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Abstract Objectives The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and clinical features for AC patients. Methods Clinical and pathological information from one hundred and ninety-seven patients with cervical AC was collected and analyzed. For each patient, 107 radiomics features were extracted from T2-weighted MRI images. Feature selection was performed using Spearman correlation and random forest (RF) algorithms, and predictive models were built using support vector machine (SVM) technique. Deep learning models were also trained with T2-weighted MRI images and clinicopathological features through Convolutional Neural Network (CNN). Kaplan-Meier curve was analyzed using significant features. In addition, information from another group of 56 AC patients was used for the independent validation. Results A total of 107 radiomics features and 6 clinicopathological features (age, FIGO stage, differentiation, invasion depth, lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) were included in the analysis. When predicting the 3-year, 4-year, and 5-year DFS, the model trained solely on radiomics features achieved AUC values of 0.659 (95%CI: 0.620–0.716), 0.791 (95%CI: 0.603–0.922), and 0.853 (95%CI: 0.745–0.912), respectively. However, the combined model, incorporating both radiomics and clinicopathological features, outperformed the radiomics model with AUC values of 0.934 (95%CI: 0.885–0.981), 0.937 (95%CI: 0.867–0.995), and 0.916 (95%CI: 0.857–0.970), respectively. For deep learning models, the MRI-based models achieved an AUC of 0.857, 0.777 and 0.828 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. And the combined deep learning models got a improved performance, the AUCs were 0.903. 0.862 and 0.969. In the independent test set, the combined model achieved an AUC of 0.873, 0.858 and 0.914 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. Conclusions We demonstrated the prognostic value of integrating MRI-based radiomics and clinicopathological features in cervical adenocarcinoma. Both radiomics and deep learning models showed improved predictive performance when combined with clinical data, emphasizing the importance of a multimodal approach in patient management.

We demonstrate a low-cost and facile photochemical solution method to prepare the ZrO2 resistive switching arrays as memristive units on flexible PET/ITO substrates. ZrO2 solution sensitive to UV light of 337 nm was synthesized using zirconium n-butyl alcohol as the precursor, and benzoylacetone as the complexing agent. After the dip-coated ZrO2 gel films were irradiated through a mask under the UV lamp (with wavelength of 325–365 nm) at room temperature and rinsed in ethanol, the ZrO2 gel arrays were obtained on PET/ITO substrates. Subsequently, the ZrO2 gel arrays were irradiated by deep UV light of 254 and 185 nm at 150 °C, resulting in the amorphous ZrO2 memristive micro-arrays. The ZrO2 units on flexible PET/ITO substrates exhibited excellent memristive properties. A high ratio of 104 of on-state and off-state resistance was obtained. The resistive switching behavior of the flexible device remained stable after being bent for 103 times. The device showed stable flexibility up to a minimum bending diameter of 1.25 cm. [ABSTRACT FROM AUTHOR]