Abstract
ObjectiveTo develop and validate a multiregional-based magnetic resonance imaging (MRI) radiomics model and combine it with clinical data for individual preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.Methods186 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 123) and testing cohorts (n = 63). Spearman’s rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Five support vector machine (SVM) classification models were built using selected clinical and semantic variables, single-regional radiomics features, multiregional radiomics features, and combinations, for predicting LN metastasis in rectal cancer. The performance of the five SVM models was evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing cohort. Differences in the AUCs among the five models were compared using DeLong’s test.ResultsThe clinical, single-regional radiomics and multiregional radiomics models showed moderate predictive performance and diagnostic accuracy in predicting LN metastasis with an AUC of 0.725, 0.702, and 0.736, respectively. A model with improved performance was created by combining clinical data with single-regional radiomics features (AUC = 0.827, (95% CI, 0.711–0.911), P = 0.016). Incorporating clinical data with multiregional radiomics features also improved the performance (AUC = 0.832 (95% CI, 0.717–0.915), P = 0.015).ConclusionMultiregional-based MRI radiomics combined with clinical data can improve efficacy in predicting LN metastasis and could be a useful tool to guide surgical decision-making in patients with rectal cancer.
Highlights
Colorectal cancer was the third most common type of malignant tumor and the second leading cause of cancer death in the world in 2018 [1]
This study aimed to develop and validate a multiregional radiomics prediction model based on Magnetic resonance imaging (MRI) and combine it with clinical-semantic data for the individualized preoperative prediction of Lymph node (LN) metastasis in rectal cancer patients
We explored the diagnostic value of multiple models which included clinical factors, single-regional radiomics, multiregional radiomics, and combinations of clinical and radiomics models based on MRI to preoperatively predict LN metastasis in patients with rectal cancer
Summary
Colorectal cancer was the third most common type of malignant tumor and the second leading cause of cancer death in the world in 2018 [1]. Accurate preoperative assessment of LN status or assessment of the N stages of regional LNs in rectal cancer patients via medical imaging is essential for precise individualized decision making and patient prognosis [2, 6, 7]. Magnetic resonance imaging (MRI) is considered the most accurate method to assess the primary staging of rectal cancer [2]. All diagnostic clues rely heavily on the size, shape, and margins of LNs, but these semantic characteristics alone are insufficient to reliably distinguish malignant from benign LNs in rectal cancer patients [2, 4, 5, 9]
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