Granata Vincenza, Fusco Roberta, De Muzio Federica, Brunese Maria Chiara, Setola Sergio Venanzio, Ottaiano Alessandro, Cardone Claudia, Avallone Antonio, Patrone Renato, Pradella Silvia, Miele Vittorio, Tatangelo Fabiana, Cutolo Carmen, Maggialetti Nicola, Caruso Damiano, Izzo Francesco, Petrillo Antonella
Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
Medical Oncology Division, Igea SpA, Naples, Italy.
Radiol Med. 2023 Nov;128(11):1310-1332. doi: 10.1007/s11547-023-01710-w. Epub 2023 Sep 11.
The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.
The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.
The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.
The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
本研究旨在评估使用计算机断层扫描（CT）和磁共振成像进行的放射组学分析在预测与患者预后相关的结直肠癌肝转移模式方面的功效：肿瘤生长前沿；分级；肿瘤芽生；黏液类型。此外，还评估了肝复发的预测情况。
这项回顾性研究包括一个内部数据集和一个验证数据集；第一个数据集由49例患者的119个肝转移灶组成，第二个数据集由28例单发病变患者组成。使用PyRadiomics提取放射组学特征。采用了包括机器学习算法在内的单变量和多变量方法。
识别肿瘤生长的最佳预测因子是小波_HLH_glcm_最大概率，准确率为84%，检测复发的最佳预测因子是小波_HLH_ngtdm_复杂度，准确率为90%，两者均从T1加权动脉期序列中提取。检测肿瘤芽生的最佳预测因子是小波_LLH_glcm_Imc1，准确率为88%，识别黏液类型的最佳预测因子是小波_LLH_glcm_联合熵，准确率为92%，两者均根据T2加权序列计算得出。使用由T2加权图像提取的15个预测因子的线性加权组合来检测肿瘤前沿生长，准确率有统计学意义的提高（90%）。使用由T1加权动脉期序列提取的11个预测因子的线性加权组合对肿瘤芽生进行分类，准确率有统计学意义的提高，达到93%。使用在CT上提取的16个预测因子的线性加权组合检测复发，准确率有统计学意义的提高，达到97%。在考虑K近邻和从T1加权动脉期序列中提取的11个显著特征的情况下，肿瘤芽生识别的准确率有统计学意义的提高。
结果证实了放射组学识别临床和组织病理学预后特征的能力，这些特征应会影响结直肠癌肝转移患者的治疗选择，以获得更个性化的治疗。
