Abstract：
OBJECTIVE: To develop and validate a model for predicting suboptimal debulking surgery (SDS) of serous ovarian carcinoma (SOC) using radiomics method, clinical and MRI features.
METHODS: 228 patients eligible from institution A (randomly divided into the training and internal validation cohorts) and 45 patients from institution B (external validation cohort) were collected and retrospectively analyzed. All patients underwent abdominal pelvic enhanced MRI scan, including T2-weighted imaging fat-suppressed fast spin-echo (T2FSE), T1-weighted dual-echo magnetic resonance imaging (T1DEI), diffusion weighted imaging (DWI), and T1 with contrast enhancement (T1CE). We extracted, selected and eliminated highly correlated radiomic features for each sequence. Then, Radiomic models were made by each single sequence, dual-sequence (T1CE + T2FSE), and all-sequence, respectively. Univariate and multivariate analyses were performed to screen the clinical and MRI independent predictors. The radiomic model with the highest area under the curve (AUC) was used to combine the independent predictors as a combined model.
RESULTS: The optimal radiomic model was based on dual sequences (T2FSE + T1CE) among the five radiomic models (AUC = 0.720, P < 0.05). Serum carbohydrate antigen 125, the relationship between sigmoid colon/rectum and ovarian mass or mass implanted in Douglas\' pouch, diaphragm nodules, and peritoneum/mesentery nodules were considered independent predictors. The AUC of the radiomic-clinical-radiological model was higher than either the optimal radiomic model or the clinical-radiological model in the training cohort (AUC = 0.908 vs. 0.720/0.854).
CONCLUSIONS: The radiomic-clinical-radiological model has an overall algorithm reproducibility and may help create individualized treatment programs and improve the prognosis of patients with SOC.
METHODS: 228 patients eligible from institution A (randomly divided into the training and internal validation cohorts) and 45 patients from institution B (external validation cohort) were collected and retrospectively analyzed. All patients underwent abdominal pelvic enhanced MRI scan, including T2-weighted imaging fat-suppressed fast spin-echo (T2FSE), T1-weighted dual-echo magnetic resonance imaging (T1DEI), diffusion weighted imaging (DWI), and T1 with contrast enhancement (T1CE). We extracted, selected and eliminated highly correlated radiomic features for each sequence. Then, Radiomic models were made by each single sequence, dual-sequence (T1CE + T2FSE), and all-sequence, respectively. Univariate and multivariate analyses were performed to screen the clinical and MRI independent predictors. The radiomic model with the highest area under the curve (AUC) was used to combine the independent predictors as a combined model.
RESULTS: The optimal radiomic model was based on dual sequences (T2FSE + T1CE) among the five radiomic models (AUC = 0.720, P < 0.05). Serum carbohydrate antigen 125, the relationship between sigmoid colon/rectum and ovarian mass or mass implanted in Douglas\' pouch, diaphragm nodules, and peritoneum/mesentery nodules were considered independent predictors. The AUC of the radiomic-clinical-radiological model was higher than either the optimal radiomic model or the clinical-radiological model in the training cohort (AUC = 0.908 vs. 0.720/0.854).
CONCLUSIONS: The radiomic-clinical-radiological model has an overall algorithm reproducibility and may help create individualized treatment programs and improve the prognosis of patients with SOC.
摘要：
目的：使用影像组学方法开发和验证用于预测浆液性卵巢癌（SOC）的次优减瘤手术（SDS）的模型，临床和MRI特征。
方法：收集并回顾性分析来自A机构（随机分为培训和内部验证队列）的228例患者和来自B机构（外部验证队列）的45例患者。所有患者均行腹部盆底增强MRI扫描,包括T2加权成像脂肪抑制快速自旋回波（T2FSE），T1加权双回波磁共振成像(T1DEI),弥散加权成像(DWI),和T1与对比度增强（T1CE）。我们提取，选择并消除每个序列的高度相关的放射学特征。然后，每个单序列都制作了放射学模型，双序列(T1CE+T2FSE),和全序列，分别。进行单变量和多变量分析以筛选临床和MRI独立预测因子。具有最高曲线下面积(AUC)的影像组学模型用于将独立预测因子组合为组合模型。
结果：在五个影像组学模型中，最佳影像组学模型基于双序列（T2FSET1CE）（AUC=0.720，P<0.05）。血清碳水化合物抗原125，乙状结肠/直肠与卵巢肿块或植入道格拉斯袋的肿块之间的关系，膈结节,腹膜/肠系膜结节被认为是独立的预测因素。在训练队列中，放射学-临床-放射学模型的AUC高于最佳放射学模型或临床-放射学模型（AUC=0.908vs.0.720/0.854）。
结论：放射学-临床-放射学模型具有整体算法的可重复性，可能有助于创建个体化治疗方案并改善SOC患者的预后。
方法：收集并回顾性分析来自A机构（随机分为培训和内部验证队列）的228例患者和来自B机构（外部验证队列）的45例患者。所有患者均行腹部盆底增强MRI扫描,包括T2加权成像脂肪抑制快速自旋回波（T2FSE），T1加权双回波磁共振成像(T1DEI),弥散加权成像(DWI),和T1与对比度增强（T1CE）。我们提取，选择并消除每个序列的高度相关的放射学特征。然后，每个单序列都制作了放射学模型，双序列(T1CE+T2FSE),和全序列，分别。进行单变量和多变量分析以筛选临床和MRI独立预测因子。具有最高曲线下面积(AUC)的影像组学模型用于将独立预测因子组合为组合模型。
结果：在五个影像组学模型中，最佳影像组学模型基于双序列（T2FSET1CE）（AUC=0.720，P<0.05）。血清碳水化合物抗原125，乙状结肠/直肠与卵巢肿块或植入道格拉斯袋的肿块之间的关系，膈结节,腹膜/肠系膜结节被认为是独立的预测因素。在训练队列中，放射学-临床-放射学模型的AUC高于最佳放射学模型或临床-放射学模型（AUC=0.908vs.0.720/0.854）。
结论：放射学-临床-放射学模型具有整体算法的可重复性，可能有助于创建个体化治疗方案并改善SOC患者的预后。
