首页|Huazhong University of Science and Technology Reports Findings in Rectal Cancer (Radiomics based on T2-weighted and diffusionweighted MR imaging for preoperative prediction of tumor deposits in rectal cancer)
Huazhong University of Science and Technology Reports Findings in Rectal Cancer (Radiomics based on T2-weighted and diffusionweighted MR imaging for preoperative prediction of tumor deposits in rectal cancer)
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New research on Oncology - Rectal Cancer is the subject of a report. According to news reporting out of Wuhan, People’s Republic of China, by NewsRx editors, research stated, “Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer.” Our news journalists obtained a quote from the research from the Huazhong University of Science and Technology, “A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n = 159) and a validation set (n = 40). The total incidence of TDs was 37.2 % (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI + ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. The Rad-score (T2WI + ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI + ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 % in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 %, 92.0 %, 83.3 %, and 82.1 %, respectively.”
WuhanPeople’s Republic of ChinaAsiaCancerCyborgsEmerging TechnologiesGastroenterologyHealth and MedicineMachine LearningOncologyRectal Cancer
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