Lacroix Maxime, Frouin Frédérique, Dirand Anne-Sophie, Nioche Christophe, Orlhac Fanny, Bernaudin Jean-François, Brillet Pierre-Yves, Buvat Irène
Service d'Imagerie Médicale, AP-HP, Hôpital Avicenne, Bobigny, France.
Laboratoire IMIV, UMR 1023 Inserm-CEA-Université Paris Sud, ERL 9218 CNRS, Université Paris Saclay, Orsay, France.
Front Oncol. 2020 Jan 31;10:43. doi: 10.3389/fonc.2020.00043. eCollection 2020.
To design and validate a preprocessing procedure dedicated to T2-weighted MR images of lung cancers so as to improve the ability of radiomic features to distinguish between adenocarcinoma and other histological types. A discovery set of 52 patients with advanced lung cancer who underwent T2-weighted MR imaging at 3 Tesla in a single center study from August 2017 to May 2019 was used. Findings were then validated using a validation set of 19 additional patients included from May to October 2019. Tumor type was obtained from the pathology report after trans-thoracic needle biopsy, metastatic lymph node or metastasis samples, or surgical excisions. MR images were preprocessed using N4ITK bias field correction and by normalizing voxel intensities with fat as a reference region. Segmentation and extraction of radiomic features were performed with LIFEx software on the raw images, on the N4ITK-corrected images and on the fully preprocessed images. Two analyses were conducted where radiomic features were extracted: (1) from the whole tumor volume (3D analysis); (2) from all slices encompassing the tumor (2D analysis). Receiver operating characteristic (ROC) analysis was used to identify features that could distinguish between adenocarcinoma and other histological types. Sham experiments were also designed to control the number of false positive findings. There were 31 (12) adenocarcinomas and 21 (7) other histological types in the discovery (validation) set. In 2D, preprocessing increased the number of discriminant radiomic features from 8 without preprocessing to 22 with preprocessing. 2D analysis yielded more features able to identify adenocarcinoma than 3D analysis (12 discriminant radiomic features after preprocessing in 3D). Preprocessing did not increase false positive findings as no discriminant features were identified in any of the sham experiments. The greatest sensitivity of the 2D analysis applied to preprocessed data was confirmed in the validation set. Correction for magnetic field inhomogeneities and normalization of voxel values are essential to reveal the full potential of radiomic features to identify the tumor histological type from MR T2-weighted images, with classification performance similar to those reported in PET/CT and in multiphase CT in lung cancers.
设计并验证一种专门用于肺癌T2加权磁共振成像的预处理程序，以提高放射组学特征区分腺癌和其他组织学类型的能力。使用了一个发现集，该发现集包含52例晚期肺癌患者，他们于2017年8月至2019年5月在单中心研究中接受了3特斯拉的T2加权磁共振成像检查。然后，使用2019年5月至10月纳入的另外19例患者的验证集对结果进行验证。肿瘤类型通过经胸针吸活检、转移性淋巴结或转移样本或手术切除后的病理报告获得。磁共振图像使用N4ITK偏置场校正并以脂肪作为参考区域对体素强度进行归一化处理来进行预处理。使用LIFEx软件在原始图像、N4ITK校正后的图像和完全预处理后的图像上进行放射组学特征的分割和提取。进行了两项提取放射组学特征的分析：（1）从整个肿瘤体积进行分析（三维分析）；（2）从包含肿瘤的所有切片进行分析（二维分析）。使用受试者操作特征（ROC）分析来识别能够区分腺癌和其他组织学类型的特征。还设计了假实验以控制假阳性结果的数量。在发现（验证）集中有31例（12例）腺癌和21例（7例）其他组织学类型。在二维分析中，预处理将无预处理时的判别放射组学特征数量从8个增加到有预处理时的22个。二维分析产生的能够识别腺癌的特征比三维分析更多（三维分析在预处理后有12个判别放射组学特征）。预处理没有增加假阳性结果，因为在任何假实验中均未识别出判别特征。应用于预处理数据的二维分析的最大敏感性在验证集中得到了证实。校正磁场不均匀性和体素值归一化对于揭示放射组学特征从磁共振T2加权图像中识别肿瘤组织学类型的全部潜力至关重要，其分类性能与肺癌的PET/CT和多期CT中报道的性能相似。
