Published online Jul 16, 2023. doi: 10.12998/wjcc.v11.i20.4852
Peer-review started: May 11, 2023
First decision: May 31, 2023
Revised: June 8, 2023
Accepted: June 21, 2023
Article in press: June 21, 2023
Published online: July 16, 2023
Breast cancer is a prevalent malignancy among women, and surgical treatment remains a widely employed approach. However, the presence of positive margins following surgery frequently results in cancer recurrence and metastasis, significantly impacting the treatment outcome and patient prognosis.
With the continuous improvement of medical technology, the treatment of breast cancer is becoming increasingly comprehensive, yet positive margins leading to recurrence and metastasis remain important factors affecting the treatment effect and patient prognosis.
This study aims to investigate the diagnostic value of various preoperative examination methods for breast cancer margin status.
A retrospective study was conducted on 323 breast cancer patients who met the criteria for undergoing breast-conserving surgery (BCS). Data on preoperative imaging, as well as intraoperative and postoperative pathological findings, were collected. The patients were categorized into groups based on the presence of positive or negative margins. The data were randomly split into a training set and a validation set. Non-conditional logistic regression and LASSO regression were applied to the validation set to identify risk factors associated with the failure of BCS.
The presence of non-mass enhancement on magnetic resonance imaging (MRI), indistinct margins on ultrasound and molybdenum target examination, as well as tumor size larger than 2 cm, were identified as factors that elevate the risk of positive margins.
The model utilizes preoperative evaluation results from ultrasound, molybdenum target, MRI, and core needle biopsy pathology, all of which were aligned with the actual situation. This provides reliable guidance for clinical decision-making concerning BCS.
This study established a computational model to forecast the success of BCS using variables such as ER-positive status, mammography tumor type, maximum intensity projection imaging feature, and MRI tumor type. This has significant implications for preoperative evaluation and the selection of appropriate surgical interventions, thus improving surgical success rates and negative margin rates.