Breast-conserving surgery with 3D-printed surgical guide: a single-center, prospective clinical study

Evaluation of the accuracy of an augmented reality-based tumor-targeting guide for breast-conserving surgery

BACKGROUND AND OBJECTIVES

Although magnetic resonance imaging (MRI) is commonly used for breast tumor detection, significant challenges remain in determining and presenting the three-dimensional (3D) morphology of tumors to guide breast-conserving surgery. To address this challenge, we have developed the augmented reality-breast surgery guide (AR-BSG) and compared its performance with that of a traditional 3D-printed breast surgical guide (3DP-BSG).

METHODS

Based on the MRI results of a breast cancer patient, a breast phantom made of skin, body, and tumor was fabricated through 3D printing and silicone-casting. AR-BSG and 3DP-BSG were executed using surgical plans based on the breast phantom's computed tomography scan images. Three operators independently inserted a catheter into the phantom using each guide. Their targeting accuracy was then evaluated using Bland-Altman analysis with limits of agreement (LoA). Differences between the users of each guide were evaluated using the intraclass correlation coefficient (ICC).

RESULTS

The entry and end point errors associated with AR-BSG were -0.34±0.68 mm (LoA: -1.71-1.01 mm) and 0.81±1.88 mm (LoA: -4.60-3.00 mm), respectively, whereas 3DP-BSG was associated with entry and end point errors of -0.28±0.70 mm (LoA: -1.69-1.11 mm) and -0.62±1.24 mm (LoA: -3.00-1.80 mm), respectively. The AR-BSG's entry and end point ICC values were 0.99 and 0.97, respectively, whereas 3DP-BSG was associated with entry and end point ICC values of 0.99 and 0.99, respectively.

CONCLUSIONS

AR-BSG can consistently and accurately localize tumor margins for surgeons without inferior guiding accuracy AR-BSG can consistently and accurately localize tumor margins for surgeons without inferior guiding accuracy compared to 3DP-BSG. Additionally, when compared with 3DP-BSG, AR-BSG can offer better spatial perception and visualization, lower costs, and a shorter setup time.

Customised 3D-Printed Surgical Guide for Breast-Conserving Surgery after Neoadjuvant Chemotherapy and Its Clinical Application

For patients eligible to undergo breast-conserving surgery (BCS) after neoadjuvant chemotherapy, accurate preoperative localisation of tumours is vital to ensure adequate tumour resection that can reduce recurrence probability effectively. For this reason, we have developed a 3D-printed personalised breast surgery guide (BSG) assisted with supine magnetic resonance imaging (MRI) and image 3D reconstruction technology, capable of mapping the tumour area identified on MRI onto the breast directly using dual positioning based on the manubrium and nipple. In addition, the BSG allows the colour dye to be injected into the breast to mark the tumour region to be removed, yielding more accurate intraoperative resection and satisfactory cosmetic outcomes. The device has been applied to 14 patients from January 2018 to July 2023, with two positive margins revealed by the intraoperative biopsy. This study showed that the BSG-based method could facilitate precise tumour resection of BCS by accurately localising tumour extent and margin, promoting the clinical efficacy in patients with breast cancer as well as simplifying the surgical process.

Radiological Society of North America (RSNA) 3D Printing Special Interest Group (SIG) clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: breast conditions

The use of medical 3D printing has expanded dramatically for breast diseases. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides updated appropriateness criteria for breast 3D printing in various clinical scenarios. Evidence-based appropriateness criteria are provided for the following clinical scenarios: benign breast lesions and high-risk breast lesions, breast cancer, breast reconstruction, and breast radiation (treatment planning and radiation delivery).

Computational Imaging to Compensate for Soft-Tissue Deformations in Image-Guided Breast Conserving Surgery

OBJECTIVE

During breast conserving surgery (BCS), magnetic resonance (MR) images aligned to accurately display intraoperative lesion locations can offer improved understanding of tumor extent and position relative to breast anatomy. Unfortunately, even under consistent supine conditions, soft tissue deformation compromises image-to-physical alignment and results in positional errors.

METHODS

A finite element inverse modeling technique has been developed to nonrigidly register preoperative supine MR imaging data to the surgical scene for improved localization accuracy during surgery. Registration is driven using sparse data compatible with acquisition during BCS, including corresponding surface fiducials, sparse chest wall contours, and the intra-fiducial skin surface. Deformation predictions were evaluated at surface fiducial locations and subsurface tissue features that were expertly identified and tracked. Among n = 7 different human subjects, an average of 22 ± 3 distributed subsurface targets were analyzed in each breast volume.

RESULTS

The average target registration error (TRE) decreased significantly when comparing rigid registration to this nonrigid approach (10.4 ± 2.3 mm vs 6.3 ± 1.4 mm TRE, respectively). When including a single subsurface feature as additional input data, the TRE significantly improved further (4.2 ± 1.0 mm TRE), and in a region of interest within 15 mm of a mock biopsy clip TRE was 3.9 ± 0.9 mm.

CONCLUSION

These results demonstrate accurate breast deformation estimates based on sparse-data-driven model predictions.

SIGNIFICANCE

The data suggest that a computational imaging approach can account for image-to-surgery shape changes to enhance surgical guidance during BCS.

3D and 4D Printing in the Fight against Breast Cancer

Breast cancer is the second most common cancer worldwide, characterized by a high incidence and mortality rate. Despite the advances achieved in cancer management, improvements in the quality of life of breast cancer survivors are urgent. Moreover, considering the heterogeneity that characterizes tumors and patients, focusing on individuality is fundamental. In this context, 3D printing (3DP) and 4D printing (4DP) techniques allow for a patient-centered approach. At present, 3DP applications against breast cancer are focused on three main aspects: treatment, tissue regeneration, and recovery of the physical appearance. Scaffolds, drug-loaded implants, and prosthetics have been successfully manufactured; however, some challenges must be overcome to shift to clinical practice. The introduction of the fourth dimension has led to an increase in the degree of complexity and customization possibilities. However, 4DP is still in the early stages; thus, research is needed to prove its feasibility in healthcare applications. This review article provides an overview of current approaches for breast cancer management, including standard treatments and breast reconstruction strategies. The benefits and limitations of 3DP and 4DP technologies are discussed, as well as their application in the fight against breast cancer. Future perspectives and challenges are outlined to encourage and promote AM technologies in real-world practice.

Tumor deformation correction for an image guidance system in breast conserving surgery

Breast cancer is the most common cancer in women, and surgical resection is standard of care for the majority of breast cancer patients. Unfortunately, current reoperation rates are 10-29%. Uncertainty in lesion localization is one of the main factors contributing to these high reoperation rates. This work uses the linearized iterative boundary reconstruction approach to model patient breast deformation due to abduction of the ipsilateral arm. A preoperative supine magnetic resonance (MR) image was obtained with the patient's arms down near the torso. A mock intraoperative breast shape was measured from a supine MR image obtained with the patient's arm up near the head. Sparse data was subsampled from the full volumetric image to represent realistic intraoperative data collection: surface fiducial points, the intra-fiducial skin surface, and the chest wall as measured with 7 tracked ultrasound images. The deformed preoperative arm-down data was compared to the ground truth arm-up data. From rigid registration to model correction the tumor centroid distance improves from 7.3 mm to 3.3 mm, average surface fiducial error across 9 synthetic fiducials and the nipple improves from 7.4 ± 2.2 to 1.3 ± 0.7, and average subsurface error across 14 corresponding features improves from 6.2 ± 1.4 mm to 3.5 ± 1.1 mm. Using preoperative supine MR imaging and sparse data in the deformed position, this modeling framework can correct for breast shape changes between imaging and surgery to more accurately predict intraoperative position of the tumor as well as 10 surface fiducials and 14 subsurface features.

Accuracy evaluation of a 3D printing surgical guide for breast-conserving surgery using a realistic breast phantom

To prevent recurrence after breast-conserving surgery (BCS), it is imperative to secure a clear resection margin, and magnetic resonance imaging (MRI) is useful for predicting this. Although MRI is highly accurate in predicting the extent of a tumor, it is difficult to quantitatively mark the tumor area directly on the patient's breast skin using MRI. Therefore, we developed a 3D-printed breast surgical guide (3DP-BSG). The 3DP-BGS is positioned on the breast using the guideline pointing to the opposite nipple and clavicle notch, centering on the nipple of the breast with the tumor. Then, the tumor was visualized by injecting blue-dye into the breast along the guide's columns using a syringe. For the quantitative evaluation of 3DP-BSG, the experiment must be done in the simulated environment. However, since it is difficult to construct the environment in the clinical field, we have fabricated a realistic breast phantom using an MRI. For modeling the 3DP-BSG, the phantom was scanned using computed tomography (CT), and. Based on these images, the 3DP-BSG was modeled to mark a 5-mm safety margin on a patient's breast skin by inserting a 16-gauge intravenous catheter. Then, the breast phantom was scanned by CT for quantitative evaluation. The insertion point measurement error (mean ± standard deviation) was 2.513 ± 0.914 mm, and the cosine similarity of the trajectories was 0.997 ± 0.005. This 3DP-BSG exhibits high accuracy in tumor targeting and is expected to facilitate precise BCS by providing a quantitative measure of the tumor area to surgeons.