Technology and Health Care

Risk factors for recurrence in patients with uterine fibroids treated with high-intensity focused ultrasound

Background Uterine fibroids, benign smooth muscle tumors prevalent in the female genital tract, affecting up to 40% of women of childbearing age. High-intensity focused ultrasound (HIFU) has emerged as a promising non-invasive approach for treating uterine fibroids, but some patients may still experience recurrence of uterine fibroids after treatment. Objective This study aims to explore the risk factors associated with uterine fibroid recurrence following HIFU treatment, and to provide a basis for formulating response measures to prevent uterine fibroid recurrence after surgery in clinical practice. Methods In this regression observational study, 120 patients with uterine fibroids who underwent HIFU therapy at our institution from Jan 2018 to Dec 2021 were included as the study subjects. Collect clinical data of all included patients, and follow up for a total of 2 years every 6 menstrual periods with gynecological ultrasound or related examinations after surgery. Univariate and logistic regression analyses were performed to identify risk factors for recurrence in potential uterine fibroid patients receiving HIFU knife treatment. Results Patients were divided into a relapse group (n = 27) and a non-relapse group (n = 93) based on recurrence during the follow-up period. The outcome of univariate analysis indicated no statistically significant difference in age, BMI, age at menarche, history of preoperative pregnancy, history of postoperative pregnancy, family history of uterine fibroids, Bcl-2, FSH, LH, E2, PRL, P, and T between the two groups ( p  > 0.05). Notably, significant differences were observed in fibroid diameter, ER, and PR ( p  < 0.05). Logistic regression analysis revealed uterine fibroid diameter (OR = 28.032, 6.074 ∼ 129.372), PR (OR = 4.642, 2.382 ∼ 9.049), and ER (OR = 3.435, 1.820 ∼ 6.483) were independent risk factors for uterine fibroid recurrence after HIFU knife treatment. ROC curve analysis showed that the AUC of uterine fibroid recurrence predicted by fibroid diameter, ER, and PR after HIFU knife treatment were 0.791, 0.738, and 0.785, respectively. Conclusion The diameter, ER, and PR of uterine fibroids are closely related to the recurrence of uterine fibroids after surgical treatment, and it is worth implementing key perioperative management in clinical practice to prevent and manage the recurrence of uterine fibroids.

Effect of virtual reality-based mindfulness training model on anxiety, depression, and cancer-related fatigue in ovarian cancer patients during chemotherapy

BACKGROUND: Although the prognosis of ovarian cancer can be significantly improved through standardized surgery and chemotherapy, 70% of epithelial ovarian cancer (EOC) patients would suffer from drug resistance and recurrence during the long chemotherapy cycle. OBJECTIVE: To explore the impact of a training mode based on the integration of virtual reality technology and mindfulness on anxiety, depression, and cancer-related fatigue in ovarian cancer patients during chemotherapy. METHOD: Through virtual reality technology, a mindfulness training software was designed and developed, and a mindfulness training mode based on virtual reality technology was constructed. Using a self-controlled design, 48 ovarian cancer patients undergoing chemotherapy who were hospitalized in a tertiary hospital in Beijing from August 2022 to May 2023 were conveniently selected as the research subjects. The patients were subjected to four weeks of mindfulness training based on virtual reality technology, and the acceptance of the mindfulness training mode using virtual reality technology was evaluated. The Hospital Anxiety and Depression Scale (HADS) and Cancer Related Fatigue Scale (CRF) were used to evaluate the anxiety, depression, and fatigue of patients before and after intervention. RESULTS: The virtual reality based mindfulness training mode includes four functional modules: personalized curriculum, intelligent monitoring, emotion tracking, and Funny Games. 48 patients had a high acceptance score (139.21 ± 10.47), and after using mindfulness training mode based on virtual reality technology, anxiety, depression, and cancer-related fatigue in ovarian cancer patients during chemotherapy were significantly reduced, with a statistically significant difference (p< 0.001). CONCLUSION: Ovarian cancer patients during chemotherapy have a high acceptance of virtual reality based mindfulness training mode. The application of this mode can reduce the psychological problems of anxiety, depression, and cancer-related fatigue in ovarian cancer patients during chemotherapy, and is worth promoting and using.

Observations of the effectiveness, dosage, and prognosis of intensity-modulated radiation therapy under ultrasonic guidance for cervical cancer patients

BACKGROUND: Volumetric modulated arc therapy (VMAT) guided by ultrasound is a novel radiation therapy technique that facilitates the delineation of the tumor target area under image guidance, enhancing the precision of radiation therapy and maximizing the protection of surrounding tissues. OBJECTIVE: The objective of this paper is to investigate the effectiveness of VMAT under ultrasonic guidance for cervical cancer patients and its impact on radiotherapy dosage and prognosis. METHODS: A retrospective analysis encompassed 128 instances of cervical cancer patients who were admitted to our medical facility between April 2019 and April 2021. The patients were categorized into an observation cohort and a control cohort, depending on variations in treatment modalities post-admission. The control group underwent conventional radiotherapy, whereas the observation group received VMAT guided by ultrasound. Clinical efficacy, average radiation dosages (in the radiotherapy target area, rectum, and bladder), radiotherapy-related toxicities during treatment, and one-year survival rates were compared between the two groups. Additionally, variances in pre- and post-treatment serum levels of squamous cell carcinoma antigen (SCC-Ag), carcinoembryonic antigen (CEA), and carbohydrate antigen 724 (CA724) were subjected to assessment. RESULTS: When compared to the control group (64.52%), the observation cohort’s comprehensive effectiveness rate was considerably greater (80.30%). The observation group saw lower average radiation exposures and a reduction in the post-treatment concentrations of CEA, SCC-Ag, and CA724. The overall incidence of adverse effects from radiation treatment also declined. The observation group had a greater one-year survival rate (90.48%) than the control group (73.33%). When comparing the observation cohort to the control group, Kaplan-Meier survival analysis showed a significantly higher one-year survival rate (Log-Rank = 6.530, P= 0.011). CONCLUSION: VMAT guided by ultrasound for patients with cervical cancer demonstrates promising short- and long-term treatment outcomes. It also leads to improvements in serum CEA, SCC-Ag, and CA724 levels, as well as reductions in the average radiation dosages to the radiotherapy target area, rectum, and bladder. This approach warrants attention from clinicians in clinical practice.

Retrospective analysis of 26 cases of pregnancy luteoma

BACKGROUND: Pregnancy luteoma is a rare hormone-dependent ovarian tumor-like lesion caused by increased androgenic activity during pregnancy. OBJECTIVE: To explore the clinical history, ultrasound manifestations, and differential diagnosis of pregnancy luteoma. METHOD: A retrospective analysis was conducted on 26 cases of pregnancy luteoma diagnosed by postoperative pathology, from 2009 to 2022. All cases were from two hospitals: Shanghai First Maternity and Infant Hospital and International Peace Maternity and Child Health Hospital. The clinical history data and ultrasound characteristics were analyzed and the relevant literature was reviewed. RESULTS: Among the 26 cases, five of them had preoperative ultrasound images. Among these five cases, three patients showed hyperechoic masses with less internal uniformity, while two demonstrated loculated anechoic zones, with clear boundary and regular morphology. Color Doppler showed no obvious internal blood flow signals, or that blood flow signals were visible within the cyst wall and hyperechoic mass. Among the cases, 16 had multiple gestations, while two visited the clinic due to sudden abdominal pain and a huge ovarian mass was found by ultrasonography. The ovarian lump was detected during routine obstetric ultrasound in three cases. The remaining were ovarian cysts found incidentally during caesarean section. Four patients presented with hairy manifestations and one patient had a deepened voice. CONCLUSION: There is no characteristic ultrasound of pregnancy luteoma, and its diagnosis is mainly based on clinical history data and laboratory tests.

Application of 3.0 T multimodal MRI in FIGO staging of cervical cancer

BACKGROUND: In malignant tumours of the female reproductive system, cervical cancer is second only to breast cancer, seriously threatening the health and safety of most women. OBJECTIVE: To evaluate the clinical value of 3.0 T multimodal nuclear magnetic resonance imaging (MRI) in the International Federation of Gynecology and Obstetrics’ (FIGO) staging of cervical cancer. METHODS: The clinical data of 30 patients with pathologically diagnosed cervical cancer admitted to our hospital from January 2018 to August 2022 were analysed retrospectively. Before treatment, all patients were examined with conventional MRI, diffusion-weighted imaging and multi-directional contrast-enhanced imaging. RESULTS: The accuracy of multimodal MRI in the FIGO staging of cervical cancer (29/30, 96.7%) was significantly higher than the accuracy obtained in a control group (21/30, 70.0%), with a statistically significant difference (p= 0.013). In addition, there was good agreement between two observers applying multimodal imaging (kappa= 0.881) and moderate agreement between two observers in the control group (kappa= 0.538). CONCLUSION: Multimodal MRI can evaluate cervical cancer comprehensively and accurately to enable accurate FIGO staging, providing significant evidence for clinical operation planning and subsequent combined therapy.

Penalized logistic regression based on L1/2 penalty for high-dimensional DNA methylation data

BACKGROUND: DNA methylation is a molecular modification of DNA that is vital and occurs in gene expression. In cancer tissues, the 5’–C–phosphate–G–3’(CpG) rich regions are abnormally hypermethylated or hypomethylated. Therefore, it is useful to find out the diseased CpG sites by employing specific methods. CpG sites are highly correlated with each other within the same gene or the same CpG island. OBJECTIVE: Based on this group effect, we proposed an efficient and accurate method for selecting pathogenic CpG sites. METHODS: Our method aimed to combine a [Formula: see text] regularized solver and a central node fully connected network to penalize group constrained logistic regression model. Consequently, both sparsity and group effect were brought in with respect to the correlated regression coefficients. RESULTS: Extensive simulation studies were used to compare our proposed approach with existing mainstream regularization in respect of classification accuracy and stability. The simulation results show that a greater predictive accuracy was attained in comparison to previous methods. Furthermore, our method was applied to over 20000 CpG sites and verified using the ovarian cancer data generated from Illumina Infinium HumanMethylation 27K Beadchip. In the result of the real dataset, not only the indicators of predictive accuracy are higher than the previous methods, but also more CpG sites containing genes are confirmed pathogenic. Additionally, the total number of CpG sites chosen is less than other methods and the results show higher accuracy rates in comparison to other methods in simulation and DNA methylation data. CONCLUSION: The proposed method offers an advanced tool to researchers in DNA methylation and can be a powerful tool for recognizing pathogenic CpG sites.

Evaluating the influence of 6MV and 10MV photon beams on cervical cancer volumetric-modulated arc therapy plans

BACKGROUND: Cervical cancer is a common gynecological cancer among women worldwide. OBJECTIVE: To determine the effects of 6 MV and 10 MV volumetric-modulated arc therapy (VMAT) photon beams on the target volume (TV) planning and critical organs in cases of cervical cancer. METHODS: Fifty patients with carcinoma of the cervix who underwent radiotherapy were selected. The transverse diameter (T) of the cross section of the upper edge of the sacroiliac joint on computerized tomography (CT) images of the patients was measured, and the mean value was calculated as 34 cm. All patients were divided into two groups: Group A (T < 34 cm) and Group B (T > 34 cm). The VMAT plans were generated using 6 MV and 10 MV plans separately. The prescription dose was 47.5 Gy, and the daily dose was 1.9 Gy. RESULTS: In Group A, the planning target volume (PTV) dose assessment parameters of 6 MV and 10 MV plans and their homogeneity and conformity indices were not statistically significantly different. A significant difference was observed between the 6 MV and 10 MV plans for the PTV dose assessment parameters and the homogeneity index of the plans for Group B. The monitor units (MUs) of the 10 MV plans were lower than in the 6 MV plans in both Groups A and B, and the difference was statistically significant. The assessment parameter V40 Gy of both the rectum and bladder in the 6 MV plans was smaller than the corresponding parameter in the 10 MV plans in Group A; in Group B, the assessment parameter V50 Gy of the rectum in the 10 MV plans was smaller than in the 6 MV plans. CONCLUSION: When T < 34 cm, 6 MV energy is more suitable for the external irradiation of cervical cancer. When T > 34 cm, 10 MV energy is more suitable for cervical cancer radiotherapy. Therefore, 10 MV should be considered for patients with a large abdominal size.

Segmentation of acetowhite region in uterine cervical image based on deep learning

BACKGROUND: Acetowhite (AW) region is a critical physiological phenomenon of precancerous lesions of cervical cancer. An accurate segmentation of the AW region can provide a useful diagnostic tool for gynecologic oncologists in screening cervical cancers. Traditional approaches for the segmentation of AW regions relied heavily on manual or semi-automatic methods. OBJECTIVE: To automatically segment the AW regions from colposcope images. METHODS: First, the cervical region was extracted from the original colposcope images by k-means clustering algorithm. Second, a deep learning-based image semantic segmentation model named DeepLab V3+ was used to segment the AW region from the cervical image. RESULTS: The results showed that, compared to the fuzzy clustering segmentation algorithm and the level set segmentation algorithm, the new method proposed in this study achieved a mean Jaccard Index (JI) accuracy of 63.6% (improved by 27.9% and 27.5% respectively), a mean specificity of 94.9% (improved by 55.8% and 32.3% respectively) and a mean accuracy of 91.2% (improved by 38.6% and 26.4% respectively). A mean sensitivity of 78.2% was achieved by the proposed method, which was 17.4% and 10.1% lower respectively. Compared to the image semantic segmentation models U-Net and PSPNet, the proposed method yielded a higher mean JI accuracy, mean sensitivity and mean accuracy. CONCLUSION: The improved segmentation performance suggested that the proposed method may serve as a useful complimentary tool in screening cervical cancer.

FCM-NPOA: A hybrid Fuzzy C-means clustering with nomadic people optimizer for ovarian cancer detection

Ovarian cancer is a highly prevalent cancer among women; However, it remains difficult to find effective pharmacological solutions to treat this deadly disease. However, early detection can significantly increase life expectancy. To address this issue, a predictive model for early diagnosis of ovarian cancer was developed by applying statistical techniques and machine learning models to clinical data from 349 patients. A hybrid evolutionary deep learning model was proposed by integrating genetic and histopathological imaging modalities within a multimodal fusion framework. Machine learning pipelines have been built using feature selection and dilution approaches to identify the most relevant genes for disease classification. A comparison was performed between the UNeT and transformer models for semantic segmentation, leading to the development of an optimized fuzzy C-means clustering algorithm (FCM-NPOA-PM-UI) for the classification of gynecological abdominopelvic tumors. Performing better than individual classifiers and other machine learning methods, the suggested ensemble model achieved an average accuracy of 98.96%, precision of 97.44%, and F1 score of 98.7%. With average Dice scores of 0.98 and 0.97 for positive tumors and 0.99 and 0.98 for malignant tumors, the Transformer model performed better in segmentation than the UNeT model. Additionally, we observed a 92.8% increase in accuracy when combining five machine learning models with biomarker data: random forest, logistic regression, SVM, decision tree, and CNN. These results demonstrate that the hybrid model significantly improves the accuracy and efficiency of ovarian cancer detection and classification, offering superior performance compared to traditional methods and individual classifiers.

An automated cervical cancer diagnosis using genetic algorithm and CANFIS approaches

BACKGROUND: Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates. OBJECTIVE: This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images. METHODS: The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation. The original cervical image undergoes smoothing using the Gaussian Filter technique, followed by the extraction of Local Binary Pattern (LBP) and Grey Level Co-occurrence Matrix (GLCM) features from the enhanced cervical images. LBP features capture pixel relationships within a mask window, while GLCM features quantify energy metrics across all pixels in the images. These features serve to distinguish normal cervical images from abnormal ones. The extracted features are optimized using Genetic Algorithm (GA) as an optimization method, and the optimized sets of features are classified using the Co-Active Adaptive Neuro-Fuzzy Inference System (CANFIS) classification method. Subsequently, a morphological segmentation technique is employed to categorize irregular cervical images, identifying and segmenting malignant regions within them. RESULTS: The proposed approach achieved a sensitivity of 99.09%, specificity of 99.39%, and accuracy of 99.36%. CONCLUSION: The proposed approach demonstrated superior performance compared to state-of-the-art techniques, and the results have been validated by expert radiologists.

Deep learning-based decision support system for cervical cancer identification in liquid-based cytology pap smears

Background Cervical cancer is the fourth most common cause of women cancer deaths worldwide. The primary etiology of cervical cancer is the persistent infection of specific high-risk strains of the human papillomavirus. Liquid-based cytology is the established method for early detection of cervical cancer. The evaluation of cellular abnormalities at a microscopic level allows for the identification of malignant or precancerous features in liquid-based cytology pap smears. This technique is characterized by its time-consuming nature and susceptibility to both inter- and intra-observer variability. Hence, the utilization of Artificial Intelligence in computer-assisted diagnosis can reduce the duration needed for diagnosing this ailment, thereby eliminating delayed diagnosis and facilitating the implementation of an efficient treatment. Objective This research presents a new deep learning-based cervical cancer identification decision support system in liquid-based cytology smear images. Methods The proposed diagnosis support system incorporates a novel hybrid feature reduction and optimization module, which integrates a sparse Autoencoder with the Binary Harris Hawk metaheuristic optimization algorithm to select the most informative features from a supplemented feature set of the input images. The supplemented feature set is retrieved by three pretrained Convolutional Neural Networks. The module utilizes an improved feature set to conduct a Bayesian-optimized K Nearest Neighbors machine learning classification of cervical cancer in input Pap smears. Results The introduced approach achieves a classification accuracy of 99.9% and demonstrates an improved ability to detect the stages of cervical cancer, with a sensitivity of 99.8%. In addition, the system has the ability to identify the lack of cervical cancer stages with a specificity rate of 99.9%. Conclusion The proposed system outpaces recent deep learning-based cervical cancer identification systems.

Study on serum miR-182 as a marker for diagnosis and prognosis of cervical cancer

BACKGROUND: Cervical cancer (CC) is a common female malignancy, with a global incidence rate second only to breast cancer. OBJECTIVE: To propose a new idea for early treatment and auxiliary diagnosis of CC by exploring the diagnostic and prognostic implications of serum miR-182 in CC. METHODS: We enrolled 70 CC patients, 35 cervical intraepithelial neoplasia (CIN) patients and 35 healthy controls (HCs), who visited The First Affiliated Hospital of Hainan Medical College Hospital between January 2015 and April 2016. miR-182 expression was quantified by real-time quantitative PCR and compared among the three groups. The correlation of serum miR-182 expression with patients’ clinical features was evaluated. The receiver operating characteristic curve (ROC) and the Kaplan-Meier method were used to evaluate the early diagnostic value and prognostic value of serum miR-182. Cox regression analysis was performed to determine serum miR-182 expression and its important role in predicting CC patients’ prognosis. RESULTS: Serum miR-182 expression was determined to be 0.345 ± 0.094, 0.369 ± 0.076, and 0.586 ± 0.157 in CC patients, CIN patients, and HCs, respectively (P< 0.001). Serum miR-182 expression had an obvious association with lymph node metastasis and pathological differentiation (P< 0.05). The area under the ROC curve (AUC) of serum miR-182 was 0.709 (95% CI: 0.622–0.795), the critical value was 0.456, the sensitivity was 81.4%, and the specificity was 52.9%. CC patients were grouped as either the low- (miR-182 < 0.3) or high-level group (miR-182 ⩾ 0.03) based on serum miR-182 levels, and a Cox regression model of OS was established. Serum miR-182 expression was identified as a factor independently influencing CC patients’ OS (P= 0.028); the death risk of the high-level group was 3.246 times that of the low-level group. CONCLUSION: Serum miR-182 expression is not only a biomarker for early diagnosis of CC, but also one of the independent factors influencing the survival and prognosis of CC patients.

A uterine cervix supporting device (Con-CapTM) for reducing canal stenosis after Loop Electrosurgical Excisional Procedure1

BACGROUND: Cervical stenoses are one of the main long-term consequences after conization of the uterine cervix. OBJECTIVE: The purpose of this study was to evaluate the safety and efficacy of a uterine cervix supporting device (Con-CapTM) in reducing uterine cervical stenosis after Loop Electrosurgical Excisional Procedure (LEEP). METHODS: We enrolled 112 patients who underwent LEEP between March 2017 to May 2019. Con-CapTM was inserted into the uterine endocervical canal for 4 weeks after LEEP. Laboratory values and clinical symptoms were evaluated. The presence of uterine cervical narrowing was determined at 2 weeks after removal of the Con-CapTM. Data were analyzed using the two-sample t test and χ2 test. RESULTS: A total of 78 women completed the 6-week study period. Thirty-four patients did not complete the study period. The diameter of the uterine cervical canal was significantly greater at postoperative 6 weeks than preoperatively (Hegar dilator No, 2.10 ± 0.56 vs. 3.21 ± 0.71, P< 0.01). The complications were acceptable. CONCLUSIONS: Con-CapTM can be used to reduce uterine cervical stenosis safely and effectively after conization of uterine cervix.

Integrating network pharmacology and Mendelian randomization to explore potential targets of matrine against ovarian cancer

BACKGROUND: Matrine has been reported inhibitory effects on ovarian cancer (OC) cell progression, development, and apoptosis. However, the molecular targets of matrine against OC and the underlying mechanisms of action remain elusive. OBJECTIVE: This study endeavors to unveil the potential targets of matrine against OC and to explore the intricate relationships between these targets and the pathogenesis of OC. METHODS: The effects of matrine on the OC cells (A2780 and AKOV3) viability, apoptosis, migration, and invasion was investigated through CCK-8, flow cytometry, wound healing, and Transwell analyses, respectively. Next, Matrine-related targets, OC-related genes, and ribonucleic acid (RNA) sequence data were harnessed from publicly available databases. Differentially expressed analyses, protein-protein interaction (PPI) network, and Venn diagram were involved to unravel the core targets of matrine against OC. Leveraging the GEPIA database, we further validated the expression levels of these core targets between OC cases and controls. Mendelian randomization (MR) study was implemented to delve into potential causal associations between core targets and OC. The AutoDock software was used for molecular docking, and its results were further validated using RT-qPCR in OC cell lines. RESULTS: Matrine reduced the cell viability, migration, invasion and increased the cell apoptosis of A2780 and AKOV3 cells (P< 0.01). A PPI network with 578 interactions among 105 candidate targets was developed. Finally, six core targets (TP53, CCND1, STAT3, LI1B, VEGFA, and CCL2) were derived, among which five core targets (TP53, CCND1, LI1B, VEGFA, and CCL2) differential expressed in OC and control samples were further picked for MR analysis. The results revealed that CCND1 and TP53 were risk factors for OC. Molecular docking analysis demonstrated that matrine had good potential to bind to TP53, CCND1, and IL1B. Moreover, matrine reduced the expression of CCND1 and IL1B while elevating P53 expression in OC cell lines. CONCLUSIONS: We identified six matrine-related targets against OC, offering novel insights into the molecular mechanisms underlying the therapeutic effects of matrine against OC. These findings provide valuable guidance for developing more efficient and targeted therapeutic approaches for treating OC.

Application and evaluation of minimally invasive surgical treatment options for early endometrial cancer

BACKGROUND: Laparoscopic and robotic-assisted techniques have gained popularity, and endometrial cancer (EC) remains a significant health problem among women. OBJECTIVE: Minimally invasive surgical (MIS) therapy options for early endometrial cancer will be evaluated for their effectiveness and safety is the aim of this paper. We also investigate the differences in oncologic outcomes between MIS and open surgery (OS) for individuals with early-stage EC. The patient was diagnosed with early-stage EC and treated with laparoscopic surgery and was the focus of a retrospective analysis. 162 patients with early EC were analyzed, with diagnoses occurring between 2002 and 2022. METHODS: The patients were fragmented into two groups, one for OS and another for laparoscopic procedures. The total tumor excision and recurrence rates were identical across the two methods, indicating similar oncologic results. Rates of complications were likewise comparable across the two groups. RESULTS: The quality of life ratings of patients with robotic-assisted surgery was higher than those with laparoscopic surgery. Sixty-two (62.2%) of the 162 patients in this research had OS, whereas Fifty-six (57.8%) had MIS. The probability of recurrence of EC from stages III to IV was significanitly higher in women who had OS. CONCLUSION: Minimally invasive procedures were shown to be effective in treating early-stage EC, and while these findings provide support for their usage, larger multicenter randomized controlled studies are required to verify these results and further examine possible long-term advantages. Patients with early-stage EC, regardless of histologic type, had superior survival rates with MIS compared to OS.