van der Kroft Gregory, Wee Leonard, Rensen Sander S, Brecheisen Ralph, van Dijk David P J, Eickhoff Roman, Roeth Anjali A, Ulmer Florian T, Dekker Andre, Neumann Ulf P, Olde Damink Steven W M
Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany.
Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands.
Front Oncol. 2023 Aug 10;13:1062937. doi: 10.3389/fonc.2023.1062937. eCollection 2023.
Computerized radiological image analysis (radiomics) enables the investigation of image-derived phenotypes by extracting large numbers of quantitative features. We hypothesized that radiomics features may contain prognostic information that enhances conventional body composition analysis. We aimed to investigate whether body composition-associated radiomics features hold additional value over conventional body composition analysis and clinical patient characteristics used to predict survival of pancreatic ductal adenocarcinoma (PDAC) patients.
Computed tomography images of 304 patients undergoing elective pancreatic cancer resection were analysed. 2D radiomics features were extracted from skeletal muscle and subcutaneous and visceral adipose tissue (SAT and VAT) compartments from a single slice at the third lumbar vertebra. The study population was randomly split (80:20) into training and holdout subsets. Feature ranking with Least Absolute Shrinkage Selection Operator (LASSO) followed by multivariable stepwise Cox regression in 1000 bootstrapped re-samples of the training data was performed and tested on the holdout data. The fitted regression predictors were used as "scores" for a clinical (C-Score), body composition (B-Score), and radiomics (R-Score) model. To stratify patients into the highest 25% and lowest 25% risk of mortality compared to the middle 50%, the Harrell Concordance Index was used.
Based on LASSO and stepwise cox regression for overall survival, ASA ≥3 and age were the most important clinical variables and constituted the C-score, and VAT-index (VATI) was the most important body composition variable and constituted the B-score. Three radiomics features (SATI_original_shape2D_Perimeter, VATI_original_glszm_SmallAreaEmphasis, and VATI_original_firstorder_Maximum) emerged as the most frequent set of features and yielded an R-Score. Of the mean concordance indices of C-, B-, and R-scores, R-score performed best (0.61, 95% CI 0.56-0.65, p<0.001), followed by the C-score (0.59, 95% CI 0.55-0.63, p<0.001) and B-score (0.55, 95% CI 0.50-0.60, p=0.03). Kaplan-Meier projection revealed that C-, B, and R-scores showed a clear split in the survival curves in the training set, although none remained significant in the holdout set.
It is feasible to implement a data-driven radiomics approach to body composition imaging. Radiomics features provided improved predictive performance compared to conventional body composition variables for the prediction of overall survival of PDAC patients undergoing primary resection.
计算机放射图像分析（影像组学）能够通过提取大量定量特征来研究图像衍生的表型。我们假设影像组学特征可能包含可增强传统身体成分分析的预后信息。我们旨在研究与身体成分相关的影像组学特征相对于用于预测胰腺导管腺癌（PDAC）患者生存的传统身体成分分析和临床患者特征是否具有额外价值。
分析了304例接受择期胰腺癌切除术患者的计算机断层扫描图像。从第三腰椎单个层面的骨骼肌、皮下和内脏脂肪组织（SAT和VAT）区域提取二维影像组学特征。研究人群被随机分为（80:20）训练集和验证集。在训练数据的1000次自抽样中，采用最小绝对收缩选择算子（LASSO）进行特征排序，随后进行多变量逐步Cox回归，并在验证数据上进行测试。拟合的回归预测因子用作临床（C评分）、身体成分（B评分）和影像组学（R评分）模型的“分数”。为了将患者分层为与中间50%相比死亡率最高的25%和最低的25%，使用了Harrell一致性指数。
基于LASSO和总体生存的逐步Cox回归，美国麻醉医师协会（ASA）≥3和年龄是最重要的临床变量，构成C评分，VAT指数（VATI）是最重要的身体成分变量，构成B评分。三个影像组学特征（SATI_original_shape2D_Perimeter、VATI_original_glszm_SmallAreaEmphasis和VATI_original_firstorder_Maximum）成为最常见的特征集，并产生R评分。在C、B和R评分的平均一致性指数中，R评分表现最佳（0.61，95%可信区间0.56 - 0.65，p<0.001），其次是C评分（0.59，95%可信区间0.55 - 0.63，p<0.001）和B评分（0.55，95%可信区间0.50 - 0.60，p = 0.03）。Kaplan-Meier投影显示，在训练集中，C、B和R评分在生存曲线中呈现明显分离，尽管在验证集中均无显著差异。
实施数据驱动的身体成分成像影像组学方法是可行的。与传统身体成分变量相比，影像组学特征在预测接受初次切除的PDAC患者总体生存方面提供了更好的预测性能。
