Diagnostic accuracy of optical coherence tomography for margin assessment in breast-conserving surgery: A systematic review and meta-analysis

Clinical evaluation of breast cancer tissue with optical coherence tomography: key findings from a large-scale study

PURPOSE

Breast cancer patients undergoing breast-conserving surgery may require a second operation if positive margins persist but current intraoperative methodologies often lack real-time and comprehensive assessments of tissue margins. This study addresses this critical gap by introducing a novel approach to enhance margin assessment in breast surgery.

METHODS

A total of 252 fresh tissue blocks from 199 patients with different types of breast lesions were scanned with a customized swept-source optical coherence tomography (SS-OCT) system, and the OCT features of normal, benign, and malignant breast tissues, were systematically analyzed.

RESULTS

The qualitative analysis results revealed that adipose tissue has high penetration depth and a typical honeycomb pattern, whereas fibrous tissue has the brightest grayscale values and a bundle-like structure. The lobular area appears as a dark region, and dilated ducts present a distinct tubular structure on B-scan images. Adenosis results in bright areas, fibroadenoma results in typical contour structures, phyllodes tumors present lobular structures, invasive carcinomas present a stellate pattern and low penetration depth, and mucinous carcinoma cancer cells are clearly visible within the low-scattering mucin.

CONCLUSIONS

Importantly, we provide comparative OCT and hematoxylin and eosin (H&E) histology images for less common conditions, such as phyllodes tumors, intraductal papillomas, and mucinous carcinoma. For the first time, we established an 3D OCT-histopathology library with a large field of view and systematically analyzed the multidimensional features. This work strongly supports the feasibility of using OCT technology intraoperatively in surgery. Additionally, the OCT-histopathology library can help pathologists better understand and identify tissue features, thereby enhancing diagnostic efficiency.

Autofluorescence imaging guided needle-type Raman spectroscopy system for breast tumor margin assessment

A trajectory-tracked, near-infrared autofluorescence imaging guided, biochemical signature-projected needle-type Raman spectroscopy (TNBN-RS) system integrated on a medical cart was developed for rapid wide-field breast tissue stratification. A wide-field (10 × 10 cm2) near-infrared autofluorescence (NIRAF) imaging subsystem was developed for gross stratification of breast tissue types based on higher NIRAF intensity associated with breast cancer, followed by projection of NIRAF-identified breast tumor margins onto the tissue of interest with a compact projector. Raman spectra were further acquired from the NIRAF projected regions for confirmed margin assessment using a needle-type Raman probe equipped with color camera-based probe trajectory tracking. The trajectory of the Raman probe and the accompanying RS biochemical signature-based margin assessment were instantly projected. A unique field of view (FOV) calibration method was proposed to calibrate the TNBN-RS FOVs, resulting in a projection accuracy of <2 mm. A graphical user interface (GUI) was developed in C# for system control, real-time processing and display of NIRAF images, Raman spectra, and projection of their results. The performance of the TNBN-RS system was validated on an ex vivo breast tissue, demonstrating its potential for rapid intraoperative breast tumor margin assessment.

Comparing the diagnostic efficacy of optical coherence tomography and frozen section for margin assessment in breast-conserving surgery: a meta-analysis

AIMS

This meta-analysis assessed the relative diagnostic accuracy of optical coherence tomography (OCT) versus frozen section (FS) in evaluating surgical margins during breast-conserving procedures.

METHODS

PubMed and Embase were searched for relevant studies published up to October 2023. The inclusion criteria encompassed studies evaluating the diagnostic accuracy of OCT or FS in patients undergoing breast-conserving surgery. Sensitivity and specificity were analysed using the DerSimonian and Laird method and subsequently transformed through the Freeman-Tukey double inverse sine method.

RESULTS

The meta-analysis encompassed 36 articles, comprising 16 studies on OCT and 20 on FS, involving 10 289 specimens from 8058 patients. The overall sensitivity of OCT was 0.93 (95% CI: 0.90 to 0.96), surpassing that of FS, which was 0.82 (95% CI: 0.71 to 0.92), indicating a significantly higher sensitivity for OCT (p=0.04). Conversely, the overall specificity of OCT was 0.89 (95% CI: 0.83 to 0.94), while FS exhibited a higher specificity at 0.97 (95% CI: 0.95 to 0.99), suggesting a superior specificity for FS (p＜0.01).

CONCLUSIONS

Our meta-analysis reveals that OCT offers superior sensitivity but inferior specificity compared with FS in assessing surgical margins in breast-conserving surgery patients. Further larger well-designed prospective studies are needed, especially those employing a head-to-head comparison design.

PROSPERO REGISTRATION NUMBER

CRD42023483751.

Evaluating the Margins of Breast Cancer Tumors by Using Digital Breast Tomosynthesis with Deep Learning: A Preliminary Assessment

BACKGROUND

The assessment information of tumor margins is extremely important for the success of the breast cancer surgery and whether the patient undergoes a second operation. However, conducting surgical margin assessments is a time-consuming task that requires pathology-related skills and equipment, and often cannot be provided in a timely manner. To address this challenge, digital breast tomosynthesis technology was utilized to generate detailed cross-sectional images of the breast tissue and integrate deep learning algorithms for image segmentation, achieving an assessment of tumor margins during surgery.

METHODS

this study utilized post-operative tissue samples from 46 patients who underwent breast-conserving treatment, and generated image sets using digital breast tomosynthesis for the training and evaluation of deep learning models.

RESULTS

Deep learning algorithms effectively identifying the tumor area. They achieved a Mean Intersection over Union (MIoU) of 0.91, global accuracy of 99%, weighted IoU of 44%, precision of 98%, recall of 83%, F1 score of 89%, and dice coefficient of 93% on the training dataset; for the testing dataset, MIoU was at 83%, global accuracy at 97%, weighted IoU at 38%, precision at 87%, recall rate at 69%, F1 score at 76%, dice coefficient at 86%.

CONCLUSIONS

The initial evaluation suggests that the deep learning-based image segmentation method is highly accurate in measuring breast tumor margins. This helps provide information related to tumor margins during surgery, and by using different datasets, this research method can also be applied to the surgical margin assessment of various types of tumors.

Digital breast tomosynthesis versus X-ray of the breast specimen for intraoperative margin assessment: A randomized trial

BACKGROUND

Involved resection margins after breast conserving surgery (BCS) often require a re-operation with increased patient anxiety and risk of impaired cosmesis. We investigated the number of re-operations due to involved resection margins after BCS comparing digital breast tomosynthesis(DBT) with X-ray for intraoperative margin evaluation. Furthermore, we assessed the diagnostic accuracy of these methods to predict histopathological margin status. Finally, we evaluated risk factors for re-operation.

METHODS

In this randomized, non-blinded study, 250 invasive breast cancer patients were randomized (1:1), whereof 241 were analyzed intraoperatively with either DBT (intervention, n = 119) or X-ray (standard, n = 122). Pearson's chi-squared test, Fisher's exact test, t-test, logistic and ordinal regression analysis was used as appropriate.

RESULTS

No difference was found in the number of re-operations between the DBT and X-ray group (16.8 % vs 19.7 %, p = 0.57), or in diagnostic accuracy to predict histopathological margin status (77.5 %, CI: 68.6-84.9 %) and (67.3 %, CI: 57.7-75.9 %), respectively. We evaluated 5 potential risk factors for re-operation: Ductal carcinoma in situ (DCIS) outside tumor, OR = 9.4 (CI: 4.3-20.6, p < 0.001); high mammographic breast density, OR = 6.1 (CI: 1.0-38.1, p = 0.047); non-evaluable margins on imaging, OR = 3.8 (CI: 1.3-10.8, p = 0.016); neoadjuvant chemotherapy, OR = 3.0 (CI: 1.0-8.8, p = 0.048); and T2 tumor-size, OR = 2.6 (CI: 1.0-6.4, p = 0.045).

CONCLUSIONS

No difference was found in the number of re-operations or in diagnostic accuracy to predict histopathological margin status between DBT and X-ray groups. DCIS outside the tumor showed the highest risk of re-operation. Intraoperative methods with improved visualization of DCIS are needed to obtain tumor free margins in BCS.