Application of automated image analysis reduces the workload of manual screening of sentinel Lymph node biopsies in breast cancer

Abstract

Introduction

Breast cancer is one of the most common cancer diseases in women with more than 1.67 million cases diagnosed worldwide each year. In breast cancer, the sentinel lymph node (SLN) pinpoints the first lymph node(s) into which the tumor spreads and it is usually located in the ipsilateral axilla. In patients with no clinical signs of metastatic disease in the axilla, a SLN biopsy (SLNB) is performed. Assessment of metastases in the SLNB is done in a conventional microscope by manually observing a metastasis and measuring its size and/or counting the number of tumor cells. This is done essentially to categorize the type of metastases as macrometastases, micrometastases or isolated tumor cells, which is used to determine which treatment the breast cancer patient will benefit mostly from. The aim of this study was to evaluate whether digital image analysis can be applied as a screening tool for SNLB assessment without compromising the diagnostic accuracy.

Materials and methods

Consecutive SLNB from 135 patients with localized breast cancer receiving surgery in the period of February to August 2015 were collected and included in this study. Of the 135 patients, 35 were received at Dept. of Pathology, Rigshospitalet, Copenhagen University Hospital, 50 at Dept. of Pathology, Zealand University Hospital, and 50 at Dept. of Pathology, Odense University Hospital. Formalin-fixed and paraffin-embedded tissue sections were analyzed by immunohistochemistry (IHC) using the BenchMark ULTRA Ventana platform. Rigshospitalet used a mixture of cytokeratin CK7 and CK19, Zealand University Hospital used pancytokeratin AE1/AE3 and Odense used pancytokeratin CAM5.2 for detection of epithelial tumor cells. Slides were stained locally. SLNB sections were assessed in a conventional microscope according to national guidelines for SLNB in breast cancer patients. The IHC stained sections were scanned by a Hamamatsu NanoZoomer-XR digital whole slide scanner and the images were analyzed by Visiopharm's software using a custommade algorithm for SLNB in breast cancer. The algorithm was optimized to the cytokeratin antibodies and the local laboratory conditions, based on staining intensity and background staining.

Results

Conventional microscopy was used as golden standard for assessment of positive tumor cells and compared with digital image analysis (DIA). The algorithm demonstrated a sensitivity of 100% (i.e. no false negative slides were observed), including 67.2%, 19.2% and 56.1% of the slides from the three pathology departments being negative, respectively. This means that on average, the workload could have been decreased by 58.2% by using the digital SLNB algorithm as a screening tool.

Discussion and conclusion

The SLNB algorithm demonstrated a sensitivity of 100% regardless of the antibody used for IHC and the staining protocol. No false negative slides were observed, which proves that the SLNB algorithm is an ideal screening tool for selecting those slides not necessary for a pathologist to see. Implementation of automated digital image analysis of SLNB in breast cancer would decrease the workload in this context for examining pathologists by almost 60%.

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