Diagnostic accuracy of radiography, digital breast tomosynthesis, micro-CT and ultrasound for margin assessment during breast surgery: A systematic review and meta-analysis

Specimen tomosynthesis provides no additional value to specimen ultrasound in ultrasound-visible malignant breast lesions

BACKGROUND

The aim of this study was to evaluate the accuracy and added value of specimen tomosynthesis (ST) to specimen ultrasound (SUS) in margin assessment of excised breast specimens in breast-conserving therapy for non-palpable US-visible breast lesions.

MATERIALS

Between January 2018 and August 2019, all consecutive patients diagnosed with non-palpable breast cancer visible by ultrasound (US), treated with breast-conserving surgery (BCS) and requiring radiological intraoperative breast specimen assessment, were included in this study. Excised breast specimens were examined with SUS by radiologists blinded to the ST results, and margins smaller than 10 mm were recorded. STs were evaluated retrospectively by experienced radiologists.

RESULTS

A total of 120 specimens were included. SUS showed a statistically significant correlation with pathological margin measurements, while ST did not and provided no additional information. The odds ratios (ORs) for SUS to predict a positive margin was 3.429 (confidence interval (CI) = 0.548-21.432) using a 10-mm cut-off point and 14.182 (CI = 2.134-94.254) using a 5-mm cut-off point, while the OR for ST were 2.528 (CI = 0.400-15.994) and 3.188 (CI = 0.318-31.998), respectively.

CONCLUSIONS

SUS was superior in evaluating intraoperative resection margins of US-visible breast resection specimens when compared to ST. Therefore, ST could be considered redundant in applicable situations.

Intraoperative evaluation of surgical margins in breast cancer

Achieving clear resection margins at the time of lumpectomy is essential for optimal patient outcomes. Margin status is traditionally determined by pathologic evaluation of the specimen and often is difficult or impossible for the surgeon to definitively know at the time of surgery, resulting in the need for re-operation to obtain clear surgical margins. Numerous techniques have been investigated to enhance the accuracy of intraoperative margin and are reviewed in this manuscript.

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.

Robot-assisted PSMA-radioguided Surgery to Assess Surgical Margins and Nodal Metastases in Prostate Cancer Patients: Report on Three Cases Using an Intraoperative PET-CT Specimen Imager

INTRODUCTION

The aim of this feasibility study was to test the intraoperative use of this brand-new specimen PET/CT to guide robot-assisted radical prostatectomy and pelvic lymph node dissection.

MATERIALS AND METHODS

Three cases of robot-assisted radical prostatectomy and pelvic lymph node dissection were performed with intraoperative use of the specimen imager. Surgeries were performed with Da Vinci Xi robot. An intravenous injection of 68Ga-PSMA-11 was performed in the OR and after complete excision, the specimens were analyzed with the imager.

RESULTS

The average nodal yield was 17.3 (5.8 SD) nodes per patient. Specimen PET/CT images showed a focal uptake in a metastatic node (TBR 13.6), and no uptake or diffuse, faint uptake in negative nodes (TBR range: 1-5.3). The specimen imager provided intraoperative PET/CT images that clearly showed negative surgical margins in two patients, whereas the results were uncertain in a locally advanced case.

CONCLUSION

The intraoperative use of the specimen PET/CT imager is safe and feasible and could improve the evaluation of prostate surgical margins and lymph node status.

Analysis of Specimen Mammography with Artificial Intelligence to Predict Margin Status

BACKGROUND

Intraoperative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy of specimen mammography in detecting microscopic margin positivity is low. We sought to develop an artificial intelligence model to predict the pathologic margin status of resected breast tumors using specimen mammography.

METHODS

A dataset of specimen mammography images matched with pathologic margin status was collected from our institution from 2017 to 2020. The dataset was randomly split into training, validation, and test sets. Specimen mammography models pretrained on radiologic images were developed and compared with models pretrained on nonmedical images. Model performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC).

RESULTS

The dataset included 821 images, and 53% had positive margins. For three out of four model architectures tested, models pretrained on radiologic images outperformed nonmedical models. The highest performing model, InceptionV3, showed sensitivity of 84%, specificity of 42%, and AUROC of 0.71. Model performance was better among patients with invasive cancers, less dense breasts, and non-white race.

CONCLUSIONS

This study developed and internally validated artificial intelligence models that predict pathologic margins status for partial mastectomy from specimen mammograms. The models' accuracy compares favorably with published literature on surgeon and radiologist interpretation of specimen mammography. With further development, these models could more precisely guide the extent of resection, potentially improving cosmesis and reducing reoperations.

Computer Vision Analysis of Specimen Mammography to Predict Margin Status

Intra-operative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy of specimen mammography in detecting microscopic margin positivity is low. We sought to develop a deep learning-based model to predict the pathologic margin status of resected breast tumors using specimen mammography. A dataset of specimen mammography images matched with pathology reports describing margin status was collected. Models pre-trained on radiologic images were developed and compared with models pre-trained on non-medical images. Model performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The dataset included 821 images and 53% had positive margins. For three out of four model architectures tested, models pre-trained on radiologic images outperformed domain-agnostic models. The highest performing model, InceptionV3, showed a sensitivity of 84%, a specificity of 42%, and AUROC of 0.71. These results compare favorably with the published literature on surgeon and radiologist interpretation of specimen mammography. With further development, these models could assist clinicians with identifying positive margins intra-operatively and decrease the rate of positive margins and re-operation in breast-conserving surgery.

New Technology for the Breast Surgeon

New innovations aid the breast surgeon with better ability to localize tumors using wireless techniques, reduce re-excision rates by intraoperative margin evaluation and perform aesthetically; pleasing, and safe surgeries. In addition to improving oncological outcomes, we can continue to improve the quality of life for our patients through evolving surgeries including nerve-sparing mastectomies, robotic mastectomies, and lymphovascular surgeries (LYMPHA). Our article reviews current and evolving techniques and technology that all breast surgeons should add to his or her armamentarium to provide optimal surgical care.