Huang Zixing, Zhang Wei, He Du, Cui Xing, Tian Song, Yin Hongkun, Song Bin
Department of Radiology, West China Hospital, Sichuan University, Chengdu.
Department of Radiology, Sichuan Provincial Corps Hospital, Chinese People' s Armed Police Forces, Leshan.
Medicine (Baltimore). 2020 Mar;99(10):e19428. doi: 10.1097/MD.0000000000019428.
Globally, colorectal cancer (CRC) is the third most commonly diagnosed cancer in males and the second in females. Rectal cancer (RC) accounts for about 28% of all newly diagnosed CRC cases. The treatment of choice for locally advanced RC is a combination of surgical resection and chemotherapy and/or radiotherapy. These patients can potentially be cured, but the clinical outcome depends on the tumor biology. Microsatellite instability (MSI) is an important biomarker in CRC, with crucial diagnostic, prognostic, and predictive implications. It is important to develop a noninvasive, repeatable, and reproducible method to reflect the microsatellite status. Magnetic resonance imaging (MRI) has been recommended as the preferred imaging examination for RC in clinical practice by both the National Comprehensive Cancer Network and the European Society for Medical Oncology guidelines. T2WI is the core sequence of MRI scanning protocol for RC. Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research.We proposed a hypothesis: A simple radiomics model based on only T2WI images can accurately evaluate the MSI status of RC preoperatively.
To develop a radiomics model based on T2WI images for accurate preoperative diagnosis the MSI status of RC.
All patients with RC were retrospectively enrolled. The dataset was randomly split into training cohort (70% of all patients) and testing cohort (30% of all patients). The radiomics features will be extracted from T2WI-MR images of the entire primary tumor region. Least absolute shrinkage and selection operator was used to select the most predictive radiomics features. Logistic regression models were constructed in the training/validation cohort to discriminate the MSI status using clinical factors, radiomics features, or their integration. The diagnostic performance of these 3 models was evaluated in the testing cohort based on their area under the curve, sensitivity, specificity, and accuracy.
This study will help us know whether radiomics model based on T2WI images to preoperative identify MSI status of RC.
在全球范围内，结直肠癌（CRC）是男性中第三大最常被诊断出的癌症，在女性中是第二大。直肠癌（RC）约占所有新诊断CRC病例的28%。局部晚期RC的首选治疗方法是手术切除与化疗和/或放疗相结合。这些患者有可能被治愈，但临床结果取决于肿瘤生物学特性。微卫星不稳定性（MSI）是CRC中的一个重要生物标志物，具有关键的诊断、预后和预测意义。开发一种无创、可重复且可再现的方法来反映微卫星状态很重要。磁共振成像（MRI）已被美国国立综合癌症网络和欧洲医学肿瘤学会指南推荐为临床实践中RC的首选影像学检查。T2WI是RC的MRI扫描方案的核心序列。放射组学是从标准医疗影像中高通量挖掘定量图像特征，使数据能够在临床决策支持系统中被提取和应用以提高诊断、预后和预测准确性，在癌症研究中越来越重要。我们提出一个假设：一个仅基于T2WI图像的简单放射组学模型能够在术前准确评估RC的MSI状态。
开发一种基于T2WI图像的放射组学模型，用于准确术前诊断RC的MSI状态。
回顾性纳入所有RC患者。数据集被随机分为训练队列（所有患者的70%）和测试队列（所有患者的30%）。将从整个原发肿瘤区域的T2WI-MR图像中提取放射组学特征。使用最小绝对收缩和选择算子来选择最具预测性的放射组学特征。在训练/验证队列中构建逻辑回归模型，以使用临床因素、放射组学特征或它们的整合来区分MSI状态。基于曲线下面积、敏感性、特异性和准确性，在测试队列中评估这3个模型的诊断性能。
本研究将帮助我们了解基于T2WI图像的放射组学模型是否能在术前识别RC的MSI状态。
