Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review

Enhancing Ovarian Tumor Diagnosis: Performance of Convolutional Neural Networks in Classifying Ovarian Masses Using Ultrasound Images

Background/Objectives: This study aims to create a strong binary classifier and evaluate the performance of pre-trained convolutional neural networks (CNNs) to effectively distinguish between benign and malignant ovarian tumors from still ultrasound images. Methods: The dataset consisted of 3510 ultrasound images from 585 women with ovarian tumors, 390 benign and 195 malignant, that were classified by experts and verified by histopathology. A 20% to80% split for training and validation was applied within a k-fold cross-validation framework, ensuring comprehensive utilization of the dataset. The final classifier was an aggregate of three pre-trained CNNs (VGG16, ResNet50, and InceptionNet), with experimentation focusing on the aggregation weights and decision threshold probability for the classification of each mass. Results: The aggregate model outperformed all individual models, achieving an average sensitivity of 96.5% and specificity of 88.1% compared to the subjective assessment's (SA) 95.9% sensitivity and 93.9% specificity. All the above results were calculated at a decision threshold probability of 0.2. Notably, misclassifications made by the model were similar to those made by SA. Conclusions: CNNs and AI-assisted image analysis can enhance the diagnosis and aid ultrasonographers with less experience by minimizing errors. Further research is needed to fine-tune CNNs and validate their performance in diverse clinical settings, potentially leading to even higher sensitivity and overall accuracy.

Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.

Accuracy of machine learning in the preoperative identification of ovarian borderline tumors: a meta-analysis

AIM

The objective of this study is to explore the diagnostic value of machine learning (ML) in borderline ovarian tumors through meta-analysis.

METHODS

Pubmed, Embase, Web of Science, and Cochrane Library databases were comprehensively retrieved from database inception untill February 16, 2023. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was adopted to evaluate the risk of bias in the original studies. Sub-group analyses of ML were conducted according to clinical features and radiomics features. We separately discussed the discriminative value of ML for borderline vs benign and borderline vs malignant tumors.

RESULTS

Eighteen studies involving 12,778 subjects were included in our analysis. The modeling variables mainly consisted of radiomics features (n=13) and a small number of clinical features (n=5). When distinguishing between borderline and benign tumors, the ML model based on radiomic features achieved a c-index of 0.782 (95% CI: 0.732-0.831), sensitivity of 0.75 (95% CI: 0.67-0.82), and specificity of 0.75 (95% CI: 0.67-0.81) in the validation set. When distinguishing between borderline and malignant tumors, the ML model based on radiomic features achieved a c-index of 0.916 (95% CI: 0.891-0.940), sensitivity of 0.86 (95% CI: 0.78-0.91), and specificity of 0.88 (95% CI: 0.82-0.92) in the validation set. In addition, we analyzed the discriminatory ability of radiologists and found that their sensitivity was 0.26 (95% CI: 0.12-0.46) and specificity was 0.94 (95% CI: 0.90-0.97).

CONCLUSIONS

ML has tremendous potential in the preoperative diagnosis and differentiation of borderline ovarian tumors and may be more accurate than radiologists in diagnosing and differentiating borderline ovarian tumors.

Applications of artificial intelligence in diagnosis of uncommon cystoid macular edema using optical coherence tomography imaging: A systematic review

Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in "detection", "classification", and "segmentation" of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96 % in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.

Natural Language Processing-Driven Artificial Intelligence Models for the Diagnosis of Lumbar Disc Herniation with L5 and S1 Radiculopathy: A Preliminary Evaluation

OBJECTIVE

To develop and validate natural language processing-driven artificial intelligence (AI) models for the diagnosis of lumbar disc herniation (LDH) with L5 and S1 radiculopathy using electronic health records (EHRs).

METHODS

EHRs of patients undergoing single-level percutaneous endoscopic lumbar discectomy for the treatment of LDH at the L4/5 or L5/S1 level between June 1, 2013, and December 31, 2021, were collected. The primary outcome was LDH with L5 and S1 radiculopathy, which was defined as nerve root compression recorded in the operative notes. Datasets were created using the history of present illness text and positive symptom text with radiculopathy (L5 or S1), respectively. The datasets were randomly split into a training set and a testing set in a 7:3 ratio. Two machine learning models, the long short-term memory network and Extreme Gradient Boosting, were developed using the training set. Performance evaluation of the models on the testing set was done using measures such as the receiver operating characteristic curve, area under the curve, accuracy, recall, F1-score, and precision.

RESULTS

The study included a total of 1681 patients, with 590 patients having L5 radiculopathy and 1091 patients having S1 radiculopathy. Among the 4 models developed, the long short-term memory model based on positive symptom text showed the best discrimination in the testing set, with precision (0.9054), recall (0.9405), accuracy (0.8950), F1-score (0.9226), and area under the curve (0.9485).

CONCLUSIONS

This study provides preliminary validation of the concept that natural language processing-driven AI models can be used for the diagnosis of lumbar disease using EHRs. This study could pave the way for future research that may develop more comprehensive and clinically impactful AI-driven diagnostic systems.

Evaluation of multi-parameter MRI in preoperative staging of endometrial carcinoma

Background

Endometrial carcinoma (EC) is a prevalent gynecological malignancy, necessitating accurate preoperative staging for effective treatment planning. This study explores the application value of multi-parameter MRI in diagnosing and staging endometrial cancer.

Methods

Seventy-six patients diagnosed with endometrial cancer underwent 3.0 T pelvic MRI within two weeks before surgery. Imaging data were analyzed based on FIGO clinical staging criteria. The study assessed the sensitivity, specificity, positive predictive value, and negative predictive value of MRI for each stage.

Results

Postoperative pathology confirmed 71 cases of endometrial adenocarcinoma, 3 serous adenocarcinoma, and 2 clear cell carcinomas. MRI staging showed a high consistency (Kappa value = 0.786) with postoperative pathology. The overall accuracy of MRI diagnosis was 86.8%. Sensitivity and specificity varied for each stage: IA (91.3%, 96.2%), IB (88.6%, 93.8%), II (97.4%, 89.2%), and III (84.2%, 100%).

Conclusion

While there was a slight misdiagnosis rate, the overall accuracy of preoperative MRI for endometrial cancer was high, aiding in precise diagnosis and clinical staging. MRI effectively identified myometrial infiltration, cervical involvement, paracentral extension, and lymph node metastasis. Further research with larger sample sizes is recommended for enhanced reliability.

The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data

Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally, we summarised the most recent developments. For better understanding, we provide explanations of key terms and approaches to solving common deep learning problems. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second focus of this work is on the analysis of the availability of open source, open data and open models. In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly present in others. Nevertheless, its impact is increasing with the years, partly self-propelled but also influenced by closely related fields. Open source, data and models are growing in number but are still scarce and unevenly distributed among different research groups. The reluctance in publishing code, data and models limits reproducibility and restricts evaluation to mono-institutional datasets. The conclusion of our analysis is that deep learning can positively change the workflow of interventional radiotherapy but there is still room for improvements when it comes to reproducible results and standardised evaluation methods.

Automated reporting of cervical biopsies using artificial intelligence

When detected at an early stage, the 5-year survival rate for people with invasive cervical cancer is 92%. Being aware of signs and symptoms of cervical cancer and early detection greatly improve the chances of successful treatment. We have developed an Artificial Intelligence (AI) algorithm, trained and evaluated on cervical biopsies for automated reporting of digital diagnostics. The aim is to increase overall efficiency of pathological diagnosis and to have the performance tuned to high sensitivity for malignant cases. Having a tool for triage/identifying cancer and high grade lesions may potentially reduce reporting time by identifying areas of interest in a slide for the pathologist and therefore improving efficiency. We trained and validated our algorithm on 1738 cervical WSIs with one WSI per patient. On the independent test set of 811 WSIs, we achieved 93.4% malignant sensitivity for classifying slides. Recognising a WSI, with our algorithm, takes approximately 1.5 minutes on the NVIDIA Tesla V100 GPU. Whole slide images of different formats (TIFF, iSyntax, and CZI) can be processed using this code, and it is easily extendable to other formats.

Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer

Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.

Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis

PURPOSE

We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC).

METHODS

Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed.

RESULTS

Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively.

CONCLUSION

The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review

BACKGROUND

A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation.

METHODS

PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789).

RESULTS

In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively.

CONCLUSION

AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.

Predicting Hypoxia Using Machine Learning: Systematic Review

Background

Hypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate time-critical interventions to counter patient health deterioration.

Objective

This systematic review aims to summarize and compare previous efforts to predict hypoxic events in the hospital setting using machine learning with respect to their methodology, predictive performance, and assessed population.

Methods

A systematic literature search was performed using Web of Science, Ovid with Embase and MEDLINE, and Google Scholar. Studies that investigated hypoxia or hypoxemia of hospitalized patients using machine learning models were considered. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.

Results

After screening, a total of 12 papers were eligible for analysis, from which 32 models were extracted. The included studies showed a variety of population, methodology, and outcome definition. Comparability was further limited due to unclear or high risk of bias for most studies (10/12, 83%). The overall predictive performance ranged from moderate to high. Based on classification metrics, deep learning models performed similar to or outperformed conventional machine learning models within the same studies. Models using only prior peripheral oxygen saturation as a clinical variable showed better performance than models based on multiple variables, with most of these studies (2/3, 67%) using a long short-term memory algorithm.

Conclusions

Machine learning models provide the potential to accurately predict the occurrence of hypoxic events based on retrospective data. The heterogeneity of the studies and limited generalizability of their results highlight the need for further validation studies to assess their predictive performance.

Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia

Cervical cancer, the fourth most common cancer among women worldwide, often proves fatal and stems from precursor lesions caused by high-risk human papillomavirus (HR-HPV) infection. Accurate and early diagnosis is crucial for effective treatment. Current screening methods, such as the Pap test, liquid-based cytology (LBC), visual inspection with acetic acid (VIA), and HPV DNA testing, have limitations, requiring confirmation through colposcopy. This study introduces CerviCARE AI, an artificial intelligence (AI) analysis software, to address colposcopy challenges. It automatically analyzes Tele-cervicography images, distinguishing between low-grade and high-grade lesions. In a multicenter retrospective study, CerviCARE AI achieved a remarkable sensitivity of 98% for high-risk groups (P2, P3, HSIL or higher, CIN2 or higher) and a specificity of 95.5%. These findings underscore CerviCARE AI's potential as a valuable diagnostic tool for highly accurate identification of cervical precancerous lesions. While further prospective research is needed to validate its clinical utility, this AI system holds promise for improving cervical cancer screening and lessening the burden of this deadly disease.

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Introduction

Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications.

Methods

The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model.

Results

The review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias.

Conclusion

This review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.

Effectiveness of a culturally tailored text messaging program for promoting cervical cancer screening in accra, Ghana: a quasi-experimental trial

INTRODUCTION

Despite breakthroughs in cervical cancer detection, resource-constrained countries continue to have a disproportionately high incidence and death rate. Mhealth has been identified as an important tool for increasing cervical cancer screening rates in Sub-Saharan Africa. We determined whether sending Ghanaian women culturally tailored one-way mobile phone SMS text messages about cervical cancer would encourage the uptake of the human papillomavirus (HPV) test.

METHODS

From August to November 2016, 88 women aged 18 to 39 living or working in an urban community (Accra, Ghana) participated in a quasi-experimental study. For 8 weeks, 32 SMS messages regarding cervical cancer were developed and sent to the personal phones of intervention arm participants (n = 42). Women in the control group (n = 46) received SMS texts with general health and lifestyle advice. Fischer's exact tests were performed to assess cervical cancer screening uptake and associated reasons for non-uptake between the intervention and control groups (p < 0.05).

RESULTS

At the baseline, women differed in terms of ethnicity and wealth. After the intervention, participants' self-reported risk factors for cervical cancer, such as early menarche, usual source of medical treatment, family history of cancer, smoking, and alcohol history, changed. None of the women in the intervention group sought cervical cancer screening after the intervention, but only one (2.2%) of the control arm participants did. Almost all the women (> 95%) agreed that an HPV test was essential and that regular healthcare check-ups could help prevent cervical cancer. Some women believed that avoiding particular foods could help prevent cervical cancer (23.8% intervention vs. 58.7% control, p < 0.001). Time constraints and out-of-pocket expenses were significant barriers to cervical cancer screening.

CONCLUSION

A one-way SMS delivered to urban women did not increase cervical cancer screening attendance. The time spent in screening facilities and the lack of coverage by the National Health Insurance Scheme limited screening uptake. We urge for the establishment of screening centers in all healthcare facilities, as well as the inclusion of cervical cancer screening in healthcare programs through cost-sharing.

Systematic reviews of machine learning in healthcare: a literature review

INTRODUCTION

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

METHODS

A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted.

RESULTS

In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively).

EXPERT OPINION

The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Background

The early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.

Methods

A systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.

Results

This systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.

Conclusion

Although the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.

Application of medical imaging in ovarian cancer: a bibliometric analysis from 2000 to 2022

Background

Ovarian cancer (OC) is the most lethal tumor within the female reproductive system. Medical imaging plays a significant role in diagnosis and monitoring OC. This study aims to use bibliometric analysis to explore the current research hotspots and collaborative networks in the application of medical imaging in OC from 2000 to 2022.

Methods

A systematica search for medical imaging in OC was conducted on the Web of Science Core Collection on August 9, 2023. All reviews and articles published from January 2000 to December 2022 were downloaded, and an analysis of countries, institutions, journals, keywords, and collaborative networks was perfomed using CiteSpace and VOSviewer.

Results

A total of 5,958 publications were obtained, demonstrating a clear upward trend in annual publications over the study peroid. The USA led in productivity with 1,373 publications, and Harvard University emerged as the most prominent institution with 202 publications. Timmerman D was the most prolific contributor with 100 publications, and Gynecological Oncology led in the number of publications with 296. The top three keywords were "ovarian cancer" (1,256), "ultrasound" (725), and "diagnosis" (712). In addition, "pelvic masses" had the highest burst strength (25.5), followed by "magnetic resonance imaging (MRI)" (21.47). Recent emergent keywords such as "apoptosis", "nanoparticles", "features", "accuracy", and "human epididymal protein 4 (HE 4)" reflect research trends in this field and may become research hotspots in the future.

Conclusion

This study provides a comprehensive summary of the key contributions of OC imaging to field's development over the past 23 years. Presently, primary areas of OC imaging research include MRI, targeted therapy of OC, novel biomarker (HE 4), and artificial intelligence. These areas are expected to influence future research endeavors in this field.

An algorithm for the pre-operative differentiation of benign ovarian tumours based on magnetic resonance imaging interpretation in a regional core hospital: A retrospective study

Objective

For selecting minimally invasive surgery (i.e. laparoscopic ovarian cystectomy) for treating ovarian tumours (OTs) in premenopausal patients, the pre-operative differentiation of benign ovarian tumours (Be-OTs) based on magnetic resonance imaging (MRI) interpretation is important. This paper describes the authors' 8-year experience of approximately 1000 OT cases, and provides information about a diagnostic algorithm to help other hospitals.

Study design

The medical records of 901 patients aged < 50 years with OTs from 1 January 2015-31 March 31 2023 were reviewed. First, the accuracy of pre-operative differentiation between Be-OTs and borderline/malignant ovarian tumours (Bo/Ma-OTs) was compared in each type of OT. Second, to identify the factors influencing differentiation between Be-OTs and Bo/Ma-OTs in 164 serous/mucinous ovarian tumours (SM-OTs), a multi-variate logistic regression analysis was performed to assess the effect of 13 factors, including MRI findings, OT size and tumour markers.

Results

In the comparison of diagnostic accuracy of pre-operative MRI for each OT type, accuracy was found to be notably high for ovarian endometrial cyst (OEC) (n = 409), ovarian mature cystic teratoma (OMCT) (n = 308), ovarian endometrioid adenocarcinoma (OEA) (n = 6) and ovarian clear cell adenocarcinoma (OCCA) (n = 14). On the other hand, discrepancies between MRI and pathological findings often occurred in SM-OTs, including ovarian serous cystadenoma (n = 86), ovarian mucinous adenocarcinoma (n = 61), ovarian serous adenocarcinoma (n = 12) and ovarian mucinous adenocarcinoma (n = 5). In the multi-variate logistic regression analysis of the latter 164 patients, in addition to MRI findings, OT size and carbohydrate antigen 125 also had an effect to some extent. The combination of MRI interpretation and OT size may enhance differentiation of Be-OTs and Bo/Ma-OTs.

Conclusions

Among four types of OTs (OEC, OMCT, OEA and OCCA), MRI interpretation was able to differentiate between Be-OTs and Bo/Ma-OTs almost perfectly. Additionally, to mitigate the difficulty in differentiating SM-OTs, OT size may be useful in combination with MRI findings, although further accumulation and analysis of OT cases is needed.

The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [18F]FDG PET/CT examinations

OBJECTIVE

To create the 3D convolutional neural network (CNN)-based system that can use whole-body [18F]FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC).

METHODS

In this study, 1224 image sets from OC patients who underwent whole-body [18F]FDG PET/CT at Kowsar Hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classification as cancerous, and non-cancerous and staging as stage III, and stage IV were determined by pathological diagnosis and specialists' interpretation. New deep neural network algorithms, the OCDAc-Net, and the OCDAs-Net were developed for diagnostic classification and staging of OC patients using [18F]FDG PET/CT images. Examinations were divided into independent training (75%), validation (10%), and testing (15%) subsets.

RESULTS

This study included 37 women (mean age 56.3 years; age range 36-83 years). Data augmentation techniques were applied to the images in two phases. There were 1224 image sets for diagnostic classification and staging. For the test set, 170 image sets were considered for diagnostic classification and staging. The OCDAc-Net areas under the receiver operating characteristic curve (AUCs) and overall accuracy for diagnostic classification were 0.990 and 0.92, respectively. The OCDAs-Net achieved areas under the receiver operating characteristic curve (AUCs) of 0.995 and overall accuracy of 0.94 for staging.

CONCLUSIONS

The proposed 3D CNN-based models provide potential tools for recurrence/post-therapy surveillance in OC. The OCDAc-Net and the OCDAs-Net model provide a new prognostic analysis method that can utilize PET images without pathological findings for diagnostic classification and staging.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.

Artificial Intelligence and Child Abuse and Neglect: A Systematic Review

All societies should carefully address the child abuse and neglect phenomenon due to its acute and chronic sequelae. Even if artificial intelligence (AI) implementation in this field could be helpful, the state of the art of this implementation is not known. No studies have comprehensively reviewed the types of AI models that have been developed/validated. Furthermore, no indications about the risk of bias in these studies are available. For these reasons, the authors conducted a systematic review of the PubMed database to answer the following questions: "what is the state of the art about the development and/or validation of AI predictive models useful to contrast child abuse and neglect phenomenon?"; "which is the risk of bias of the included articles?". The inclusion criteria were: articles written in English and dated from January 1985 to 31 March 2023; publications that used a medical and/or protective service dataset to develop and/or validate AI prediction models. The reviewers screened 413 articles. Among them, seven papers were included. Their analysis showed that: the types of input data were heterogeneous; artificial neural networks, convolutional neural networks, and natural language processing were used; the datasets had a median size of 2600 cases; the risk of bias was high for all studies. The results of the review pointed out that the implementation of AI in the child abuse and neglect field lagged compared to other medical fields. Furthermore, the evaluation of the risk of bias suggested that future studies should provide an appropriate choice of sample size, validation, and management of overfitting, optimism, and missing data.

Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer

Accurate and early detection of malignant pelvic mass is important for a suitable referral, triage, and for further care for the women diagnosed with a pelvic mass. Several deep learning (DL) methods have been proposed to detect pelvic masses but other methods cannot provide sufficient accuracy and increase the computational time while classifying the pelvic mass. To overcome these issues, in this manuscript, the evolutionary gravitational neocognitron neural network optimized with nomadic people optimizer for gynecological abdominal pelvic masses classification is proposed for classifying the pelvic masses (EGNNN-NPOA-PM-UI). The real time ultrasound pelvic mass images are augmented using random transformation. Then the augmented images are given to the 3D Tsallis entropy-based multilevel thresholding technique for extraction of the ROI region and its features are further extracted with the help of fast discrete curvelet transform with the wrapping (FDCT-WRP) method. Therefore, in this work, EGNNN optimized with nomadic people optimizer (NPOA) was utilized for classifying the gynecological abdominal pelvic masses. It was executed in PYTHON and the efficiency of the proposed method analyzed under several performance metrics. The proposed EGNNN-NPOA-PM-UI methods attained 99.8%. Ultrasound image analysis using the proposed EGNNN-NPOA-PM-UI methods can accurately predict pelvic masses analyzed with the existing methods.

Automated Assessment of Digital Images of Uterine Cervix Captured Using Transvaginal Device-A Pilot Study

In low-resource settings, a point-of-care test for cervical cancer screening that can give an immediate result to guide management is urgently needed. A transvaginal digital device, "Smart Scope®" (SS), with an artificial intelligence-enabled auto-image-assessment (SS-AI) feature, was developed. In a single-arm observational study, eligible consenting women underwent a Smart Scope®-aided VIA-VILI test. Images of the cervix were captured using SS and categorized by SS-AI in four groups (green, amber, high-risk amber (HRA), red) based on risk assessment. Green and amber were classified as SS-AI negative while HRA and red were classified as SS-AI positive. The SS-AI-positive women were advised colposcopy and guided biopsy. The cervix images of SS-AI-negative cases were evaluated by an expert colposcopist (SS-M); those suspected of being positive were also recommended colposcopy and guided biopsy. Histopathology was considered a gold standard. Data on 877 SS-AI, 485 colposcopy, and 213 histopathology were available for analysis. The SS-AI showed high sensitivity (90.3%), specificity (75.3%), accuracy (84.04%), and correlation coefficient (0.670, p = 0.0) in comparison with histology at the CINI+ cutoff. In conclusion, the AI-enabled Smart Scope® test is a good alternative to the existing screening tests as it gives a real-time accurate assessment of cervical health and an opportunity for immediate triaging with visual evidence.

Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images

BACKGROUND

Ovarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images.

METHODS

We have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan-Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response.

RESULTS

The study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results.

CONCLUSIONS

This type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities.

Analysis of factors affecting nonalcoholic fatty liver disease in Chinese steel workers and risk assessment studies

BACKGROUND

The global incidence of nonalcoholic fatty liver disease (NAFLD) is rapidly escalating, positioning it as a principal public health challenge with significant implications for population well-being. Given its status as a cornerstone of China's economic structure, the steel industry employs a substantial workforce, consequently bringing associated health issues under increasing scrutiny. Establishing a risk assessment model for NAFLD within steelworkers aids in disease risk stratification among this demographic, thereby facilitating early intervention measures to protect the health of this significant populace.

METHODS

Use of cross-sectional studies. A total of 3328 steelworkers who underwent occupational health evaluations between January and September 2017 were included in this study. Hepatic steatosis was uniformly diagnosed via abdominal ultrasound. Influential factors were pinpointed using chi-square (χ2) tests and unconditional logistic regression analysis, with model inclusion variables identified by pertinent literature. Assessment models encompassing logistic regression, random forest, and XGBoost were constructed, and their effectiveness was juxtaposed in terms of accuracy, area under the curve (AUC), and F1 score. Subsequently, a scoring system for NAFLD risk was established, premised on the optimal model.

RESULTS

The findings indicated that sex, overweight, obesity, hyperuricemia, dyslipidemia, occupational dust exposure, and ALT serve as risk factors for NAFLD in steelworkers, with corresponding odds ratios (OR, 95% confidence interval (CI)) of 0.672 (0.487-0.928), 4.971 (3.981-6.207), 16.887 (12.99-21.953), 2.124 (1.77-2.548), 2.315 (1.63-3.288), 1.254 (1.014-1.551), and 3.629 (2.705-4.869), respectively. The sensitivity of the three models was reported as 0.607, 0.680 and 0.564, respectively, while the precision was 0.708, 0.643, and 0.701, respectively. The AUC measurements were 0.839, 0.839, and 0.832, and the Brier scores were 0.150, 0.153, and 0.155, respectively. The F1 score results were 0.654, 0.661, and 0.625, with log loss measures at 0.460, 0.661, and 0.564, respectively. R2 values were reported as 0.789, 0.771, and 0.778, respectively. Performance was comparable across all three models, with no significant differences observed. The NAFLD risk score system exhibited exceptional risk detection capabilities with an established cutoff value of 86.

CONCLUSIONS

The study identified sex, BMI, dyslipidemia, hyperuricemia, occupational dust exposure, and ALT as significant risk factors for NAFLD among steelworkers. The traditional logistic regression model proved equally effective as the random forest and XGBoost models in assessing NAFLD risk. The optimal cutoff value for risk assessment was determined to be 86. This study provides clinicians with a visually accessible risk stratification approach to gauge the propensity for NAFLD in steelworkers, thereby aiding early identification and intervention among those at risk.

The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies-A Systematic Review

(1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myometrial invasion in MRI studies in women with endometrial cancer. (2) Methods: A systematic search was conducted in PubMed, SCOPUS, Embase, and clinicaltrials.gov databases for research papers from inception to May 2023. As keywords, we used: "endometrial cancer artificial intelligence", "endometrial cancer AI", "endometrial cancer MRI artificial intelligence", "endometrial cancer machine learning", and "endometrial cancer machine learning MRI". We excluded studies that did not evaluate myometrial invasion. (3) Results: Of 1651 screened records, eight were eligible. The size of the dataset was between 50 and 530 participants among the studies. We evaluated the models by accuracy scores, area under the curve, and sensitivity/specificity. A quantitative analysis was not appropriate for this study due to the high heterogeneity among studies. (4) Conclusions: High accuracy, sensitivity, and specificity rates were obtained among studies using different AI systems. Overall, the existing studies suggest that they have the potential to improve the accuracy and efficiency of the myometrial invasion evaluation of MRI images in endometrial cancer patients.

Gynecologic and Breast Cancers: What's New in Chemoresistance and Chemosensitivity Tests?

Gynecological and breast cancers affect women's health worldwide. Although chemotherapy is one of the principal treatments for cancer, it also has limitations owing to toxicity and tumor resistance to the drugs used. Thus, individualized treatment based on personal tumor characteristics is essential for improving therapeutic outcomes and patient survival. Chemoresistance and chemosensitivity tests can be useful for predicting tumor response and guiding chemotherapy choices. This methodology has already been applied to breast, ovarian, cervical, and endometrial cancers, identifying successfully which drugs cause resistance and sensitivity responses for each individual person, influencing their progression-free survival and overall response. In addition, more recent techniques, such as organoids and patient-derived xenografts, can also recapitulate patients' tumor characteristics and contribute to chemo response evaluation. Therefore, this review compiles information on chemoresistance and chemosensitivity tests performed in gynecologic and breast cancers and their main results for women's health improvement.

An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy

Introduction

It has been estimated that 19,880 new cases of ovarian cancer had been diagnosed in 2022. Most epithelial ovarian cancer are sporadic, while in 15%-25% of cases, there is evidence of a familial or inherited component. Approximately 20%-25% of high-grade serous carcinoma cases are caused by germline mutations in the BRCA1 and BRCA2 genes. However, owing to a lack of effective early detection methods, women with BRCA mutations are recommended to undergo bilateral risk-reducing salpingo-oophorectomy (RRSO) after childbearing. Determining the right timing for this procedure is a difficult decision. It is crucial to find a clinical signature to identify high-risk BRCA-mutated patients and determine the appropriate timing for performing RRSO.

Methods

In this work, clinical data referred to a cohort of 184 patients, of whom 7.6% were affected by adnexal tumors including invasive carcinomas and intraepithelial lesions after RSSO has been analyzed. Thus, we proposed an explainable machine learning (ML) ensemble approach using clinical data commonly collected in clinical practice to early identify BRCA-mutated patients at high risk of ovarian cancer and consequentially establish the correct timing for RRSO.

Results

The ensemble model was able to handle imbalanced data achieving an accuracy value of 83.2%, a specificity value of 85.3%, a sensitivity value of 57.1%, a G-mean value of 69.8%, and an AUC value of 71.1%.

Discussion

In agreement with the promising results achieved, the application of suitable ML techniques could play a key role in the definition of a BRCA-mutated patient-centric clinical signature for ovarian cancer risk and consequently personalize the management of these patients. As far as we know, this is the first work addressing this task from an ML perspective.

Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review

BACKGROUND

Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data. New privacy-preserving technologies, such as decentralized learning, make it possible to create health models without mobilizing data sets by using distributed computation principles. Several multinational partnerships, including a recent agreement between the United States and the European Union, are adopting these techniques for next-generation data science. While these approaches are promising, there is no clear and robust evidence synthesis of health care applications.

OBJECTIVE

The main aim is to compare the performance among health data models (eg, automated diagnosis and mortality prediction) developed using decentralized learning approaches (eg, federated and blockchain) to those using centralized or local methods. Secondary aims are comparing the privacy compromise and resource use among model architectures.

METHODS

We will conduct a systematic review using the first-ever registered research protocol for this topic following a robust search methodology, including several biomedical and computational databases. This work will compare health data models differing in development architecture, grouping them according to their clinical applications. For reporting purposes, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram will be presented. CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies)-based forms will be used for data extraction and to assess the risk of bias, alongside PROBAST (Prediction Model Risk of Bias Assessment Tool). All effect measures in the original studies will be reported.

RESULTS

The queries and data extractions are expected to start on February 28, 2023, and end by July 31, 2023. The research protocol was registered with PROSPERO, under the number 393126, on February 3, 2023. With this protocol, we detail how we will conduct the systematic review. With that study, we aim to summarize the progress and findings from state-of-the-art decentralized learning models in health care in comparison to their local and centralized counterparts. Results are expected to clarify the consensuses and heterogeneities reported and help guide the research and development of new robust and sustainable applications to address the health data privacy problem, with applicability in real-world settings.

CONCLUSIONS

We expect to clearly present the status quo of these privacy-preserving technologies in health care. With this robust synthesis of the currently available scientific evidence, the review will inform health technology assessment and evidence-based decisions, from health professionals, data scientists, and policy makers alike. Importantly, it should also guide the development and application of new tools in service of patients' privacy and future research.

TRIAL REGISTRATION

PROSPERO 393126; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=393126.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/45823.

Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors

BACKGROUND

To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors.

METHODS

We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set.

RESULTS

 Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists.

CONCLUSIONS

 We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.

Artificial intelligence in colposcopic examination: A promising tool to assist junior colposcopists

Introduction

Well-trained colposcopists are in huge shortage worldwide, especially in low-resource areas. Here, we aimed to evaluate the Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) to detect abnormalities based on digital colposcopy images, especially focusing on its role in assisting junior colposcopist to correctly identify the lesion areas where biopsy should be performed.

Materials and methods

This is a hospital-based retrospective study, which recruited the women who visited colposcopy clinics between September 2021 to January 2022. A total of 366 of 1,146 women with complete medical information recorded by a senior colposcopist and valid histology results were included. Anonymized colposcopy images were reviewed by CAIADS and a junior colposcopist separately, and the junior colposcopist reviewed the colposcopy images with CAIADS results (named CAIADS-Junior). The diagnostic accuracy and biopsy efficiency of CAIADS and CAIADS-Junior were assessed in detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+), CIN3+, and cancer in comparison with the senior and junior colposcipists. The factors influencing the accuracy of CAIADS were explored.

Results

For CIN2 + and CIN3 + detection, CAIADS showed a sensitivity at ~80%, which was not significantly lower than the sensitivity achieved by the senior colposcopist (for CIN2 +: 80.6 vs. 91.3%, p = 0.061 and for CIN3 +: 80.0 vs. 90.0%, p = 0.189). The sensitivity of the junior colposcopist was increased significantly with the assistance of CAIADS (for CIN2 +: 95.1 vs. 79.6%, p = 0.002 and for CIN3 +: 97.1 vs. 85.7%, p = 0.039) and was comparable to those of the senior colposcopists (for CIN2 +: 95.1 vs. 91.3%, p = 0.388 and for CIN3 +: 97.1 vs. 90.0%, p = 0.125). In detecting cervical cancer, CAIADS achieved the highest sensitivity at 100%. For all endpoints, CAIADS showed the highest specificity (55–64%) and positive predictive values compared to both senior and junior colposcopists. When CIN grades became higher, the average biopsy numbers decreased for the subspecialists and CAIADS required a minimum number of biopsies to detect per case (2.2–2.6 cut-points). Meanwhile, the biopsy sensitivity of the junior colposcopist was the lowest, but the CAIADS-assisted junior colposcopist achieved a higher biopsy sensitivity.

Conclusion

Colposcopic Artificial Intelligence Auxiliary Diagnostic System could assist junior colposcopists to improve diagnostic accuracy and biopsy efficiency, which might be a promising solution to improve the quality of cervical cancer screening in low-resource settings.

Gynecological cancer prognosis using machine learning techniques: A systematic review of last three decades (1990–2022)

OBJECTIVE

Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs.

METHODS

Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly.

RESULTS

Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes.

CONCLUSION

There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.

The trend of change in cervical tumor size and time to death of hospitalized patients in northwestern Ethiopia during 2018-2022: Retrospective study design

Background and Aims

Cervical cancer is the fourth most common cause of cancer-related death in the world. The objective of this study was to determine factors that affect the longitudinal change of tumor size and the time to death of outpat.

Methods

A retrospective follow-up study was carried out among 322 randomly selected patients with cervical cancer at the University of Gondar Referral Hospital from May 15, 2018 to May 15, 2022. Data were extracted from the patient's chart from all patients' data records. Kaplan-Meier estimator, log-rank test, the Cox proportional-hazard model, and the joint model for the two response variables simultaneously were used.

Results

Among 322 outpatients with cervical cancer, 148 (46%) of them were human immunodeficiency virus (HIV) positive and 107 (33.3%) of them died. The results of joint and separate models show that there is an association between survival and the longitudinal data in the analysis; it indicates that there is a dependency between longitudinal terms of cervical tumor size and time-to-death events. A unit centimeter square rise in tumor size, corresponding to an exp(0.8502) = 2.34 times, significantly raised the mortality risk.

Conclusion

The study showed that HIV, stage of cancer, treatment, weight, history of abortion, oral contraceptive use, smoking status, and visit time were statistically significant factors for the two outcomes jointly.

Implications

As a result, adequate health services and adequate resource allocations are critical for cervical cancer control and prevention programs. Therefore, the government should provide adequate funding and well-trained health professionals to hospitals to sustain screening programs with appropriate coverage of cervical cancer patient treatments.

Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T1 and T2 Relaxation Times with Application to Cancer Cell Culture

Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing the PubMed database from the last 5 years (2017 to 2021), we see that the number of responses to the query "neural network in medicine" exceeds 10,500 papers. Deep learning algorithms are of particular importance in oncology. This paper presents the use of neural networks to analyze the magnetic resonance imaging (MRI) images used to determine MRI relaxometry of the samples. Relaxometry is becoming an increasingly common tool in diagnostics. The aim of this work was to optimize the processing time of DICOM images by using a neural network implemented in the MATLAB package by The MathWorks with the patternnet function. The application of a neural network helps to eliminate spaces in which there are no objects with characteristics matching the phenomenon of longitudinal or transverse MRI relaxation. The result of this work is the elimination of aerated spaces in MRI images. The whole algorithm was implemented as an application in the MATLAB package.

Artificial intelligence for prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal women

BACKGROUND

The current approach to endometrial cancer screening requires that all patients be able to recognize symptoms, report them, and carry out appropriate interventions. The current approach to endometrial cancer screening could become a problem in the future, especially for Black women and women from minority groups, and could lead to disparities in receiving proper care. Moreover, there is a lack of literature on artificial intelligence in the prediction and diagnosis of endometrial intraepithelial neoplasia and endometrial cancer.

OBJECTIVE

This study analyzed different artificial intelligence methods to help in clinical decision-making and the prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal women. This study aimed to investigate whether artificial intelligence may help to overcome the challenges that statistical and diagnostic tests could not.

STUDY DESIGN

This study included 564 patients. The features that were collected included age, menopause status, premenopausal abnormal bleeding and postmenopausal bleeding, obesity, hypertension, diabetes mellitus, smoking, endometrial thickness, and history of breast cancer. Endometrial sampling was performed on all women with postmenopausal bleeding and asymptomatic postmenopausal women with an endometrial thickness of at least 3 mm. Endometrial biopsy was performed on premenopausal women with abnormal uterine bleeding and asymptomatic premenopausal women with suspected endometrial lesions. Python was used to model machine learning algorithms. Random forest, logistic regression, multilayer perceptron, Catboost, Xgboost, and Naive Bayes methods were used for classification. The synthetic minority oversampling technique was used to correct the class imbalance in the training sets. In addition, tuning and boosting were used to increase the performance of the models with a 5-fold cross-validation approach using a training set. Accuracy, sensitivity, specificity, positive predictive value, and F1 score were calculated.

RESULTS

The prevalence of endometrial or preuterine cancer was 7.9%. Data from 451 patients were randomly assigned to the training group, and data from another 113 patients were used for internal validation. Of note, 3 of 9 features were selected by the Boruta algorithm for use in the final modeling. Age, body mass index, and endometrial thickness were all associated with a high risk of developing precancerous and cancerous diseases, after fine-tuning for the multilayer computer to have the highest area below the receiver operating characteristic curve (area under the curve, 0.938) to predict a precancerous disease. The accuracy was 0.94 for predicting a precancerous disease. Precision, recall, and F1 scores for the test group were 0.71, 0.50, and 0.59, respectively.

CONCLUSION

Our study found that artificial intelligence can be used to identify women at risk of endometrial intraepithelial neoplasia and endometrial cancer. The model is not contingent on menopausal status or symptoms. This may be an advantage over the traditional methodology because many women, especially Black women and women from minority groups, could not recognize them. We have proposed to include patients to provide age and body mass index, and measurement of endometrial thickness by either sonography or artificial intelligence may help improve healthcare for women in rural or minority communities.

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence

In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either "malignant", "other or benign" or "insufficient". An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with "malignant" and "other or benign" areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being "malignant" or "other or benign". Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either "malignant", "other or benign" or "insufficient". The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists' workload.

Cervical Cancer in SAARC Countries

Purvish M. ParikhIn the year 2020, a total of 342 000 women were estimated to die of cervical cancer, of which 90%) were expected amongst low- and middle-income countries (LMIC). Globally incidence of cervical cancer has reduced as a result of improved personal hygiene, better living conditions and higher application of opportunistic screening programs. Yet GLOBOCAN shows that absolute number of cases are still increasing. We therefore conducted a 21 question multiple choice questionnaire online survey in Jan 2023 amongst 9 SAARC countries. A total of 367 replies were received and the representative answers for each country are being reported in this manuscript. A good possibility of achieving World Health Assembly target (Nov 17, 2020) was felt only by Bhutan and Nepal. For screening, most countries (Bhutan, India, Myanmar, Nepal, Pakistan and Sri Lanka) recommend for all asymptomatic eligible patients. Public health experts have suggested VIA / VILI as the best solution for LMICs. However, a dual screening strategy (HPV DNA plus) cytology was preferred by doctors in Afghanistan, Bhutan, India, Myanmar, Pakistan and Sri Lanka. Screening, triage and then treatment was the preferred by Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka. HPV vaccination was recommended in all girls between ages 10 to 26 years in Bangladesh, India, Myanmar, Nepal, Pakistan and Sri Lanka. All the 9 countries would use HPV vaccination to all eligible patients if the cost of the vaccine was reasonably low. Our survey clearly outlines challenges faced in tackling cervical cancer in SAARC countries. We also provide consensus regarding several potential solutions that can be used in both public and private cervical cancer control programs.

Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status

Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.

Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging

Background

Upon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of distinguishing between benign and malignant ovarian tumors. Numerous types of ovarian tumors exist, many of which exhibit similar characteristics that increase the ambiguity in clinical diagnosis. Using deep learning technology, we aimed to develop a method that rapidly and accurately assists the different diagnosis of ovarian tumors in ultrasound images.

Methods

Based on deep learning method, we used ten well-known convolutional neural network models (e.g., Alexnet, GoogleNet, and ResNet) for training of transfer learning. To ensure method stability and robustness, we repeated the random sampling of the training and validation data ten times. The mean of the ten test results was set as the final assessment data. After the training process was completed, the three models with the highest ratio of calculation accuracy to time required for classification were used for ensemble learning pertaining. Finally, the interpretation results of the ensemble classifier were used as the final results. We also applied ensemble gradient-weighted class activation mapping (Grad-CAM) technology to visualize the decision-making results of the models.

Results

The highest mean accuracy, mean sensitivity, and mean specificity of ten single CNN models were 90.51 ± 4.36%, 89.77 ± 4.16%, and 92.00 ± 5.95%, respectively. The mean accuracy, mean sensitivity, and mean specificity of the ensemble classifier method were 92.15 ± 2.84%, 91.37 ± 3.60%, and 92.92 ± 4.00%, respectively. The performance of the ensemble classifier is better than that of a single classifier in three evaluation metrics. Moreover, the standard deviation is also better which means the ensemble classifier is more stable and robust.

Conclusion

From the comprehensive perspective of data quantity, data diversity, robustness of validation strategy, and overall accuracy, the proposed method outperformed the methods used in previous studies. In future studies, we will continue to increase the number of authenticated images and apply our proposed method in clinical settings to increase its robustness and reliability.

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

OBJECTIVE

The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions.

MATERIALS AND METHODS

Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.

RESULTS

The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively.

CONCLUSION

The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery

Objective

To evaluate the value of ultrasound-based radiomics in the preoperative prediction of type I and type II epithelial ovarian cancer.

Methods

A total of 154 patients with epithelial ovarian cancer were enrolled retrospectively. There were 102 unilateral lesions and 52 bilateral lesions among a total of 206 lesions. The data for the 206 lesions were randomly divided into a training set (53 type I + 71 type II) and a test set (36 type I + 46 type II) by random sampling. ITK-SNAP software was used to manually outline the boundary of the tumor, that is, the region of interest, and 4976 features were extracted. The quantitative expression values of the radiomics features were normalized by the Z-score method, and the 7 features with the most differences were screened by using the Lasso regression tenfold cross-validation method. The radiomics model was established by logistic regression. The training set was used to construct the model, and the test set was used to evaluate the predictive efficiency of the model. On the basis of multifactor logistic regression analysis, combined with the radiomics score of each patient, a comprehensive prediction model was established, the nomogram was drawn, and the prediction effect was evaluated by analyzing the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve.

Results

The AUCs of the training set and test set in the radiomics model and comprehensive model were 0.817 and 0.731 and 0.982 and 0.886, respectively. The calibration curve showed that the two models were in good agreement. The clinical decision curve showed that both methods had good clinical practicability.

Conclusion

The radiomics model based on ultrasound images has a good predictive effect for the preoperative differential diagnosis of type I and type II epithelial ovarian cancer. The comprehensive model has higher prediction efficiency.

Diffusion-Weighted MRI in the Genitourinary System

Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

Cervical optical coherence tomography image classification based on contrastive self-supervised texture learning

BACKGROUND

Cervical cancer seriously affects the health of the female reproductive system. Optical coherence tomography (OCT) emerged as a non-invasive, high-resolution imaging technology for cervical disease detection. However, OCT image annotation is knowledge-intensive and time-consuming, which impedes the training process of deep-learning-based classification models.

PURPOSE

This study aims to develop a computer-aided diagnosis (CADx) approach to classifying in-vivo cervical OCT images based on self-supervised learning.

METHODS

In addition to high-level semantic features extracted by a convolutional neural network (CNN), the proposed CADx approach designs a contrastive texture learning (CTL) strategy to leverage unlabeled cervical OCT images' texture features. We conducted ten-fold cross-validation on the OCT image dataset from a multi-center clinical study on 733 patients from China.

RESULTS

In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion and cervical cancer, our method achieved an area-under-the-curve value of 0.9798 ± 0.0157 with a sensitivity of 91.17 ± 4.99% and a specificity of 93.96 ± 4.72% for OCT image patches; also, it outperformed two out of four medical experts on the test set. Furthermore, our method achieved a 91.53% sensitivity and 97.37% specificity on an external validation dataset containing 287 3D OCT volumes from 118 Chinese patients in a new hospital using a cross-shaped threshold voting strategy.

CONCLUSIONS

The proposed contrastive-learning-based CADx method outperformed the end-to-end CNN models and provided better interpretability based on texture features, which holds great potential to be used in the clinical protocol of "see-and-treat." This article is protected by copyright. All rights reserved.

Research and Application of Artificial Intelligence (AI) based on Electronic Health Records from Patients with Cancer: a Systematic Review (Preprint)

Background

With the accumulation of electronic health records and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of artificial intelligence based on electronic health records in cancer care.

Objective

The aim of this study was to conduct a review to introduce the current state and limitations of artificial intelligence based on electronic health records of patients with cancer and to summarize the performance of artificial intelligence in mining electronic health records and its impact on cancer care.

Methods

Three databases were systematically searched to retrieve potentially relevant papers published from January 2009 to October 2020. Four principal reviewers assessed the quality of the papers and reviewed them for eligibility based on the inclusion criteria in the extracted data. The summary measures used in this analysis were the number and frequency of occurrence of the themes.

Results

Of the 1034 papers considered, 148 papers met the inclusion criteria. Cancer care, especially cancers of female organs and digestive organs, could benefit from artificial intelligence based on electronic health records through cancer emergencies and prognostic estimates, cancer diagnosis and prediction, tumor stage detection, cancer case detection, and treatment pattern recognition. The models can always achieve an area under the curve of 0.7. Ensemble methods and deep learning are on the rise. In addition, electronic medical records in the existing studies are mainly in English and from private institutional databases.

Conclusions

Artificial intelligence based on electronic health records performed well and could be useful for cancer care. Improving the performance of artificial intelligence can help patients receive more scientific-based and accurate treatments. There is a need for the development of new methods and electronic health record data sharing and for increased passion and support from cancer specialists.