Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy.
Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
World J Gastroenterol. 2023 May 21;29(19):2888-2904. doi: 10.3748/wjg.v29.i19.2888.
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting and gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
结直肠癌的主要治疗选择是非转移性疾病的手术切除和辅助化疗。然而,评估具有高复发风险的患者的整体辅助化疗益处具有挑战性。放射影像学可以提供数据来源,这些数据可以通过使用自动基于计算机的技术进行分析,这些技术可作用于数字成像和通信在医学文件中编码的信息:这种图像数字分析被称为“放射组学”。放射组学允许从放射学图像中提取定量特征,这些特征主要肉眼不可见,然后可以通过人工智能算法进一步分析。放射组学在肿瘤学领域不断发展,既可以了解肿瘤生物学,也可以开发用于诊断、分期和预后、预测治疗反应以及疾病监测和随访的成像生物标志物。已经做出了一些努力来使用计算机断层扫描 (CT) 图像为结直肠癌患者开发放射组学特征,目的不同:术前预测淋巴结转移、检测和基因突变。此外,使用 delta-radiomics 可以分析从不同时间点进行的 CT 扫描中提取的放射组学参数的变化。大多数关于放射组学和磁共振成像 (MRI) 的已发表研究主要集中在接受新辅助治疗的晚期肿瘤的反应上。淋巴结状态是辅助化疗的主要决定因素。因此,已经开发了几种基于 MRI 的放射组学模型,特别是在 T2 加权图像和 ADC 图上,用于预测直肠癌的淋巴结转移。目前的研究主要集中在放射组学在正电子发射断层扫描/CT 中的应用,用于预测根治性手术后的生存和新辅助放化疗后的反应评估。由于大约 25%的结直肠癌患者会发展为结直肠肝转移,因此放射组学的主要诊断任务应该是检测同步和异时性病变。放射组学可以成为临床环境中的一种附加工具,尤其是在识别高危疾病患者方面。然而,放射组学存在许多缺点,使其日常使用极其困难。需要进一步研究来评估放射组学在高危疾病患者分层中的性能。