PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value forp≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.
目的：本研究旨在比较三种不同放射组学模型（逻辑回归、随机森林和支持向量机）与临床列线图（Briganti、MSKCC、Yale和Roach）在预测前列腺癌患者淋巴结受累方面的性能。材料与方法：这项回顾性研究纳入了95例接受多参数磁共振成像、根治性前列腺切除术及盆腔淋巴结清扫术的前列腺癌患者。收集每位患者的影像学数据（T2、DWI、ADC序列信号强度及PIRADS评分）、临床数据（年龄和MRI前PSA值）、病理学数据（Gleason评分、TNM分期、组织学类型、包膜侵犯、精囊侵犯和神经血管束侵犯）以及临床列线图（Yale、Roach、MSKCC和Briganti）。使用开源软件对每位患者的靶病灶进行手动分割，并基于Pyradiomics库从各序列中提取放射组学特征。通过ChatGPT软件独立训练和测试三种放射组学模型，计算各特征系数值，并采用准确率和曲线下面积评估模型性能。结果：本研究共识别出每位患者343个特征，其中随机森林模型在DWI和ADC序列中表现出最佳预测性能。结论：在整合放射组学与语义学数据构建的预测模型中，随机森林模型在前列腺癌淋巴结受累预测方面较临床列线图具有更高的诊断准确性。
肿瘤(癌症)患者之家