Granata Vincenza, Fusco Roberta, De Muzio Federica, Cutolo Carmen, Setola Sergio Venanzio, Grassi Roberta, Grassi Francesca, Ottaiano Alessandro, Nasti Guglielmo, Tatangelo Fabiana, Pilone Vincenzo, Miele Vittorio, Brunese Maria Chiara, Izzo Francesco, Petrillo Antonella
Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
Medical Oncology Division, Igea SpA, Napoli, Italy.
Radiol Med. 2022 May;127(5):461-470. doi: 10.1007/s11547-022-01477-6. Epub 2022 Mar 26.
To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients.
This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered.
The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet_LHL_gldm_DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet_LLH_glcm_Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet_LLH_glcm_JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet_LLL_glcm_Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%.
Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.
评估通过T2加权序列获得的影像组学特征预测结直肠癌肝转移患者肝切除术后临床结局的效能。
本回顾性分析经当地伦理委员会批准，检索2018年1月至2021年5月的放射学数据库，以选择术前有病理证实且行MRI检查的肝转移患者。患者队列包括一个训练集和一个外部验证集。内部训练集包括51例患者，中位年龄61岁，有121个肝转移灶。验证队列共有30例单发病灶患者，中位年龄60岁。使用PyRadiomics提取每个感兴趣体积的851个影像组学特征作为中位数。考虑了非参数检验、组内相关性、受试者操作特征（ROC）分析、线性回归建模和模式识别方法（支持向量机（SVM）、k近邻（KNN）、人工神经网络（NNET）和决策树（DT））。
区分肿瘤生长的膨胀性与浸润性前沿的最佳预测指标是wavelet_LHL_gldm_DependenceNonUniformityNormalized，准确率为82%；区分高分级与低分级或无分级的是wavelet_LLH_glcm_Imc1，准确率为88%；区分黏液性肿瘤类型的是wavelet_LLH_glcm_JointEntropy，准确率为92%，而识别肿瘤复发的是wavelet_LLL_glcm_Correlation，准确率为85%。线性回归模型仅在区分肿瘤生长的膨胀性与浸润性前沿方面提高了单变量分析的性能，准确率达到90%，敏感性为95%，特异性为80%。考虑到用模式识别方法测试的显著纹理指标，KNN在考虑四个纹理预测指标区分肿瘤芽生方面表现最佳，准确率为93%，敏感性为81%，特异性为97%。
我们的结果证实了影像组学能够识别多种可作为生物标志物的预后特征，这些特征可能影响肝转移患者的治疗选择，从而获得更个性化的治疗方法。
