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三维表面分析在乳房体积术前预测中的验证研究。

Three-Dimensional Surface Analysis for Preoperative Prediction of Breast Volume: A Validation Study.

机构信息

From the Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center.

Division of Plastic Surgery, Department of Surgery, Icahn School of Medicine at Mount Sinai.

出版信息

Plast Reconstr Surg. 2023 Dec 1;152(6):1153-1162. doi: 10.1097/PRS.0000000000010473. Epub 2023 Mar 30.

Abstract

BACKGROUND

Few studies have examined whether preoperative three-dimensional surface imaging can accurately predict breast volume. Reliably predicting breast volume preoperatively can assist with breast reconstruction planning, patient education, and perioperative risk stratification.

METHODS

The authors conducted a review of patients who underwent mastectomy from 2020 to 2021 and included all patients who had preoperative VECTRA XT three-dimensional imaging. VECTRA Analysis Module (VAM) and VECTRA Body Sculptor (VBS) were used for volumetric analysis using standard anatomical breast borders. Breast weights were obtained intraoperatively. Predictive accuracy was defined as VAM estimates ±10% of mastectomy specimen weight or ±100 g of mastectomy weight.

RESULTS

The study included 179 patients (266 breasts). There was no significant difference ( P = 0.22) between mean mastectomy weight of 620.8 ± 360.3 g and mean VAM estimate of 609.5 ± 361.9 g. Mean VBS estimate was 498.9 ± 337.6 g, which differed from mean mastectomy weight ( P < 0.001). When defining predictive accuracy as ±100 g, 58.7% of VAM and 44.4% of VBS estimates were accurate. Body mass index, body surface area, and ptosis grade significantly affected VAM and VBS breast volume predictions.

CONCLUSIONS

VAM is more accurate at predicting mastectomy weight than VBS, likely because of VAM's analysis of surface topography rather than discrete surface landmarks. Discrepancies between VECTRA estimates and mastectomy weight were likely attributable to differences between surgical mastectomy borders and breast borders used in volumetric analysis. Surgeons should consider the physical characteristics of patients when using three-dimensional imaging.

CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, I.

摘要

背景

很少有研究探讨术前三维表面成像是否能准确预测乳房体积。术前可靠地预测乳房体积可以辅助乳房重建规划、患者教育和围手术期风险分层。

方法

作者对 2020 年至 2021 年间接受乳房切除术的患者进行了回顾性研究,纳入所有接受术前 VECTRA XT 三维成像的患者。使用 VECTRA 分析模块(VAM)和 VECTRA 身体雕塑家(VBS),使用标准解剖学乳房边界进行容积分析。术中获得乳房重量。预测准确性定义为 VAM 估计值±10%的乳房切除术标本重量或±100g 的乳房切除术重量。

结果

研究纳入 179 例患者(266 侧乳房)。平均乳房切除术重量 620.8 ± 360.3g 与平均 VAM 估计值 609.5 ± 361.9g 之间无显著差异(P=0.22)。平均 VBS 估计值为 498.9 ± 337.6g,与平均乳房切除术重量有差异(P<0.001)。当定义预测准确性为±100g 时,VAM 有 58.7%的估计值和 VBS 有 44.4%的估计值是准确的。体重指数、体表面积和乳房下垂程度显著影响 VAM 和 VBS 乳房体积预测。

结论

VAM 比 VBS 更准确地预测乳房切除术的重量,这可能是因为 VAM 分析的是表面形貌,而不是离散的表面标志。VECTRA 估计值与乳房切除术重量之间的差异可能归因于手术乳房切除术边界和容积分析中使用的乳房边界之间的差异。外科医生在使用三维成像时应考虑患者的身体特征。

临床问题/证据水平:诊断,I。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/929e/11404560/a1b3c0fcf022/nihms-2000842-f0001.jpg

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