ArticleGland surgery2025
Development and internal validation of a nomogram for predicting pectoralis major fascia invasion in breast cancer patients using preoperative magnetic resonance imaging parameters.
Article in Gland surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Accurate preoperative identification of pectoralis major fascia (PMF) invasion in breast cancer is essential to guide surgical planning, particularly when considering fascia preservation to reduce reconstructive complications. However, no validated preoperative tool currently exists for assessing PMF invasion risk. This study aimed to develop and internally validate a nomogram based on preoperative magnetic resonance imaging (MRI) parameters to predict the risk of PMF invasion in breast cancer patients. Methods: We retrospectively analyzed data from 235 breast cancer patients who underwent surgery between January 2019 and February 2022. Postoperative paraffin pathology served as the gold standard for diagnosing PMF invasion. MRI-based variables included tumor location, axillary lymph node metastasis, carbohydrate antigen (CA)-153 levels, tumor diameter, the shortest distance from tumor to PMF in contrast-enhanced MRI (D1), the distance from the posterior edge of the gland to PMF in lipid-suppressed MRI (D2), and the D1/D2 ratio. Patients were randomly split into training and validation cohorts (2:1). Logistic regression identified predictors used to construct a nomogram, evaluated by receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results: PMF invasion was present in 16 out of 235 patients (6.8%). Significant differences in D1, D2, and D1/D2 were found between invasion and non-invasion groups. Logistic regression identified D2 [P=0.012, odds ratio (OR) =0.091, 95% confidence interval (CI): 0.014-0.585] and D1/D2 (P=0.007, OR =0.217, 95% CI: 0.071-0.664) as independent predictors. The nomogram yielded an area under the curve (AUC) of 0.81 in the training set and 0.87 in the validation set, with good calibration and clinical utility. Conclusions: This study presents a preliminary nomogram based on MRI-derived features for preoperative prediction of PMF invasion in breast cancer. While internally validated, the model's utility is limited by the small number of invasion cases and retrospective design. Further prospective studies with larger cohorts are necessary to confirm clinical applicability.
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