Preoperative prediction nomogram based on primary tumor miRNAs signature and clinical-related features for axillary lymph node metastasis in early-stage invasive breast cancer

Evaluating MicroRNAs as Diagnostic Tools for Lymph Node Metastasis in Breast Cancer: Findings from a Systematic Review and Meta-Analysis

Lymph node metastasis (LNM) significantly affects the prognosis and clinical management of breast cancer (BC) patients. This systematic review and meta-analysis aim to identify microRNAs (miRNAs) associated with LNM in BC and evaluate their potential diagnostic and prognostic value. Following PRISMA guidelines, a comprehensive literature search was conducted in PubMed, Web of Science, and SCOPUS databases, to assess the role of miRNAs in LNM BC. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate the quality of included studies. A total of 84 miRNAs were identified as differentially expressed in BC patients with LNM. Of these, a meta-analysis was performed in two microRNAs that were present in at least 3 different articles with a coherent expression direction: miR-155 and miR-34a. The meta-analysis returned a pooled a Log2 fold change of 1.50 for miR-155 (upregulated) and -0.53 for miR-34a (downregulated) with no evidence of publication bias, and a low risk of bias and applicability concerns. To conclude, this study names miR-155 and miR-34a as potential diagnostic biomarkers for LNM in BC, although further experimental validation is necessary to confirm these findings and develop non-invasive diagnostic tools for clinical use.

Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer

OBJECTIVES

To explore the clinical value of the nomogram based on spectral Doppler ultrasound combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer.

METHODS

We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively.

RESULTS

Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93).

CONCLUSION

Axillary ultrasound combined with Ki-67 and spectral ultrasound parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

Investigation of the regulation of EGF signaling by miRNAs, delving into the underlying mechanism and signaling pathways in cancer

The EGF receptors (EGFRs) signaling pathway is essential for tumorigenesis and progression of cancer. Emerging evidence suggests that miRNAs are essential regulators of EGF signaling, influencing various pathway components and tumor behavior. This article discusses the underlying mechanisms and clinical implications of miRNA-mediated regulation of EGF signaling in cancer. miRNAs utilize multiple mechanisms to exert their regulatory effects on EGF signaling. They can target EGF ligands, including EGF and TGF-directly, inhibiting their expression and secretion. In addition, miRNAs can modulate EGF signaling indirectly by targeting EGF receptors, downstream signaling molecules, and transcription factors implicated in regulating the EGF pathway. These miRNAs can disrupt the delicate equilibrium of EGF signaling, resulting in aberrant activation and fostering tumor cell proliferation, survival, angiogenesis, and metastasis. The dysregulation of the expression of specific miRNAs has been linked to clinical outcomes in numerous types of cancer. Specific profiles of miRNA expression have been identified as prognostic markers, reflecting tumor characteristics, invasiveness, metastatic potential, and therapeutic response. These miRNAs can serve as potential therapeutic targets for interventions that modulate EGF signaling and improve patient outcomes. Understanding the intricate relationship between miRNAs and EGF signaling in cancer can transform cancer diagnosis, prognosis, and treatment. The identification of specific miRNAs involved in the regulation of the EGF pathway opens the door to the development of targeted therapies and personalized medicine approaches. In addition, miRNA-based interventions promise to overcome therapeutic resistance and improve the efficacy of existing treatments. miRNAs are crucial regulators of EGF signaling in cancer, affecting tumor behavior and clinical outcomes. Further research is required to decipher the complex network of miRNA-mediated EGF signaling regulation and translate these findings into clinically applicable strategies for enhanced cancer treatment.

A nomogram based on inflammation and nutritional biomarkers for predicting the survival of breast cancer patients

Background

We aim to develop a new prognostic model that incorporates inflammation, nutritional parameters and clinical-pathological features to predict overall survival (OS) and disease free survival (DFS) of breast cancer (BC) patients.

Methods

The study included clinicopathological and follow-up data from a total of 2857 BC patients between 2013 and 2021. Data were randomly divided into two cohorts: training (n=2001) and validation (n=856) cohorts. A nomogram was established based on the results of a multivariate Cox regression analysis from the training cohorts. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. Furthermore, decision curve analysis (DCA) was performed to assess the clinical value of the nomogram.

Results

A nomogram was developed for BC, incorporating lymphocyte, platelet count, hemoglobin levels, albumin-to-globulin ratio, prealbumin level and other key variables: subtype and TNM staging. In the prediction of OS and DFS, the concordance index (C-index) of the nomogram is statistically greater than the C-index values obtained using TNM staging alone. Moreover, the time-dependent AUC, exceeding the threshold of 0.7, demonstrated the nomogram's satisfactory discriminative performance over different periods. DCA revealed that the nomogram offered a greater overall net benefit than the TNM staging system.

Conclusion

The nomogram incorporating inflammation, nutritional and clinicopathological variables exhibited excellent discrimination. This nomogram is a promising instrument for predicting outcomes and defining personalized treatment strategies for patients with BC.

A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status

Axillary staging is commonly performed via sentinel lymph node biopsy for patients with early breast cancer (EBC) presenting with clinically negative axillary lymph nodes (cN0). The present study aimed to investigate the association between axillary lymph node metastasis (ALNM), clinicopathological characteristics of tumors and results from axillary ultrasound (US) scanning. Moreover, a nomogram model was developed to predict the risk for ALNM based on relevant factors. Data from 998 patients who met the inclusion criteria were retrospectively reviewed. These patients were then randomly divided into a training and validation group in a 7:3 ratio. In the training group, receiver operating characteristic curve analysis was used to identify the cutoff values for continuous measurement data. R software was used to identify independent ALNM risk variables in the training group using univariate and multivariate logistic regression analysis. The selected independent risk factors were incorporated into a nomogram. The model differentiation was assessed using the area under the curve (AUC), while calibration was evaluated through calibration charts and the Hosmer-Lemeshow test. To assess clinical applicability, a decision curve analysis (DCA) was conducted. Internal verification was performed via 1000 rounds of bootstrap resampling. Among the 998 patients with EBC, 228 (22.84%) developed ALNM. Multivariate logistic analysis identified lymphovascular invasion, axillary US findings, maximum diameter and molecular subtype as independent risk factors for ALNM. The Akaike Information Criterion served as the basis for both nomogram development and model selection. Robust differentiation was shown by the AUC values of 0.855 (95% CI, 0.817-0.892) and 0.793 (95% CI, 0.725-0.857) for the training and validation groups, respectively. The Hosmer-Lemeshow test yielded P-values of 0.869 and 0.847 for the training and validation groups, respectively, and the calibration chart aligned closely with the ideal curve, affirming excellent calibration. DCA showed that the net benefit from the nomogram significantly outweighed both the 'no intervention' and the 'full intervention' approaches, falling within the threshold probability interval of 12-97% for the training group and 17-82% for the validation group. This underscores the robust clinical utility of the model. A nomogram model was successfully constructed and validated to predict the risk of ALNM in patients with EBC and cN0 status. The model demonstrated favorable differentiation, calibration and clinical applicability, offering valuable guidance for assessing axillary lymph node status in this population.

Nomograms for metastasis of non-sentinel lymph nodes or more than three lymph nodes in patients with one or two positive sentinel lymph nodes

Purpose

The purpose of this study was to provide advice for the indication of regional nodal irradiation (RNI) in patients with one to two positive sentinel lymph nodes (SLNs) without axillary lymph node dissection (ALND).

Methods

We conducted a retrospective study in Shandong Cancer Hospital, Fudan University Shanghai Cancer Center, and West China Hospital. Logistic analysis was performed in order to explore the influencing factors of positive non-SLNs (NSLNs) and >3 positive nodes among patients with one to two SLNs+. Then, nomograms were constructed.

Results

Between May 2010 and 2020, among the 2,845 patients with one to two SLNs+ undergoing ALND (1,992 patients in the training set and 853 patients in the validation set), there were 34.3% harbored NSLNs+ and 15.6% harbored >3 positive nodes. Multivariate analysis showed that cN stage, the number of positive/negative SLN, pathological tumor stage, lympho-vascular invasion (LVI), multicenter, and molecular subtypes were significantly associated with NSLN metastasis. Similarly, multivariate analysis also showed that cN stage, the number of positive/negative SLNs, pathological tumor stage, and LVI could be independent predictors of >3 positive nodes. Then, nomograms for NSLN metastasis and >3 positive nodes were constructed using these parameters, respectively.

Conclusions

The nomograms will be useful in estimating positive NSLNs and >3 positive nodes, and they might provide advice for the optimization of RNI.

Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review

Sentinel node biopsy (SNB) is routinely performed in people with node-negative early breast cancer to assess the axilla. SNB has no proven therapeutic benefit. Nodal status information obtained from SNB helps in prognostication and can influence adjuvant systemic and locoregional treatment choices. However, the redundancy of the nodal status information is becoming increasingly apparent. The accuracy of radiological assessment of the axilla, combined with the strong influence of tumour biology on systemic and locoregional therapy requirements, has prompted many to consider alternative options for SNB. SNB contributes significantly to decreased quality of life in early breast cancer patients. Substantial improvements in workflow and cost could accrue by removing SNB from early breast cancer treatment. We review the current viewpoints and ideas for alternative options for assessing and managing a clinically negative axilla in patients with early breast cancer (EBC). Omitting SNB in selected cases or replacing SNB with a non-invasive predictive model appear to be viable options based on current literature.

Using MammaPrint on core needle biopsy to guide the need for axillary staging during breast surgery

BACKGROUND

At present, the only opportunity to omit axillary staging is with Choosing Wisely criteria for women ages >70 y with cT1 2N0 estrogen receptor-positive/human epidermal growth factor receptor 2-negative breast cancer. However, many women are diagnosed when pathologic node status-negative, raising the question of additional opportunities to omit sentinel lymph node biopsy. We sought to investigate the association between MammaPrint, a genomic test that estimates estrogen receptor-positive breast cancer recurrence risk, and pathologic node status, with the aim that low-risk MammaPrint could be considered for omission of sentinel lymph node biopsy if associated with pathologic node status-negative.

METHODS

A single-institution database was queried for all women with cT1 2N0 estrogen receptor-positive/human epidermal growth factor receptor 2-negative invasive breast cancer with breast surgery as their first treatment and MammaPrint performed from 2020 to 2021. Patient and tumor factors, including MammaPrint score, were compared with axillary node status for correlation.

RESULTS

A total of 668 women met inclusion criteria, with a median age of 66 y. MammaPrint was low-risk luminal A in 481 (72%) and high-risk luminal B in 187 (28%). At the time of breast surgery, 588 (88%) had sentinel lymph node biopsy, 27 (4%) had axillary lymph node dissection, and 53 (7.9%) had no axillary staging. Most women in both the pathologic node status-negative and pathologic node status-positive cohorts had low-risk MammaPrint (355 [73.3%] pathologic node status-negative vs 91 [69.5%] pathologic node status-positive), and women with low-risk MammaPrint did not have a significantly lower risk of pathologic node status-positive (P = .377).

CONCLUSION

Low-risk MammaPrint does not predict lower risk of pathologic node status-positive breast cancer. Based on our results, genomic testing does not appear to provide additional personalization for the ability to omit sentinel lymph node biopsy for patients outside of the Choosing Wisely guidelines.

PIWI-interacting RNA-YBX1 inhibits proliferation and metastasis by the MAPK signaling pathway via YBX1 in triple-negative breast cancer

Breast cancer is the second leading cause of death in women worldwide, with triple-negative breast cancer (TNBC) having the worst prognosis. Although there are numerous studies on TNBC, there is no effective treatment for it, and it is still a major problem today. Studies on PIWI-interacting RNAs (piRNAs) are increasing and investigating the mechanism of piRNAs in the proliferation and metastasis of TNBC may lead to new potential treatment targets. Here, we identified a novel piRNA, piR-YBX1, which was downregulated in TNBC compared to matched normal breast tissue. Overexpression of piR-YBX1 significantly inhibited the proliferation, migration, invasion ability of TNBC cells both in vivo and in vitro. Mechanistically, piR-YBX1 could bind directly to mRNA of Y-box binding protein 1 (YBX1) and overexpression of piR-YBX1 downregulated YBX1 in both mRNA and protein levels, while the function of piR-YBX1 could be partly rescued by overexpression of YBX1. In addition, YBX1 could bind to RAF1 which is the key molecule in the MAPK signaling pathway, and overexpression of piR-YBX1 inhibited the p-MEK and p-ERK1/2, which can be reverted by YBX1. In conclusion, our findings discovered that the piR-YBX1/YBX1/MAPK axis suppresses the proliferation and metastasis of TNBC and therefore piR-YBX1 has the potential to be an effective therapeutic agent for breast cancer.

Recent advances in novel miRNA mediated approaches for targeting breast cancer

Breast cancer (BC) is considered one of the most frequent cancers among woman worldwide. While conventional therapy has been successful in treating many cases of breast cancer, drug resistance, heterogenicity, tumour features and recurrence, invasion, metastasis and the presence of breast cancer stem cells can hinder the effect of treatments, and can reduce the quality of life of patients. MicroRNAs (miRNAs) are short non-coding RNA molecules that play a crucial role in the development and progression of breast cancer. Several studies have reported that aberrant expression of specific miRNAs is associated with the pathogenesis of breast cancer. However, miRNAs are emerging as potential biomarkers and therapeutic targets for breast cancer. Understanding their role in breast cancer biology could help develop more effective treatments for this disease. The present study discusses the biogenesis and function of miRNAs, as well as miRNA therapy approaches for targeting and treating breast cancer cells.

Intra- and Peritumoral Radiomics of Contrast-Enhanced Mammography Predicts Axillary Lymph Node Metastasis in Patients With Breast Cancer: A Multicenter Study

RATIONALE AND OBJECTIVES

This multicenter study aimed to explore the feasibility of radiomics based on intra- and peritumoral regions on preoperative breast cancer contrast-enhanced mammography (CEM) to predict axillary lymph node (ALN) metastasis.

MATERIALS AND METHODS

A total of 809 patients with preoperative breast cancer CEM images from two centers were retrospectively recruited. Least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features extracted from CEM images in regions of the tumor and peritumoral area of five and ten mm as well as construct radiomics signature. A nomogram, including the optimal radiomics signature and clinicopathological factors, was then constructed. Nomogram performance was evaluated using AUC and compared with breast radiologists directly.

RESULTS

In the internal testing set, AUCs of peritumoral signatures decreased when the peritumoral area increased and signaturetumor + 10mm demonstrated the best performance with an AUC of 0.712. The nomogram incorporating signaturetumor + 10mm, tumor diameter, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and CEM-reported lymph node status yielded maximum AUCs of 0.753 and 0.732 in internal and external testing sets, respectively. Moreover, the nomogram outperformed radiologists and improved diagnostic performance of radiologists.

CONCLUSION

The nomogram based on CEM intra- and peritumoral regions may provide a noninvasive auxiliary tool to guide treatment strategy of ALN metastasis in breast cancer.

A novel nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer

BACKGROUND OR PURPOSE

A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status.

METHODS

Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n = 472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models.

RESULTS

In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p = 0.003); Her-2 status (p = 0.049); and ALN-US shape (p = 0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p = 0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability.

CONCLUSION

This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.

miRNA in Molecular Diagnostics

MicroRNAs are a class of small non-coding RNA molecules that regulate gene expression on post-transcriptional level. Their biogenesis consists of a complex series of sequential processes, and they regulate expression of many genes involved in all cellular processes. Their function is essential for maintaining the homeostasis of a single cell; therefore, their aberrant expression contributes to development and progression of many diseases, especially malignant tumors and viral infections. Moreover, they can be associated with certain states of a specific disease, obtained in the least invasive manner for patients and analyzed with basic molecular methods used in clinical laboratories. Because of this, they have a promising potential to become very useful biomarkers and potential tools in personalized medicine approaches. In this review, miRNAs biogenesis, significance in cancer and infectious diseases, and current available test and methods for their detection are summarized.

Construction and validation of a risk prediction model for clinical axillary lymph node metastasis in T1-2 breast cancer

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.

A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases

Background

We developed a new nomogram combining serum biomarkers with clinicopathological features to improve the accuracy of prediction of nonsentinel lymph node (SLN) metastases in Chinese breast cancer patients.

Methods

We enrolled 209 patients with breast cancer who underwent SLN biopsy and axillary lymph node dissection. We evaluated the relationships between non-SLN metastases and clinicopathologic features, as well as preoperative routine tests of blood indexes, tumor markers, and serum lipids, including lipoprotein a (Lp(a)). Risk factors for non-SLN metastases were identified by logistic regression analysis. The nomogram was created using the R program to predict the risk of non-SLN metastases in the training set. Receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in the validation set.

Results

Lp(a) was significantly associated with non-SLN metastasis status. Compared with the MSKCC model, the predictive ability of our new nomogram that combined Lp(a) level and clinical variables (pathologic tumor size, lymphovascular invasion, multifocality, and positive/negative SLN numbers) was significantly greater (AUC: 0.732, 95% CI: 0.643–0.821) (C-index: 0.703, 95% CI: 0.656–0.791) in the training cohorts and also performed well in the validation cohorts (C-index: 0.773, 95% CI: 0.681–0.865). Moreover, the new nomogram with Lp(a) improved the accuracy (12.10%) of identification of patients with non-SLN metastases (NRI: 0.121; 95% CI: 0.081–0.202; P = 0.011).

Conclusions

This novel nomogram based on preoperative serum indexes combined with clinicopathologic features facilitates accurate prediction of risk of non-SLN metastases in Chinese patients with breast cancer.

microRNA-based diagnostic and therapeutic applications in cancer medicine

It has been almost two decades since the first link between microRNAs and cancer was established. In the ensuing years, this abundant class of short noncoding regulatory RNAs has been studied in virtually all cancer types. This tremendously large body of research has generated innovative technological advances for detection of microRNAs in tissue and bodily fluids, identified the diagnostic, prognostic, and/or predictive value of individual microRNAs or microRNA signatures as potential biomarkers for patient management, shed light on regulatory mechanisms of RNA-RNA interactions that modulate gene expression, uncovered cell-autonomous and cell-to-cell communication roles of specific microRNAs, and developed a battery of viral and nonviral delivery approaches for therapeutic intervention. Despite these intense and prolific research efforts in preclinical and clinical settings, there are a limited number of microRNA-based applications that have been incorporated into clinical practice. We review recent literature and ongoing clinical trials that highlight most promising approaches and standing challenges to translate these findings into viable microRNA-based clinical tools for cancer medicine. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Models for Predicting Sentinel and Non-sentinel Lymph Nodes Based on Pre-operative Ultrasonic Breast Imaging to Optimize Axillary Strategies

Axillary strategy decisions have become more complex and controversial in considering minimally traumatic therapy instead of sentinel lymph node biopsy, axillary lymph node dissection or regional nodal irradiation for people with breast cancer. The purpose of this study was to noninvasively predict sentinel lymph node (SLN) and non-sentinel lymph node (NSLN) status based on pre-operative sonographic and clinicopathologic features to determine optimal decisions regarding axillary therapy. In total, 701 patients with breast cancer from two independent centers were retrospectively analyzed. The SLN model (SLNM) for predicting SLN status and the NSLN model (NSLNM) for predicting NSLN status were trained based on a training set using the random-forest algorithm, and their performance was validated using an independent external test set. A receiver operating characteristic curve was drawn to obtain the area under the curve, which was used to assess performance. The area under the curve for the SLNM in the training and test, respectively, was 94.2% and 83.0%, and for the NSLNM, 99.5% and 92.7%. The SLNM and NSLNM accurately predicted that 61.46% (319/519) and 17.53% (91/519), respectively, of our participants were non-metastatic. The overall benefit of the three models was 78.99% in our participants. The two models for predicting SLN and NSLN status showed excellent application potential in optimizing axillary strategies.

Predictive Nomogram of Subsequent Liver Metastasis After Mastectomy or Breast-Conserving Surgery in Patients With Nonmetastatic Breast Cancer

Background:

Metastasis accounts for the majority of deaths in patients with breast cancer. Liver metastasis is reported common for breast cancer patients. The purpose of this study was to construct a nomogram to predict the likelihood of subsequent liver metastasis in patients with nonmetastatic breast cancer, thus high-risk patient populations can be prevented and monitored.

Methods:

A total of 1840 patients with stage I-III breast cancer were retrospectively included and analyzed. A nomogram was constructed to predict liver metastasis based on multivariate logistic regression analysis. SEER database was used for external validation. C-index, calibration curve and decision curve analysis were used to evaluate the predictive performance of the model.

Results:

The nomogram included 3 variables related to liver metastasis: HER2 status (odds ratio (OR) 1.86, 95%CI 1.02 to 3.41; P = 0.045), tumor size (OR 3.62, 1.91 to 6.87; P < 0.001) and lymph node metastasis (OR 2.26, 1.18 to 4.34; P = 0.014). The C index of the training cohort, internal validation cohort and external validation cohort were 0.699, 0.814 and 0.791, respectively. The nomogram was well-calibrated, with no statistical difference between the predicted and the observed probabilities.

Conclusion:

We have developed and validated a robust tool enabled to predict subsequent liver metastasis in patients with nonmetastatic breast cancer. Distinguishing a population of patients at high risk of liver metastasis will facilitate preventive treatment or monitoring of liver metastasis.

Development of a prognostic signature for bladder cancer based on immune-related genes

Background

Although the prognosis of patients with bladder cancer (BC) has improved significantly with the use of multimodal therapy, reliable prognostic biomarkers are still urgently needed due to the heterogeneity of tumors. Our aim was to develop an individualized immune-related gene pair (IRGP) signature that could precisely predict prognosis in BC patients.

Methods

Gene expression profiles and corresponding clinical information were collected from eight microarray data sets and one RNA-Seq data set.

Results

Among 1,811 immune genes, a 30-IRGP signature consisting of 52 unique genes was generated in the training cohort, which significantly stratified patients into low- and high-risk groups in terms of overall survival. In the testing and validation cohorts, the IRGP signature was also associated with patient prognosis in the univariate and multivariate Cox regression analyses. Several biological processes, including the immune response, chemotaxis, and the inflammatory response, were enriched among genes in the IRGP signature. When the signature was integrated with the TNM stage, an IRGP nomogram was developed and showed improved prognostic accuracy relative to the IRGP signature alone.

Conclusions

In short, we identified a robust IRGP signature for estimating overall survival in BC patients that could also be used as a promising biomarker for identifying high-risk patients for individualized therapy.

Development of a Novel Serum Exosomal MicroRNA Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma

Preoperative prediction of lymph node (LN) metastasis is accepted as a crucial independent risk factor for treatment decision-making for esophageal squamous cell carcinoma (ESCC) patients. Our study aimed to establish a non-invasive nomogram to identify LN metastasis preoperatively in ESCC patients. Construction of the nomogram involved three sequential phases with independent patient cohorts. In the discovery phase (N = 20), LN metastasis-associated microRNAs (miRNAs) were selected from next-generation sequencing (NGS) assay of human ESCC serum exosome samples. In the training phase (N = 178), a nomogram that incorporated exosomal miRNA model and clinicopathologic was developed by multivariate logistic regression analysis to preoperatively predict LN status. In the validation phase (n = 188), we validated the predicted nomogram's calibration, discrimination, and clinical usefulness. Four differently expressed miRNAs (chr 8-23234-3p, chr 1-17695-5p, chr 8-2743-5p, and miR-432-5p) were tested and selected in the serum exosome samples from ESCC patients who have or do not have LN metastasis. Subsequently, an optimized four-exosomal miRNA model was constructed and validated in the clinical samples, which could effectively identify ESCC patients with LN metastasis, and was significantly superior to preoperative computed tomography (CT) report. In addition, a clinical nomogram consisting of the four-exosomal miRNA model and CT report was established in training cohort, which showed high predictive value in both training and validation cohorts [area under the receiver operating characteristic curve (AUC): 0.880 and 0.869, respectively]. The Hosmer–Lemeshow test and decision curve analysis implied the nomogram's clinical applicability. Our novel non-invasive nomogram is a robust prediction tool with promising clinical potential for preoperative LN metastasis prediction of ESCC patients, especially in T1 stage.

Preoperative Prediction Nomogram Based on Integrated Profiling for Glioblastoma Multiforme in Glioma Patients

Introduction: Traditional classification that divided gliomas into glioblastoma multiformes (GBM) and lower grade gliomas (LGG) based on pathological morphology has been challenged over the past decade by improvements in molecular stratification, however, the reproducibility and diagnostic accuracy of glioma classification still remains poor. This study aimed to establish and validate a novel nomogram for the preoperative diagnosis of GBM by using integrated data combined with feasible baseline characteristics and preoperative tests. Material and method: The models were established in a primary cohort that included 259 glioma patients who had undergone surgical resection and were pathologically diagnosed from March 2014 to May 2016 in the First Affiliated Hospital of Xi'an Jiaotong University. The preoperative data were used to construct three models by the best subset regression, the forward stepwise regression, and the least absolute shrinkage and selection operator, and to furthermore establish the nomogram among those models. The assessment of nomogram was carried out by the discrimination and calibration in internal cohorts and external cohorts. Results and discussion: Out of all three models, model 2 contained eight clinical-related variables, which exhibited the minimum Akaike Information Criterion (173.71) and maximum concordance index (0.894). Compared with the other two models, the integrated discrimination index for model 2 was significantly improved, indicating that the nomogram obtained from model 2 was the most appropriate model. Likewise, the nomogram showed great calibration and significant clinical benefit according to calibration curves and the decision curve analysis. Conclusion: In conclusion, our study showed a novel preoperative model that incorporated clinically relevant variables and imaging features with laboratory data that could be used for preoperative prediction in glioma patients, thus providing more reliable evidence for surgical decision-making.

Construction of an immune-related genes nomogram for the preoperative prediction of axillary lymph node metastasis in triple-negative breast cancer

Immune system disorder is associated with metastasis of triple-negative breast cancers (TNBCs). A robust, individualized immune-related genes (IRGs)-based classifier was aimed to develop and validate in our study to precisely estimate the axillary lymph node (ALN) status preoperatively in patients with early-stage TNBC. We first analyzed RNA sequencing profiles in TNBC patients from The Cancer Genome Atlas database by using bioinformatics approaches, and screened 23 differentially expressed IRGs. A 9-gene panel was generated with an area under the curve (AUC) of 0.77 [95% confidence interval (95% CI), 0.68-0.87]. We detected the 9 ALN-status-related IRGs in the training set (n = 133) and developed a reduced and optimized five-IRGs signature, which effectively distinguished TNBC patients with ALN metastasis (AUC, 0.80; 95% CI, 0.65-0.86), and was superior to preoperative ultrasound-based ALN status (AUC, 0.73; 95% CI, 0.53-0.93). Predictive efficiency (AUC, 0.77; 95% CI 0.61-0.93) of this five-IRGs signature was validated in the validation set (n = 81). Furthermore, IRGs nomogram incorporated IRGs signature with US-based ALN status showed higher ALN status prediction efficacy than US-based ALN status and five-IRGs signature alone in both training and validation sets. IRGs nomogram may aid in identifying patients who can be exempted from axillary surgery.Novelty and impact: An immune-related genes (IRGs) nomogram was first developed and externally validated in our study, which incorporated the IRGs signature with ultrasound (US)-based axillary lymph nodes (ALN) status. IRGs nomogram is superior to IRGs signature alone for preoperative estimation of ALN metastasis in patients with triple-negative breast cancer (TNBC). It is a favourable biomarker for preoperatively predicting ALN metastasis risk and may aid in clinical decision-making in early-stage TNBCs.

Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients

OBJECTIVES

To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions.

METHODS

Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results.

RESULTS

In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790-0.902) for the training cohort and 0.733 (95% CI, 0.613-0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations.

CONCLUSIONS

Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics.

KEY POINTS

• A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer.

Development and external validation of a nomogram to predict four or more positive nodes in breast cancer patients with one to three positive sentinel lymph nodes

Objective

To develop a nomogram for predicting the possibility of four or more positive nodes in breast cancer patients with 1–3 positive sentinel lymph nodes (SLN).

Materials and methods

Retrospective analysis of data of patients from two institutions was conducted. The inclusion criteria were: invasive breast cancer; clinically node negative; received lumpectomy or mastectomy plus SLN biopsy followed by axillary lymph node dissection (ALND); and pathologically confirmed T1-2 tumor, with 1–3 positive SLNs. Patients from one institution formed the training group and patients from the other the validation group. Univariate and multivariate analyses were performed to identify the predictors of four or more positive nodes. These predictors were used to build the nomogram. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model.

Results

Of the 1480 patients (966 patients in the training group, 514 in the validation group), 306 (20.7%) had four or more positive nodes. Multivariate stepwise logistic regression showed number of positive (p < .001) and negative SLN (p < .001), extracapsular extension (p < .001), pT stage (p = .016), and tumor location in outer upper quadrant (p = .031) to be independent predictors of four or more positive nodes. The nomogram was built using these five factors. The AUC was 0.845 in the training group and 0.804 in the validation group.

Conclusion

The proposed nomogram appears to accurately estimate the likelihood of four or more positive nodes and could help radiation oncologists to decide on use of regional nodal irradiation (RNI) for breast cancer patients with 1–3 positive nodes but no ALND.

•

Five predictors of four or more positive nodes in breast cancer patients were identified.

•

A nomogram was built using these five factors.

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The nomogram was validated on an external cohort.

•

The proposed nomogram predicts four or more positive nodes with high accuracy.

•

The nomogram can help in decision making on use of regional nodal irradiation.

Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer

BACKGROUND

The aim of our study is to find microRNAs (miRNAs) associated with sentinel lymph node metastasis (SLNM) and to develop a prediction model for SLNM in ER-positive and HER2-negative (ER+/HER2-) breast cancer.

PATIENTS AND METHODS

In the present study, only ER+/HER2- primary breast cancer was considered. The discovery set for SLNM-associated miRNAs included 10 tumors with and 10 tumors without SLNM. The training and validation sets both included 100 tumors. miRNA expression in tumors was examined comprehensively by miRNA microarray in the discovery set and by droplet digital PCR in the training and validation sets.

RESULTS

In the discovery set, miR-98, miR-22, and miR-223 were found to be significantly (P < 0.001, fold-change > 2.5) associated with SLNM. In the training set, we constructed the prediction model for SLNM using miR-98, tumor size, and lymphovascular invasion (LVI) with high accuracy (AUC, 0.877). The accuracy of this prediction model was confirmed in the validation set (AUC, 0.883), and it outperformed the conventional Memorial Sloan Kettering Cancer Center nomogram. In situ hybridization revealed the localization of miR-98 expression in tumor cells.

CONCLUSIONS

We developed a prediction model consisting of miR-98, tumor size, and LVI for SLNM with high accuracy in ER+/HER2- breast cancer. This model might help decide the indication for SLN biopsy in this subtype.

Early Detection of Non-Small Cell Lung Cancer by Using a 12-microRNA Panel and a Nomogram for Assistant Diagnosis

Background: We previously identified a 12-microRNA (miRNA) panel (miRNA-17, miRNA-146a, miRNA-200b, miRNA-182, miRNA-155, miRNA-221, miRNA-205, miRNA-126, miRNA-7, miRNA-21, miRNA-145, and miRNA-210) that aided in the early diagnosis of non-small cell lung cancer (NSCLC). We validated the diagnostic value of this miRNA panel and compared it with that of traditional tumor markers and radiological diagnosis. We constructed a nomogram based on the miRNA panel's results to predict the risk of NSCLC.

Methods: Eighty-two participants with pulmonary nodules on a CT scan and who underwent a pathological examination and surgical treatment were enrolled in our study. Patients were randomly divided into a training group or a validation group. The miRNA concentrations were quantified by RT-PCR and log-transformed for analysis. The cutoff value was determined in the training group and then applied in the validation group. A comparison between the miRNAs and traditional tumor markers [CEA, NSE, and cytokeratin 19 fragment 21-1 (Cyfra21-1)] and radiological diagnosis was performed. A nomogram based on the miRNA panel's results to predict the risk of NSCLC was constructed.

Results: The expression level of these 12 miRNAs was significantly higher in NSCLC patients than in benign patients. In the validation group, the specificity and positive predictive value were 96.4 and 95.8%, respectively, which were significantly higher than those using traditional tumor markers or radiological diagnosis. The sensitivity was 42.6%, which was also higher than that using tumor markers. Moreover, the sensitivity increased to 63.6% when the nodule diameters were larger than 2 cm. The miRNAs and seven clinical factors were integrated into the nomogram, and the calibration curves showed optimal agreement between the predicted and actual probabilities.

Conclusions: Our miRNA panel has clinical value for the early detection of NSCLC. A nomogram was constructed and internally validated, and the results indicate that it can assist clinicians in making treatment recommendations in the clinic.

Facilitating validation of prediction models: a comparison of manual and semi-automated validation using registry-based data of breast cancer patients in the Netherlands

Background

Clinical prediction models are not routinely validated. To facilitate validation procedures, the online Evidencio platform (https://www.evidencio.com) has developed a tool partly automating this process. This study aims to determine whether semi-automated validation can reliably substitute manual validation.

Methods

Four different models used in breast cancer care were selected: CancerMath, INFLUENCE, Predicted Probability of Axillary Metastasis, and PREDICT v.2.0. Data were obtained from the Netherlands Cancer Registry according to the inclusion criteria of the original development population. Calibration (intercepts and slopes) and discrimination (area under the curve (AUC)) were compared between semi-automated and manual validation.

Results

Differences between intercepts and slopes of all models using semi-automated validation ranged from 0 to 0.03 from manual validation, which was not clinically relevant. AUCs were identical for both validation methods.

Conclusions

This easy to use semi-automated validation option is a good substitute for manual validation and might increase the number of validations of prediction models used in clinical practice. In addition, the validation tool was considered to be user-friendly and to save a lot of time compared to manual validation. Semi-automated validation will contribute to more accurate outcome predictions and treatment recommendations in the target population.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0761-5) contains supplementary material, which is available to authorized users.

MicroRNA profiling of pancreatic ductal adenocarcinoma (PDAC) reveals signature expression related to lymph node metastasis

Lymph node (LN) metastasis occurs frequently in pancreatic ductal adenocarcinoma (PDAC), representing an advanced disease stage and independently predicting patient survival. Current nodal staging is inadequate preoperatively and even less so postoperatively, and molecular biomarkers are needed to improve prognostication and selection of therapy. Recent data have suggested important roles of miRNAs in PDAC tumorigenesis and progression. The aim of the present study was to identify miRNA expression signature for nodal spread in PDAC patients. Using PDAC human tissue specimens, we identified 39 miRNAs which were differently expressed in LN positive compared to LN negative PDAC samples. Of them, six miRNAs have been reported to play a role in cancer invasion and metastasis. A high versus low six- miRNA signature score was predictive of LN metastasis in the PDAC validation cohort. We demonstrated a similar expression pattern of four out of the six miRNAs in the plasma of PDAC patients. The results of our in-vitro studies revealed that miR-141 and miR-720 are involved in the process of epithelial to mesenchymal-transition in PDAC. These miRNAs significantly inhibited in vitro proliferation, migration and invasion of PDAC cells as evidence by gain- and loss- of function studies, specifically, via ZEB-1 and TWIST1 transcription factors, as well as through the activation of the MAP4K4/JNK signaling pathway.

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method

It is difficult to accurately assess axillary lymph nodes metastasis and the diagnosis of axillary lymph nodes in patients with breast cancer is invasive and has low-sensitivity preoperatively. This study aims to develop a mammography-based radiomics nomogram for the preoperative prediction of ALN metastasis in patients with breast cancer. This study enrolled 147 patients with clinicopathologically confirmed breast cancer and preoperative mammography. Features were extracted from each patient's mammography images. The least absolute shrinkage and selection operator regression method was used to select features and build a signature in the primary cohort. The performance of the signature was assessed using support vector machines. We developed a nomogram by incorporating the signature with the clinicopathologic risk factors. The nomogram performance was estimated by its calibration ability in the primary and validation cohorts. The signature was consisted of 10 selected ALN-status-related features. The AUC of the signature from the primary cohort was 0.895 (95% CI, 0.887-0.909) and 0.875 (95% CI, 0.698-0.891) for the validation cohort. The C-Index of the nomogram from the primary cohort was 0.779 (95% CI, 0.752-0.793) and 0.809 (95% CI, 0.794-0.833) for the validation cohort. Our nomogram is a reliable and non-invasive tool for preoperative prediction of ALN status and can be used to optimize current treatment strategy for breast cancer patients.

Clinical Implication of MicroRNAs in Molecular Pathology: An Update for 2018

MicroRNAs (miRNAs) are poised to provide diagnostic, prognostic, and therapeutic targets for several diseases including malignancies for precision medicine applications. The miRNAs have immense potential in the clinical arena because they can be detected in the blood, serum, tissues (fresh and formalin-fixed paraffin-embedded), and fine-needle aspirate specimens. The most attractive feature of miRNA-based therapy is that a single miRNA could be useful for targeting multiple genes that are deregulated in cancers, which can be further investigated through systems biology and network analysis that may provide cancer-specific personalized therapy.

Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer

Purpose: To develop and validate nomogram models using noninvasive imaging parameters with related clinical variables to predict the extent of axillary nodal involvement and stratify treatment options based on the essential cut-offs for axillary surgery according to the ACOSOG Z0011 criteria.

Materials and Methods: From May 2007 to December 2017, 1799 patients who underwent preoperative breast and axillary magnetic resonance imaging (MRI) were retrospectively studied. Patients with data on axillary ultrasonography (AUS) were enrolled. The MRI images were interpreted according to Breast Imaging Reporting and Data system (BI-RADS). Using logistic regression analyses, nomograms were developed to visualize the associations between the predictors and each lymph node (LN) status endpoint. Predictive performance was assessed based on the area under the receiver operating characteristic curve (AUC). Bootstrap resampling was performed for internal validation. Goodness-of-fit of the models was evaluated using the Hosmer-Lemeshow test.

Results: Of 397 early breast cancer patients, 200 (50.4%) had disease-free axilla, 119 (30.0%) had 1 or 2 positive LNs, and 78 (19.6%) had ≥3 positive LNs. Patient age, MRI features (mass margin, LN margin, presence/absence of LN hilum, and LN symmetry/asymmetry), and AUS descriptors (presence of cortical thickening or hilum) were identified as predictors of nodal disease. Nomograms with these predictors showed good calibration and discrimination; the AUC was 0.809 for negative axillary node (N0) vs. any LN metastasis, 0.749 for 1 or 2 involved nodes vs. N0, and 0.874 for ≥3 nodes vs. ≤2 metastatic nodes. The predictive ability of the 3 nomograms with additional pathological variables was significantly greater.

Conclusion: The nomograms could predict the extent of ALN metastasis and facilitate decision-making preoperatively.

MicroRNA-139 suppresses hepatocellular carcinoma cell proliferation and migration by directly targeting Topoisomerase I

microRNAs (miRNAs) have been determined to be associated with cancer progression and metastasis. Mir-139 is located on 11q13.4 and exhibits anti-oncogenic and anti-metastatic activity in human cancers. It is downregulated in various malignant tumor types. In the present study, the potential functions and targets of miR-139 in hepatocellular carcinoma (HCC) were explored. Using a combinational analysis of four miRNA target prediction tools and biological experiments, it was determined that Topoisomerase I (TOP1) is a direct target of miR-139 in HCC. Several traditional topoisomerase inhibitors have demonstrated anticancer activity, but their side effects outnumbered their anticancer potential. The present study determined that overexpression of miR-139 significantly inhibits HCC cell proliferation (P<0.05) and migration (P<0.05), which is largely due to TOP1 downregulation. The present study indicated that miR-139 exerts a tumor-suppressive effect during hepatocarcinogenesis via the suppression of expression of TOP1; therefore, miR-139 is a promising target for the treatment of HCC.

Serum miRNA-based Prediction of Axillary Lymph Node Metastasis in Breast Cancer

PURPOSE

Sentinel lymph node biopsy (SLNB) is the gold-standard procedure for evaluating axillary lymph node (ALN) status in patients with breast cancer. However, the morbidity of SLNB is not negligible, and the procedure is invasive for patients without ALN metastasis. Here, we developed a diagnostic model for evaluating ALN status using a combination of serum miRNAs and clinicopathologic factors as a novel less-invasive biomarker.Experimental Design: Preoperative serum samples were collected from patients who underwent surgery for primary breast cancer or breast benign diseases between 2008 and 2014. A total of 958 serum samples (921 cases of primary breast cancer, including 630 cases in the no ALN metastasis group and 291 cases in the ALN metastasis group, and 37 patients with benign breast diseases) were analyzed by miRNA microarray. Samples were randomly divided into training and test sets. Logistic LASSO regression analysis was used to construct diagnostic models in the training set, which were validated in the test set.

RESULTS

An optimal diagnostic model was identified using a combination of two miRNAs (miR-629-3p and miR-4710) and three clinicopathologic factors (T stage, lymphovascular invasion, and ultrasound findings), which showed a sensitivity of 0.88 (0.84-0.92), a specificity of 0.69 (0.61-0.76), an accuracy of 0.818, and an area under the receiver operating characteristic curve of 0.86 in the test set.

CONCLUSIONS

Serum miRNA profiles may be useful for the diagnosis of ALN metastasis before surgery in a less-invasive manner than SLNB.

Development of a preoperative prediction nomogram for lymph node metastasis in colorectal cancer based on a novel serum miRNA signature and CT scans

BACKGROUND

Preoperative prediction of lymph node (LN) status is of crucial importance for appropriate treatment planning in patients with colorectal cancer (CRC). In this study, we sought to develop and validate a non-invasive nomogram model to preoperatively predict LN metastasis in CRC.

METHODS

Development of the nomogram entailed three subsequent stages with specific patient sets. In the discovery set (n = 20), LN-status-related miRNAs were screened from high-throughput sequencing data of human CRC serum samples. In the training set (n = 218), a miRNA panel-clinicopathologic nomogram was developed by logistic regression analysis for preoperative prediction of LN metastasis. In the validation set (n = 198), we validated the above nomogram with respect to its discrimination, calibration and clinical application.

FINDINGS

Four differently expressed miRNAs (miR-122-5p, miR-146b-5p, miR-186-5p and miR-193a-5p) were identified in the serum samples from CRC patients with and without LN metastasis, which also had regulatory effects on CRC cell migration. The combined miRNA panel could provide higher LN prediction capability compared with computed tomography (CT) scans (P < .0001 in both the training and validation sets). Furthermore, a nomogram integrating the miRNA-based panel and CT-reported LN status was constructed in the training set, which performed well in both the training and validation sets (AUC: 0.913 and 0.883, respectively). Decision curve analysis demonstrated the clinical usefulness of the nomogram.

INTERPRETATION

Our nomogram is a reliable prediction model that can be conveniently and efficiently used to improve the accuracy of preoperative prediction of LN metastasis in patients with CRC.

Predicting prognosis in resected esophageal squamous cell carcinoma using a clinical nomogram and recursive partitioning analysis

BACKGROUND

Development demand of precise medicine in resectable esophageal squamous cell carcinoma (ESCC) require to recognize patients at high risk treated by surgery alone. Thus, our aim was to construct a clinical nomogram and recursive partitioning analysis (RPA) to predict long-term survival in ESCC treated by surgery alone.

METHODS

Based on the patients with ESCC who treated by three-incisional esophagectomy and two-field lymphadenectomy alone, we identified and integrated significant prognostic factors for survival to build a nomogram. The nomogram was calibrated for overall survival (OS) and the predictive accuracy and discriminative ability was measured by concordance index (c-index) and Akaike information criterion (AIC). Based on the nomogram, the RPA was performed for risk stratification.

RESULTS

A total of 747 patients were included for analysis. Five independent prognostic factors were identified and entered into the nomogram. The calibration curves for probability of 1-, 3-, and 5-year OS showed optimal agreement between nomogram prediction and actual observation. The AIC value of the nomogram was lower than that of the 7th edition staging system, whereas the c-index of the nomogram was higher than that of the 7th edition staging system. The risk groups stratified by RPA allowed significant distinction between survival curves within respective TNM categories.

CONCLUSION

The RPA based on a clinical nomogram appears to be suitable for risk stratification in OS for resected ESCC. This practical system may help clinicians in decision making and design of clinical studies.