Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
J Magn Reson Imaging. 2022 Dec;56(6):1659-1668. doi: 10.1002/jmri.28237. Epub 2022 May 19.
Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear.
To develop a DL method based on T2-weighted imaging (T2WI) and clinical factors for noninvasively evaluating KRAS mutation in rectal cancer.
Retrospective.
A total of 376 patients (108 women [28.7%]) with histopathology-confirmed rectal adenocarcinoma and KRAS mutation status.
FIELD STRENGTH/SEQUENCE: A 3 T, turbo spin echo T2WI and single-shot echo-planar diffusion-weighted imaging (b = 0, 1000 sec/mm ).
A clinical model was constructed with clinical factors (age, gender, carcinoembryonic antigen level, and carbohydrate antigen 199 level) and MRI features (tumor length, tumor location, tumor stage, lymph node stage, and extramural vascular invasion), and two DL models based on modified MobileNetV2 architecture were evaluated for diagnosing KRAS mutation based on T2WI alone (image model) or both T2WI and clinical factors (combined model). The clinical usefulness of these models was evaluated through calibration analysis and decision curve analysis (DCA).
Mann-Whitney U test, Chi-squared test, Fisher's exact test, logistic regression analysis, receiver operating characteristic curve (ROC), Delong's test, Hosmer-Lemeshow test, interclass correlation coefficients, and Fleiss kappa coefficients (P < 0.05 was considered statistically significant).
All the nine clinical-MRI characteristics were included for clinical model development. The clinical model, image model, and combined model in the testing cohort demonstrated good calibration and achieved areas under the curve (AUCs) of 0.668, 0.765, and 0.841, respectively. The combined model showed improved performance compared to the clinical model and image model in two cohorts. DCA confirmed the higher net benefit of the combined model than the other two models when the threshold probability is between 0.05 and 0.85.
The proposed combined DL model incorporating T2WI and clinical factors may show good diagnostic performance. Thus, it could potentially serve as a supplementary approach for noninvasively evaluating KRAS mutation in rectal cancer.
3 TECHNICAL EFFICACY: Stage 2.
最近的研究表明，基于 MRI 的深度学习（DL）在评估直肠癌治疗反应方面具有潜力，但基于 MRI 的 DL 在评估 Kirsten 大鼠肉瘤病毒致癌基因同源物（KRAS）突变中的作用尚不清楚。
开发一种基于 T2 加权成像（T2WI）和临床因素的 DL 方法，用于无创评估直肠癌中的 KRAS 突变。
回顾性。
共 376 名（108 名女性[28.7%]）经组织病理学证实的直肠腺癌患者和 KRAS 突变状态。
磁场强度/序列：3T，涡轮自旋回波 T2WI 和单次回波平面扩散加权成像（b=0，1000 sec/mm）。
构建了一个包含临床因素（年龄、性别、癌胚抗原水平和碳水化合物抗原 199 水平）和 MRI 特征（肿瘤长度、肿瘤位置、肿瘤分期、淋巴结分期和外膜血管侵犯）的临床模型，并评估了两种基于改进的 MobileNetV2 架构的 DL 模型，以基于 T2WI （图像模型）或 T2WI 和临床因素（联合模型）诊断 KRAS 突变。通过校准分析和决策曲线分析（DCA）评估这些模型的临床实用性。
Mann-Whitney U 检验、卡方检验、Fisher 精确检验、逻辑回归分析、受试者工作特征曲线（ROC）、Delong 检验、Hosmer-Lemeshow 检验、组内相关系数和 Fleiss kappa 系数（P<0.05 被认为具有统计学意义）。
所有九个临床-MRI 特征均被纳入临床模型开发。在测试队列中，临床模型、图像模型和联合模型均表现出良好的校准度，曲线下面积（AUCs）分别为 0.668、0.765 和 0.841。联合模型在两个队列中的表现均优于临床模型和图像模型。DCA 证实，当阈值概率在 0.05 到 0.85 之间时，联合模型的净获益高于其他两个模型。
所提出的联合 DL 模型结合了 T2WI 和临床因素，可能具有良好的诊断性能。因此，它可能有潜力作为一种无创评估直肠癌 KRAS 突变的补充方法。
3 级技术功效。
