Lymphovascular space invasion (LVSI) is an important determinant for selecting treatment plan in cervical cancer (CC). For CC patients without LVSI, conization is recommended; otherwise, if LVSI is observed, hysterectomy and pelvic lymph node dissection are required. Despite the importance, current identification of LVSI can only be obtained by pathological examination through invasive biopsy or after surgery. In this study, we provided a non-invasive and preoperative method to identify LVSI by radiomics analysis on T2-magnetic resonance image (MRI), aiming at assisting personalized treatment planning. We enrolled 120 CC patients with T2 image and clinical information, and allocated them into a training set (n = 80) and a testing set (n= 40) according to the diagnostic time. Afterwards, 839 image features were extracted to reflect the intensity, shape, and high-dimensional texture information of CC. Among the 839 radiomic features, 3 features were identified to be discriminative by Least absolute shrinkage and selection operator (Lasso)-Logistic regression. Finally, we built a support vector machine (SVM) to predict LVSI status by the 3 radiomic features. In the independent testing set, the radiomics model achieved area under the receiver operating characteristic curve (AUC) of 0.7356, classification accuracy of 0.7287. The radiomics signature showed significant difference between non-LVSI and LVSI patients (p<0.05). Furthermore, we compared the radiomics model with clinical model that uses clinical information, and the radiomics model showed significant improvement than clinical factors (AUC=0.5967 in the validation cohort for clinical model).
The Locally advanced rectal cancer (LARC) patients were routinely treated with neoadjuvant chemoradiotherapy (CRT)
firstly and received total excision afterwards. While, the LARC patients might relieve to T1N0M0/T0N0M0 stage after
the CRT, which would enable the patients be qualified for local excision. However, accurate pathological TNM stage
could only be obtained by the pathological examination after surgery. We aimed to conduct a Radiomics analysis of
Diffusion weighted Imaging (DWI) data to identify the patients in T1N0M0/T0N0M0 stages before surgery, in hope of
providing clinical surgery decision support. 223 routinely treated LARC patients in Beijing Cancer Hospital were
enrolled in current study. DWI data and clinical characteristics were collected after CRT. According to the pathological
TNM stage, the patients of T1N0M0 and T0N0M0 stages were labelled as 1 and the other patients were labelled as 0.
The first 123 patients in chronological order were used as training set, and the rest patients as validation set. 563 image
features extracted from the DWI data and clinical characteristics were used as features. Two-sample T test was conducted to pre-select the top 50% discriminating features. Least absolute shrinkage and selection operator
(Lasso)-Logistic regression model was conducted to further select features and construct the classification model. Based
on the 14 selected image features, the area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8781,
classification Accuracy (ACC) of 0.8432 were achieved in the training set. In the validation set, AUC of 0.8707, ACC
(ACC) of 0.84 were observed.
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