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Abstract：
OBJECTIVE: Exploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model.
METHODS: A total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM.
RESULTS: The AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively.
CONCLUSIONS: The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.
METHODS: A total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM.
RESULTS: The AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively.
CONCLUSIONS: The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.
摘要：
目的：探讨在建立预测模型的特征选择过程中加入相关分析(盆腔转移淋巴结和原发灶的影像组学特征(RFs))筛选原发灶RFs的价值。
方法：共有394名前列腺癌（PCa）患者（训练组263名，来自两家三级医院的内部验证组中的74例和外部验证组中的57例)被纳入研究。训练组盆腔淋巴结转移(PLNM)阳性患者经活检或MRI诊断为短轴直径≥1.5cm,训练组的PLNM阴性病例和验证组的所有病例均接受了根治性前列腺切除术（RP）和扩大盆腔淋巴结清扫术（ePLND）。从T2WI和表观扩散系数(ADC)图谱中提取训练组PLNM阴性病灶和PLNM阳性组织包括原发灶及其转移淋巴结(MLNs)的RFs,通过5倍交叉验证建立以下两个模型:病灶模型,根据t检验和绝对收缩和选择算子(LASSO)选择的原发病变RFs建立；病变相关模型，根据Pearson相关性分析选择的原发病灶RFs(原发病灶及其MLN的RFs，相关系数>0.9)，t测试和LASSO。最后,我们比较了这两种模型在预测PLNM方面的表现。
结果：病变模型和病变相关模型的AUC和AUC的DeLong检验如下：训练组（0.8053，0.8466，p=0.0002），内部验证组（0.7321，0.8268，p=0.0429），和外部验证组(0.6445，0.7874，p=0.0431)，分别。
结论：根据与MLN相关的原发肿瘤特征建立的病变相关模型在预测PLNM方面比病变模型更具优势。
方法：共有394名前列腺癌（PCa）患者（训练组263名，来自两家三级医院的内部验证组中的74例和外部验证组中的57例)被纳入研究。训练组盆腔淋巴结转移(PLNM)阳性患者经活检或MRI诊断为短轴直径≥1.5cm,训练组的PLNM阴性病例和验证组的所有病例均接受了根治性前列腺切除术（RP）和扩大盆腔淋巴结清扫术（ePLND）。从T2WI和表观扩散系数(ADC)图谱中提取训练组PLNM阴性病灶和PLNM阳性组织包括原发灶及其转移淋巴结(MLNs)的RFs,通过5倍交叉验证建立以下两个模型:病灶模型,根据t检验和绝对收缩和选择算子(LASSO)选择的原发病变RFs建立；病变相关模型，根据Pearson相关性分析选择的原发病灶RFs(原发病灶及其MLN的RFs，相关系数>0.9)，t测试和LASSO。最后,我们比较了这两种模型在预测PLNM方面的表现。
结果：病变模型和病变相关模型的AUC和AUC的DeLong检验如下：训练组（0.8053，0.8466，p=0.0002），内部验证组（0.7321，0.8268，p=0.0429），和外部验证组(0.6445，0.7874，p=0.0431)，分别。
结论：根据与MLN相关的原发肿瘤特征建立的病变相关模型在预测PLNM方面比病变模型更具优势。
