Computer-aided classification of breast cancer using histopathological images can play a significant role in clinical practice by detecting the distinct type of malignant and/or benign tumor. However, currently proposed deep learning models developed using the BreakHis dataset only conduct a binary classification between benign and malignant tumors, and are also scale-dependent. This study utilizes a ResNet-50 implementation to transform images from the four magnification factors such that all images can be used for training the deep neural network. This process yields a larger training set that is also scale-independent. For this paper, we utilized a dual step approach with the first pass being binary classification and the second pass being a multi-class classifier of malignant tumors that offers higher clinical utility.
Significance: Re-excision rates for women with invasive breast cancer undergoing breast conserving surgery (or lumpectomy) have decreased in the past decade but remain substantial. This is mainly due to the inability to assess the entire surface of an excised lumpectomy specimen efficiently and accurately during surgery.
Aim: The goal of this study was to develop a deep-ultraviolet scanning fluorescence microscope (DUV-FSM) that can be used to accurately and rapidly detect cancer cells on the surface of excised breast tissue.
Approach: A DUV-FSM was used to image the surfaces of 47 (31 malignant and 16 normal/benign) fresh breast tissue samples stained in propidium iodide and eosin Y solutions. A set of fluorescence images were obtained from each sample using low magnification (4 × ) and fully automated scanning. The images were stitched to form a color image. Three nonmedical evaluators were trained to interpret and assess the fluorescence images. Nuclear–cytoplasm ratio (N/C) was calculated and used for tissue classification.
Results: DUV-FSM images a breast sample with subcellular resolution at a speed of 1.0 min / cm2. Fluorescence images show excellent visual contrast in color, tissue texture, cell density, and shape between invasive carcinomas and their normal counterparts. Visual interpretation of fluorescence images by nonmedical evaluators was able to distinguish invasive carcinoma from normal samples with high sensitivity (97.62%) and specificity (92.86%). Using N/C alone was able to differentiate patch-level invasive carcinoma from normal breast tissues with reasonable sensitivity (81.5%) and specificity (78.5%).
Conclusions: DUV-FSM achieved a good balance between imaging speed and spatial resolution with excellent contrast, which allows either visual or quantitative detection of invasive cancer cells on the surfaces of a breast surgical specimen.
Breast cancer is the most commonly diagnosed cancer among women. Positive margin status after breast-conserving surgery (BCS) is a predictor of higher rates of local recurrence. Intraoperative margin detection helps to complete tumor excision at the first operation. A margin tool that is capable of imaging all six margins of large lumpectomy specimens with both high resolution and fast speed (within 20 min) is yet to be developed. Deep UV light allows simultaneous excitation of multiple fluorophores and generating surface fluorescence images. We have developed a deep UV fluorescence scanning microscope (DUV-FSM) for slide-free, high-resolution and rapid examination of tumor specimens during BCS. The DUV-FSM uses a deep UV LED for oblique back illumination of freshly excised breast tissues stained with propidium iodide and Eosin Y and motorized XY stages for mosaic scanning. Fluorescence images are captured by a color CCD camera. Both invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) images showed excellent contrast from that of the normal cells in color, tissue texture, and cell density and shapes. This contrast have been consistently observed in all samples (n = 20) we have imaged so far. Statistical analysis showed a significant difference (p<0.0001) in nucleus-to-cytoplasm (NC) ratio between normal and invasive tissues. Thus, it may be utilized either visually by a trained individual or quantitatively by an algorithm to detect positive margins of lumpectomy specimens intraoperatively.