Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning

Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter study

Purpose

This study was designed to determine the diagnostic performance of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics-based machine learning (ML) in the classification of cervical adenocarcinoma (AC) and squamous cell carcinoma (SCC).

Methods

Pretreatment 18F-FDG PET/CT data were retrospectively collected from patients who were diagnosed with locally advanced cervical cancer at two centers. Radiomics features were extracted and selected by the Pearson correlation coefficient and least absolute shrinkage and selection operator regression analysis. Six ML algorithms were then applied to establish models, and the best-performing classifier was selected based on accuracy, sensitivity, specificity, and area under the curve (AUC). The performance of different model was assessed and compared using the DeLong test.

Results

A total of 227 patients with locally advanced cervical cancer were enrolled in this study (N=136 for the training cohort, N=59 for the internal validation cohort, and N=32 for the external validation cohort). The PET radiomics model constructed based on the lightGBM algorithm had an accuracy of 0.915 and an AUC of 0.851 (95% confidence interval [CI], 0.715-0.986) in the internal validation cohort, which were higher than those of the CT radiomics model (accuracy: 0.661; AUC: 0.513 [95% CI, 0.339-0.688]). The DeLong test revealed no significant difference in AUC between the combined radiomics model and the PET radiomics model in either the training cohort (z=0.940, P=0.347) or the internal validation cohort (z=0.285, P=0.776). In the external validation cohort, the lightGBM-based PET radiomics model achieved good discrimination between SCC and AC (AUC = 0.730).

Conclusions

The lightGBM-based PET radiomics model had great potential to predict the fine histological subtypes of locally advanced cervical cancer and might serve as a promising noninvasive approach for the diagnosis and management of locally advanced cervical cancer.

(Nano)biotechnological approaches in the treatment of cervical cancer: integration of engineering and biology

Cervical cancer is one of the most malignant gynaecological tumors characterised with the aggressive behaviour of the tumor cells. In spite of the development of different strategies for the treatment of cervical cancer, the tumor cells have developed resistance to conventional therapeutics. On the other hand, nanoparticles have been recently applied for the treatment of human cancers through delivery of drugs and facilitate tumor suppression. The stimuli-sensitive nanostructures can improve the release of therapeutics at the tumor site. In the present review, the nanostructures for the treatment of cervical cancer are discussed. Nanostructures can deliver both chemotherapy drugs and natural compounds to increase anti-cancer activity and prevent drug resistance in cervical tumor. Moreover, the genetic tools such as siRNA can be delivered by nanoparticles to enhance their accumulation at tumor site. In order to enhance selectivity, the stimuli-responsive nanoparticles such as pH- and redox-responsive nanocarriers have been developed to suppress cervical tumor. Moreover, nanoparticles can induce photo-thermal and photodynamic therapy to accelerate cell death in cervical tumor. In addition, nanobiotechnology demonstrates tremendous potential in the treatment of cervical cancer, especially in the context of tumor immunotherapy. Overall, metal-, carbon-, lipid- and polymer-based nanostructures have been utilized in cervical cancer therapy. Finally, hydrogels have been developed as novel kinds of carriers to encapsulate therapeutics and improve anti-cancer activity.

Highlighting the role of CD44 in cervical cancer progression: immunotherapy's potential in inhibiting metastasis and chemoresistance

Cervical cancer affects thousands of women globally with recurring high-risk HPV infections being at the centre of cervical pathology. Oncological treatment strategies are continually challenged by both chemoresistance and metastasis within patients. Although both work hand-in-hand, targeting their individual mechanisms could prove highly beneficial for treatment outcomes. Such targets include the metastatic-promoting stem cell marker, CD44, which is abundant in cervical cancer cells and is common to both chemoresistance and metastatic mechanisms. Seeing that many existing advanced-stage cervical cancer treatment regimes, such as platinum-based chemotherapy regimens, remain limited and are rarely curative, alternative treatment options within the field of immunology are being considered. The use of immune checkpoint inhibition therapy, which targets immune checkpoints, CTLA-4 and PD-1/PD-L1, has shown promise as an alternate standard of care for patients suffering from advanced-stage cervical cancer. Therefore, this review aims to assess whether immune checkpoint inhibition can mitigate the pathological effects of CD44-induced EMT, metastasis, and chemoresistance in cervical cancer patients.

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.

Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials

Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.

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.

Imaging Biomarkers and Liquid Biopsy in Assessment of Cervical Cancer

ABSTRACT

The role of imaging has been increasing in pretherapy planning and response assessment in cervical cancer, particularly in high-resource settings that provide access to computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). In 2018, imaging was incorporated into the International Federation of Gynecology and Obstetrics staging system for cervical cancer. Magnetic resonance imaging is advantageous over CT for evaluation of the primary cervical cancer size and extent, because of superior contrast resolution. Furthermore, quantitative methods, including diffusion-weighted and dynamic contrast-enhanced MRI, show promise in improving treatment response and prognosis evaluation. Molecular imaging with fluorodeoxyglucose-PET/CT and PET/MRI can be particularly helpful in the detection of nodal disease and distant metastases. Semiautomated delineation of 3-dimensional tumor regions of interest has facilitated the development of novel PET-derived biomarkers that include metabolic volume and radiomics textural analysis features for prediction of outcomes. However, posttreatment inflammatory changes can be a confounder and lymph node evaluation is challenging, even with the use of PET/CT. Liquid biopsy has emerged as a promising tool that may be able to overcome some of the drawbacks inherent with imaging, such as limited ability to detect microscopic metastases or to distinguish between postchemoradiotherapy changes and residual tumor. Preliminary evidence suggests that liquid biopsy may be able to identify cervical cancer treatment response and resistance earlier than traditional methods. Future work should prioritize how to best synergize imaging and liquid biopsy as an integrated approach for optimal cervical cancer management.

Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques

Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model's primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F1–Score, and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.