Analysis of sentinel node biopsy - a single-institution experience supporting the use of serial sectioning and immunohistochemistry for detection of micrometastases by comparing four different histopathological laboratory protocols

Cytomorphological Characterization of Individual Metastatic Tumor Cells from Gastrointestinal Cancer Patient Lymph Nodes with Imaging Flow Cytometry

The presence or absence of tumor cells within patient lymph nodes is an important prognostic indicator in a number of cancer types and an essential element of the staging process. However, patients with the same pathological stage will not necessarily have the same outcome. Therefore, additional factors may aid in identifying patients at a greater risk of developing metastasis. In this proof of principle study, initially, spiked tumor cells in rat lymph nodes were used to mimic a node with a small cancer deposit. Next, human lymph nodes were obtained from cancer patients for morphological characterization. Nodes were dissociated with a manual tissue homogenizer and stained with fluorescent antibodies against CD45 and Pan-Cytokeratin and then imaging flow cytometry (AMNIS ImageStreamX Mark II) was performed. We show here that imaging flow cytometry can be used for the detection and characterization of small numbers of cancer cells in lymph nodes and we also demonstrate the phenotypical and morphological characterization of cancer cells in gastrointestinal cancer patient lymph nodes. When used in addition to conventional histological techniques, this high throughput detection of tumor cells in lymph nodes may offer additional information assisting in the staging process with therapeutic and prognostic applications.

The accuracy of preoperative axillary nodal staging in primary breast cancer by ultrasound is modified by nodal metastatic load and tumor biology

Background The outcome of axillary ultrasound (AUS) with fine-needle aspiration biopsy (FNAB) in the diagnostic work-up of primary breast cancer has an impact on therapy decisions. We hypothesize that the accuracy of AUS is modified by nodal metastatic burden and clinico-pathological characteristics. Material and methods The performance of AUS and AUS-guided FNAB for predicting nodal metastases was assessed in a prospective breast cancer cohort subjected for surgery during 2009–2012. Predictors of accuracy were included in multivariate analysis. Results AUS had a sensitivity of 23% and a specificity of 95%, while AUS-guided FNAB obtained 73% and 100%, respectively. AUS-FNAB exclusively detected macro-metastases (median four metastases) and identified patients with more extensive nodal metastatic burden in comparison with sentinel node biopsy. The accuracy of AUS was affected by metastatic size (OR 1.11), obesity (OR 2.46), histological grade (OR 4.43), and HER2-status (OR 3.66); metastatic size and histological grade were significant in the multivariate analysis. Conclusions The clinical utility of AUS in low-risk breast cancer deserves further evaluation as the accuracy decreased with a low nodal metastatic burden. The diagnostic performance is modified by tumor and clinical characteristics. Patients with nodal disease detected by AUS-FNAB represent a group for whom neoadjuvant therapy should be considered.

Causes of false-negative sentinel node biopsy in patients with breast cancer

Background

Sentinel lymph node (SLN) biopsy has replaced axillary lymph node dissection as the routine staging procedure in clinically node-negative breast cancer. False-negative SLN biopsy results in misclassification and may cause undertreatment of the disease. The aim of this study was to investigate whether serial sectioning of SLNs reveals metastases more frequently in patients with false-negative SLNs than in patients with true-negative SLNs.

Methods

This was a case–control study. Tissue blocks from patients with false-negative SLNs, defined as tumour-positive lymph nodes excised at completion axillary dissection or a subsequent axillary tumour recurrence, were reassessed by serial sectioning and immunohistochemical staining. For each false-negative node, two true-negative SLN biopsies were analysed. Tumour and node characteristics in patients with false-negative SLNs were compared with those in patients with a positive SLN by univariable and multivariable regression analysis.

Results

Undiagnosed SLN metastases were discovered in nine (18 per cent) of 50 patients in the false-negative group and in 12 (11.2 per cent) of 107 patients in the true-negative group (P = 0.245). The metastases were represented by isolated tumour cells in 14 of these 21 patients. The risk of a false-negative SLN was higher in patients with hormone receptor-negative (odds ratio (OR) 2.50, 95 per cent confidence interval 1.17 to 5.33) or multifocal tumours (OR 3.39, 1.71 to 6.71), or if only one SLN was identified (OR 3.57, 1.98 to 6.45).

Conclusion

SLN serial sectioning contributes to a higher rate of detection of SLN metastasis. The rate of upstaging of the tumour is similar in false- and true-negative groups of patients.

Three-dimensional quantitative ultrasound for detecting lymph node metastases

PURPOSE

Detection of metastases in lymph nodes (LNs) is critical for cancer management. Conventional histological methods may miss metastatic foci. To date, no practical means of evaluating the entire LN volume exists. The aim of this study was to develop fast, reliable, operator-independent, high-frequency, quantitative ultrasound (QUS) methods for evaluating LNs over their entire volume to effectively detect LN metastases.

METHODS

We scanned freshly excised LNs at 26 MHz and digitally acquired echo-signal data over the entire three-dimensional (3D) volume. A total of 146 LNs of colorectal, 26 LNs of gastric, and 118 LNs of breast cancer patients were enrolled. We step-sectioned LNs at 50-μm intervals and later compared them with 13 QUS estimates associated with tissue microstructure. Linear-discriminant analysis classified LNs as metastatic or nonmetastatic, and we computed areas (Az) under receiver-operator characteristic curves to assess classification performance. The QUS estimates and cancer probability values derived from discriminant analysis were depicted in 3D images for comparison with 3D histology.

RESULTS

Of 146 LNs of colorectal cancer patients, 23 were metastatic; Az = 0.952 ± 0.021 (95% confidence interval [CI]: 0.911-0.993); sensitivity = 91.3% (specificity = 87.0%); and sensitivity = 100% (specificity = 67.5%). Of 26 LNs of gastric cancer patients, five were metastatic; Az = 0.962 ± 0.039 (95% CI: 0.807-1.000); sensitivity = 100% (specificity = 95.3%). A total of 17 of 118 LNs of breast cancer patients were metastatic; Az = 0.833 ± 0.047 (95% CI: 0.741-0.926); sensitivity = 88.2% (specificity = 62.5%); sensitivity = 100% (specificity = 50.5%). 3D cancer probability images showed good correlation with 3D histology.

CONCLUSIONS

These results suggest that operator- and system-independent QUS methods allow reliable entire-volume LN evaluation for detecting metastases. 3D cancer probability images can help pathologists identify metastatic foci that could be missed using conventional methods.