area under the receiver operating characteristic curve (AUC).

RESULTS

We developed and validated a nomogram to predict DM in patients with SGC-NLNM, showing that race, age, primary site, depth, size, and grade were independent risk factors. The built nomogram had a good discriminatory performance, with a C-index of 0.836 (95% confidence interval [CI]: 0.813-0.859). Calibration plots showed that the predicted DM probability was identical to the actual observations in both the training and validation sets. AUC was 0.846 (95% CI: 0.820-0.871) and 0.801 (95% CI: 0.751-0.850) in the training and validation sets, respectively. The results of the survival analysis revealed that surgery (hazard ratio [HR] = 0.249; 95% CI, 0.125-0.495), chemotherapy (HR = 0.473; 95% CI, 0.353-0.633), and grade (HR = 1.374; 95% CI, 1.018-1.854) were independent prognostic factors associated with cancer-specific survival (CSS), but radiotherapy was not (log-rank test, p = 0.676).

CONCLUSIONS

We constructed a sensitive and discriminative nomogram to identify high-risk patients with SGC-NLNM who may harbor dissemination at initial diagnosis. The tumor size and primary site were the largest contributors to DM prediction. Compared with radiotherapy, aggressive surgery, and chemotherapy may be better options for patients with DM.

A nomogram to predict skip metastasis in papillary thyroid cancer

Background

Skip metastases are defined as lateral lymph node metastasis (LNM) without the involvement of central LNM in papillary thyroid cancer (PTC), and it is difficult to predict in clinical practice. Our study aimed to investigate the risk factors of skip metastasis and establish a nomogram for predicting the probability of skip metastasis in PTC patients.

Patients and methods

A total of 378 consecutive PTC patients with clinically suspected LNM who underwent modified radical neck dissection (MRND) from March 2018 to July 2019 in our hospital were enrolled. Univariate and multivariate analyses were used to examine risk factors of skip metastasis, and a nomogram prediction model was established and internally validated.

Results

The incidence of skip metastases was 11.6% (44/378). Primary tumor size of ≤ 1 cm (OR = 2.703; 95% CI, 1.342–5.464; P = 0.005), age (OR = 1.051; 95% CI, 1.017–1.805; P = 0.005), and primary tumor location in the upper portion (OR = 6.799; 95% CI, 2.710–17.060; P < 0.001) were found to be independent risk factors for skip metastasis in PTC patients. A nomogram based upon these predictors performed well. The area under the curve (AUC) was 0.806 (95% CI, 0.736–0.876), and the P value of the Hosmer-Lemeshow goodness of fit test was 0.66. Decision curve analysis revealed that the nomogram was clinically useful.

Conclusion

Based on the risk factors of skip metastasis, a high-performance nomogram was established, which can provide an individual risk assessment and can guide treatment decisions for patients.

Evaluating overall survival and competing risks of survival in patients with early-stage breast cancer using a comprehensive nomogram

BACKGROUND

Patients with early-stage breast cancer (BC) live long but have competing comorbidities. This study aimed to estimate the effect of cancer and other causes of death in patients with early-stage BC and further quantify the survival differences.

MATERIALS AND METHODS

Data of patients diagnosed with BC between 2010 and 2016 were collected from the Surveillance, Epidemiology, and End Results database. The cumulative incidence function for breast cancer-specific mortality (BCSM) and other cause-specific mortality (OCSM) was estimated, and the differences were tested using the Gray test. The nomogram for estimating 3-, 4-, and 5-year overall survival (OS), breast cancer-specific survival, and other cause-specific survival was established based on Cox regression analysis and Fine and Gray competing risk analysis. The discriminative ability, calibration, and precision of the nomogram were evaluated and compared using C statistics, calibration plots, and area under the receiver operating characteristic curve.

RESULTS

A total of 196 304 eligible patients with early-stage BC were identified in this study. Of these, 12 417 (6.3%) patients died: 5628 (45.3%) due to BC and 6789 (54.7%) due to other causes. Five validated variables were incorporated to develop the prognostic nomogram: age, grade, tumor size, subtype, and surgery of primary site (Figure 3). Age was a strong predictive factor, which was more obvious in OCSM. The effect of surgery was more prominent in BCSM. Increased tumor size was correlated with OS and BCSM and slightly correlated with OCSM. Grade and subtype differences were more predominant in BCSM than in OCSM. The established nomogram was well calibrated and displayed good discrimination.

CONCLUSIONS

We evaluate OS and competing risks of death in patients with early-stage BC, establishing the first comprehensive prognostic nomogram.

Preoperative Axillary Ultrasound versus Sentinel Lymph Node Biopsy in Patients with Early Breast Cancer

Background and objectives: With improved diagnostic means of early breast cancer, the percentage of cases with metastasis in axillary lymph nodes has decreased from 50–75% to 15–30%. Lymphadenectomy and sentinel lymph node biopsy are not treatment procedures, as they aim at axillary nodal staging in breast cancer. Being surgical interventions, they can lead to various complications. Therefore, recently much attention has been paid to the identification of non-invasive methods for axillary nodal staging. In many countries, ultrasound is a first-line method to evaluate axillary lymph node status. The aim of this study was to evaluate the prognostic value of ultrasound in detecting intact axillary lymph nodes and to assess the accuracy of ultrasound in detecting a heavy nodal disease burden. The additional objective was to evaluate patients’ and tumor characteristics leading to false-negative results. Materials and Methods: A total of 227 women with newly diagnosed pT1 breast cancer were included to this prospective study conducted at the Breast Surgery Unit, Clinic of Surgery, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, between May 1, 2016, and May 31, 2018. All patients underwent preoperative axillary ultrasound examination. Ultrasound data were compared with the results of histological examination. The accuracy and true-negative rate of ultrasound were calculated. The reasons of false-negative results were analyzed. Results: Of the 189 patients who had normally appearing axillary lymph nodes on preoperative ultrasound (PAUS-negative), 173 (91.5%) patients were also confirmed to have intact axillary lymph nodes (node-negative) by histological examination after surgery. The accuracy and the negative predictive value of ultrasound examination were 84.1% and 91.5%, respectively. In ≥3 node-positive cases, the accuracy and the negative predictive value increased to 88.7% and 98.3%, respectively. In total, false-negative results were found in 8.5% of the cases (n = 16); in the PAUS-negative group, false-negative results were recorded only in 1.6% of the cases (n = 3). The results of PAUS and pathological examination differed significantly between patients without and with lymphovascular invasion (LV0 vs. LV1, p < 0.001) as well as those showing no human epidermal growth factor receptor 2 (HER2) expression and patients with weakly or strongly expressed HER2 (HER2(0) vs. HER2(1), p = 0.024). Paired comparisons revealed that the true-negative rate was significantly different between the LV0 and LV1 groups (91% vs. 66.7%, p < 0.05), and the false-negative rate was statistically significant different between the HER2(0) and HER2(1) groups (10.5% vs. 1.2%, p < 0.05). Evaluation of other characteristics showed both the groups to be homogenous. Conclusions: Negative axillary ultrasound excluded axillary metastatic disease in 91.5% of the patients. PAUS had an accuracy of 88.7% in detecting a heavy nodal disease burden. With the absence of lymphovascular invasion (LV0), we can rely on PAUS examination that axillary lymph nodes are intact (PAUS-negative), and this patients’ group could avoid sentinel lymph node biopsy. Patients without HER2 expression are at a greater likelihood of false-negative results; therefore, the findings of ultrasound that axillary lymph nodes are intact (PAUS-negative results) should be interpreted with caution.