Table_1_Development and validation of a radiomics-based nomogram for the prediction of postoperative malnutrition in stage IB1-IIA2 cervical carcinoma.DOCX

ObjectiveIn individuals with stage IB1-IIA2 cervical cancer (CC) who received postoperative radiotherapy ± chemotherapy (PORT/CRT), the interaction between sarcopenia and malnutrition remains elusive, let alone employing a nomogram model based on radiomic features of psoas extracted at the level of the third lumbar vertebra (L3). This study was set to develop a radiomics-based nomogram model to predict malnutrition as per the Patient-Generated Subjective Global Assessment (PG-SGA) for individuals with CC.
MethodsIn total, 120 individuals with CC underwent computed tomography (CT) scans before PORT/CRT. The radiomic features of psoas at L3 were obtained from non-enhanced CT images. Identification of the optimal features and construction of the rad-score formula were conducted utilizing the least absolute shrinkage and selection operator (LASSO) logistic regression to predict malnutrition in the training dataset (radiomic model). Identification of the major clinical factors in the clinical model was performed by means of binary logistic regression analysis. The radiomics-based nomogram was further developed by integrating radiomic signatures and clinical risk factors (combined model). The receiver operating characteristic (ROC) curves and decision curves analysis (DCA) were employed for the evaluation and comparison of the three models in terms of their predictive performance.
ResultsTwelve radiomic features in total were chosen, and the rad-score was determined with the help of the non-zero coefficient from LASSO regression. Multivariate analysis revealed that besides rad-score, age and Eastern Cooperative Oncology Group performance status could independently predict malnutrition. As per the data of this analysis, a nomogram prediction model was constructed. The area under the ROC curves (AUC) values of the radiomic and clinical models were 0.778 and 0.847 for the training and 0.776 and 0.776 for the validation sets, respectively. An increase in the AUC was observed up to 0.972 and 0.805 in the training and validation sets, respectively, in the combined model. DCA also confirmed the clinical benefit of the combined model.
ConclusionThis radiomics-based nomogram model depicted potential for use as a marker for predicting malnutrition in stage IB1-IIA2 CC patients who underwent PORT/CRT and required further investigation with a large sample size.

目的：对于接受术后放疗±化疗（PORT/CRT）的IB1-IIA2期宫颈癌（CC）患者，肌肉减少症与营养不良之间的相互作用仍不明确，基于第三腰椎（L3）水平腰大肌放射组学特征（radiomic features）构建列线图（nomogram）模型的相关研究更是匮乏。本研究拟开发一种基于放射组学的列线图模型，以患者主观整体营养评估（PG-SGA）为评价标准，预测宫颈癌患者的营养不良发生风险。
方法：本研究共纳入120例宫颈癌患者，均在接受PORT/CRT前完成计算机断层扫描（CT）检查。从平扫CT图像中提取L3水平腰大肌的放射组学特征（radiomic features）。采用最小绝对收缩和选择算子（LASSO）logistic回归筛选最优特征并构建放射组学评分（rad-score）公式，在训练集中构建放射组学模型以预测营养不良。通过二元logistic回归分析筛选临床模型的主要临床危险因素。将放射组学特征与临床危险因素相结合，进一步构建基于放射组学的联合列线图模型（联合模型）。采用受试者工作特征（ROC）曲线及决策曲线分析（DCA）对三种模型的预测性能进行评估与比较。
结果：最终共筛选出12个放射组学特征，通过LASSO回归得到的非零系数确定放射组学评分。多因素分析显示，除放射组学评分外，年龄及东部肿瘤协作组体能状态评分（ECOG PS）均可独立预测营养不良发生风险。基于上述分析结果构建列线图预测模型。放射组学模型与临床模型在训练集的曲线下面积（AUC）分别为0.778和0.847，在验证集的AUC均为0.776；联合模型在训练集与验证集的AUC分别提升至0.972与0.805。决策曲线分析同样证实联合模型具备临床获益价值。
结论：本研究构建的基于放射组学的列线图模型，在接受PORT/CRT的IB1-IIA2期宫颈癌患者中具备预测营养不良的应用潜力，后续可通过大样本量研究进一步验证其临床价值。