## 4 Discussion and Conclusions  
When investigating the potential of hyperspectral imaging for intraoperative breast cancer detection, it is challenging to image the actual resection surface to allow for intraoperative feedback while obtaining a high correlation with histopathology. As such, previous research of our group showed the potential of hyperspectral imaging to discriminate tumor from healthy tissue using fresh breast tissue slices [14; 15]. By imaging tissue \begin{table}
\begin{tabular}{c c c c} \hline  & & **Before Wavelength Reduction**[15] & **After Wavelength Reduction** \\  & **Number of** & **VIS** & **VIS** + NIR** \\  & **Spectra** & **NIR (450–1650 nm)** & **(450–602 \& 1187–1224 \& 1379–1551 nm)** \\ \hline
**Tumor** & 3312 & 99\% & 79\% \\ IC & 3200 & 99\% & 80\% \\ DCIS & 112 & 95\% & 77\% \\
**Healthy** & 21,227 & 99\% & 90\% \\ Connective & 468 & 94\% & 68\% \\ Adipose & 20,759 & 100\% & 96\% \\ \hline \end{tabular}
\end{table} 
Table 4: Classification results before and after wavelength reduction: Recall for each tissue type. Figure 11: The number of locations in the lumpectomy dataset that are specified as a specific tissue type for all the locations that do not contain malignant tissue. slices, an extensive reliable hyperspectral database was created with a high correlation with histopathology and enough measurements on both healthy and tumorous tissue to develop reliable classification algorithms. In the current study, we imaged the resection surface of lumpectomy specimens and presented a method that allowed us to (1) obtain a high correlation with histopathology and (2) increased the likelihood to measure tumor-positive resection margins. Next, we compared the hyperspectral data obtained on the tissue slices and lumpectomy specimens. Due to the difference in tissue thickness and the penetration depth of light, spectra in both datasets varied. After reducing the number of wavelengths so that only wavelengths with a penetration depth less than 2.5 mm (minimum thickness of tissue slices) remained, the spectral similarity increased. However, with the remaining wavelength, the classification performance on both tissue slices and lumpectomy specimens were insufficient for clinical application. Therefore, even though the tissue slices dataset could be used to investigate the potential of hyperspectral imaging as a margin assessment technique for breast surgery, algorithms developed with the slices data cannot be used directly to classify lumpectomy specimens. The penetration depth of light varies both with wavelength and tissue composition and was estimated with a model based on diffusion theory. This model assumes an optically homogeneous and infinite medium and extracts optical parameters from a measured diffuse reflectance spectrum. However, in the lumpectomy data that we measured, the breast tissue was inhomogeneous. Therefore, different wavelengths in one diffuse reflectance spectrum reflect different measurement volumes, that can have different optical properties. As the model assumes that the diffuse reflectance spectrum reflects a single set of optical properties, extracting optical properties from a spectrum reflecting multiple optical properties might be prone to errors. This explains why the fitted and measured spectra in Figure 8 did not completely overlap and why, for some parameters, the confidence intervals are relatively high. When the penetration depth is known, a fitting algorithm can be applied on separate wavelength ranges with a uniform measurement volume, reflecting a single set of optical properties [26]. However, in the present work, the penetration depth was unknown. Therefore, we used the fitted optical parameters to obtain an estimation of the penetration depth. By excluding wavelengths with penetration depth higher than 2.5 mm, spectra of the slices and lumpectomy datasets are more similar (Figure 7 vs. Figure 10). The highest difference between the two datasets and the lowest SCM was observed in the VIS wavelength range.