Sentinel node biopsy for breast cancer: Using local results for estimation of risk to the patient

Background: Sentinel node biopsy (SNB) is being increasingly used but its place outside randomized trials has not yet been established. Methods: The first 114 sentinel node (SN) biopsies performed for breast cancer at the Princess Alexandra Hospital from March 1999 to June 2001 are presented. In 111 cases axillary dissection was also performed, allowing the accuracy of the technique to be assessed. A standard combination of preoperative lymphoscintigraphy, intraoperative gamma probe and injection of blue dye was used in most cases. Results are discussed in relation to the risk and potential consequences of understaging. Results: Where both probe and dye were used, the SN was identified in 90% of patients. A significant number of patients were treated in two stages and the technique was no less effective in patients who had SNB performed at a second operation after the primary tumour had already been removed. The interval from radioisotope injection to operation was very wide (between 2 and 22 h) and did not affect the outcome. Nodal metastases were present in 42 patients in whom an SN was found, and in 40 of these the SN was positive, giving a false negative rate of 4.8% (2/42), with the overall percentage of patients understaged being 2%. For this particular group as a whole, the increased risk of death due to systemic therapy being withheld as a consequence of understaging (if SNB alone had been employed) is estimated at less than 1/500. The risk for individuals will vary depending on other features of the particular primary tumour. Conclusion: For patients who elect to have the axilla staged using SNB alone, the risk and consequences of understaging need to be discussed. These risks can be estimated by allowing for the specific surgeon's false negative rate for the technique, and considering the likelihood of nodal metastases for a given tumour. There appears to be no disadvantage with performing SNB at a second operation after the primary tumour has already been removed. Clearly, for a large number of patients, SNB alone will be safe, but ideally participation in randomized trials should continue to be encouraged.

Clinical features and management of tumors affecting the lacrimal drainage apparatus

Purpose: To report the clinical features of a series of patients with lacrimal drainage apparatus tumors and present guidelines for management based on histopathology. Methods: A noncomparative retrospective chart review of the clinical, imaging, and pathologic findings of 37 patients presenting to four regional orbital Surgery departments with tumors affecting the lacrimal drainage apparatus between 1990 and 2004. Results: There were 37 patients, of whom 62% were male. The mean age at referral was 54 years. Epiphora, a palpable mass, and dacryocystitis were the most common presentations. Two thirds of the tumors were epithelial. with carcinomas being the most frequent (38%). followed by papillomas (27%). Lymphomas were the most common nonepithelial malignancy (30%). Epithelial tumors were more common in men (87%), whereas lymphomas were more common in women (57%). Treatment modalities included surgery, in addition to radiotherapy and/or chemotherapy and immunotherapy. Mean follow-up was 38 months. Thirty-three patients (89%) remain alive without evidence of disease and 4 patients died of recurrence and/or metastases. Conclusions: Lacrimal drainage apparatus tumors require careful initial management to ensure adequate local and systemic disease control. Atypical mucosa encountered during dacryocystorhinostomy should be biopsied and small papillomas or pedunculated tumors excised and analyzed with frozen sections. If a diffuse or infiltrative mass is encountered, it should be biopsied and managed on the basis of histopathology and extent of disease. Lymphomas should be treated according to protocols. whereas noninvasive carcinoma and extensive papillomas require complete excision of the system. Invasive disease requires en bloc excision. Long-term follow-up is essential for early detection of recurrence.

Indexing and integrating multiple features for WWW images

In this paper, we present a novel indexing technique called Multi-scale Similarity Indexing (MSI) to index image's multi-features into a single one-dimensional structure. Both for text and visual feature spaces, the similarity between a point and a local partition's center in individual space is used as the indexing key, where similarity values in different features are distinguished by different scale. Then a single indexing tree can be built on these keys. Based on the property that relevant images have similar similarity values from the center of the same local partition in any feature space, certain number of irrelevant images can be fast pruned based on the triangle inequity on indexing keys. To remove the dimensionality curse existing in high dimensional structure, we propose a new technique called Local Bit Stream (LBS). LBS transforms image's text and visual feature representations into simple, uniform and effective bit stream (BS) representations based on local partition's center. Such BS representations are small in size and fast for comparison since only bit operation are involved. By comparing common bits existing in two BSs, most of irrelevant images can be immediately filtered. To effectively integrate multi-features, we also investigated the following evidence combination techniques-Certainty Factor, Dempster Shafer Theory, Compound Probability, and Linear Combination. Our extensive experiment showed that single one-dimensional index on multi-features improves multi-indices on multi-features greatly. Our LBS method outperforms sequential scan on high dimensional space by an order of magnitude. And Certainty Factor and Dempster Shafer Theory perform best in combining multiple similarities from corresponding multiple features.

Indexing text and visual features for WWW images

In this paper, we present a novel indexing technique called Multi-scale Similarity Indexing (MSI) to index imagersquos multi-features into a single one-dimensional structure. Both for text and visual feature spaces, the similarity between a point and a local partitionrsquos center in individual space is used as the indexing key, where similarity values in different features are distinguished by different scale. Then a single indexing tree can be built on these keys. Based on the property that relevant images haves similar similarity values from the center of the same local partition in any feature space, certain number of irrelevant images can be fast pruned based on the triangle inequity on indexing keys. To remove the ldquodimensionality curserdquo existing in high dimensional structure, we propose a new technique called Local Bit Stream (LBS). LBS transforms imagersquos text and visual feature representations into simple, uniform and effective bit stream (BS) representations based on local partitionrsquos center. Such BS representations are small in size and fast for comparison since only bit operation are involved. By comparing common bits existing in two BSs, most of irrelevant images can be immediately filtered. Our extensive experiment showed that single one-dimensional index on multi-features improves multi-indices on multi-features greatly. Our LBS method outperforms sequential scan on high dimensional space by an order of magnitude.