Lee Hyunjong, Moon Seung Hwan, Hong Jung Yong, Lee Jeeyun, Hyun Seung Hyup
Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea.
Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea.
Cancers (Basel). 2023 Jul 28;15(15):3841. doi: 10.3390/cancers15153841.
We assessed the performance of F-18 fluorodeoxyglucose positron emission tomography (FDG PET)-based radiomics for the prediction of tumor mutational burden (TMB) and prognosis using a machine learning (ML) approach in patients with stage IV colorectal cancer (CRC).
Ninety-one CRC patients who underwent pretreatment FDG PET/computed tomography (CT) and palliative chemotherapy were retrospectively included. PET-based radiomics were extracted from the primary tumor on PET imaging using the software LIFEx. For feature selection, PET-based radiomics associated with TMB were selected by logistic regression analysis. The performances of seven ML algorithms to predict high TMB were compared by the area under the receiver's operating characteristic curves (AUCs) and validated by five-fold cross-validation. A PET radiomic score was calculated by averaging the z-score of each radiomic feature. The prognostic power of the PET radiomic score was assessed using Cox proportional hazards regression analysis.
Ten significant radiomic features associated with TMB were selected: surface-to-volume ratio, total lesion glycolysis, tumor volume, area, compacity, complexity, entropy, correlation, coarseness, and zone size non-uniformity. The k-nearest neighbors model obtained the good performance for prediction of high TMB (AUC: 0.791, accuracy: 0.814, sensitivity: 0.619, specificity: 0.871). On multivariable Cox regression analysis, the PET radiomic score (Hazard ratio = 4.498, 95% confidential interval = 1.024-19.759; = 0.046) was a significant independent prognostic factor for OS.
This study demonstrates that PET-based radiomics are useful image biomarkers for the prediction of TMB status in stage IV CRC. PET radiomic score, which integrates significant radiomic features, has the potential to predict survival in stage IV CRC patients.
我们采用机器学习（ML）方法，评估基于F-18氟脱氧葡萄糖正电子发射断层扫描（FDG PET）的放射组学在预测IV期结直肠癌（CRC）患者肿瘤突变负荷（TMB）及预后方面的表现。
回顾性纳入91例接受过治疗前FDG PET/计算机断层扫描（CT）及姑息化疗的CRC患者。使用LIFEx软件从PET成像上的原发性肿瘤中提取基于PET的放射组学特征。对于特征选择，通过逻辑回归分析选择与TMB相关的基于PET的放射组学特征。通过受试者操作特征曲线下面积（AUC）比较7种ML算法预测高TMB的表现，并通过五折交叉验证进行验证。通过对每个放射组学特征的z分数求平均值来计算PET放射组学评分。使用Cox比例风险回归分析评估PET放射组学评分的预后能力。
选择了10个与TMB相关的显著放射组学特征：表面积与体积比、总病变糖酵解、肿瘤体积、面积、紧密度、复杂性、熵、相关性、粗糙度和区域大小不均匀性。k近邻模型在预测高TMB方面表现良好（AUC：0.791，准确率：0.814，灵敏度：0.619，特异性：0.871）。在多变量Cox回归分析中，PET放射组学评分（风险比 = 4.498，95%置信区间 = 1.024 - 19.759；P = 0.046）是总生存期的显著独立预后因素。
本研究表明，基于PET的放射组学是预测IV期CRC患者TMB状态的有用图像生物标志物。整合了显著放射组学特征的PET放射组学评分有潜力预测IV期CRC患者的生存情况。
