2020
DOI: 10.1007/s00330-019-06572-3
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Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer

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Cited by 85 publications
(65 citation statements)
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“…Another machine learning approach radiomics rapidly developed in recent years can be widely available through open-source software and the radiomics signature is easily utilized. The potential for diagnosing and predicting outcomes of different lesions has been proven in the prior reproducible investigations [14,15], as well as our previous studies in predicting preoperative synchronous distant metastasis in patients with rectal cancer [30,31]. In this study, 8 radiomics features, mainly focus on the textural features, were selected to build the radiomics signature and the proposed combined radiomics model performed well not only in the training cohort but also in the validation and testing cohorts with AUCs of 1.00, 0.98, and 0.93, respectively.…”
Section: Discussionmentioning
confidence: 90%
“…Another machine learning approach radiomics rapidly developed in recent years can be widely available through open-source software and the radiomics signature is easily utilized. The potential for diagnosing and predicting outcomes of different lesions has been proven in the prior reproducible investigations [14,15], as well as our previous studies in predicting preoperative synchronous distant metastasis in patients with rectal cancer [30,31]. In this study, 8 radiomics features, mainly focus on the textural features, were selected to build the radiomics signature and the proposed combined radiomics model performed well not only in the training cohort but also in the validation and testing cohorts with AUCs of 1.00, 0.98, and 0.93, respectively.…”
Section: Discussionmentioning
confidence: 90%
“…Furthermore, this study only refers to T2-weighted (T2w) MRI leaving the door open for further research including other magnetic resonance imaging sequences. In other study, Cui et al also presented a T2w MRI-based radiomics signature for predicting KRAS mutation in rectal cancer [32]. The evaluation on an external validation dataset (n = 86) yielded an accuracy, sensitivity and specificity lower than ours (i.e., 69.8%, 71.1%, and 68.8%, respectively).…”
Section: Discussionmentioning
confidence: 56%
“…Another machine learning approach radiomics rapidly developed in recent years can be widely available through open-source software and the radiomics signature is easily utilized. The potential for diagnosing and predicting outcomes of different lesions has been proven in the prior reproducible investigations [14,15], as well as our previous studies in predicting preoperative synchronous distant metastasis in patients with rectal cancer [28,29]. In this study, 8 radiomics features, mainly focus on the textural features, were selected to build the radiomics signature and the proposed combined radiomics model performed well not only in the training cohort but also in the validation and testing cohorts with AUCs of 1.00, 0.98, and 0.93, respectively.…”
Section: Discussionmentioning
confidence: 90%