Contrast Media & Molecular Imaging

Metformin in Combination with Progesterone Improves the Pregnancy Rate for Patients with Early Endometrial Cancer

Objective. To study the therapeutic effects of metformin in combination with medroxyprogesterone in the early endometrial cancer patients with fertility requirements. A total of 120 patients with early endometrial cancer admitted to and treated in our hospital were enrolled and evenly assigned into two groups according to different therapeutic regimens, namely, metformin group (metformin combined with medroxyprogesterone acetate) and control group (medroxyprogesterone acetate alone). The objective response rate (ORR) and disease control rate (DCR) were 71.7% (43/60) and 90.0% (54/60) in the metformin group and 53.3% (32/60) and 78.3% (47/60) in the control group, respectively. Adverse reactions such as gastrointestinal reaction, headache, and insomnia were mainly observed in patients. The body mass index (BMI) declined from (34.43 ± 4.34) kg/m2 to (24.77 ± 2.39) kg/m2 in the metformin group and from (33.37 ± 4.49) kg/m2 to (31.28 ± 3.55) kg/m2 in the control group after treatment. After treatment, serum levels of vascular endothelial growth factor (VEGF), angiotensin‐2 (Ang‐2), carbohydrate antigen 125 (CA125), and CA19‐9 in the metformin group were significantly lower than those in the control group (P = 0.005, P < 0.001, P = 0.002, and P < 0.001). During follow‐up, the pregnancy rate was 81.7% (49/60) in the metformin group and 61.7% (37/60) in the control group, and the former was prominently higher than the latter (P = 0.025). Metformin in combination with progesterone is effective in treating early endometrial cancer patients with fertility requirements, which significantly reduced the BMI of patients and increased the pregnancy rate after treatment.

Sentinel Node Mapping in Ovarian Tumors: A Study Using Lymphoscintigraphy and SPECT/CT

Purpose. Ovarian cancer in the early stage requires a complete surgical staging, including radical lymphadenectomy, implying subsequent risk of morbidity and complications. Sentinel lymph node (SLN) mapping is a procedure that attempts to reduce radical lymphadenectomy-related complications and morbidities. Our study evaluates the feasibility of SLN mapping in patients with ovarian tumors by the use of intraoperative Technetium-99m-Phytate (Tc-99m-Phytate) and postoperative lymphoscintigraphy using tomographic (single-photon emission computed tomography/computed tomography (SPECT/CT)) acquisition. Materials and Methods. Thirty-two patients with ovarian mass participated in this study. Intraoperative injection of the radiopharmaceutical was performed just after laparotomy and before the removal of tumor in utero-ovarian and suspensory ligaments of the ovary just beneath the peritoneum. Subsequently, pelvic and para-aortic lymphadenectomy was performed for malignant masses, and the presence of tumor in the lymph nodes was assessed through histopathological examination. Conversely, lymphadenectomy was not performed in patients with benign lesions or borderline ovarian tumors. Lymphoscintigraphy was performed within 24 hr using tomographic acquisition (SPECT/CT) of the abdomen and pelvis. Results. Final pathological examination showed 19 patients with benign pathology, 5 with borderline tumors, and 6 with malignant ovarian tumors. SPECT/CT identified SLNs in para-aortic-only areas in 6 (20%), pelvic/para-aortic areas in 14 (47%), and pelvic-only areas in 7 (23%) cases. Notably, additional unusual SLN locations were revealed in perirenal, intergluteal, and posterior to psoas muscle regions in three patients. We were not able to calculate the false negative rate due to the absence of patients with involved lymph nodes. Conclusion. SLN mapping using intraoperative injection of radiotracers is safe and feasible. Larger studies with more malignant cases are needed to better evaluate the sensitivity of this method for lymphatic staging of ovarian malignancies.

[Retracted] Relationship between Prognosis, Immune Infiltration Level, and Differential Expression of PARVG Gene in Uterine Corpus Endometrial Carcinoma

Endometrial cancer (UCEC) is very common in gynecological diseases and ranks second in the death cause of gynecological cancer in developed countries. The connection between the overall survival of UCEC patients and immune invasion of the tumor microenvironment is positive. The PARVG gene has not been given notice in cancer, and its mechanism is unknown. The research utilized TCGA data to test the function of PARVG in UCEC. The manifestation of PARVG in UCEC was studied by GEPIA. By assessing the survival module, the authors learned the impact of PARVG on the survival of people with UCEC and then obtained UCEC information from TCGA. This study uses logistic regression to prove the possible relationship between PARVG expression and clinical information. From the research of Cox regression, clinicopathological characteristics of people with TCGA were connected with overall survival. Furthermore, the “correlation” module of GEPIA and CIBERSORT was used to study the association between cancer immune invasion and PARVG . Using univariate logistic regression analysis with PARVG expression as a categorical variable (median expression value of 2.5), the result suggested that raised PARVG expression was considerably connected with tumor status, pathological stage, and lymph nodes. Multiple factor studies have shown that upregulation of PARVG, distant metastasis, and negative pathological stage are absolute elements of excellent prognosis. In addition, CIBERSORT analysis was utilized to determine that raised PARVG expression has a positive connection with immune infiltration by T cells, mast cells, neutrophils, and B cells. This is recognized in GEPIA’s “correlation” module. The above outcomes show us that the raised expression of PARVG is associated with a good prognosis and it raises the proportion of immune cells (such as T cells, mast cells, neutrophils, and B cells) in UCEC. These outcomes tell us that PARVG can be utilized as a possible biomarker to evaluate UCEC’s immune infiltration levels and prognosis.

Application of Pelvic Magnetic Resonance Imaging Scan Combined with Serum Pyruvate Kinase Isozyme M2, Neutrophil Gelatinase‐Associated Lipocalin, and Soluble Leptin Receptor Detection in Diagnosing Endometrial Carcinoma

Objective. To explore the application value of pelvic magnetic resonance imaging (MRI) scan combined with serum pyruvate kinase isozyme M2 (PKM2), neutrophil gelatinase‐associated lipocalin (NGAL), and soluble leptin receptor (sOB‐R) detection in diagnosing endometrial carcinoma (EC). Methods. The clinical data of 45 patients with pathologically confirmed EC treated in our hospital from May 2019 to May 2020 were retrospectively analyzed. All patients received pelvic MRI scan, serum PKM2, NGAL and sOB‐R detection was performed, and the combination of the two was performed so as to analyze the diagnostic application value of the three modalities. Results. Compared with the joint detection, the number of true positive cases, sensitivity, specificity, and accuracy rate obtained by a single application of pelvic MRI or serum PKM2, NGAL, and sOB‐R detection were obviously lower; the area under the ROC curve of the joint detection was obviously larger than that of single detection; the results of the joint detection were better than those of single detection (P < 0.05); the combined diagnosis obtained the highest sensitivity. Conclusion. Combining pelvic MRI with serum PKM2, NGAL, and sOB‐4 detection can effectively promote the diagnostic accuracy for EC, presenting significant clinical diagnostic value.

Evaluation and Monitoring of Endometrial Cancer Based on Magnetic Resonance Imaging Features of Deep Learning

This study was aimed to compare and analyze the magnetic resonance imaging (MRI) manifestations and surgical pathological results of endometrial cancer (EC) and to explore the clinical research of MRI in the diagnosis and staging of EC. Methods. 80 patients with EC admitted to the hospital were selected as the research objects. The ResNet network was used to optimize the network. When the depth was added, the accuracy of the model was improved, the network parameters were iteratively updated, and the damage function of the minimized network was obtained. The recognition efficiency of MRI images was analyzed using three network modes: shallow CNN network, Res‐Net network, and optimized network. The images of EC patients were analyzed, and a quantitative and timed MRI was achieved using simulated datasets in deep learning neural networks, which provided the basis for the formulation of single‐scan MRI parameters. All patients underwent preoperative MRI examination using coronal and sagittal T1WI and T2WI imaging. The results showed that the accuracy and specificity of T2 weighted imaging and enhanced scanning in MRI were 88.75% and 95%, respectively. Sensitivity was 87.5%, negative predictive value was 93.75%, and positive predictive value was 86.25%. By MRI examination, 80 cases of EC in patients with stage I diagnosis were 72 cases, accounting for 90%, with endometrial thickening and uneven enhancement. In conclusion, the MRI manifestations of EC are diversified, and MRI has a high value for the staging of EC. MRI examination is conducive to improving diagnostic accuracy.

ADNEX Model-Based Diagnosis of Ovarian Cancer Using MRI Images

This exploration aims to investigate the important role of magnetic resonance imaging (MRI) in the diagnosis of ovarian cancer under the ADNEX. From March 2017 to December 2019, 84 patients with ovarian cancer confirmed by pathological operation were selected as the research objects. The consistency of ADNEX, MRI, and ADNEX ∗ MRI in the diagnosis and staging of ovarian cancer was calculated separately. SPSS 26.0 statistical software was used to compare the accuracy, sensitivity, specificity, and diagnostic value of the two diagnostic methods. The results show that the accuracy and sensitivity of ADNEX are 78.6% and 93.2%, respectively. The accuracy and sensitivity of MRI are 81.2% and 89.4%, respectively. There is no significant difference between the two methods ( p < 0.05 ). The overall consistency rates of ADNEX ∗ MRI, MRI diagnosis, and ADNEX for ovarian cancer staging are 94.2%, 74%, and 65.4%, respectively. There was a significant difference ( p < 0.05 ). ADNEX ∗ MRI and MRI diagnosis were compared with each stage of ADNEX. There is a significant difference between the second and fourth stages ( p < 0.05 ), and there is also a significant difference in the fourth stage ( p < 0.017 ). It is concluded that MRI diagnosis of ovarian cancer based on ADNEX is superior to ADNEX and MRI examination alone, which provides a certain reference value for clinical staging of ovarian cancer.

Sacral Insufficiency Fracture after Radiotherapy for Cervical Cancer: Appearance and Dynamic Changes on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography

Objective. With the increasing application of radiotherapy for cervical cancer, the incidence of sacral insufficiency fracture (SIF) is increasing gradually. Incorrect or untimely treatment caused by misdiagnosis may lead to serious adverse clinical consequences. This study retrospectively analyzed SIF caused by radiotherapy regarding the appearance and dynamic changes in 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (18F-FDG) positive emission tomography (PET)/computed tomography (CT) images to improve the understanding of SIF. Materials and Methods. We retrospectively examined cervical cancer patients who underwent pelvic radiotherapy and 18F-FDG PET/CT between January 2014 and January 2021. Comparative analysis of the imaging performance and follow-up data was conducted. In total, 38 patients with ages ranging from 28 to 81 years (mean age 59.2 ± 10.6 y, median age 56 y) participated in the study. The respective characteristics of the 38 patients were summarized, and diagnosis was confirmed by follow-up changes. Results. Twenty-five (65.8%) of the 38 patients suffered from unilateral SIF, and 13 (34.2%) suffered from bilateral SIF. After receiving radiotherapy, SIF first appeared in 3–42 months (median, 13 months). The main 18F-FDG PET/CT manifestations of SIF were increased bone density (35/38, 92.1%), anterior sacral fracture line (28/38, 73.7%), and diffuse or linear uptake patterns parallel to the sacroiliac joint (37/38, 97.3%), with the maximum standard uptake value (SUVmax) ranging from 1.8 to 5.9 (average, 3.1). Follow-up lasted 3–59 months (mean, 14 months). The main changes in SIF were increases in the bone density and high-density range and decreases in the FDG uptake intensity and hypermetabolism range. Three patients had secondary sacral or sacroiliac joint infection (3/38, 7.9%), and 3 patients had secondary fracture and/or pelvic deformation (3/38, 7.9%). Conclusions. 18F-FDG PET/CT is an effective technique for diagnosing SIF. A small fracture line in the anterior sacrum and diffuse or linear areas of high density or metabolism parallel to the sacroiliac joint were the characteristic features of SIF. The main changes in SIF were increases in the bone density and high-density range and decreases in the FDG uptake intensity and hypermetabolism range.

[Retracted] Circ TYMP1 Inhibits Carcinogenesis and Cisplatin Resistance in Ovarian Cancer by Reducing Smad2/3 Phosphorylation via a MicroRNA‐182A‐3p/TGF1B Axis

TYMP1 is a cancer driver in several human malignancies. However, its significance in ovarian cancer (ovarian carcinoma) remains uncertain. This research aims to understand the TYMP1’s role in ovarian carcinoma carcinogenesis and cisplatin (DDP) resistance and its molecular ovarian marchionesses. Circ TYMP1 overexpression in ovarian carcinoma samples led to an accelerated tumor stage. Bioinformatics identified miR‐182A‐3p as the TYMP1’s target transcript. Circ TYMP1 functioned as a sponge for miR‐182A‐3p, lowering its inhibitory effect on TGF1B. Downregulating circ TYMP1 decreased A2780‐Res cell proliferation, invasion, and death resistance. Malignant ovarian carcinoma cells recovered following miR‐182A‐3p downregulation. TGF1B overexpression boosted A2780‐Res cell proliferation, aggression, and cisplatin resistance while reducing Smad2/3 phosphorylation. TYMP1 sequesters miR‐182A‐3p and promotes TGF1B in ovarian carcinoma, boosting carcinogenesis and cisplatin resistance. This might lead to novel ovarian carcinoma treatments.

Genetic Polymorphism of E2F1 Influences Susceptibility to Ovarian Cancer in a Chinese Population

Purpose. The present study is aimed at exploring whether rs3213172, rs3213173, and rs3213176 polymorphisms of the E2F1 gene confer risk for ovarian cancer. Methods. A total of 80 patients with ovarian cancer were selected from the first affiliated hospital of Soochow University in Jiangsu Province from January 2016 to June 2021, including 48 cases that were premenopausal and 32 cases that were menopausal. 130 healthy women who participated in normal physical examinations during the same period were selected as the control group. The rs3213172, rs3213173, and rs3213176 polymorphisms of the E2F1 gene were detected by the fluorescent probe method. Results. For rs3213173 and rs3213176 loci, there were no statistical significances in genotype distribution frequency between the ovarian cancer group and the control group (P > 0.05). For rs3213172 loci, a significant difference was observed in CT genotype between the ovarian cancer group and the control group (P = 0.024). Conclusion.E2F1 gene rs3213173 and rs3213176 polymorphisms confer no risk to ovarian cancer risk. The CT genotype of E2F1 gene rs3213172 polymorphism is associated with an increased risk of ovarian cancer, and E2F1 gene rs3213172 polymorphism may be a novel marker for the risk prediction of ovarian cancer.

Application Value of Combined Detection of DCE‐MRI and Serum Tumor Markers HE4, Ki67, and HK10 in the Diagnosis of Ovarian Cancer

Objective. To investigate the application value of the combined detection of DCE‐MRI and serum tumor markers (HE4, Ki67, and HK10) in the diagnosis of ovarian cancer. Methods. The clinical data of 40 patients with advanced ovarian cancer (AOC) confirmed by surgery and pathology in our hospital from February 2019 to February 2020 were retrospectively analyzed. All patients received DCE‐MRI, the detection of serum tumor markers HE4, Ki67, and HK10, and the combined detection of DCE‐MRI and the serum tumor markers (HE4, Ki67, and HK10). The application value of the three detection methods was analyzed. Results. The number of true positives in the single detection (DCE‐MRI detection and the detection of serum HE4, Ki67, and HK10) was notably lower than that in the combined detection. The sensitivity, specificity, and accuracy of the single detection were notably lower compared with the combined detection. The area under the curve in the ROC of the combined detection was notably larger than that of the single detection. The results of the combined detection were better than those of the single detection (P < 0.05), with the highest sensitivity of the combined detection. Conclusion. The combined detection of DCE‐MRI and the serum tumor markers (HE4, Ki67, and HK10) can effectively improve the diagnostic accuracy of AOC patients, with high sensitivity and specificity, which has an important diagnostic value in clinic.

The Evaluation Value of CT in the Efficacy of Neoadjuvant Chemotherapy in Ovarian Cancer Patients

Aim. To discuss the evaluation value of CT in the efficacy of neoadjuvant chemotherapy in patients with ovarian cancer. Methods. The clinical, pathological, and CT imaging information of 72 patients with ovarian cancer treated in our hospital from January 2018 to January 2022 were retrospectively analyzed. CT examination and pathological examination were compared to evaluate the efficacy of neoadjuvant chemotherapy. Results. Using the CRS grading system, 26 cases (36.11%) scored 1, 42 cases (58.33%) scored 2, and 4 cases (5.56%) scored 3. CRS grading system scores of 1, 2, 3, and 4–7 patients were compared, P > 0.05. The CT manifestations of lymphadenectasis, degree of peritoneal thickening, ascites, and maximum length diameter of the mass were compared between the patients before and after chemotherapy, P < 0.05. According to RECIST 1.1, there were 1 (1.39%) CR, 38 (52.78%) PR, 29 (40.28%) SD, and 4 (5.56%) PD. The comparison was done between RECIST 1.1 and CRS grading system, P > 0.05. Conclusion. CT could be used to evaluate the efficacy of neoadjuvant chemotherapy for ovarian cancer.

Diagnostic Value of Combined Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging with Diffusion Tensor Imaging in Predicting Parametrial Infiltration in Cervical Cancer

Objective. This study sought to determine the diagnostic value of combined intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) in predicting parametrial infiltration (PMI) in patients with cervical cancer. Materials and Methods. We enrolled 65 patients with cervical cancer confirmed by radical hysterectomy (25 PMI-negative and 40 PMI-positive) who underwent IVIM and DTI pretreatment. The parameters of IVIM (ADC, D, D   ∗ , and f) and DTI (average diffusion coefficient (DCavg) and fractional anisotropy (FA)) were recorded by two observers. All parameter differences were tested, and the receiver operating characteristic (ROC) curves were generated to estimate the diagnostic performance of significant metrics and their combinations. Results. Compared to the PMI-negative group, the PMI-positive group had significantly lower D (0.632 ± 0.017 vs. 0.773 ± 0.024, p < 0.001 ) and lower FA (0.073 ± 0.002 vs. 0.085 ± 0.003, p = 0.003 ). The area under the ROC curve (AUC) of D and FA was 0.801 and 0.726, respectively, and the combination of D and FA improved the AUC to 0.931, with a sensitivity and specificity of 80.0% and 97.5%, respectively. Conclusion. D and FA values could be used to help diagnose PMI in patients with cervical cancer. The combination of IVIM and DTI was more valuable than either option alone.

Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm

The purpose of this study is to explore the application value of artificial intelligence algorithm in multimodal MRI image diagnosis of cervical cancer. Based on the traditional convolutional neural network (CNN), an artificial intelligence 3D-CNN algorithm is designed according to the characteristics of cervical cancer. 70 patients with cervical cancer were selected as the experimental group, and 10 healthy people were selected as the reference group. The 3D-CNN algorithm was applied to the diagnosis of clinical cervical cancer multimodal MRI images. The value of the algorithm was comprehensively evaluated by the image quality and diagnostic accuracy. The results showed that compared with the traditional CNN algorithm, the convergence rate of the loss curve of the artificial intelligence 3D-CNN algorithm was accelerated, and the segmentation accuracy of whole-area tumors (WT), core tumor areas (CT), and enhanced tumor areas (ET) was significantly improved. In addition, the clarity of the multimodal MRI image and the recognition performance of the lesion were significantly improved. Under the artificial intelligence 3D-CNN algorithm, the Dice values of WT, ET, and CT regions were 0.78, 0.71, and 0.64, respectively. The sensitivity values were 0.92, 0.91, and 0.88, respectively. The specificity values were 0.93, 0.92, and 0.9 l, respectively. The Hausdorff (Haus) distances were 0.93, 0.92, and 0.90, respectively. The data of various indicators were significantly better than those of the traditional CNN algorithm ( P  < 0.05). In addition, the diagnostic accuracy of the artificial intelligence 3D-CNN algorithm was 93.11 ± 4.65%, which was also significantly higher than that of the traditional CNN algorithm (82.45 ± 7.54%) ( P  < 0.05). In summary, the recognition and segmentation ability of multimodal MRI images based on artificial intelligence 3D-CNN algorithm for cervical cancer lesions were significantly improved, which can significantly enhance the clinical diagnosis rate of cervical cancer.

Diagnostic Accuracy of 18F-FDG-PET/CT and MRI in Predicting the Tumor Response in Locally Advanced Cervical Carcinoma Treated by Chemoradiotherapy: A Meta-Analysis

Objective. The aim of this meta-analysis was to compare the diagnostic accuracy of 18F-FDG-PET/CT and MRI in predicting the tumor response in locally advanced cervical carcinoma (LACC) treated by chemoradiotherapy (CRT). Method. This meta-analysis has been performed according to PRISMA guidelines. Systematic searches were conducted using PubMed and Embase databases for articles published from January 1, 2010, to January 1, 2020. By using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, the reviewers assessed the methodological quality scores of the selected studies. We analyzed the sensitivity, specificity, and accuracy of two diagnostic methods using Meta-DiSc 1.4 and Stata 15. Results. An overall of 15 studies including 1132 patients were included. Sensitivities of PET/CT and MRI were 83.5% and 82.7%, while the corresponding rates for specificities were 77.8% and 68.4%, respectively. The DOR, PLR, and NLR for MRI were 15.140, 2.92, and 22.6. PET/CT had a DOR of 25.21. The PLR and NLR for PET/CT were 4.13 and 0.215, respectively. The diagnostic sensitivity and specificity of PET/CT for the detection of residual tumor were 86% and 95%, respectively. The corresponding rates for MRI were 73% and 96%, respectively. The diagnostic sensitivity and specificity of PET/CT for the detection of tumor metastases were 97% and 99%, while the corresponding rates for MRI were 31% and 98%, respectively. Conclusion. 18F-FDG PET/CT seemed to have a better overall diagnostic accuracy in the evaluation of treatment response to chemoradiotherapy in LACC patients. MRI showed a really poor sensitivity in the detection of metastases, and PET/CT performed significantly better. However, the difference between these two methods in the detection of residual disease was not significant. More studies are needed to be conducted in order to approve that 18F-FDG PET/CT can be a standard option to assess the treatment response.

Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features

Purpose. This study aims to determine whether IVIM-DWI combined with texture features based on preoperative IVIM-DWI could be used to predict the Ki-67 PI, which is a widely used cell proliferation biomarker in CSCC. Methods. A total of 70 patients were included. Among these patients, 16 patients were divided into the Ki-67 PI <50% group and 54 patients were divided into the Ki-67 PI ≥50% group based on the retrospective surgical evaluation. All patients were examined using a 3.0T MRI unit with one standard protocol, including an IVIM-DWI sequence with 10 b values (0–1,500 sec/mm2). The maximum level of CSCC with a b value of 800 sec/mm2 was selected. The parameters (diffusion coefficient (D), microvascular volume fraction (f), and pseudodiffusion coefficient (D ∗ )) were calculated with the ADW 4.6 workstation, and the texture features based on IVIM-DWI were measured using GE AK quantitative texture analysis software. The texture features included the first order, GLCM, GLSZM, GLRLM, and wavelet transform features. The differences in IVIM-DWI parameters and texture features between the two groups were compared, and the ROC curve was performed for parameters with group differences, and in combination. Results. The D value in the Ki-67 PI ≥50% group was lower than that in the Ki-67 PI <50% group ( P < 0.05 ). A total of 1,050 texture features were obtained using AK software. Through univariate logistic regression, mPMR feature selection, and multivariate logistic regression, three texture features were obtained: wavelet_HHL_GLRLM_ LRHGLE, lbp_3D_k_ firstorder_IR, and wavelet_HLH_GLCM_IMC1. The AUC of the prediction model based on the three texture features was 0.816, and the combined D value and three texture features was 0.834. Conclusions. Texture analysis on IVIM-DWI and its parameters was helpful for predicting Ki-67 PI and may provide a noninvasive method to investigate important imaging biomarkers for CSCC.

SRSF3 Restriction Eases Cervical Cancer Cell Viability and Metastasis via Adjusting PI3K/AKT/mTOR Signaling Pathway

Objective. To investigate the effect of SRSF3 on the viability and metastasis of cervical cancer (CC) SiHa and Hela cells. Methods. In vitro, HeLa cells and SiHa cells were cultured. In cervical cancer cells, RNA interference technology was utilized to lessen the SRSF3 level, and via RT‐PCR utilization, the SRSF3 level in every group of cells was revealed. By employing the CCK‐8 method, the OD value was revealed in every group at 24, 48, 72, and 96 h. On the migration of cervical cancer SiHa and HeLa cells via transwell utilizing, the consequence of SRSF3 was surveyed. Through western blotting utilizing, the PI3K/AKT/mTOR signaling pathway‐connected proteins levels was revealed. Results. In SiHa cells, contrasted to the NC‐SiHa group, the SRSF3 level, the number of invasive cells per unit area, the p‐PI3K/PI3K level, the p‐AKT/AKT level, and the p‐mTOR/mTOR level in the si‐SRSF3 group were substantially lessened. The OD value at 490 nm of the si‐SRSF3 group had no impressive divergence, contrasted to the NC‐SiHa group at 24 h. At 48 h, the OD value of the si‐SRSF3 group was impressively lessened than that of the NC‐SiHa group. This connection was time‐dependent. In HeLa cells, the SRSF3 level, the number of invasive cells per unit area, the level of p‐PI3K/PI3K, the level of p‐AKT/AKT, and the level of p‐mTOR/mTOR in the cells of the si‐SRSF3 group in the NC‐HeLa group were impressively lessened than those in the NC‐Hela group. Between the NC‐HeLa group and the si‐SRSF3 group at 24 h, there was no impressive divergence in the OD value at 490 nm. At 48 h, the OD value of the si‐SRSF3 group was impressively lessened than that of the NC‐SiHa group. This connection is time‐dependent. Conclusion. Reducing the SRSF3 level can restrain the viability and metastasis of cervical cancer cells via restraining the PI3K/AKT/mTOR signaling pathway.

HPV‐Induced MiR‐21 Promotes Epithelial Mesenchymal Transformation and Tumor Progression in Cervical Cancer Cells through the TGFβ R2/hTERC Pathway

Cervical cancer (CC) is a common malignant tumor in women. It ranks first among the malignant tumors of woman reproductive organs and is one of the most important cancers in the world. Current studies suggest that human papillomavirus (HPV) infection, especially high‐risk persistent infection, is the basic cause of cervical precancerous lesions and cervical cancer. MicroRNA‐21 (miR‐21) plays a role similar to oncogenes in the occurrence and growth of malignant tumors and can be developed as a potential target for treating malignant tumors. Recently, the study of the mechanism of malignant invasion and metastasis has made great progress. The current consensus is that the invasion and metastasis of malignant tumors is a complicated biological process with multistep and multigene control; the process of epithelial mesenchymal transition (EMT) may be the initial event of invasion and metastasis of epithelial malignant tumors. EMT means that epithelial cells obtain the characteristics of mesenchymal cells, which has main characteristics such as the loss of epithelial cell characteristics and the achievement of mesenchymal cell features, and then induce epithelial cells to acquire the ability of migration and invasion, and participate in many physiological and pathological processes of human body, including embryogenesis, organ differentiation, tissue inflammation, and wound healing. Research has proved that miR‐21 is associated with the invasion and metastasis of cervical cancer, and its specific mechanism has not been completely clear; EMT exerts a significant effect on the invasion and metastasis of epithelial malignant tumors; we speculate whether miR‐21 regulates the EMT process of cervical cancer cells. ELISA and RT‐PCR studied HPV‐induced cervical cancer cells, and it was found that HPV may induce miR‐21 to pass through the TGF β R2/hTERC pathway which promotes epithelial stromal transformation and tumor progression of cervical cancer cells.

Biomedical Application of Identified Biomarkers Gene Expression Based Early Diagnosis and Detection in Cervical Cancer with Modified Probabilistic Neural Network

Cervical squamous cell carcinoma (CSC) is expected to rise to become the fourth most prevalent cancer in women globally and to replace breast cancer as the top cause of death in women in the future years, according to the World Health Organization. According to the World Health Organization, developing countries are responsible for 86 percent of all cervical cancer cases globally in women aged 15 to 44 (WHO). Cancer mortality is associated with the largest amount of monotonous antecedent in low‐ and middle‐income nations, while cancer mortality is associated with the least amount of monotonous antecedent in high‐income countries. Cervical cancer is thought to be caused by aberrant proliferation of cells in the cervix that is capable of stealing or invading other human organs, according to current thinking. Cancer of the cerebral cell is the most prevalent kind of cancer in women. It is expected that cervical squamous cell carcinoma (CSC) will be the fourth most frequent cancer in the world and the main cause of death in women by the year 2050. Despite the fact that technology has improved tremendously since then, this is still the case. When compared to high‐income countries, low‐ and middle‐income countries have the highest consistent antecedent for cancer mortality, according to the World Cancer Research Fund. Cancerous growths of cells in the cervix, such as cervical cancer, are caused by cells that have the ability to steal from or invade auxiliary organs of the body, as is the case with cervical cancer. Although technological advances have been made in recent years, gene expression profiling continues to be a prominent approach in the investigation of cervical cancer. Since then, researchers have had the opportunity to examine a gene coexpression network, which has evolved into an exceptionally comprehensive technique for microarray research. This has helped them to get a better understanding of the human genome. When a specific biological issue is addressed, gene coexpression networks retain a considerable percentage of their once vast component of physiognomy, which was previously immense. When comparing the properties of genes in a population, it is well known that feature selection may be used to choose genes that outperform the rest of the genes in the population. There are several benefits to feature selection, and this is only one of them. Typically used gene selection approaches have been shown to be insufficient in acquiring the best potential sequence of genes for training purposes, and as a result, the accuracy of the classifier has likely suffered as a result of this. Recently, a considerable number of scientists have advocated for the use of optimization approaches in the process of gene selection, and this trend is expected to continue. A metaheuristic algorithm may be used to choose a suitable subset of genes, according to the preceding assertion, which is also consistent with the metaheuristic approach. A Modified Probabilistic Neural Network differs from other networks in that the underlying gene expression associated with DEGs and standard data in a Modified Probabilistic Neural Network is not uniformly distributed as it is in other networks (MPN). As previously said, selecting the most relevant genes or repeating genes is a vital step in the prediction process. It was this technique that was used in the research of cervical cancer. Since then, researchers have had the opportunity to examine a gene coexpression network, which has evolved into an exceptionally comprehensive technique for microarray research. This has helped them to get a better understanding of the human genome. When a specific biological issue is addressed, gene coexpression networks are able to preserve a previously major section of the face that had been lost. When comparing the properties of genes in a population, it is well known that feature selection may be used to choose genes that outperform the rest of the genes in the population. There are several benefits to feature selection, and this is only one of them. Typically used gene selection approaches have been shown to be insufficient in acquiring the best potential sequence of genes for training purposes, and as a result, the accuracy of the classifier has likely suffered as a result of this. In the field of gene selection, several scholars have argued in favor of the employment of optimization approaches. A metaheuristic algorithm may be used to choose a suitable subset of genes, according to the preceding assertion, which is also consistent with the metaheuristic approach. It was discovered that Modified Probabilistic Neural Networks (MPNs) had a different distribution of gene expression linked with DEGs and normal data than other networks, which had not been previously seen. This was previously unknown. Following what has been said before, selecting the most appropriate or repeated genes is a critical task throughout the prediction process.

[Retracted] Investigation of the Clinical Application Value of HR‐HPV DNA Combined with Liquid Based Cytology in Colposcopy of Cervical Cancer

In order to investigate the clinical application value of HR‐HPV DNA combined with liquid‐based cytology in colposcopy of cervical cancer, a retrospective analysis is performed on 428 patients who underwent a cervical pathological examination in our hospital from May 2020 to December 2021. The pathological biopsy results are used as the gold standard to determine whether cervical lesions occurred in patients, and patients with positive gold standard results are included in the study group. The positive rates of liquid‐based cytology and HR‐HPV DNA are observed and recorded, and the patients with negative gold standard results are used as the control group. The positive rate of HR‐HPV DNA and liquid‐based cytology will increase with the aggravation of pathological results. The ROC curve shows that HR‐HPV DNA combined with liquid‐based cytology has high diagnostic efficiency in colposcopy of cervical lesions. It is clearly evident that both liquid‐based cytology and HR‐HPV DNA tests have certain advantages in the screening of cervical lesions, and the combined detection can further improve the screening value of cervical lesions.

The 22‐Item Benefit Finding Scale: Validation and Application among Patients with Cervical Cancer in Ethnic Minority Areas of Southwestern China

Recently, the Benefit Finding Scale (BFS) has been translated and culturally adapted for use in China. However, further validation of the instrument is required before it can be used in the management of patients with cervical cancer in China. In this study, we conducted the questionnaire survey and examined its properties. This methodological study was conducted at a tumor hospital located in southwestern China. Patients with cervical cancer who had been reexamined in the outpatient department of the hospital and hospitalized from June to August 2019 were selected. The item analysis, exploratory factor analysis (EFA), and reliability analysis were tested. The relationships between benefit finding and sociodemographic and disease‐related variables were analyzed by ANOVA and regression models. A total of 247 patients were assessed (mean age: 48.0 ± 13.3 years). Educational level, self‐perceived disease severity, and physical exercise were the predictors of benefit finding. The correlation coefficient between 22 items and their dimensions was the best. EFA analysis supported a five‐factor model for structure validity. All Cronbach’s alpha for the Chinese version of the BFS (BFS‐C) was greater than 0.80. The results demonstrated the good reliability and validity of BFS‐C. It appears to be a useful scale to assess experience of benefit finding among patients with cervical cancer in China.

Application and Clinical Value of Machine Learning‐Based Cervical Cancer Diagnosis and Prediction Model in Adjuvant Chemotherapy for Cervical Cancer: A Single‐Center, Controlled, Non‐Arbitrary Size Case‐Control Study

Objective. A case‐control study was conducted to explore the application and clinical value of machine learning‐based cervical cancer (CC) diagnosis and prediction model in adjuvant chemotherapy of CC. Methods. From August 2019 to August 2021, 46 patients with stage IA CC (study group) and 55 patients with high‐grade squamous intraepithelial lesions (HSIL) (control group) were retrospectively analyzed. All patients completed routine MRI examinations, the ADC values of diseased CC and normal cervix and cervical tissues in different stages were compared, and the changes of ADC values in CC tissues before and after chemotherapy were analyzed. The training set (IA = 37, HSIL = 44) and test set (IA = 9, HSIL = 11) are set in a ratio of 4 : 1. The preoperative MRI images were collected and uploaded to the radiomics cloud platform after preprocessing, and the cervix was manually delineated layer by layer on OSag‐T2WI, OAx‐T1WI, and OAx‐T2FS, respectively, to obtain a three‐dimensional volume of interest (VOI) of the cervix to extract omics features. Variance Threshold analysis, univariate feature selection (SelectKBest), and least absolute shrinkage and selection operator (LASSO) are adopted to reduce the dimension of data and enroll features. The arbitrary forest model was adopted for machine learning, the ROC curve was drawn, and the diagnostic performance of different sequence omics models was analyzed. Results. Compared with ADC of stage A CC and HSIL, the ADC value of CC was remarkably lower than that of normal CC (P < 0.05). The ROC curve analysis of ADC value to differentiate CC and normal cervix indicated that the AUC was 0.838 and the 95% confidence interval was 0.721–0.955. According to the maximum Youden index of 0.848, the optimal critical value of ADC was 1.267 × 10−3 mm2/s and the sensitivity and specificity were 92.21% and 9.48%, respectively. All results are indicated in Table 2. After CC treatment, 12 patients were effective (CR + PR) and 4 patients were ineffective (PD + SD). When the b value was 1000 s/mm2, the ADC value of the effective patients after the second chemotherapy was significantly higher than that of the first chemotherapy and before treatment (P < 0.05). There was no significant difference between the ADC value after the first chemotherapy and before treatment, compared with before treatment (P > 0.05). There was no significant difference in ADC value between the ineffective patients before treatment and after the first and second chemotherapy (P > 0.05). A total of 8 omics features were extracted based on OSag‐T2WI, all of which were wavelet features, including 7 texture features and 1 first‐order feature. A total of 10 omics features were extracted based on OAx‐T1WI, including 6 wavelet first‐order features, 2 gradient first‐order features, and 2 wavelet texture features. Based on OAx‐T2FS, 6 omics features were extracted, including 3 wavelet texture features, 2 original shape features, and 1 logarithmic first‐order feature. Based on OSag‐T2WI&OAx‐T2FS, 9 histological features were extracted, 4 from OSag‐T2WI and 5 from OAx‐T2FS. The diagnostic performance of the four arbitrary forest models is indicated in Table 1, and the ROC curve is indicated in Figure 6. The diagnostic performance of the omics model based on OSag‐T2WI&OAx‐T2FS was the best in both the training set and the test set. The AUC of the training set was 0.991 (95% CI (0.94, 1.00)), and the accuracy rate was 0.925. The AUC of the test set was 0.894 (95% CI (0.75, 1.00)), and the accuracy rate was 0.835. On the other hand, the diagnostic efficiency of the group model based on OAx‐T1WI was the worst in both the training set and the test set. The AUC of the training set was 0.713 (95% CI (0.52, 0.92)), and the accuracy rate was 0.71. The AUC of test set is 0.513 (95% CI (0.24, 0.77)), and the accuracy rate was 0.56, which has no practical clinical significance. Conclusion. A CC diagnosis and prediction model based on machine learning can better distinguish stage IA CC from HSIL in the absence of clear lesions, which is of great significance for reducing invasive examination before surgery, guiding surgical procedures and adjuvant chemotherapy for CC.

Manifestation of Urinary Tract Injury during Cervical Cancer Surgery Based on CT Urography Secretion Phase Images

Object. CT imaging can be processed by computer, and the absorption coefficient of each voxel to X‐ray can be obtained by calculation, which can effectively improve the efficiency of surgery. Traditional treatment is based on the patient’s age, fertility requirements, and general conditions, often using a comprehensive treatment plan with surgery and radiotherapy as the mainstay, supplemented by chemotherapy, which has great limitations and side effects, in order to alleviate the loss of various body functions, especially the urinary tract during cervical cancer surgery. Methods. We grouped the patients who had undergone cervical cancer surgery in a hospital in this article and compared the nanodrug carrier system under CT imaging with traditional laparoscopy. The postoperative physical parameters of surgical patients are collected from cervical cancer patients of different degrees, and the parameters and prognostic health of patients after different operations are compared. Results. The results of the study show that the postoperative patient’s body parameters of the nanodrug delivery system under the CT imaging technology used in this article are better than those of the traditional surgery group, and the average intraoperative blood loss is about 20% less than that of the traditional surgery. Postoperative complications occur. The situation is even lower, more than 30% lower than traditional surgery. Conclusion. This shows that the operation of the nanodrug delivery system based on CT imaging technology has broken through some of the limitations of the development of laparoscopic technology and has played an important role in the surgical treatment of cervical cancer.

Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm

This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the diagnosis of early cervical cancer. 64 patients diagnosed with early cervical cancer clinicopathologically were included, and according to the examination methods, they were divided into A group with conventional multimodal MRI examination and B group with the multimodal MRI examination under the alternating direction algorithm. The diagnostic results of two types of multimodal MRI for early cervical cancer staging were compared with the results of clinicopathological examination to judge the application value in the early diagnosis of cervical cancer. The results showed that in the 6 randomly selected samples of early cervical cancer patients, the peak signal‐to‐noise ratio (PSNR) and structural similarity image measurement (SSIM) of multimodal MRI images under the alternating direction algorithm were significantly higher than those of conventional multimodal MRI images and the image reconstruction was clearer under this algorithm. By comparing MRI multimodal staging, statistical analysis showed that the staging accuracy of B group was 75%, while that of A group was only 59.38%. For the results of postoperative medical examinations, the examination consistency of B group was better than that of A group, with a statistically significant difference (P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) of B group was larger than that of A group; thus, sensitivity was improved and misdiagnosis was reduced significantly. Multimodal MRI under the alternating direction algorithm was superior to conventional multimodal MRI examination in the diagnosis of early cervical cancer, as the lesions were displayed more clearly, which was conducive to the detection rate of small lesions and the staging accuracy. Therefore, it could be used as an ideal MRI method for the assistant diagnosis of cervical cancer staging.

A Combination Analysis of IVIM‐DWI Biomarkers and T2WI‐Based Texture Features for Tumor Differentiation Grade of Cervical Squamous Cell Carcinoma

Purpose. To explore the value of intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) and texture analysis on T2‐weighted imaging (T2WI) for evaluating pathological differentiation of cervical squamous cell carcinoma. Method. This retrospective study included a total of 138 patients with pathologically confirmed poor/moderate/well‐differentiated (71/49/18) who underwent conventional MRI and IVIM‐DWI scans. The values of ADC, D, D∗, and f and 58 T2WI‐based texture features (18 histogram features, 24 gray‐level co‐occurrence matrix features, and 16 gray‐level run length matrix features) were obtained. Multiple comparison, correlation, and regression analyses were used. Results. For IVIM‐DWI, the ADC, D, D∗, and f were significantly different among the three groups (p < 0.05). ADC, D, and D∗ were positively correlated with pathological differentiation (r = 0.262, 0.401, 0.401; p < 0.05), while the correlation was negative for f (r = −0.221; p < 0.05). The comparison of 52 parameters of texture analysis on T2WI reached statistically significant levels (p < 0.05). Multivariate logistic regression analysis incorporated significant IVIM‐DWI, and texture features on T2WI showed good diagnostic performance both in the four differentiation groups (poorly vs. moderately, area under the curve(AUC) = 0.797; moderately vs. well, AUC = 0.954; poorly vs. moderately and well, AUC = 0.795; and well vs. moderately and poorly, AUC = 0.952). The AUCs of each parameters alone were smaller than that of each regression model (0.503∼0.684, 0.547∼0.805, 0.511∼0.712, and 0.636∼0.792, respectively; pairwise comparison of ROC curves between regression model and individual variables, p < 0.05). Conclusions. IVIM‐DWI biomarkers and T2WI‐based texture features had potential to evaluate the pathological differentiation of cervical squamous cell carcinoma. The combination of IVIM‐DWI with texture analysis improved the predictive performance.