Development and Validation of a Radiomics Model Based on 3-Dimensional Endoanal Rectal Ultrasound of Rectal Cancer for Predicting Lymph Node Metastasis
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Abstract
Background: Development of a radiomics model for predicting lymph node metastasis status in rectal cancer patients based on 3-dimensional endoanal rectal ultrasound images.
Methods: This study retrospectively included 79 patients (41 with lymph node metastasis positive and 38 with lymph node metastasis negative) diagnosed with rectal cancer in our hospital from January 2018 to February 2022. The tumor’s region of interest is first delineated by radiologists, from which radiomics features are extracted. Radiomics features were then selected by independent samples t-test, correlation coefficient analysis between features, and least absolute shrinkage and regression with selection operator. Finally, a multilayer neural network model is developed using the selected radiomics features, and nested cross-validation is performed on it. These models were validated by assessing their diagnostic performance and comparing the areas under the curve and recall rate curve in the test set.
Results: The areas under the curve of radiologist was 0.662 and the F1 score was 0.632. Thirty-four radiomics features were significantly associated with lymph node metastasis (P < .05), and 10 features were finally selected for developing multilayer neural network models. The areas under the curve of the multilayer neural network models were 0.787, 0.761, 0.853, and the mean areas under the curve was 0.800. The F1 scores of the multilayer neural network models were 0.738, 0.740, and 0.818, and the mean F1 score was 0.771.
Conclusions: Radiomics models based on 3-dimensional endoanal rectal ultrasound can be used to identify lymph node metastasis status in rectal cancer patient with good diagnostic performance.
Cite this article as: Li J, Chen S, Lin Y, et al. Development and validation of a radiomics model based on 3-dimensional endoanal rectal ultrasound of rectal cancer for predicting lymph node metastasis. Turk J Gastroenterol. 2023;34(5):542-551.
Methods: This study retrospectively included 79 patients (41 with lymph node metastasis positive and 38 with lymph node metastasis negative) diagnosed with rectal cancer in our hospital from January 2018 to February 2022. The tumor’s region of interest is first delineated by radiologists, from which radiomics features are extracted. Radiomics features were then selected by independent samples t-test, correlation coefficient analysis between features, and least absolute shrinkage and regression with selection operator. Finally, a multilayer neural network model is developed using the selected radiomics features, and nested cross-validation is performed on it. These models were validated by assessing their diagnostic performance and comparing the areas under the curve and recall rate curve in the test set.
Results: The areas under the curve of radiologist was 0.662 and the F1 score was 0.632. Thirty-four radiomics features were significantly associated with lymph node metastasis (P < .05), and 10 features were finally selected for developing multilayer neural network models. The areas under the curve of the multilayer neural network models were 0.787, 0.761, 0.853, and the mean areas under the curve was 0.800. The F1 scores of the multilayer neural network models were 0.738, 0.740, and 0.818, and the mean F1 score was 0.771.
Conclusions: Radiomics models based on 3-dimensional endoanal rectal ultrasound can be used to identify lymph node metastasis status in rectal cancer patient with good diagnostic performance.
Cite this article as: Li J, Chen S, Lin Y, et al. Development and validation of a radiomics model based on 3-dimensional endoanal rectal ultrasound of rectal cancer for predicting lymph node metastasis. Turk J Gastroenterol. 2023;34(5):542-551.