Breast surgery after neoadjuvant therapy

Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network

BACKGROUND

After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) may be used to detect surgical clips in follow-up mammograms after BCS.

METHODS

884 mammograms and 517 tomosynthetic images depicting surgical clips and calcifications were manually segmented and classified. A U-Net-based segmentation network was trained with 922 images and validated with 394 images. An external test dataset consisting of 39 images was annotated by two radiologists with up to 7 years of experience in breast imaging. The network's performance was compared to that of human readers using accuracy and interrater agreement (Cohen's Kappa).

RESULTS

The overall classification accuracy on the validation set after 45 epochs ranged between 88.2% and 92.6%, indicating that the model's performance is comparable to the decisions of a human reader. In 17.4% of cases, calcifications have been misclassified as post-operative clips. The interrater reliability of the model compared to the radiologists showed substantial agreement (κreader1 = 0.72, κreader2 = 0.78) while the readers compared to each other revealed a Cohen's Kappa of 0.84, thus showing near-perfect agreement.

CONCLUSIONS

With this study, we show that surgery clips can adequately be identified by an AI technique. A potential application of the proposed technique is patient triage as well as the automatic exclusion of post-operative cases from PGMI (Perfect, Good, Moderate, Inadequate) evaluation, thus improving the quality management workflow.

The importance of the multidisciplinary team in the decision-making process of patients undergoing neoadjuvant chemotherapy for breast cancer

BACKGROUND AND OBJECTIVES

Recent literature suggests that rates of breast conservation surgery (BCS) are lower than expected in patients submitted to neoadjuvant chemotherapy (NAC) for breast cancer. The aim of this study was to underscore the role of the multidisciplinary team (MDT) in the decision-making process of patients who underwent breast surgery after NAC.

METHODS

We conducted a retrospective study on patients with breast cancer treated according to an algorithm developed at the Breast Unit of Northern Sardinia between January 2019 and May 2023. Data collected included demographics, tumor characteristics, upfront treatment (surgery or NAC), type of primary surgery (BCS or mastectomy [Ma]) and patients' adherence to the treatment proposed by the MDT.

RESULTS

Overall, 1061 women were treated during the study period, of whom 164 received NAC (Group A) and 897 upfront surgery (Group B). In group A, conversion from BCS ineligibility to BCS eligibility was observed in 47 patients (40.1%). Final surgery in patients who became BCS-eligible after NAC was BCS in 42 cases (89.3%) and Ma in 5 (10.6%). Rates of patients' adherence to the treatment proposed by the MDT were significantly better in the Group A (p = 0.02).

CONCLUSIONS

Our results suggest that the MDT has a pivotal role in increasing the rates of breast conservation in women submitted to NAC.