Hepatic arterial infusion pump chemotherapy combined with systemic chemotherapy for borderline resectable and unresectable colorectal liver metastases: phase II feasibility study.

BACKGROUND: Hepatic arterial infusion pump chemotherapy combined with systemic chemotherapy (HAIP-SYS) for liver-only colorectal liver metastases (CRLMs) has shown promising results but has not been adopted worldwide. This study evaluated the feasibility of HAIP-SYS in the Netherlands. METHODS: This was a single-arm phase II study of patients with CRLMs who received HAIP-SYS consisting of floxuridine with concomitant systemic FOLFOX or FOLFIRI. Main inclusion and exclusion criteria were borderline resectable or unresectable liver-only metastases, suitable arterial anatomy and no previous local treatment. Patients underwent laparotomy for pump implantation and primary tumour resection if in situ. Primary end point was feasibility, defined as ≥70% of patients completing two cycles of HAIP-SYS. Sample size calculations led to 31 patients. Secondary outcomes included safety and tumour response. RESULTS: Thirty-one patients with median 13 CRLMs (i.q.r. 6-23) were included. Twenty-eight patients (90%) received two HAIP-SYS cycles. Three patients did not get two cycles due to extrahepatic disease at pump placement, definitive pathology of a recto-sigmoidal squamous cell carcinoma, and progressive disease. Five patients experienced grade 3 surgical or pump device-related complications (16%) and 11 patients experienced grade ≥3 chemotherapy toxicity (38%). At first radiological evaluation, disease control rate was 83% (24/29 patients) and hepatic disease control rate 93% (27/29 patients). At 6 months, 19 patients (66%) had experienced grade ≥3 chemotherapy toxicity and the disease control rate was 79%. CONCLUSION: HAIP-SYS for borderline resectable and unresectable CRLMs was feasible and safe in the Netherlands. This has led to a successive multicentre phase III randomized trial investigating oncological benefit (EUDRA-CT 2023-506194-35-00). Current trial registration number: clinicaltrials.gov (NCT04552093).

Margin assessment during breast conserving surgery using diffuse reflectance spectroscopy.

Significance: During breast-conserving surgeries, it is essential to evaluate the resection margins (edges of breast specimen) to determine whether the tumor has been removed completely. In current surgical practice, there are no methods available to aid in accurate real-time margin evaluation. Aim: In this study, we investigated the diagnostic accuracy of diffuse reflectance spectroscopy (DRS) combined with tissue classification models in discriminating tumorous tissue from healthy tissue up to 2 mm in depth on the actual resection margin of in vivo breast tissue. Approach: We collected an extensive dataset of DRS measurements on ex vivo breast tissue and in vivo breast tissue, which we used to develop different classification models for tissue classification. Next, these models were used in vivo to evaluate the performance of DRS for tissue discrimination during breast conserving surgery. We investigated which training strategy yielded optimum results for the classification model with the highest performance. Results: We achieved a Matthews correlation coefficient of 0.76, a sensitivity of 96.7% (95% CI 95.6% to 98.2%), a specificity of 90.6% (95% CI 86.3% to 97.9%) and an area under the curve of 0.98 by training the optimum model on a combination of ex vivo and in vivo DRS data. Conclusions: DRS allows real-time margin assessment with a high sensitivity and specificity during breast-conserving surgeries.

Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images.

(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial-spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor's reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach.

Purpose: Training and evaluation of the performance of a supervised deep-learning model for the segmentation of hepatic tumors from intraoperative US (iUS) images, with the purpose of improving the accuracy of tumor margin assessment during liver surgeries and the detection of lesions during colorectal surgeries. Approach: In this retrospective study, a U-Net network was trained with the nnU-Net framework in different configurations for the segmentation of CRLM from iUS. The model was trained on B-mode intraoperative hepatic US images, hand-labeled by an expert clinician. The model was tested on an independent set of similar images. The average age of the study population was 61.9 ± 9.9 years. Ground truth for the test set was provided by a radiologist, and three extra delineation sets were used for the computation of inter-observer variability. Results: The presented model achieved a DSC of 0.84 (p=0.0037), which is comparable to the expert human raters scores. The model segmented hypoechoic and mixed lesions more accurately (DSC of 0.89 and 0.88, respectively) than hyper- and isoechoic ones (DSC of 0.70 and 0.60, respectively) only missing isoechoic or >20 mm in diameter (8% of the tumors) lesions. The inclusion of extra margins of probable tumor tissue around the lesions in the training ground truth resulted in lower DSCs of 0.75 (p=0.0022). Conclusion: The model can accurately segment hepatic tumors from iUS images and has the potential to speed up the resection margin definition during surgeries and the detection of lesion in screenings by automating iUS assessment.

Toward the use of diffuse reflection spectroscopy for intra-operative tissue discrimination during sarcoma surgery.

Significance: Accurately distinguishing tumor tissue from normal tissue is crucial to achieve complete resections during soft tissue sarcoma (STS) surgery while preserving critical structures. Incomplete tumor resections are associated with an increased risk of local recurrence and worse patient prognosis. Aim: We evaluate the performance of diffuse reflectance spectroscopy (DRS) to distinguish tumor tissue from healthy tissue in STSs. Approach: DRS spectra were acquired from different tissue types on multiple locations in 20 freshly excised sarcoma specimens. A k-nearest neighbors classification model was trained to predict the tissue types of the measured locations, using binary and multiclass approaches. Results: Tumor tissue could be distinguished from healthy tissue with a classification accuracy of 0.90, sensitivity of 0.88, and specificity of 0.93 when well-differentiated liposarcomas were included. Excluding this subtype, the classification performance increased to an accuracy of 0.93, sensitivity of 0.94, and specificity of 0.93. The developed model showed a consistent performance over different histological subtypes and tumor locations. Conclusions: Automatic tissue discrimination using DRS enables real-time intra-operative guidance, contributing to more accurate STS resections.

Point Projection Mapping System for Tracking, Registering, Labeling, and Validating Optical Tissue Measurements.

The validation of newly developed optical tissue-sensing techniques for tumor detection during cancer surgery requires an accurate correlation with the histological results. Additionally, such an accurate correlation facilitates precise data labeling for developing high-performance machine learning tissue-classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with the histopathology results is proposed and validated on a case study. The proposed framework provides a more-robust and accurate method for the tracking and validation of optical tissue-sensing techniques, which saves time and resources compared to the available conventional techniques.

Ultrasound guidance in navigated liver surgery: toward deep-learning enhanced compensation of deformation and organ motion.

PURPOSE: Accuracy of image-guided liver surgery is challenged by deformation of the liver during the procedure. This study aims at improving navigation accuracy by using intraoperative deep learning segmentation and nonrigid registration of hepatic vasculature from ultrasound (US) images to compensate for changes in liver position and deformation. METHODS: This was a single-center prospective study of patients with liver metastases from any origin. Electromagnetic tracking was used to follow US and liver movement. A preoperative 3D model of the liver, including liver lesions, and hepatic and portal vasculature, was registered with the intraoperative organ position. Hepatic vasculature was segmented using a reduced 3D U-Net and registered to preoperative imaging after initial alignment followed by nonrigid registration. Accuracy was assessed as Euclidean distance between the tumor center imaged in the intraoperative US and the registered preoperative image. RESULTS: Median target registration error (TRE) after initial alignment was 11.6 mm in 25 procedures and improved to 6.9 mm after nonrigid registration (p = 0.0076). The number of TREs above 10 mm halved from 16 to 8 after nonrigid registration. In 9 cases, registration was performed twice after failure of the first attempt. The first registration cycle was completed in median 11 min (8:00-18:45 min) and a second in 5 min (2:30-10:20 min). CONCLUSION: This novel registration workflow using automatic vascular detection and nonrigid registration allows to accurately localize liver lesions. Further automation in the workflow is required in initial alignment and classification accuracy.

Tumor Segmentation in Colorectal Ultrasound Images Using an Ensemble Transfer Learning Model: Towards Intra-Operative Margin Assessment.

Tumor boundary identification during colorectal cancer surgery can be challenging, and incomplete tumor removal occurs in approximately 10% of the patients operated for advanced rectal cancer. In this paper, a deep learning framework for automatic tumor segmentation in colorectal ultrasound images was developed, to provide real-time guidance on resection margins using intra-operative ultrasound. A colorectal ultrasound dataset was acquired consisting of 179 images from 74 patients, with ground truth tumor annotations based on histopathology results. To address data scarcity, transfer learning techniques were used to optimize models pre-trained on breast ultrasound data for colorectal ultrasound data. A new custom gradient-based loss function (GWDice) was developed, which emphasizes the clinically relevant top margin of the tumor while training the networks. Lastly, ensemble learning methods were applied to combine tumor segmentation predictions of multiple individual models and further improve the overall tumor segmentation performance. Transfer learning outperformed training from scratch, with an average Dice coefficient over all individual networks of 0.78 compared to 0.68. The new GWDice loss function clearly decreased the average tumor margin prediction error from 1.08 mm to 0.92 mm, without compromising the segmentation of the overall tumor contour. Ensemble learning further improved the Dice coefficient to 0.84 and the tumor margin prediction error to 0.67 mm. Using transfer and ensemble learning strategies, good tumor segmentation performance was achieved despite the relatively small dataset. The developed US segmentation model may contribute to more accurate colorectal tumor resections by providing real-time intra-operative feedback on tumor margins.

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases.

BACKGROUND: We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). METHODS: In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated. RESULTS: In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95-0.96) and 0.80 (IQR 0.67-0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29-0.76) for tumor segmentation. CONCLUSIONS: Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. RELEVANCE STATEMENT: Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist's workload and increasing accuracy and consistency. KEY POINTS: • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations.

Diffuse reflectance spectroscopy for accurate margin assessment in breast-conserving surgeries: importance of an optimal number of fibers.

During breast-conserving surgeries, it remains challenging to accomplish adequate surgical margins. We investigated different numbers of fibers for fiber-optic diffuse reflectance spectroscopy to differentiate tumorous breast tissue from healthy tissue ex vivo up to 2 mm from the margin. Using a machine-learning classification model, the optimal performance was obtained using at least three emitting fibers (Matthew's correlation coefficient (MCC) of 0.73), which was significantly higher compared to the performance of using a single-emitting fiber (MCC of 0.48). The percentage of correctly classified tumor locations varied from 75% to 100% depending on the tumor percentage, the tumor-margin distance and the number of fibers.

EORTC 1409 GITCG/ESSO 01 - A prospective colorectal liver metastasis database for borderline or initially unresectable diseases (CLIMB): Lessons learnt from real life. From paradigm to unmet need.

AIM: Multidisciplinary management of metastatic colorectal liver metastases (CRLM) is still challenging. To assess postoperative complications in initially unresectable or borderline resectable CRLM, the prospective EORTC-1409 ESSO 01-CLIMB trial capturing 'real-life data' of European centres specialized in liver surgery was initiated. MATERIAL AND METHODS: A total of 219 patients were registered between May 2015 and January 2019 from 15 centres in nine countries. Eligible patients had borderline or initially unresectable CRLM assessed by pre-operative multidisciplinary team discussion (MDT). Primary endpoints were postoperative complications, 30-day and 90-days mortality post-surgery, and quality indicators. We report the final results of the 151 eligible patients that underwent at least one liver surgery. RESULTS: Perioperative chemotherapy with or without targeted treatment were administered in 100 patients (69.4%). One stage resection (OSR) was performed in 119 patients (78.8%). Two stage resections (TSR, incl. Associating Liver Partition and Portal Vein Ligation for Staged hepatectomy (ALPPS)) were completed in 24 out of 32 patients (75%). Postoperative complications were reported in 55.5% (95% CI: 46.1-64.6%), 64.0% (95% CI: 42.5-82%), and 100% (95% CI: 59-100%) of the patients in OSR, TSR and ALPPS, respectively. Post-hepatectomy liver failure occurred in 6.7%, 20.0%, and 28.6% in OSR, TSR, and ALPPS, respectively. In total, four patients (2.6%) died after surgery. CONCLUSION: Across nine countries, OSR was more often performed than TSR and tended to result in less postoperative complications. Despite many efforts to register patients across Europe, it is still challenging to set up a prospective CRLM database.

Size and depth of residual tumor after neoadjuvant chemoradiotherapy in rectal cancer - implications for the development of new imaging modalities for response assessment.

With the shift towards organ preserving treatment strategies in rectal cancer it has become increasingly important to accurately discriminate between a complete and good clinical response after neoadjuvant chemoradiotherapy (CRT). Standard of care imaging techniques such as CT and MRI are well equipped for initial staging of rectal tumors, but discrimination between a good clinical and complete response remains difficult due to their limited ability to detect small residual vital tumor fragments. To identify new promising imaging techniques that could fill this gap, it is crucial to know the size and invasion depth of residual vital tumor tissue since this determines the requirements with regard to the resolution and imaging depth of potential new optical imaging techniques. We analyzed 198 pathology slides from 30 rectal cancer patients with a Mandard tumor regression grade 2 or 3 after CRT that underwent surgery. For each patient we determined response pattern, size of the largest vital tumor fragment or bulk and the shortest distance from the vital tumor to the luminal surface. The response pattern was shrinkage in 14 patients and fragmentation in 16 patients. For both groups combined, the largest vital tumor fragment per patient was smaller than 1mm for 38% of patients, below 0.2mm for 12% of patients and for one patient as small as 0.06mm. For 29% of patients the vital tumor remnant was present within the first 0.01mm from the luminal surface and for 87% within 0.5mm. Our results explain why it is difficult to differentiate between a good clinical and complete response in rectal cancer patients using endoscopy and MRI, since in many patients submillimeter tumor fragments remain below the luminal surface. To detect residual vital tumor tissue in all patients included in this study a technique with a spatial resolution of 0.06mm and an imaging depth of 8.9mm would have been required. Optical imaging techniques offer the possibility of detecting majority of these cases due to the potential of both high-resolution imaging and enhanced contrast between tissue types. These techniques could thus serve as a complimentary tool to conventional methods for rectal cancer response assessment.

An improved camera model for oblique-viewing laparoscopes: high reprojection accuracy independent of telescope rotation.

Objective. Oblique-viewing laparoscopes are popular in laparoscopic surgeries where the target anatomy is located in narrow areas. Their viewing direction can be shifted by telescope rotation without changing the laparoscope pose. This rotation also changes laparoscope camera parameters that are estimated by camera calibration to be able to reproject an anatomical model onto the laparoscopic view, creating augmented reality (AR). The aim of this study was to develop a camera model that accounts for these changes, achieving high reprojection accuracy for any telescope rotation.Approach. Camera parameters were acquired by calibrations encompassing a wide telescope rotation range. For those parameters showing periodic changes upon rotation, interpolation models were created and used to establish an updatable camera model. With this model, corner points of a tracked checkerboard were reprojected onto the checkerboard laparoscopic images, at random rotation angles. Root-mean-square reprojection errors (RMSEs) were calculated between the reprojected and imaged corner points.Main results. Reprojection RMSEs were low and approximately independent on telescope rotation angle, over a wide rotation range of 320°. The mean reprojection RMSE was 2.8±0.7 pixels for a conventional laparoscope and 3.6±0.7 pixels for a chip-on-the-tip (COTT) laparoscope, corresponding to 0.3±0.1 mm and 0.4±0.1 mm in world coordinates respectively. Worst-case reprojection errors were about 9 pixels (0.8 mm) for both laparoscopes.Significance. The camera model developed in this study improves on existing models for oblique-viewing laparoscopes because it provides high reprojection accuracy independent of the telescope rotation angle and is applicable for conventional and chip-on-a-tip oblique-viewing laparoscopes. The work presented here is an important step towards creating accurate AR in image-guided interventions where oblique-viewing laparoscopes are used while simultaneously providing the surgeon the flexibility to rotate the telescope to any desired rotation angle.Acronyms. CC: camera coordinates; CCToolbox: camera calibration toolbox; COTT: chip-on-the-tip; CS: camera sensor; DD: decentering distortion; FL: focal length; OTS: optical tracking system; PP: principal point; RD: radial distortion; SI: supplementary information;tHE:hand-eye translation component.

Tissue Classification of Breast Cancer by Hyperspectral Unmixing.

(1) Background: Assessing the resection margins during breast-conserving surgery is an important clinical need to minimize the risk of recurrent breast cancer. However, currently there is no technique that can provide real-time feedback to aid surgeons in the margin assessment. Hyperspectral imaging has the potential to overcome this problem. To classify resection margins with this technique, a tissue discrimination model should be developed, which requires a dataset with accurate ground-truth labels. However, establishing such a dataset for resection specimens is difficult. (2) Methods: In this study, we therefore propose a novel approach based on hyperspectral unmixing to determine which pixels within hyperspectral images should be assigned to the ground-truth labels from histopathology. Subsequently, we use this hyperspectral-unmixing-based approach to develop a tissue discrimination model on the presence of tumor tissue within the resection margins of ex vivo breast lumpectomy specimens. (3) Results: In total, 372 measured locations were included on the lumpectomy resection surface of 189 patients. We achieved a sensitivity of 0.94, specificity of 0.85, accuracy of 0.87, Matthew's correlation coefficient of 0.71, and area under the curve of 0.92. (4) Conclusion: Using this hyperspectral-unmixing-based approach, we demonstrated that the measured locations with hyperspectral imaging on the resection surface of lumpectomy specimens could be classified with excellent performance.

Microcamera Visualisation System to Overcome Specular Reflections for Tissue Imaging.

In vivo tissue imaging is an essential tool for medical diagnosis, surgical guidance, and treatment. However, specular reflections caused by glossy tissue surfaces can significantly degrade image quality and hinder the accuracy of imaging systems. In this work, we further the miniaturisation of specular reflection reduction techniques using micro cameras, which have the potential to act as intra-operative supportive tools for clinicians. In order to remove these specular reflections, two small form factor camera probes, handheld at 10 mm footprint and miniaturisable to 2.3 mm, are developed using different modalities, with line-of-sight to further miniaturisation. (1) The sample is illuminated via multi-flash technique from four different positions, causing a shift in reflections which are then filtered out in a post-processing image reconstruction step. (2) The cross-polarisation technique integrates orthogonal polarisers onto the tip of the illumination fibres and camera, respectively, to filter out the polarisation maintaining reflections. These form part of a portable imaging system that is capable of rapid image acquisition using different illumination wavelengths, and employs techniques that lend themselves well to further footprint reduction. We demonstrate the efficacy of the proposed system with validating experiments on tissue-mimicking phantoms with high surface reflection, as well as on excised human breast tissue. We show that both methods can provide clear and detailed images of tissue structures along with the effective removal of distortion or artefacts caused by specular reflections. Our results suggest that the proposed system can improve the image quality of miniature in vivo tissue imaging systems and reveal underlying feature information at depth, for both human and machine observers, leading to better diagnosis and treatment outcomes.

Intersurgeon Variability in Local Treatment Planning for Patients with Initially Unresectable Colorectal Cancer Liver Metastases: Analysis of the Liver Expert Panel of the Dutch Colorectal Cancer Group.

BACKGROUND: Consensus on resectability criteria for colorectal cancer liver metastases (CRLM) is lacking, resulting in differences in therapeutic strategies. This study evaluated variability of resectability assessments and local treatment plans for patients with initially unresectable CRLM by the liver expert panel from the randomised phase III CAIRO5 study. METHODS: The liver panel, comprising surgeons and radiologists, evaluated resectability by predefined criteria at baseline and 2-monthly thereafter. If surgeons judged CRLM as resectable, detailed local treatment plans were provided. The panel chair determined the conclusion of resectability status and local treatment advice, and forwarded it to local surgeons. RESULTS: A total of 1149 panel evaluations of 496 patients were included. Intersurgeon disagreement was observed in 50% of evaluations and was lower at baseline than follow-up (36% vs. 60%, p < 0.001). Among surgeons in general, votes for resectable CRLM at baseline and follow-up ranged between 0-12% and 27-62%, and for permanently unresectable CRLM between 3-40% and 6-47%, respectively. Surgeons proposed different local treatment plans in 77% of patients. The most pronounced intersurgeon differences concerned the advice to proceed with hemihepatectomy versus parenchymal-preserving approaches. Eighty-four percent of patients judged by the panel as having resectable CRLM indeed received local treatment. Local surgeons followed the technical plan proposed by the panel in 40% of patients. CONCLUSION: Considerable variability exists among expert liver surgeons in assessing resectability and local treatment planning of initially unresectable CRLM. This stresses the value of panel-based decisions, and the need for consensus guidelines on resectability criteria and technical approach to prevent unwarranted variability in clinical practice.

Correction: Groen et al. Use of Image-Guided Surgical Navigation during Resection of Locally Recurrent Rectal Cancer. Life 2022, 12, 645.

The authors wish to make the following corrections to this paper [...].

Toward Intraoperative Margin Assessment Using a Deep Learning-Based Approach for Automatic Tumor Segmentation in Breast Lumpectomy Ultrasound Images.

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.