Breast-conserving surgery (BCS) – lumpectomy – involves the removal of a cancerous lump and some sur­rounding tissue. BCS is suitable for women with early-stage breast cancer or small lumps, as it preserves more of the breast compared to mastectomy. After BCS, it is crucial to ensure that all cancer cells are removed to determine if further surgery is needed. This is done through a tumor margin assess­ment, where the edges of the removed area (tumor margins) are examined for the presence of cancer cells. Typically, tumor margin assess­ments involve staining tissue samples with dyes and examining them under a microscope to dis­tinguish healthy cells from cancer cells. However, emerging optical imaging methods offer faster alter­natives for this assessment.

Now, researchers from the United States intro­duced hyper­spectral dark-field microscopy (HSDFM) as a method to rapidly and accurately differentiate between cancerous and healthy cells, as well as to identify different tumor subtypes within breast tissues following lumpectomy procedures. “We success­fully identified specific regions containing carcinoma subtypes, including invasive ductal carcinoma and invasive mucinous carcinoma, in freshly excited tissues by applying machine learning algorithms to the imaging data,” Jeeseong Hwang from National Institute of Standards and Technology explains.

In HSDFM, tissue samples are illuminated by multiple wavelengths of light, and the differences in the intensity of scattered light as a function of wavelength from cellular and molecular substances are analyzed to produce unique spectral signatures of the tissues. The method produces a two-dimensional image where each pixel contains spectral information across multiple wave­lengths, making it capable of identi­fying the composition of tissues. This imaging method specifically addresses issues persistent in most hyper­spectral tumor margin imaging techniques, which rely on reflectance to gather spectral data of tissue samples. Reflec­tance-based methods encounter challenges due to the uneven absorption of light by biological substances such as oxyhemo­globin in blood, leading to inconsistent spectral signatures across multiple samples.

The researchers analyzed HSDFM images of breast lumpectomy samples taken from multiple patients. To classify the pixels according to the tissue type, they used two different approaches: supervised and unsuper­vised machine learning. For the supervised approach, the researchers utilized spectral angle mapping, which compares the spectral signature of each pixel in the hyperspectral image to known spectral signatures of tumor subtypes and tissue types (such as fat, inter­connected tissue, and blood) identified through histo­pathological analysis. For the unsuper­vised approach, they utilized the K-means clustering algorithm. This method groups pixels with similar spectral signatures into clusters, facili­tating the identification of tumor regions without prior knowledge of reference spectra or tissue types.

The spectral signatures obtained by both methods were quite similar and effectively pinpointed regions with invasive ductal carcinoma, the most common type of breast cancer, representing 75 percent of all breast cancer cases, as well as invasive mucinous carcinoma, a rare type charac­terized by cancer cells developing in mucus.

Breast cancer is a leading cause of cancer-related deaths in women. This study shows that the unsupervised method is validated by the supervised method, therefore HSDFM imaging data can be used to develop unsuper­vised algorithms for the rapid and accurate identification of cancerous tissues. Thus, it is expected to enhance post­surgical care for BCS and faci­litate timely corrective actions. (Source: SPIE)

Reference: J. Hwang et al.: Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery, J. Biomed. Opt. 29, 093503 (2024); DOI: 10.1117/1.JBO.29.9.093503

Link: Molecular and Bio-Photonics Group, National Institute of Standards and Technology NIST, Boulder, USA