Huang Yi-Ching, Tsai Yi-Shan, Li Chung-I, Chan Ren-Hao, Yeh Yu-Min, Chen Po-Chuan, Shen Meng-Ru, Lin Peng-Chan
Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.
Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.
Cancers (Basel). 2022 Apr 8;14(8):1895. doi: 10.3390/cancers14081895.
To evaluate whether adjusted computed tomography (CT) scan image-based radiomics combined with immune genomic expression can achieve accurate stratification of cancer recurrence and identify potential therapeutic targets in stage III colorectal cancer (CRC), this cohort study enrolled 71 patients with postoperative stage III CRC. Based on preoperative CT scans, radiomic features were extracted and selected to build pixel image data using covariate-adjusted tensor classification in the high-dimension (CATCH) model. The differentially expressed RNA genes, as radiomic covariates, were identified by cancer recurrence. Predictive models were built using the pixel image and immune genomic expression factors, and the area under the curve (AUC) and F1 score were used to evaluate their performance. Significantly adjusted radiomic features were selected to predict recurrence. The association between the significantly adjusted radiomic features and immune gene expression was also investigated. Overall, 1037 radiomic features were converted into 33 × 32-pixel image data. Thirty differentially expressed genes were identified. We performed 100 iterations of 3-fold cross-validation to evaluate the performance of the CATCH model, which showed a high sensitivity of 0.66 and an F1 score of 0.69. The area under the curve (AUC) was 0.56. Overall, ten adjusted radiomic features were significantly associated with cancer recurrence in the CATCH model. All of these methods are texture-associated radiomics. Compared with non-adjusted radiomics, 7 out of 10 adjusted radiomic features influenced recurrence-free survival. The adjusted radiomic features were positively associated with , , , , , and expression. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. Adjusted CT scan image-based radiomics with immune genomic expression covariates using the CATCH model can efficiently predict cancer recurrence. The correlation between adjusted radiomic features and immune genomic expression can provide biological relevance and individualized therapeutic targets.
为了评估基于调整后的计算机断层扫描（CT）图像的放射组学与免疫基因组表达相结合是否能够实现对癌症复发的准确分层，并识别III期结直肠癌（CRC）的潜在治疗靶点，这项队列研究纳入了71例III期CRC术后患者。基于术前CT扫描，提取并选择放射组学特征，使用高维协变量调整张量分类（CATCH）模型构建像素图像数据。通过癌症复发确定差异表达的RNA基因作为放射组学协变量。使用像素图像和免疫基因组表达因子构建预测模型，并使用曲线下面积（AUC）和F1分数评估其性能。选择显著调整后的放射组学特征来预测复发。还研究了显著调整后的放射组学特征与免疫基因表达之间的关联。总体而言，1037个放射组学特征被转换为33×32像素的图像数据。识别出30个差异表达基因。我们进行了100次3折交叉验证以评估CATCH模型的性能，该模型显示出0.66的高敏感性和0.69的F1分数。曲线下面积（AUC）为0.56。总体而言，在CATCH模型中，10个调整后的放射组学特征与癌症复发显著相关。所有这些方法都是与纹理相关的放射组学。与未调整的放射组学相比，10个调整后的放射组学特征中有7个影响无复发生存。调整后的放射组学特征与 、 、 、 、 和 表达呈正相关。我们为复发的III期CRC患者提供基于调整后的放射组学特征的个体化癌症治疗策略。使用CATCH模型结合免疫基因组表达协变量的基于调整后的CT扫描图像的放射组学能够有效预测癌症复发。调整后的放射组学特征与免疫基因组表达之间的相关性可以提供生物学相关性和个体化治疗靶点。
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