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Abstract：
BACKGROUND: To investigate the association between Kirsten rat sarcoma viral oncogene homolog (KRAS) / neuroblastoma rat sarcoma viral oncogene homolog (NRAS) /v-raf murine sarcoma viral oncogene homolog B (BRAF) mutations and the tumor habitat-derived radiomic features obtained during pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in patients with colorectal cancer (CRC).
METHODS: We retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model.
RESULTS: The model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585-0.909) on the training cohort and that of 0.701 (95% CI: 0.468-0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection.
CONCLUSIONS: The habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.
METHODS: We retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model.
RESULTS: The model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585-0.909) on the training cohort and that of 0.701 (95% CI: 0.468-0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection.
CONCLUSIONS: The habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.
摘要：
背景：研究Kirsten大鼠肉瘤病毒癌基因同源物（KRAS）/神经母细胞瘤大鼠肉瘤病毒癌基因同源物（NRAS）/v-raf鼠肉瘤病毒癌基因同源物B（BRAF）突变与CRC患者（FDF-脱氧葡萄糖（G）正电子断层扫描）预处理过程中获得的肿瘤栖息地来源的放射组学特征之间的关系。
方法：我们回顾性招募了62例CRC患者，这些患者在治疗开始前于2017年1月至2022年7月接受了18F-FDGPET/计算机断层扫描。患者被随机分为训练和验证队列，比例为6:4。整个肿瘤区域的放射学特征，栖息地衍生的放射学特征，从18F-FDGPET图像中提取代谢参数。减少特征尺寸并选择有意义的特征后，利用支持向量机构建了KRAS/NRAS/BRAF突变的层次模型。利用学习曲线对模型的收敛性进行了评价,并根据受试者工作特征曲线下面积（AUC）评估其性能，校正曲线,和决策曲线分析。Shapley加法扩张用于解释各种特征对模型预测的贡献。
结果：使用栖息地衍生的放射学特征构建的模型对KRAS/NRAS/BRAF突变具有足够的预测能力，训练队列的AUC为0.759（95％CI：0.585-0.909），验证队列的AUC为0.701（95％CI：0.468-0.916）。模型表现出良好的收敛性，合适的校准，和临床应用价值。Shapley加法解释的结果表明，瘤周生境和高代谢生境对模型预测的影响最大。在特征选择过程中，没有保留有意义的整个肿瘤区域放射学特征或代谢参数。
结论：研究发现栖息地来源的放射学特征有助于对CRC患者的KRAS/NRAS/BRAF状态进行分层。本文提出的方法对CRC患者的辅助治疗决策具有重要意义。并且需要在更大的前瞻性队列中进一步验证。
方法：我们回顾性招募了62例CRC患者，这些患者在治疗开始前于2017年1月至2022年7月接受了18F-FDGPET/计算机断层扫描。患者被随机分为训练和验证队列，比例为6:4。整个肿瘤区域的放射学特征，栖息地衍生的放射学特征，从18F-FDGPET图像中提取代谢参数。减少特征尺寸并选择有意义的特征后，利用支持向量机构建了KRAS/NRAS/BRAF突变的层次模型。利用学习曲线对模型的收敛性进行了评价,并根据受试者工作特征曲线下面积（AUC）评估其性能，校正曲线,和决策曲线分析。Shapley加法扩张用于解释各种特征对模型预测的贡献。
结果：使用栖息地衍生的放射学特征构建的模型对KRAS/NRAS/BRAF突变具有足够的预测能力，训练队列的AUC为0.759（95％CI：0.585-0.909），验证队列的AUC为0.701（95％CI：0.468-0.916）。模型表现出良好的收敛性，合适的校准，和临床应用价值。Shapley加法解释的结果表明，瘤周生境和高代谢生境对模型预测的影响最大。在特征选择过程中，没有保留有意义的整个肿瘤区域放射学特征或代谢参数。
结论：研究发现栖息地来源的放射学特征有助于对CRC患者的KRAS/NRAS/BRAF状态进行分层。本文提出的方法对CRC患者的辅助治疗决策具有重要意义。并且需要在更大的前瞻性队列中进一步验证。
