Detailed evaluation of one step nucleic acid (OSNA) molecular assay for intra-operative diagnosis of sentinel lymph node metastasis and prediction of non-sentinel nodal involvement: experience from a London teaching hospital

The diagnostic performance of the one-step nucleic acid amplification assay for the detection of sentinel lymph node metastases in cytokeratin 19-positive breast cancer: a PRISMA-compliant meta-analysis

Background

The status of the sentinel lymph nodes (SLNs) is an important prognostic factor for many different types of cancer. The one-step nucleic acid amplification (OSNA) assay has emerged as a rapid intraoperative molecular diagnostic tool for LN metastasis detection. We aimed to evaluate and summarize the value of the OSNA assay for the diagnosis of SLN metastasis in cytokeratin 19 (CK19)-positive breast cancer.

Methods

To evaluate the diagnostic value, the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were pooled. The threshold effect, followed by subgroup analysis, was performed to explore the source of heterogeneity. A sensitivity analysis was performed to assess the stability of this meta-analysis model. Fagan plots and likelihood ratio scattergrams were used to explore the potential clinical significance.

Results

A total of 29 eligible studies, which consisted of 5,331 patients with 10,343 SLNs, were included in this meta-analysis. The pooled sensitivity, specificity, PLR, NLR, and DOR were 0.86 (95% CI: 0.85-0.88), 0.94 (95% CI, 0.94-0.95), 18.00 (95% CI, 13.54-23.92), 0.13 (95% CI, 0.10-0.17), and 138.99 (95% CI, 86.66-222.92), respectively. The AUC was 0.97 (95% CI, 0.95-0.98). Sensitivity analysis showed that four studies had an impact on the pooled results and mainly contributed to the heterogeneity. Fagan's nomogram revealed that the prior probability was 50%, the post-probability positive was 95%, and the post-probability negative was 11%.

Discussion

Our results suggested that OSNA can predict the occurrence of SLN metastasis in CK19-positive breast cancer. However, more well-designed and multicenter diagnostic tests are needed to validate our results.

Diagnostic value of one-step nucleic acid amplification for sentinel lymph node metastasis in cytokeratin 19-positive tumors: evidence from bioinformatics and meta-analysis

Background

The status of the sentinel lymph nodes (SLNs) was an important prognostic factor in varies cancers. A one-step nucleic acid amplification (OSNA) assay, a molecular-based whole-node analysis method based on CK19 mRNA copy number, was developed to diagnose lymph node metastases. We aimed to evaluate the value of OSNA for the diagnosis of sentinel lymph node metastasis in CK19 positive cancers. CK19 mRNA and protein expression for pan-caner analysis were obtained from TCGA and the Human protein atlas database.

Methods

Two researchers independently searched the PubMed, Cochrane Library and Web of Science databases for qualified articles published before December 1, 2023. A meta-analysis was performed using MetaDisc and STATA. Risk bias and quality assessments of the included studies were evaluated, and a subgroup analysis was performed. Ten cancer types were found to be CK19 positively expressed and 7 of 10 had been reported to use OSNA for SLN detection.

Results

After literature review, there were 61 articles included in the meta-analysis, which consisted of 7115 patients with 18007 sentinel lymph nodes. The pooled sensitivity and specificity of OSNA were 0.87 and 0.95 in overall patients. Moreover, we found the background CK19 expression in normal tissue affected the diagnostic accuracy of OSNA. In breast cancer, we performed subgroup analysis. OSNA exhibited to be a stable method across different population groups and various medical centers. In addition, when 250 copies/μl was chosen as the cutoff point of CK19 mRNA, there were a relatively higher sensitivity and AUC in detecting SLN micro-metastasis than 5000 copies/μl.

Discussion

OSNA can predict the occurrence of SLN metastasis accurately in CK19 positive cancers, especially in breast cancer, colorectal cancer, lung cancer, gastric cancer and endometrial cancer. Our study warrants future studies investigating the clinical application of OSNA in pancreatic, ovarian and bladder cancers.

Application of One-Step Nucleic Acid Amplification (OSNA) in different cancer entities and usefulness in prostate cancer: a systematic review

Background

Lymph node (LN) status is a key prognostic factor in the decision-making process of different cancer entities, including prostate cancer (PCa). Sectioning and haematoxylin and eosin (H&E) staining technique remain the gold standard for the evaluation of LN metastases despite some limitations, especially low sensitivity in detecting an accurate tumour burden within the LN, as well as a subjective and time-consuming result. One-step nucleic acid amplification (OSNA) quantifies mRNA copies of cytokeratin 19 (CK19) in a fast, objective, automated, and reproducible way, raising a general interest to explore its utility for lymphatic metastasis identification in different malignancies.

Methods

To present the latest evidence related to the detection of LN metastases in several tumours by using OSNA compared with the conventional H&E method, a systematic review of articles published since March 2021 was conducted using PubMed, Cochrane Library, and Web of Science databases. References from primary papers and review articles were checked to obtain further potential studies. Our procedure for evaluating records identified during the literature search followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses criteria. With the aim to design and justify future clinical routine use of OSNA in PCa, novel PCa evidence has been included in this review for the first time.

Results

Twenty five studies were included. LN from six different groups of tumours: breast, gastrointestinal, gynecological, lung, head and neck and prostate cancers has been assessed. OSNA was compared with post-operative formalin-fixed paraffin-embedded tissue sections with H&E staining as the reference standard. Contingency tables were created, and concordance rate, sensitivity, specificity and predictive values were reported. Seventeen studies analysed the discordant cases using different techniques.

Conclusion

OSNA method has a high diagnostic accuracy for the detection of LN metastases in several CK19 expressing tumours. Available evidence might encourage future investigations about its usage in PCa patients to improve LN staging and prognosis.

Intra-operative assessment of sentinel lymph nodes for breast cancer surgery: An update

Lymph node (LN) involvement is the strongest prognostic factor in operable breast cancer (BC). Therefore, accurate assessment of LN status is essential for management of BC patients. The introduction of sentinel LN approach reduced the need for extensive axillary surgery to achieve accurate staging. However, positive sentinel LN as determined on postoperative histological examination often leads to a second axillary operation to ensure an accurate staging and that positive non-sentinel LNs are removed. Although preoperative assessment of LN has improved significantly, its accuracy remains insufficient to avoid further axillary surgery and is not sufficient to predict the status of the LN. Therefore, intraoperative evaluation of the sentinel LN to determine the need for completing lymph node dissection in case of metastasis can provide an important approach to guide BC management decision making. This article reviews the techniques available and under development for intraoperative detection of sentinel LN metastasis in BC surgery. The key features of each technique are described in detail, emphasising the benefits offered by label-free optical techniques: minimal sample preparation, high spatial resolution, and immediate on-site implementation. Optical techniques have the potential to provide a cost-effective and accurate intraoperative platform for the assessment of SLN within the operating theatre.

Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features

The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.

A new application to one-step nucleic acid amplification-discarded sample in breast cancer: Preliminary results

The one-step nucleic acid amplification (OSNA) assay is a molecular method used for detecting breast cancer (BC) metastasis in sentinel lymph nodes (SLNs). However, this method has a major disadvantage, since it prevents tissue structure analysis, while only one molecular marker can be evaluated, namely cytokeratin 19 mRNA. The aim of the present study was to evaluate whether an OSNA-discarded sample could be suitable for the gene expression analysis of the SLN microenvironment. The remaining intermediate phase of the centrifuged SLN homogenate obtained from the OSNA assay of samples from two patients with BC was used for mRNA extraction. Subsequently, the expression of five genes, namely forkhead box, cluster of differentiation 4 and three control genes, was determined by reverse transcription-quantitative PCR analysis. The results demonstrated that high-quality RNA was extracted. Therefore, this RNA may be used for gene expression analyses to predict novel molecular biomarkers associated with immuno-inflammatory microenvironment.

A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.

Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study

Simple Summary

Sentinel lymph node biopsy procedure is time consuming and expensive, but it is still the intra-operative exam capable of the best performance. However, sometimes, surgery is achieved without a clear diagnosis, so clinical decision support systems developed with artificial intelligence techniques are essential to assist current diagnostic procedures. In this work, we evaluated the usefulness of a CancerMath tool in the sentinel lymph nodes positivity prediction for clinically negative patients. We tested it on 993 patients referred to our institute characterized by sentinel lymph node status, tumor size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor, HER2, and Ki-67. By training the CancerMath (CM) model on our dataset, we reached a sensitivity value of 72%, whereas the online one was 46%, despite a specificity reduction. It was found the addiction of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients.

Abstract

In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.

The performance of one-step nucleic acid amplification assay for intraoperative detection of sentinel lymph node macrometastasis in breast cancer: An updated meta-analysis

BACKGROUND

One-step nucleic acid amplification (OSNA) assay is a molecular diagnostic method for intraoperative detection of lymph node metastasis by amplifying cytokeratin 19 mRNA. We aim to further evaluate the performance of OSNA assay for the intraoperative diagnosis of sentinel lymph node metastasis in breast cancer.

METHODS

PubMed, Cochrane Library, Web of Science databases were searched to retrieve related literature published up until December 2017. This meta-analysis was performed using a random-effects model. Risks of bias and quality assessments of included studies were evaluated and subgroup analysis was performed.

RESULTS

Nineteen studies were included in this meta-analysis. For overall metastasis, the pooled sensitivity, specificity and area under the summary receiver-operating characteristic curve (AUC) were 0.90, 0.96 and 0.98, respectively. For macrometastasis, the pooled sensitivity, specificity and AUC were 0.85, 0.98 and 0.94, respectively.

CONCLUSION

OSNA assay is an accurate molecular diagnosis for intraoperative detection of sentinel lymph node macrometastasis in breast cancer.

One-step nucleic acid amplification: the possible value in assessing sentinel lymph node metastasis during mastectomy

Breast cancer is the most common cancer in women, worldwide, and 1,400 deaths per day are attributed to it. The success of national screening programs has seen breast cancers being diagnosed at an earlier stage. With conservative surgery to the breast demonstrating equivalent long-term outcomes, the last 10 years have seen a growing interest in the safety of less invasive management for the axilla in breast cancer patients. One-step nucleic acid amplification (OSNA) is a validated, reliable, and efficient tool in identifying micro- and macro-metastases intraoperatively. It is the most widely used intraoperative analysis tool within the United Kingdom, and is employed by over 320 units across Europe and Asia. Recent evidence from the AMAROS, IBCSG 23-01, and ACOSOG Z0011 trials has changed surgical practice in managing the axilla of patients with breast cancer. We propose a clinical algorithm demonstrating the role of OSNA as an intraoperative analysis tool in today's management of breast cancer as well as prospects for the future use of OSNA.

p53 expression in breast cancer predicts tumors with low probability of non-sentinel nodes infiltration

AIM

Several predictive tools of non-sentinel lymph nodes neoplastic involvement when a positive sentinel lymph node is found have been described. However, molecular factors have been rarely evaluated to build these tools. The aim of this study was to establish which factors predicted non-sentinel lymph nodes infiltration in our setting, including some molecular factors.

MATERIAL AND METHODS

We carried out a retrospective review of 161 patients with breast cancer and a positive sentinel lymph node who had undergone axillary lymph node dissection, none of whom had received neoadjuvant treatment. Features evaluated as predictive factors for non-sentinel node positivity were: menopausal status, tumor size, histological subtype, histological grade, lymphovascular invasion, extracapsular invasion, Ki67 index, hormonal receptors, CerbB2 and p53 expression, size of sentinel lymph node metastases and number of sentinel lymph nodes affected.

RESULTS

Tumor size (P = 0.001), size of sentinel lymph node metastases (P = 0.001), lobular invasive carcinoma (P = 0.05) and lymphovascular invasion (P = 0.006) were significantly associated with non-sentinel lymph node positivity. Tumor p53 positive expression was strongly associated with non-sentinel lymph node negativity (P = 0.000). In multivariate analysis, all these factors but tumor size maintained their significance. The discrimination power of the model calculated by the area under the receiver-operator curve was 0.811 (95% confidence interval, 0.741-0.880).

CONCLUSION

p53 expression in breast cancer was highly predictive of non-sentinel lymph node negativity in our study. New studies should evaluate if it would be useful to add p53 expression to other existing predictive tools.

Clinical usefulness of one-step nucleic acid amplification assay in the new era of sentinel lymph node biopsy for breast cancer

SUMMARY 

According to the results of the ACOSOG Z0011 trial and IBCSG trial 23-01, rapid intraoperative examinations of the sentinel lymph nodes (SLN) are needed in only a limited number of breast cancer patients, including those with more than three sentinel nodes or who undergo mastectomy. In this new era of SLN biopsies, one-step nucleic acid amplification assay is a promising method for accurately assessing metastasis in the whole SLN, and which help to accurately select patients requiring axillary dissection and reliably evaluate the risk of recurrence. One-step nucleic acid amplification assay may therefore be an alternative to conventional pathology in detecting metastasis in the SLNs, when used for both intraoperative and postoperative examinations.

Positive sentinel lymph node: the evolution of axillary surgery and intraoperative assessment of sentinel lymph nodes

SUMMARY 

Axillary lymph node dissection (ALND) has been the treatment for breast cancer in patients with sentinel lymph node (SLN) biopsy metastasis for prognostic information, local control and maybe for a small survival benefit. In recent years, clinicians have been questioning the need for axillary dissection in patients with positive SLN as the rate of axillary recurrences remains low when no ALND is performed in this group. Several variables incorporated in nomograms have been examined to predict axillary metastasis in patients with SLN metastasis and these nomograms have helped to determine which patients can spare the morbidity of the ALND. The combined multimodality in breast cancer treatments and the improvement in targeted therapies based in tumor biology have contributed to the low recurrence rates in early-stage breast cancer. As the multimodal treatment and the screening programs will improve, more patients with SLN metastasis will spare an ALND without compromising their oncologic outcome.