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Development and validation of a stacking nomogram for predicting regional lymph node metastasis status in rectal cancer via deep learning and hand-crafted radiomics
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| J. Liu, L. Sun, X. Lu, Y. Geng, Z. Zhang |
| Department of Radiology, The fourth Affiliated Hospital of China Medical University, East Chongshan Road, Shenyang, 110000, Liaoning, China , liujiaxuan666666@163.com |
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Abstract: (125 Views) |
Background: Preoperative assessment of lymph node metastasis (LNM) status is the basis of individual treatment for rectal cancer (RC). However, conventional imaging methods are not accurate enough. Materials and Methods: We collected 282 RC patients who were divided into the training dataset (n=225) and the test dataset (n=57) with an 8:2 scale. A large number of deep learning (DL) features and hand-crafted radiomics (HCR) features of primary tumors were extracted from the arterial and venous phases of the computed tomography (CT) images. Three machine learning models, including support vector machine (SVM), k-nearest neighbor (KNN),and multi-layer perceptron (MLP) were utilized to predict LNM status in RC patients. A stacking nomogram was constructed by selecting optimal machine learning models for arterial and venous phases, respectively, combined with predictive clinical features. Results: The stacking nomogram performed well in predicting LNM status, with an area under the curve (AUC) of 0.914 [95% confidence interval (CI): 0.874-0.953] in the training dataset, and an AUC of 0.942 (95%CI: 0.886-0.997) in the test dataset. The AUC of the stacking nomogram were higher than those of CT_reported_N_status, ASVM, and VSVM model in the training dataset (P <0.05). However, in the test dataset, although the AUC of the stacking nomogram was higher than the VSVM, the difference was not obvious (P =0.1424). Conclusion: The developed deep learning radiomics stacking nomogram showed to be effective in predicting the preoperative LNM status in RC patients. |
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| Keywords: Rectal cancer, lymph node metastasis, radiomics, deep learning, machine learning. |
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Full-Text [PDF 1371 kb]
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Type of Study: Original Research |
Subject:
Radiation Biology
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