Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction

Abstract The aim of this study is to evaluate cosmetic outcomes of the reconstructed breast in breast cancer patients, using anomaly score (AS) detected by generative adversarial network (GAN) deep learning algorithm. A total of 251 normal breast images from patients who underwent breast-conserving surgery were used for training anomaly GAN network. GAN-based anomaly detection was used to calculate abnormalities as an AS, followed by standardization by using z-score. Then, we reviewed 61 breast cancer patients who underwent mastectomy followed by reconstruction with autologous tissue or tissue expander. All patients were treated with adjuvant radiation therapy (RT) after reconstruction and computed tomography (CT) was performed at three-time points with a regular follow-up; before RT (Pre-RT), one year after RT (Post-1Y), and two years after RT (Post-2Y). Compared to Pre-RT, Post-1Y and Post-2Y demonstrated higher AS, indicating more abnormal cosmetic outcomes (Pre-RT vs. Post-1Y, P = 0.015 and Pre-RT vs. Post-2Y, P = 0.011). Pre-RT AS was higher in patients having major breast complications (P = 0.016). Patients with autologous reconstruction showed lower AS than those with tissue expander both at Pre-RT (2.00 vs. 4.19, P = 0.008) and Post-2Y (2.89 vs. 5.00, P = 0.010). Linear mixed effect model revealed that days after baseline were associated with increased AS (P = 0.007). Also, tissue expander was associated with steeper rise of AS, compared to autologous tissue (P = 0.015). Fractionation regimen was not associated with the change of AS (P = 0.389). AS detected by deep learning might be feasible in predicting cosmetic outcomes of RT-treated patients with breast reconstruction. AS should be validated in prospective studies.