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The University of Adelaide
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Detection of Metastatic Breast Cancer Using Sentinel Node Biopsy

Pradhan, M. and Farshid, G.

Introduction

Breast cancer is a major cause of premature death in women. The status of the axillary lymph nodes is one of the most powerful predictors of outcome for these patients and the number of involved nodes influences further treatment choices including chemotherapy and radiation therapy to the axilla[ 1, 2 ].

Staging currently involves an axillary clearance (AC) and histologic documentation of metastatic disease. This operation has a significant complication rate; including chronic lymphedema (5%), damage to the intercostobrachial nerve causing paraesthesia and pain (40%), limitation in the range of movement of the shoulder and angiosarcoma in the affected arm [ 3 ] .

Several large scale studies[ 4-12 ] , suggest that Sentinel nodes (SN) which first receive the lymphatic drainage from an area, are the ones most likely to contain cancer cells dislodged from a tumour in that region. If the SN is uninvolved, the other nodes are unlikely to bear metastases[ 4, 9 ] . The SN is isolated through the injection of an appropriate blue dye or radioactive isotope into the region bearing the carcinoma. The node which first receives the injected substance is identified by direct visualisation or lymphoscintigraphy and designated the SN. This is submitted for histologic evaluation. If the SN is histologically negative, the patient may not need further axillary surgery and may not be offered chemotherapy.

Numerous studies show SNs can predict axillary basin status in over 90% of the patients[ 4-12 ]. However, these studies have highlighted two potential problems. One problem is the incidence of a false negative rate (FNR)(0- 29.4%[ 4, 14, 15 ]) from skip metastases in non sentinel nodes. This may be due to small metastases missed on pathological examination. While the inadequacy of routine pathologic examination of lymph nodes has been documented repeatedly [ 29, 32, 33 ] there has not been a principled approach to formulating new solutions to this problem.

Figure 1. The effect of sampling strategy on the likelihood of detecting nodal micrometastases.
In (A), a SN is bisected and one slice is processed. Only one section is examined histologically; the metastasis has a high chance of being missed. In (B), the node is first sliced fine, all the slices are submitted for processing and multiple sections are examined histologically. The probability of identifying the micrometastasis improves, but the cost of processing and examination increases. (Adapted from Friedman, S. et al. Acta Oncol 27, 483-487 1988).

The second problem is that little information exists to predict the likelihood of the SN being the only node with metastases[ 31 ] .

Projects

To assess the efficacy of a number of sectioning strategies we propose to use a computer generated model of a SN bearing micrometastases of variable diameter[ 34] . We will also develop a detailed cost model that will allow us to assess the cost/benefit of each strategy. Genetic algorithms (GAs) will be used to search for optimal policies given cost constraints [ 40 41] .

To investigate the presence of a correlation between various attributes of SN metastases and the likelihood of non-SN involvement. We propose to perform regression analyses.

References

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