Background: Breast cancer surgeons struggle with differentiating healthy tissue from cancer at the resection margin during surgery. We report on the feasibility of using diffuse reflectance spectroscopy (DRS) for real-time in vivo tissue characterization. Methods: Evaluating feasibility of the technology requires a setting in which measurements, imaging and pathology have the best possible correlation. For this purpose an optical biopsy needle was used that had integrated optical fibers at the tip of the needle. This approach enabled the best possible correlation between optical measurement volume and tissue histology. With this optical biopsy needle we acquired real-time DRS data of normal tissue and tumor tissue in 27 patients that underwent an ultrasound guided breast biopsy procedure. Five additional patients were measured in continuous mode in which we obtained DRS measurements along the entire biopsy needle trajectory. We developed and compared three different support vector machine based classification models to classify the DRS measurements. Results: With DRS malignant tissue could be discriminated from healthy tissue. The classification model that was based on eight selected wavelengths had the highest accuracy and Matthews Correlation Coefficient (MCC) of 0.93 and 0.87, respectively. In three patients that were measured in continuous mode and had malignant tissue in their biopsy specimen, a clear transition was seen in the classified DRS measurements going from healthy tissue to tumor tissue. This transition was not seen in the other two continuously measured patients that had benign tissue in their biopsy specimen. Conclusions: It was concluded that DRS is feasible for integration in a surgical tool that could assist the breast surgeon in detecting positive resection margins during breast surgery. Trail registration NIH US National Library of Medicine–clinicaltrails.gov, NCT01730365. Registered: 10/04/2012 https://clinicaltrials.gov/ct2/show/study/NCT01730365

Successful breast conserving surgery consists of complete removal of the tumor while sparing healthy surrounding tissue. Despite currently available imaging and margin assessment tools, recognizing tumor tissue at a resection margin during surgery is challenging. Diffuse reflectance spectroscopy (DRS), which uses light for tissue characterization, can potentially guide surgeons to prevent tumor positive margins. However, inter-patient variation and changes in tissue physiology occurring during the resection might hamper this light-based technology. Here we investigate how inter-patient variation and tissue status (in vivo vs ex vivo) affect the performance of the DRS optical parameters. In vivo and ex vivo measurements of 45 breast cancer patients were obtained and quantified with an analytical model to acquire the optical parameters. The optical parameter representing the ratio between fat and water provided the best discrimination between normal and tumor tissue, with an area under the receiver operating characteristic curve of 0.94. There was no substantial influence of other patient factors such as menopausal status on optical measurements. Contrary to expectations, normalization of the optical parameters did not improve the discriminative power. Furthermore, measurements taken in vivo were not significantly different from the measurements taken ex vivo. These findings indicate that DRS is a robust technology for the detection of tumor tissue during breast conserving surgery.

There is an unmet clinical need for an accurate, rapid and reliable tool for margin assessment during breast-conserving surgeries. Ultrasound offers the potential for a rapid, reproducible, and non-invasive method to assess margins. However, it is challenged by certain drawbacks, including a low signal-to-noise ratio, artifacts, and the need for experience with the acquirement and interpretation of images. A possible solution might be computer-aided ultrasound evaluation. In this study, we have developed new ensemble approaches for automated breast tumor segmentation. The ensemble approaches to predict positive and close margins (distance from tumor to margin ≤ 2.0 mm) in the ultrasound images were based on 8 pre-trained deep neural networks. The best optimum ensemble approach for segmentation attained a median Dice score of 0.88 on our data set. Furthermore, utilizing the segmentation results we were able to achieve a sensitivity of 96% and a specificity of 76% for predicting a close margin when compared to histology results. The promising results demonstrate the capability of AI-based ultrasound imaging as an intraoperative surgical margin assessment tool during breast-conserving surgery.

Background: Although the incidence of positive resection margins in breast-conserving surgery has decreased, both incomplete resection and unnecessary large resections still occur. This is especially the case in the surgical treatment of ductal carcinoma in situ (DCIS). Diffuse reflectance spectroscopy (DRS), an optical technology based on light tissue interactions, can potentially characterize tissue during surgery thereby guiding the surgeon intraoperatively. DRS has shown to be able to discriminate pure healthy breast tissue from pure invasive carcinoma (IC) but limited research has been done on (1) the actual optical characteristics of DCIS and (2) the ability of DRS to characterize measurements that are a mixture of tissue types. Methods: In this study, DRS spectra were acquired from 107 breast specimens from 107 patients with proven IC and/or DCIS (1488 measurement locations). With a generalized estimating equation model, the differences between the DRS spectra of locations with DCIS and IC and only healthy tissue were compared to see if there were significant differences between these spectra. Subsequently, different classification models were developed to be able to predict if the DRS spectrum of a measurement location represented a measurement location with “healthy” or “malignant” tissue. In the development and testing of the models, different definitions for “healthy” and “malignant” were used. This allowed varying the level of homogeneity in the train and test data. Results: It was found that the optical characteristics of IC and DCIS were similar. Regarding the classification of tissue with a mixture of tissue types, it was found that using mixed measurement locations in the development of the classification models did not tremendously improve the accuracy of the classification of other measurement locations with a mixture of tissue types. The evaluated classification models were able to classify measurement locations with > 5% malignant cells with a Matthews correlation coefficient of 0.41 or 0.40. Some models showed better sensitivity whereas others had better specificity. Conclusion: The results suggest that DRS has the potential to detect malignant tissue, including DCIS, in healthy breast tissue and could thus be helpful for surgical guidance.

Breast cancer is the most frequently diagnosed type of cancer among women globally, with an estimated incidence of 2.26 million cases in the year 2020.¹ It is the leading cause of cancer death among females, with an estimated number of 685,000 deaths in the year 2020.¹ The standard of care for early-stage breast cancer is breast-conserving therapy (BCT), which involves breast-conserving surgery (BCS) followed by adjuvant radiotherapy.^2,3 In BCS, the surgeon attempts to excise the entire tumor along with a small margin of healthy tissue while providing a satisfactory cosmetic outcome. Multiple randomized clinical trials have demonstrated equivalent survival outcomes for mastectomy and BCT if clear resection margins (edges of a breast cancer specimen) are achieved during BCS.^{4 – 7} To assess whether the margins of a specimen are clear, and therefore no cancerous cells remain in the adjacent tissue, a histopathological examination is performed after surgery. If positive (tumor-involved) margins are found, the patient is usually recommended to undergo a re-excision and/or additional doses of radiotherapy,⁸ as tumor positive margins are associated with a twofold increase in ipsilateral breast tumor recurrence and an overall lower long-term survival.^{9 – 13} The required additional therapy in patients with positive margins may result in anxiety, increased clinical complications,^14,15 unsatisfactory cosmetic outcome, and^{16 – 19} increased health care costs.^{15,20 – 22} Globally, there is a lack of consensus on what constitutes a “positive margin,” and the reported rates of positive margins after BCS in the literature vary from 9% to as high as 36% for invasive breast cancer and from 4% to as high as 23% for ductal carcinoma in situ (DCIS).²³ According to Dutch guidelines, a positive margin is defined as invasive carcinoma (IC) cells reaching the inked margin over a trajectory $>4\mathrm{mm}$ or DCIS cells reaching the inked margin over any trajectory.²⁴ Focally positive margins are defined as IC cells reaching the inked margin over a trajectory $\le 4\mathrm{mm}$.²⁴ Otherwise, the margins are considered negative. For the United States, according to the guidelines of the Society of Surgical Oncology (SSO) and the American Society for Radiation Oncology (ASTRO), a margin is considered tumor-positive when ink touches the IC or when there is DCIS present within 2 mm from the resection margin.⁹