Dalli J, Nguyen C L, Jindal A, Epperlein J P, Hardy N P, Pulitano C, Warrier S, Cahill R A
UCD Centre for Precision Surgery, School of Medicine, UCD, Dublin, Ireland.
Department of Breast Surgery, Chris O'Brien Lifehouse, Camperdown, Australia.
JPRAS Open. 2024 Jan 26;40:32-47. doi: 10.1016/j.jpra.2024.01.012. eCollection 2024 Jun.
Immediate post-mastectomy breast reconstruction offers benefits; however, complications can compromise outcomes. Intraoperative indocyanine green fluorescence angiography (ICGFA) may mitigate perfusion-related complications (PRC); however, its interpretation remains subjective. Here, we examine and develop methods for ICGFA quantification, including machine learning (ML) algorithms for predicting complications.
ICGFA video recordings of flap perfusion from a previous study of patients undergoing nipple-sparing mastectomy (NSM) with either immediate or staged immediate (delayed by a week due to perfusion insufficiency) reconstructions were analysed. Fluorescence intensity time series data were extracted, and perfusion parameters were interrogated for overall/regional associations with postoperative PRC. A naïve Bayes ML model was subsequently trained on a balanced data subset to predict PRC from the extracted meta-data.
The analysable video dataset of 157 ICGFA featured females (average age 48 years) having oncological/risk-reducing NSM with either immediate (n=90) or staged immediate (n=26) reconstruction. For those delayed, peak brightness at initial ICGFA was lower (p<0.001) and significantly improved (both quicker-onset and brighter p=0.001) one week later. The overall PRC rate in reconstructed patients (n=116) was 11.2%, with such patients demonstrating significantly dimmer (overall, p=0.018, centrally, p=0.03, and medially, p=0.04) and slower-onset (p=0.039) fluorescent peaks with shallower slopes (p=0.012) than uncomplicated patients with ICGFA. Importantly, such relevant parameters were converted into a whole field of view heatmap potentially suitable for intraoperative display. ML predicted PRC with 84.6% sensitivity and 76.9% specificity.
Whole breast quantitative ICGFA assessment reveals statistical associations with PRC that are potentially exploitable via ML.
乳房切除术后立即进行乳房重建有诸多益处；然而，并发症可能会影响手术效果。术中吲哚菁绿荧光血管造影（ICGFA）或许能减轻与灌注相关的并发症（PRC）；然而，其解读仍具有主观性。在此，我们研究并开发ICGFA定量方法，包括用于预测并发症的机器学习（ML）算法。
分析了先前一项针对接受保留乳头乳房切除术（NSM）并行即刻或分期即刻（因灌注不足延迟一周）重建的患者的皮瓣灌注ICGFA视频记录。提取荧光强度时间序列数据，并探究灌注参数与术后PRC的整体/区域关联。随后在一个平衡数据子集上训练朴素贝叶斯ML模型，以根据提取的元数据预测PRC。
可分析的157例ICGFA视频数据集的女性患者（平均年龄48岁）接受了肿瘤切除/降低风险的NSM手术，并行即刻（n = 90）或分期即刻（n = 26）重建。对于延迟重建的患者，初次ICGFA时的峰值亮度较低（p < 0.001），而一周后显著改善（发作更快且亮度更高，p = 0.001）。重建患者（n = 116）的总体PRC发生率为11.2%，与未发生并发症的ICGFA患者相比，此类患者的荧光峰值明显更暗（总体，p = 0.018；中央区域，p = 0.03；内侧区域，p = 0.04）、发作更慢（p = 0.039）且斜率更浅（p = 0.012）。重要的是，这些相关参数被转换为一个可能适用于术中显示的全视野热图。ML预测PRC的敏感性为84.6%，特异性为76.9%。
全乳定量ICGFA评估揭示了与PRC的统计学关联，这些关联有可能通过ML加以利用。
