Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

https://www.mdpi.com/1424-8220/20/10/2809/pdf

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods.

Diagnostics is the key in screening and treatment of cancer. As an emerging tool in precision medicine, metabolic analysis detects end products of pathways, and thus is more distal than proteomic/genetic analysis. However, metabolic analysis is far from ideal in clinical diagnosis due to the sample complexity and metabolite abundance in patient specimens. A further challenge is real-time and accurate tracking of treatment effect, e.g., radiotherapy. Here, Pd-Au synthetic alloys are reported for mass-spectrometry-based metabolic fingerprinting and analysis, toward medulloblastoma diagnosis and radiotherapy evaluation. A core-shell structure is designed using magnetic core particles to support Pd-Au alloys on the surface. Optimized synthetic alloys enhance the laser desorption/ionization efficacy and achieve direct detection of 100 nL of biofluids in seconds. Medulloblastoma patients are differentiated from healthy controls with average diagnostic sensitivity of 94.0%, specificity of 85.7%, and accuracy of 89.9%, by machine learning of metabolic fingerprinting. Furthermore, the radiotherapy process of patients is monitored and a preliminary panel of serum metabolite biomarkers is identified with gradual changes. This work will lead to the application-driven development of novel materials with tailored structural design and establishment of new protocols for precision medicine in near future.

Metabolic Fingerprinting on Synthetic Alloys for Medulloblastoma Diagnosis and Radiotherapy Evaluation.

Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis.

https://www.mdpi.com/2072-6694/11/3/431/pdf

Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach.

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

/pdf/a-systematic-review-of-artificial-intelligence-techniques-in-1wo9rqflz9.pdf

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.

https://www.mdpi.com/2072-6694/11/12/2007/pdf

Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach

Kidney cancer is one of the deadliest diseases and its diagnosis and subtype classification are crucial for patients’ survival. Thus, developing automated tools that can accurately determine kidney cancer subtypes is an urgent challenge. It has been confirmed by researchers in the biomedical field that miRNA dysregulation can cause cancer. In this paper, we propose a machine learning approach for the classification of kidney cancer subtypes using miRNA genome data. Through empirical studies we found 35 miRNAs that possess distinct key features that aid in kidney cancer subtype diagnosis. In the proposed method, Neighbourhood Component Analysis (NCA) is employed to extract discriminative features from miRNAs and Long Short Term Memory (LSTM), a type of Recurrent Neural Network, is adopted to classify a given miRNA sample into kidney cancer subtypes. In the literature, only a couple of kidney subtypes have been considered for classification. In the experimental study, we used the miRNA quantitative read counts data, which was provided by The Cancer Genome Atlas data repository (TCGA). The NCA procedure selected 35 of the most discriminative miRNAs. With this subset of miRNAs, the LSTM algorithm was able to group kidney cancer miRNAs into five subtypes with average accuracy around 95% and Matthews Correlation Coefficient value around 0.92 under 10 runs of randomly grouped 5-fold cross-validation, which were very close to the average performance of using all miRNAs for classification.

A Machine Learning Approach for the Classification of Kidney Cancer Subtypes Using miRNA Genome Data

miRNA expression profiles are heterogeneously expressed among cancer types, with miRNAs serving as highly tissue specific tumor suppressors and oncogenes. Machine learning methodologies have been used to develop high performance pan-cancer classification models and identify potentially novel miRNA biomarkers for clinical investigation. However, it is important to understand how such data science techniques correlate to established biological processes to advance integration into clinical environments. This research aims to assess how the top miRNA features selected by machine learning models relate to clinically and biologically verified miRNA biomarkers. We developed Support Vector Machine and Random Forest machine learning models for cancer classification, iteratively adding cancer classes to the multiclass models. The relationship between the selected top features (miRNAs) and clinically verified miRNA biomarkers was assessed through percent relevance, i.e., the number of verified miRNAs vs the number of selected features. We found that as the number of cancer classes increased, the performance metrics decreased, yet the percentage relevance of the miRNA feature selection signature slightly increased before stabilizing. Additionally, after conducting principal component analysis, the non-cancer tissues from all samples had very similar expression visualizations, while all cancerous tissues had unique profiles. The results indicated that models with a greater number of cancer classes shift towards focusing on cancer-diverse miRNAs of greater relevance with characterized functionality. This work suggests that miRNAs may be highly unique to specific cancerous tissues and can be strong biomarkers for detection and classification, but current verified biomarkers fall toward more cancer-wide miRNAs when detecting cancer.

Oneeb Rehman

Papers

Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach.

A Machine Learning Approach for the Classification of Kidney Cancer Subtypes Using miRNA Genome Data

Exploration of the Relevance of MicroRNA Signatures for Cancer Detection and Multiclass Cancer Classification