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Tumor volume calculated by VitaWorks ® in the groups with or without LNM and the groups of low-or intermediate-risk and high-risk patients and ROC curve of tumor volume in prediction of high-risk cervical cancer patients. (A) Tumor volume in the groups with or without LNM. (B) Tumor volume in the groups of low-or intermediate-risk and high-risk patients. (C) ROC curve of tumor volume in prediction of high-risk patients. LNM, lymph node metastasis; ROC, receiver operating characteristic.
Source publication
Background:
Cervical tumors usually have an irregular morphology. It is often difficult to estimate tumor size or volume based on a diameter measurement from a two-dimensional magnetic resonance imaging slice. This study aimed to explore the use of magnetic resonance imaging-based three-dimensional reconstruction in cervical cancer.
Methods:
We...
Contexts in source publication
Context 1
... for patients without LNM and 18.8 mL (range: 2.3-24.0 mL) for patients with LNM ( Figure 2A). There was a significant difference in tumor volume between the low-or intermediate-risk and high-risk groups (P<0.05), as well as a non-significant difference in maximum tumor diameter (P>0.5). ...
Context 2
... for the low-or intermediate-risk group and 18.8 mL (range: 2.3-24.0 mL) for the high-risk group ( Figure 2B). A larger tumor volume had predicted value for high-risk patients (P<0.05), with a cut-off value of ≥18.6 mL providing 60% sensitivity, 96.7% specificity, 75% positive predictive value (PPV) and 93.5% negative predictive value (NPV) (area under the ROC curve: 0.807, 95% confidence interval: 0.588-1.00) ...
Context 3
... larger tumor volume had predicted value for high-risk patients (P<0.05), with a cut-off value of ≥18.6 mL providing 60% sensitivity, 96.7% specificity, 75% positive predictive value (PPV) and 93.5% negative predictive value (NPV) (area under the ROC curve: 0.807, 95% confidence interval: 0.588-1.00) ( Figure 2C). ...
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Citations
... In previous clinical research, we uncovered a signi cant correlation between the cervical tumor volume and adverse pathological factors [9]. Furthermore, we discovered that 3D reconstruction technology provides a more precise method for assessing tumor size, and we successfully utilized these models for more accurate staging of cervical cancer and predicting high-risk patients, making it highly potential for application in clinical diagnosis and treatment [10]. In the aforementioned study, 3D reconstruction models can display the location, extent, and relationship with surrounding tissues of the lesion more intuitively, which can assist clinicians in diagnosis and treatment. ...
... In our previous study, the pre-treatment magnetic resonance imaging data from 54 patients with cervical cancer have been used to construct 3D reconstruction models of individual patients [10]. Four patients at different stages were selected (Table 1). ...
Background
The landscape of medical education is rapidly evolving, driven by advancements in technology. This evolution has ushered in a new era characterized by digitization, connectivity, and intelligence. In this era, traditional teaching methods are being augmented with innovative technologies such as virtual learning, artificial intelligence platforms, and access to information clouds. One notable advancement is the integration of three-dimensional (3D) reconstruction models into medical education, particularly in fields like gynecological oncology.
Methods
This study introduces 3D reconstruction models based on real cervical cancer cases as a teaching tool for undergraduate gynecological oncology education. The aim was to assess the impact of these models on students' learning efficiency and perspectives. Participants were fourth-year Clinical Medicine students from Wuhan University, China. Pre- and post-tests were conducted to evaluate students' comprehension of cervical intraepithelial lesions, cervical cancer, female pelvic anatomy, and the 2018 International Federation of Gynecology and Obstetrics staging system for cervix uteri.
Results
A significant improvement in students' post-test scores across all areas. Feedback from students underscored the visual benefits and engaging nature of the models, with many expressing that the 3D models provided a clearer representation of cervical cancer and enhanced their learning experience.
Conclusion
The integration of 3D reconstruction models into medical education represents a promising approach to address the complexities of teaching intricate subjects like anatomy and gynecological oncology. These models offer a more intuitive and meticulous visualization of anatomical structures and pathological processes, fostering a hands-on and exploratory learning experience for students. Further research and integration of such technologies into medical curricula are warranted to continue enhancing the educational experience for future medical professionals in the digital age.
Objective:
To compare the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis derived from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) diffusion-weighted imaging (DWI) in evaluating normal-sized lymph node metastasis (LNM) in cervical cancer.
Methods:
Seventy-six pathologically confirmed cervical cancer patients (stages IB and IIA) were enrolled, including 61 patients with non-LNM (group A) and 15 patients with normal-sized LNM (group B). The recorded tumor volume on T2-weighted imaging was the reference against which both DWIs were evaluated. Each ADC histogram parameter (including ADCmax, ADC90, ADCmedian, ADCmean, ADC10, ADCmin, ADCskewness, ADCkurtosis, and ADCentropy) was compared between SS-EPI and RS-EPI and between the 2 groups.
Results:
There was no significant difference in tumor volume between the 2 DWIs and T2-weighted imaging (both P > 0.05). Higher ADCmax and ADCentropy but lower ADC10, ADCmin and ADCskewness were found in SS-EPI than those in RS-EPI (all P < 0.05). For SS-EPI, lower ADC90 and higher ADCkurtosis were found in group B than those in group A (both P < 0.05). For RS-EPI, lower ADC90 and higher ADCkurtosis and ADCentropy were found in group B than those in group A (all P < 0.05). Readout-segmented echo-planar imaging ADCkurtosis showed the highest area under the curve of 0.792 in the differentiation of the 2 groups (sensitivity, 80%; specificity, 73.77%).
Conclusions:
Compared with SS-EPI, the ADC histogram parameters derived from RS-EPI were more accurate, and ADCkurtosis held great potential in differentiating normal-sized LNM in cervical cancer.
