Individualized Analysis of Nipple-Sparing Mastectomy Versus Modified Radical Mastectomy Using Deep Learning

This study aimed to evaluate the impact of nipple-sparing mastectomy (NSM) and modified radical mastectomy (MRM) on individual survival outcomes and to assess the potential of neoadjuvant systemic therapy (NST) in reducing surgical intervention requirements.

To develop treatment recommendations for breast cancer patients, five machine learning models were trained. To mitigate bias in treatment allocation, advanced statistical methods, including propensity score matching (PSM) and inverse probability treatment weighting (IPTW), were applied.

NSM demonstrated either superior or noninferior survival outcomes compared with MRM across all breast cancer stages, irrespective of adjustments for IPTW and PSM. Among all models and National Comprehensive Cancer Network guidelines, the Balanced Individual and Mixture Effect (BIME) for survival regression model proposed in this study showed the strongest protective effects in treatment recommendations, as evidenced by an IPTW hazard ratio of 0.39 (95% CI: 0.26-0.59), an IPTW risk difference of 19.66% (95% CI: 18.20-21.13), and an IPTW difference in restricted mean survival time of 17.77 months (95% CI: 16.37-19.21). NST independently reduced the probability of surgical intervention by 1.4% (95% CI: 0.9%-2.0%), with the greatest impact observed in patients with locally advanced breast cancer, in whom a 4.5% reduction (95% CI: 3.8%-5.2%) in surgical selection was noted.

The BIME model provides superior accuracy in recommending surgical approaches for breast cancer patients, leading to improved survival outcomes. These findings underscore the potential of BIME to enhance clinical decision-making. However, further investigation incorporating comprehensive prognostic evaluation is needed to optimize the surgical selection process and refine its clinical utility.