RESUMO
Purpose: To evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA). Methods: Data of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models' performance was evaluated using the area under the curve (AUC). Results: There were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798). Conclusion: Misdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT.
RESUMO
Pelvic tumors can be both complicated and challenging, and computed tomography (CT) has played an important role in the diagnosis and treatment planning of these conditions. Cinematic rendering (CR) is a new method of 3D imaging using CT volumetric data. Unlike traditional 3D methods, CR uses the global illumination model to produce high-definition surface details and shadow effects to generate photorealistic images. In this pictorial review, a series of primary pelvic tumor cases are presented to demonstrate the potential value of CR relative to conventional volume rendering (VR). This technique holds great potential in disease diagnosis, preoperative planning, medical education and patient communication.
RESUMO
BACKGROUND: Ovarian cancer is the second most common gynecologic malignancy. As the primary imaging modality, computed tomography (CT) can provide staging information for preoperative planning and determination of surgical resectability. As a new three-dimensional postprocessing tool for CT images, cinematic rendering (CR) has the potential to depict anatomic details accurately. CASE PRESENTATION: (Case 1) A 44-year-old married woman was diagnosed with recurrent ovarian cancer. CT images indicated the recurrent nodules and masses in the pelvic cavity and the upper middle abdominal peritoneum. The CR image showed that the multiple metastatic lesions and lymph nodes could not be completely removed by reoperation. The patient agreed to receive continued chemotherapy. (Case 2) A 51-year-old woman was admitted to our hospital due to abdominal distension and defecation that had increased for 6 months, with aggravation over the past 3 days. CT examination found cystic and solid masses in the bilateral ovarian area. The CR image demonstrated that the ovarian mass violated the posterior wall of the bladder and the anterior rectal wall. The preoperational imaging evaluation ensured the safety of the operation. CONCLUSION: CR could improve the visualization of ovarian cancer masses, metastatic lymph nodes, and peritoneal metastases. CR has a good clinical value and will be more helpful in the preoperational evaluation of ovarian cancer.
