Prognostic value of CT radiomics in evaluating lymphovascular invasion in rectal cancer: Diagnostic performance based on different volumes of interest.

OBJECTIVES: This study aims to evaluate diagnostic performance of radiomic analysis using computed tomography (CT) to identify lymphovascular invasion (LVI) in patients diagnosed with rectal cancer and assess diagnostic performance of different lesion segmentations. METHODS: The study is applied to 169 pre-treatment CT images and the clinical features of patients with rectal cancer. Radiomic features are extracted from two different volumes of interest (VOIs) namely, gross tumor volume and peri-tumor tissue volume. The maximum relevance and the minimum redundancy, and the least absolute shrinkage selection operator based logistic regression analyses are performed to select the optimal feature subset on the training cohort. Then, Rad and Rad-clinical combined models for LVI prediction are built and compared. Finally, the models are externally validated. RESULTS: Eighty-three patients had positive LVI on pathology, while 86 had negative LVI. An optimal multi-mode radiology nomogram for LVI estimation is established. The area under the receiver operating characteristic curves of the Rad and Rad-clinical combined model in the peri-tumor VOI group are significantly higher than those in the tumor VOI group (Rad: peri-tumor vs. tumor: 0.85 vs. 0.68; Rad-clinical: peri-tumor vs. tumor: 0.90 vs 0.82) in the validation cohort. Decision curve analysis shows that the peri-tumor-based Rad-clinical combined model has the best performance in identifying LVI than other models. CONCLUSIONS: CT radiomics model based on peri-tumor volumes improves prediction performance of LVI in rectal cancer compared with the model based on tumor volumes.

Radiomics analysis of multicenter CT images for discriminating mucinous adenocarcinoma from nomucinous adenocarcinoma in rectal cancer and comparison with conventional CT values.

OBJECTIVE: To investigate the value of CT-based radiomics signature for preoperatively discriminating mucinous adenocarcinoma (MA) from nomucinous adenocarcinoma (NMA) in rectal cancer and compare with conventional CT values. METHOD: A total of 225 patients with histologically confirmed MA or NMA of rectal cancer were retrospectively enrolled. Radiomics features were computed from the entire tumor volume segmented from the post-contrast phase CT images. The maximum relevance and minimum redundancy (mRMR) and LASSO regression model were performed to select the best preforming features and build the radiomics models using a training cohort of 155 cases. Then, predictive performance of the models was validated using a validation cohort of 70 cases and receiver operating characteristics (ROC) analysis method. Meanwhile, CT values in post- and pre-contrast phase, as well as their difference (D-values) of tumors in two cohorts were measured by two radiologists. ROC curves were also calculated to assess diagnostic efficacies. RESULTS: One hundred and sixty-three patients were confirmed by pathology as NMA and 62 cases were MA. The radiomics signature comprised 19 selected features and showed good discrimination performance in both the training and validation cohorts. The areas under ROC curves (AUC) are 0.93 (95% confidence interval [CI]: 0.89-0.98) in training cohort and 0.93 (95% CI: 0.87-0.99) in validation cohort, respectively. Three sets of CT values of MA in pre- and post-contrast phase, and their difference (D-value) (31±7.0, 51±12.6 and 20±9.3, respectively) were lower than those of NMA (37±5.6, 69±13.3 and 32±11.7, respectively). Comparing to the radiomics signature, using three sets of conventional CT values yielded relatively low diagnostic performance with AUC of 0.84 (95% CI: 0.78-0.88), 0.75 (95% CI: 0.69-0.81) and 0.78 (95% CI: 0.72-0.83), respectively. CONCLUSION: This study demonstrated that CT radiomics features could be utilized as a noninvasive biomarker to identify MA patients from NMA of rectal cancer preoperatively, which is more accurate than using the conventional CT values.

Suppression of miR-93-5p inhibits high-risk HPV-positive cervical cancer progression via targeting of BTG3.

This study explores the role of miR-93-5p in high-risk HPV-positive (HR-HPV) cervical cancer by targeting of BTG3. Cervical tissues were collected from 332 patients with conditions of chronic cervicitis (n = 42), low-grade cervical intraepithelial neoplasia (CIN I, n = 51), CIN II (n = 49), CIN III (n = 43), cervical cancer (n = 90), and normal cervical tissues (n = 57). HR-HPV DNA was detected by Hybrid Capture 2, and the expressions of miR-93-5p and BTG3 were determined by qRT-PCR and Western blot. The target relationship between miR-93-5p and BTG3 was verified by dual-luciferase reporter gene assay. HPV-positive cervical cancer cells (CaSki and HeLa) were divided into control, NC, inhibitor, BTG3, and mimic + BTG3 groups. CCK-8, Annexin V-APC/PI, and Transwell assays were applied to evaluate cell biological activities. MiR-93-5p was positively related but BTG3 was inversely related to HR-HPV infection. Additionally, miR-93-5p expression was negatively correlated with BTG3 expression in cervical cancer tissues infected with HR-HPV. HPV-positive cervical cancer cells showed higher miR-93-5p and lower BTG3 levels than negative cells. CaSki and HeLa cells in the inhibitor group showed increased BTG3 compared with the control group. After transfection with miR-93-5p inhibitor or BTG3 activation plasmid, proliferation and metastasis were inhibited, but apoptosis was promoted. The mimic + BTG3 group showed increased cell proliferation and metastasis but decreased cell apoptosis compared with the BTG3 group. Upregulated miR-93-5p was positively related but downregulated BTG3 was inversely related to HR-HPV infection, and inhibition of miR-93-5p may have blocked HPV-positive cervical cancer development by targeting of BTG3.