Multi‑parametric magnetic resonance imaging radiomics nomogram can effectively predict lymphovascular invasion in rectal cancer before surgery
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摘要:
目的 探讨基于多参数磁共振影像组学结合临床危险因素构建的列线图模型在术前预测直肠癌淋巴血管浸润的价值。 方法 回顾性分析蚌埠医学院第一附属医院术前行多参数MRI检查且术后病理证实为直肠腺癌的患者112例, 收集患者的临床和盆腔影像资料, 以7:3的比例随机分为训练集和验证集。通过单-多因素Logistic回归分析筛选与直肠癌淋巴血管浸润相关的临床独立危险因素; 分别于T2WI、扩散加权成像和T1WI增强序列手动勾画感兴趣区并提取影像组学特征, 经特征降维筛选最优影像组学特征构建影像组学模型; 结合临床预测因子与影像组学评分标签构建列线图模型。采用ROC曲线下面积、校准曲线、决策曲线分析评价模型的预测效能。 结果 列线图模型的预测效能最佳, 其曲线下面积在训练集和验证集分别为0.876(95%CI: 0.799~0.952)、0.769(95%CI: 0.600~0.938), 显著高于单独影像组学模型(0.818、0.741)和临床模型(0.714、0.548)。 结论 本研究构建的列线图模型在预测直肠癌淋巴血管浸润方面具有较高的诊断性能, 可以术前为临床决策提供重要指导。 Abstract:Objective To explore the value of a nomogram model based on multi-parametric MRI radiomics combined with clinical risk factors in preoperative prediction of lymphovascular invasion of rectal cancer. Methods A total of 112 patients who underwent preoperative multi-parametric MRI examination and were confirmed as rectal adenocarcinomas by postoperative pathology in the First Affiliated Hospital of Bengbu Medical College were retrospectively analyzed. The clinical and pelvic imaging data of the patients were collected, and they were randomly divided into training set and validation set at a ratio of 7:3. Clinical independent risk factors related to lymphovascular invasion in rectal cancer were selected through single-multiple Logistic regression analysis. Regions of interest were manually delineated on T2WI, diffusion weighted imaging and T1WI enhanced sequences, and radiomics characteristics were extracted. The optimal radiomics characteristics were selected through characteristics dimension reduction, and a radiomics model was constructed. A nomogram model was built by combining clinical predictive factors with radiomics score labels. The predictive efficiency of the model was evaluated adopting area under the ROC curve, calibration curve and decision curve analysis. Results The nomogram model showed excellent predictive efficiency, with an area under the curve of 0.876(95% CI: 0.799-0.952)and 0.769(95% CI: 0.600-0.938)for the training set and validation set respectively, which was significantly higher than the radiomics model (0.818, 0.741) and clinical model (0.714, 0.548). Conclusion The nomogram model exhibited excellent predictive efficiency in predicting lymphovascular invasion of rectal cancer, which can provide important guidance for clinical decision-making preoperatively. -
Key words:
- rectal cancer /
- lymphovascular invasion /
- radiomics /
- magnetic resonance imaging /
- nomogram model
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图 1 男，66岁，病理为直肠腺癌
Figure 1. Male, 66 years old, with a pathological diagnosis of rectal adenocarcinoma. A-C: Regions of interest were manually delineated on T2WI, DWI and T1WI enhanced sequences, respectively.
图 2 利用LASSO算法筛选出的17个最优影像学特征
Figure 2. The 17 optimal imaging features selected by LASSO algorithm.
图 3 预测直肠癌LVI的列线图模型。
Figure 3. Nomogram model for predicting LVI in rectal cancer. Rad-score: radiomics score constructed by weighted summation of optimal imaging features; CEA: serum carcinoembryonic antigen level (0: 0-5 ng/mL; 1: > 5 ng/mL). The higher the sum of the final score, the greater the risk of lymphovascular invasion in colorectal cancer.
图 4 3个模型的ROC曲线对比
Figure 4. Comparison of ROC curves for three models. A: Training set; B: Validation set.
图 5 列线图模型的校准曲线
Figure 5. Calibration curve of nomogram model. A: Training set; B: Validation set.
图 6 3个模型的DCA对比
Figure 6. Comparative analysis of DCA for three models. A: Training set; B: Validation Set.
表 1 训练集和验证集、LVI阴性组和阳性组两组间临床、病理及影像特征的比较
Table 1. Comparison of clinical, pathological, and imaging features between the training and validation sets, as well as between the LVI negative and positive groups.
Variables Train (n=77) Test (n=35) P LVI(-) (n=67) LVI(+) (n=45) P Age (year, Mean±SD) 64.62±9.16 67.40±10.98 0.166 66.22±9.83 64.40±9.76 0.337 AFP [ng/mL, M(P25, P75)] 2.45 (1.84, 3.49) 3.31 (2.16, 3.94) 0.074 2.70（2.07, 3.70） 2.39(1.70, 3.98) 0.549 CA199 [U/mL, M(P25, P75)] 8.23 (4.77, 17.11) 11.16 (4.98, 26.95) 0.104 9.44（5.33, 21.02） 9.50(3.49, 19.47) 0.531 Diameter [cm, M(P25, P75)] 4.00 (3.50, 5.00) 4.00 (3.50, 4.75) 0.648 4.00（3.50, 5.00） 4.50(3.50, 5.00) 0.410 Gender [n(%)] 0.276 0.372 female 13 (16.88) 9 (25.71) 15(22.4) 7(15.6) male 64 (83.12) 26 (74.29) 52(77.6) 38(84.4) CEA [n(%), ng/mL] 0.854 0.001 0~5 47 (61.04) 22 (62.86) 50(74.6) 19(42.2) ≥5 30 (38.96) 13 (37.14) 17(25.4) 26(57.8) Pathological grade [n(%)] 0.367 0.012 Ⅰ-Ⅱ 63 (81.82) 31 (88.57) 61(91.0) 33(73.3) Ⅲ 14 (18.18) 4 (11.43) 6(9.0) 12(26.7) Pathological type [n(%)] 0.614 0.120 Infiltrating 1 (1.30) 0 (0.00) 0(0.0) 1(2.2) Ulcerative 66 (85.71) 33 (94.29) 57(85.1) 42(93.3) Cauliflower 2 (2.60) 1 (2.86) 2(3.0) 1(2.2) Uplift 8 (10.39) 1 (2.86) 8(11.9) 1(2.2) pT stage [n(%)] 0.195 0.019 T2 22 (28.57) 6 (17.14) 22(32.8) 6(13.3) T3 55 (71.43) 29 (82.86) 45(67.2) 39(86.7) MR T stage [n(%)] 0.081 0.813 T1-2 14 (18.18) 2 (5.71) 10(14.9) 6(13.3) T3-4 63 (81.82) 33 (94.29) 57(85.1) 39(86.7) MR N stage [n(%)] 0.697 0.122 N0 9 (11.69) 5 (14.29) 9(13.4) 5(11.1) N1 21 (27.27) 7 (20.00) 21(31.3) 7(15.6) N2 47 (61.04) 23 (65.71) 37(55.2) 33(73.3) LVI: Lymphovascular invasion. 
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表 2 单-多因素Logistic回归分析筛选LVI临床危险因素
Table 2. Single-multiple factor logistic regression analysis for screening clinical risk factors of LVI.
Variables Univariate Logistic regression Multivariate Logistic regression B OR (95% CI) P B OR (95% CI) P Rad-score 0.08 0.92(0.88-0.97) 0.003 0.08 0.92(0.87-0.98) 0.009 CEA (ng/mL) 1.39 4.02(1.79-9.03) < 0.001 1.00 2.72(1.13-6.59) 0.026 Pathological grade 1.31 3.70(1.27-10.75) 0.016 1.11 3.03(0.94-9.80) 0.064 pT stage 1.16 3.18(1.17-8.63) 0.023 0.92 2.51(0.83-7.59) 0.104
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表 3 不同模型预测效能比较
Table 3. Comparison of prediction performance among different models
Models Group AUC 95% CI Sensitivity Specificity Accuracy Youden index Positive predictive value Negative predictive value Nomogram Train 0.876 0.799-0.952 0.935 0.761 0.831 0.696 0.725 0.946 Test 0.769 0.600-0.938 0.643 0.857 0.657 0.500 0.750 0.783 Radiomics Train 0.818 0.725-0.911 0.677 0.848 0.779 0.525 0.750 0.796 Test 0.741 0.559-0.924 0.714 0.762 0.743 0.476 0.667 0.800 Clinical Train 0.714 0.609-0.819 0.645 0.783 0.727 0.428 0.667 0.766 Test 0.548 0.378-0.717 0.429 0.667 0.571 0.090 0.461 0.636
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