Machine learning applications in cancer prognosis and prediction

A human-interpretable machine learning pipeline based on ultrasound to support leiomyosarcoma diagnosis

The preoperative evaluation of myometrial tumors is essential to avoid delayed treatment and to establish the appropriate surgical approach. Specifically, the differential diagnosis of leiomyosarcoma (LMS) is particularly challenging due to the overlapping of clinical, laboratory and ultrasound features between fibroids and LMS. In this work, we present a human-interpretable machine learning (ML) pipeline to support the preoperative differential diagnosis of LMS from leiomyomas, based on both clinical data and gynecological ultrasound assessment of 68 patients (8 with LMS diagnosis). The pipeline provides the following novel contributions: (i) end-users have been involved both in the definition of the ML tasks and in the evaluation of the overall approach; (ii) clinical specialists get a full understanding of both the decision-making mechanisms of the ML algorithms and the impact of the features on each automatic decision. Moreover, the proposed pipeline addresses some of the problems concerning both the imbalance of the two classes by analyzing and selecting the best combination of the synthetic oversampling strategy of the minority class and the classification algorithm among different choices, and the explainability of the features at global and local levels. The results show very high performance of the best strategy (AUC = 0.99, F1 = 0.87) and the strong and stable impact of two ultrasound-based features (i.e., tumor borders and consistency of the lesions). Furthermore, the SHAP algorithm was exploited to quantify the impact of the features at the local level and a specific module was developed to provide a template-based natural language (NL) translation of the explanations for enhancing their interpretability and fostering the use of ML in the clinical setting.

Development and validation of an artificial intelligence prediction model and a survival risk stratification for lung metastasis in colorectal cancer from highly imbalanced data: A multicenter retrospective study

BACKGROUND

To assist clinicians with diagnosis and optimal treatment decision-making, we attempted to develop and validate an artificial intelligence prediction model for lung metastasis (LM) in colorectal cancer (CRC) patients.

METHODS

The clinicopathological characteristics of 46037 CRC patients from the Surveillance, Epidemiology, and End Results (SEER) database and 2779 CRC patients from a multi-center external validation set were collected retrospectively. After feature selection by univariate and multivariate analyses, six machine learning (ML) models, including logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest, and balanced random forest (BRF), were developed and validated for the LM prediction. In addition, stratified LM patients by risk score were utilized for survival analysis.

RESULTS

Extremely low rates of LM with 2.59% and 4.50% were present in the development and validation set. As the imbalanced learning strategy, the BRF model with an Area under the receiver operating characteristic curve (AUC) of 0.874 and an average precision (AP) of 0.184 performed best compares with other models and clinical predictor. Patients with LM in the high-risk group had significantly poorer survival (P＜0.001) and failed to benefit from resection (P = 0.125).

CONCLUSIONS

In summary, we have utilized the BRF algorithm to develop an effective, non-invasive, and practical model for predicting LM in CRC patients based on highly imbalanced datasets. In addition, we have implemented a novel approach to stratify the survival risk of CRC patients with LM based the output of the model.

Antenatal prediction models for outcomes of extremely and very preterm infants based on machine learning

PURPOSE

Predicting individual risks for adverse outcomes in preterm infants is necessary for perinatal management and antenatal counseling for their parents. To evaluate whether a machine learning approach can improve the prediction of severe infant outcomes beyond the performance of conventional logistic models, and to identify maternal and fetal factors that largely contribute to these outcomes.

METHODS

A population-based retrospective study was performed using clinical data of 31,157 infants born at < 32 weeks of gestation and weighing ≤ 1500 g, registered in the Neonatal Research Network of Japan between 2006 and 2015. We developed a conventional logistic model and 6 types of machine learning models based on 12 maternal and fetal factors. Discriminative ability was evaluated using the area under the receiver operating characteristic curves (AUROCs), and the importance of each factor in terms of its contribution to outcomes was evaluated using the SHAP (SHapley Additive exPlanations) value.

RESULTS

The AUROCs of the most discriminative machine learning models were better than those of the conventional models for all outcomes. The AUROCs for in-hospital death and short-term adverse outcomes in the gradient boosting decision tree were significantly higher than those in the conventional model (p = 0.015 and p = 0.002, respectively). The SHAP value analyses showed that gestational age, birth weight, and antenatal corticosteroid treatment were the three most important factors associated with severe infant outcomes.

CONCLUSION

Machine learning models improve the prediction of severe infant outcomes. Moreover, the machine learning approach provides insight into the potential risk factors for severe infant outcomes.

Prediction of HER2 status via random forest in 3257 Chinese patients with gastric cancer

The accurate evaluation of human epidermal growth factor receptor 2 (HER2) is crucial for successful trastuzumab-based therapy in individuals with gastric cancer (GC). The present study, involving a retrospective cohort (N = 2865) from Wuhan Union Hospital and a prospective cohort (N = 392) from Renmin Hospital of Wuhan University, evaluated the benefits of clinical features using random forest and logistic regression models for the detection of HER2 status in patients with GC. Patients from the Union cohort were randomly assigned to either a training (N = 2005) or an internal validation (N = 860) group. Data processing and feature selection were done in Python, which was also used to build random forest and logistic regression models for the prediction of HER2 overexpression. The Renmin cohort (N = 392) was used as the external validation group. Ten features were closely correlated with HER2 overexpression, including age, albumin/globulin ratio, globulin, activated partial thromboplastin time, tumor stage, node stage, tumor node metastasis stage, tumor size, tumor differentiation, and neuron-specific enolase (NSE). Random forest and logistic regression had areas under the curve (AUC) of 0.9995 and 0.6653 in the training group and 0.923 and 0.667 in the internal validation group, respectively. When the two predictive models were validated using data from the Renmin cohort, random forest and logistic regression had AUCs of 0.9994 and 0.627, respectively. This is the first multicenter study to predict HER2 overexpression in individuals with GC, based on clinical variables. The random forest model significantly outperformed the logistic regression model.

Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery

BACKGROUND

This study aimed to investigate the clinical significance of machine-learning (ML) algorithms based on serum inflammatory markers to predict survival outcomes for patients with colorectal cancer (CRC).

METHODS

The study included 941 patients with stages I to III CRC. Based on random forest algorithms using 15 compositions of inflammatory markers, four different prediction scores (DFS score-1, DFS score-2, DFS score-3, and DFS score-4) were developed for the Yonsei cohort (training set, n = 803) and tested in the Ulsan cohort (test set, n = 138). The Cox proportional hazards model was used to determine correlation between prediction scores and disease-free survival (DFS). Harrell's concordance index (C-index) was used to compare the predictive ability of prediction scores for each composition.

RESULTS

The multivariable analysis showed the DFS score-4 to be an independent prognostic factor after adjustment for clinicopathologic factors in both the training and test sets (hazard ratio [HR], 8.98; 95% confidence interval [CI] 6.7-12.04; P < 0.001 for the training set and HR, 2.55; 95% CI 1.1-5.89; P = 0.028 for the test set]. With regard to DFS, the highest C-index among single compositions was observed in the lymphocyte-to-C-reactive protein ratio (LCR) (0.659; 95% CI 0.656-0.662), and the C-index of DFS score-4 (0.727; 95% CI 0.724-0.729) was significantly higher than that of LCR in the test set. The C-index of DFS score-3 (0.725; 95% CI 0.723-0.728) was similar to that of DFS score-4, but higher than that of DFS score-2 (0.680; 95% CI 0.676-0.683).

CONCLUSIONS

The ML-based approaches showed prognostic utility in predicting DFS. They could enhance clinical use of inflammatory markers in patients with CRC.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Feasibility of machine learning-based modeling and prediction using multiple centers data to assess intrahepatic cholangiocarcinoma outcomes

BACKGROUND AND AIMS

Currently, there are still no definitive consensus in the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aimed to build a clinical decision support tool based on machine learning using the Surveillance, Epidemiology, and End Results (SEER) database and the data from the Fifth Medical Center of the PLA General Hospital in China.

METHODS

4,398 eligible patients from the SEER database and 504 eligible patients from the hospital data, who presented with histologically proven iCCA, were enrolled for modeling by cross-validation based on machine learning. All the models were trained using the open-source Python library scikit-survival version 0.16.0. Shapley additive explanations method was used to help clinicians better understand the obtained results. Permutation importance was calculated using library ELI5.

RESULTS

All involved treatment modalities could contribute to a better prognosis. Three models were derived and tested using different data sources, with concordance indices of 0.67, 0.69, and 0.73, respectively. The prediction results were consistent with those under actual situations involving randomly selected patients. Model 2, trained using the hospital data, was selected to develop an online tool, due to its advantage in predicting short-term prognosis.

CONCLUSION

The prediction model and tool established in this study can be applied to predict the prognosis of iCCA after treatment by inputting the patient's clinical parameters or TNM stages and treatment options, thus contributing to optimal clinical decisions.KEY MESSAGESA prognostic model related to disease staging and treatment mode was conducted using the method of machine learning, based on the big data of multi centers.The online calculator can predict the short-term survival prognosis of intrahepatic cholangiocarcinoma, thus, help to make the best clinical decision.The online calculator built to calculate the mortality risk and overall survival can be easily obtained and applied.

DNA-methylation signature accurately differentiates pancreatic cancer from chronic pancreatitis in tissue and plasma

OBJECTIVE

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy. Differentiation from chronic pancreatitis (CP) is currently inaccurate in about one-third of cases. Misdiagnoses in both directions, however, have severe consequences for patients. We set out to identify molecular markers for a clear distinction between PDAC and CP.

DESIGN

Genome-wide variations of DNA-methylation, messenger RNA and microRNA level as well as combinations thereof were analysed in 345 tissue samples for marker identification. To improve diagnostic performance, we established a random-forest machine-learning approach. Results were validated on another 48 samples and further corroborated in 16 liquid biopsy samples.

RESULTS

Machine-learning succeeded in defining markers to differentiate between patients with PDAC and CP, while low-dimensional embedding and cluster analysis failed to do so. DNA-methylation yielded the best diagnostic accuracy by far, dwarfing the importance of transcript levels. Identified changes were confirmed with data taken from public repositories and validated in independent sample sets. A signature of six DNA-methylation sites in a CpG-island of the protein kinase C beta type gene achieved a validated diagnostic accuracy of 100% in tissue and in circulating free DNA isolated from patient plasma.

CONCLUSION

The success of machine-learning to identify an effective marker signature documents the power of this approach. The high diagnostic accuracy of discriminating PDAC from CP could have tremendous consequences for treatment success, once the result from still a limited number of liquid biopsy samples would be confirmed in a larger cohort of patients with suspected pancreatic cancer.

The Use of Artificial Intelligence Algorithms in the Prognosis and Detection of Lymph Node Involvement in Head and Neck Cancer and Possible Impact in the Development of Personalized Therapeutic Strategy: A Systematic Review

Given the increasingly important role that the use of artificial intelligence algorithms is taking on in the medical field today (especially in oncology), the purpose of this systematic review is to analyze the main reports on such algorithms applied for the prognostic evaluation of patients with head and neck malignancies. The objective of this paper is to examine the currently available literature in the field of artificial intelligence applied to head and neck oncology, particularly in the prognostic evaluation of the patient with this kind of tumor, by means of a systematic review. The paper exposes an overview of the applications of artificial intelligence in deriving prognostic information related to the prediction of survival and recurrence and how these data may have a potential impact on the choice of therapeutic strategy, making it increasingly personalized. This systematic review was written following the PRISMA 2020 guidelines.

Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors

Background

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction.

Methods

In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images.

Results

After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments.

Conclusion

Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. With ongoing advancements in model algorithms and their integration into various imaging systems, deep learning stands poised to become an invaluable tool, significantly impacting stem cell research.

Application of machine learning in predicting survival outcomes involving real-world data: a scoping review

BACKGROUND

Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.

METHODS

PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).

RESULTS

Of 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6-0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).

CONCLUSIONS

The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

Material Extrusion Filament Width and Height Prediction via Design of Experiment and Machine Learning

The dimensions of material extrusion 3D printing filaments play a pivotal role in determining processing resolution and efficiency and are influenced by processing parameters. This study focuses on four key process parameters, namely, nozzle diameter, nondimensional nozzle height, extrusion pressure, and printing speed. The design of experiment was carried out to determine the impact of various factors and interaction effects on filament width and height through variance analysis. Five machine learning models (support vector regression, backpropagation neural network, decision tree, random forest, and K-nearest neighbor) were built to predict the geometric dimension of filaments. The models exhibited good predictive performance. The coefficients of determination of the backpropagation neural network model for predicting line width and line height were 0.9025 and 0.9604, respectively. The effect of various process parameters on the geometric morphology based on the established prediction model was also studied. The order of influence on line width and height, ranked from highest to lowest, was as follows: nozzle diameter, printing speed, extrusion pressure, and nondimensional nozzle height. Different nondimensional nozzle height settings may cause the extruded material to be stretched or squeezed. The material being in a stretched state leads to a thin filament, and the regularity of processing parameters on the geometric size is not strong. Meanwhile, the nozzle diameter exhibits a significant impact on dimensions when the material is in a squeezing state. Thus, this study can be used to predict the size of printing filament structures, guide the selection of printing parameters, and determine the size of 3D printing layers.

Trends in using deep learning algorithms in biomedical prediction systems

In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.

Medical image analysis using deep learning algorithms

In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.

A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset

Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. One approach is to use ML to analyse genetic data, or patient demographics and medical history, to predict the likelihood of colon cancer. However, due to the challenges imposed by variable gene expression and the high dimensionality of cancer-related datasets, traditional transductive ML applications have limited accuracy and risk overfitting. In this paper, we propose a new hybrid feature selection model called HMLFSM-Hybrid Machine Learning Feature Selection Model to improve colon cancer gene classification. We developed a multifilter hybrid model including a two-phase feature selection approach, combining Information Gain (IG) and Genetic Algorithms (GA), and minimum Redundancy Maximum Relevance (mRMR) coupling with Particle Swarm Optimization (PSO). We critically tested our model on three colon cancer genetic datasets and found that the new framework outperformed other models with significant accuracy improvements (95%, ~97%, and ~94% accuracies for datasets 1, 2, and 3 respectively). The results show that our approach improves the classification accuracy of colon cancer detection by highlighting important and relevant genes, eliminating irrelevant ones, and revealing the genes that have a direct influence on the classification process. For colon cancer gene analysis, and along with our experiments and literature review, we found that selective input feature extraction prior to feature selection is essential for improving predictive performance.

The Crying Need for a Better Response Assessment in Rectal Cancer

OPINION STATEMENT

Since total neoadjuvant treatment achieves almost 30% pathologic complete response, organ preservation has been increasingly debated for good responders after neoadjuvant treatment for patients diagnosed with rectal cancer. Two organ preservation strategies are available: a watch and wait strategy and a local excision strategy including patients with a near clinical complete response. A major issue is the selection of patients according to the initial tumor staging or the response assessment. Despite modern imaging improvement, identifying complete response remains challenging. A better selection could be possible by radiomics analyses, exploiting numerous image features to feed data characterization algorithms. The subsequent step is to include baseline and/or pre-therapeutic MRI, PET-CT, and CT radiomics added to the patients' clinicopathological data, inside machine learning (ML) prediction models, with predictive or prognostic purposes. These models could be further improved by the addition of new biomarkers such as circulating tumor biomarkers, molecular profiling, or pathological immune biomarkers.

Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study

Background

Identifying patients with hepatocellular carcinoma (HCC) at high risk of recurrence after hepatectomy can help to implement timely interventional treatment. This study aimed to develop a machine learning (ML) model to predict the recurrence risk of HCC patients after hepatectomy.

Methods

We retrospectively collected 315 HCC patients who underwent radical hepatectomy at the Third Affiliated Hospital of Sun Yat-sen University from April 2013 to October 2017, and randomly divided them into the training and validation sets at a ratio of 7:3. According to the postoperative recurrence of HCC patients, the patients were divided into recurrence group and non-recurrence group, and univariate and multivariate logistic regression were performed for the two groups. We applied six machine learning algorithms to construct the prediction models and performed internal validation by 10-fold cross-validation. Shapley additive explanations (SHAP) method was applied to interpret the machine learning model. We also built a web calculator based on the best machine learning model to personalize the assessment of the recurrence risk of HCC patients after hepatectomy.

Results

A total of 13 variables were included in the machine learning models. The multilayer perceptron (MLP) machine learning model was proved to achieve optimal predictive value in test set (AUC = 0.680). The SHAP method displayed that γ-glutamyl transpeptidase (GGT), fibrinogen, neutrophil, aspartate aminotransferase (AST) and total bilirubin (TB) were the top 5 important factors for recurrence risk of HCC patients after hepatectomy. In addition, we further demonstrated the reliability of the model by analyzing two patients. Finally, we successfully constructed an online web prediction calculator based on the MLP machine learning model.

Conclusion

MLP was an optimal machine learning model for predicting the recurrence risk of HCC patients after hepatectomy. This predictive model can help identify HCC patients at high recurrence risk after hepatectomy to provide early and personalized treatment.

Machine-learning-based approach for nonunion prediction following osteoporotic vertebral fractures

PURPOSE

An osteoporotic vertebral fracture (OVF) is a common disease that causes disabilities in elderly patients. In particular, patients with nonunion following an OVF often experience severe back pain and require surgical intervention. However, nonunion diagnosis generally takes more than six months. Although several studies have advocated the use of magnetic resonance imaging (MRI) observations as predictive factors, they exhibit insufficient accuracy. The purpose of this study was to create a predictive model for OVF nonunion using machine learning (ML).

METHODS

We used datasets from two prospective cohort studies for OVF nonunion prediction based on conservative treatment. Among 573 patients with acute OVFs exceeding 65 years in age enrolled in this study, 505 were analyzed. The demographic data, fracture type, and MRI observations of both studies were analyzed using ML. The ML architecture utilized in this study included a logistic regression model, decision tree, extreme gradient boosting (XGBoost), and random forest (RF). The datasets were processed using Python.

RESULTS

The two ML algorithms, XGBoost and RF, exhibited higher area under the receiver operating characteristic curves (AUCs) than the logistic regression and decision tree models (AUC = 0.860 and 0.845 for RF and XGBoost, respectively). The present study found that MRI findings, anterior height ratio, kyphotic angle, BMI, VAS, age, posterior wall injury, fracture level, and smoking habit ranked as important features in the ML algorithms.

CONCLUSION

ML-based algorithms might be more effective than conventional methods for nonunion prediction following OVFs.

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.

Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier

Background

Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse. Our study aimed to evaluate the clinical and genomic prognosticators for lung cancer recurrence by comparing the predictive accuracy of four ML models.

Methods

A total of 41 early-stage lung cancer patients who underwent surgery between June 2007 and October 2014 at New York University Langone Medical Center were included (with recurrence, n=16; without recurrence, n=25). All patients had tumor tissue and buffy coat collected at the time of resection. The CIBERSORT algorithm quantified tumor-infiltrating immune cells (TIICs). Protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to unearth potential molecular drivers of tumor progression. The data was split into training (75%) and validation sets (25%). Ensemble linear kernel support vector machine (SVM) ML models were developed using optimized clinical and genomic features to predict tumor recurrence.

Results

Activated natural killer (NK) cells, M0 macrophages, and M1 macrophages showed a positive correlation with progression. Conversely, T CD4+ memory resting cells were negatively correlated. In the PPI network, TNF and IL6 emerged as prominent hub genes. Prediction models integrating clinicopathological prognostic factors, tumor gene expression (45 genes), and buffy coat gene expression (47 genes) yielded varying receiver operating characteristic (ROC)-area under the curves (AUCs): 62.7%, 65.4%, and 59.7% in the training set, 58.3%, 83.3%, and 75.0% in the validation set, respectively. Notably, merging gene expression with clinical data in a linear SVM model led to a significant accuracy boost, with an AUC of 92.0% in training and 91.7% in validation.

Conclusions

Using ML algorithm, immune gene expression data from tumor tissue and buffy coat may enhance the precision of lung cancer recurrence prediction.

Extracting cancer concepts from clinical notes using natural language processing: a systematic review

BACKGROUND

Extracting information from free texts using natural language processing (NLP) can save time and reduce the hassle of manually extracting large quantities of data from incredibly complex clinical notes of cancer patients. This study aimed to systematically review studies that used NLP methods to identify cancer concepts from clinical notes automatically.

METHODS

PubMed, Scopus, Web of Science, and Embase were searched for English language papers using a combination of the terms concerning "Cancer", "NLP", "Coding", and "Registries" until June 29, 2021. Two reviewers independently assessed the eligibility of papers for inclusion in the review.

RESULTS

Most of the software programs used for concept extraction reported were developed by the researchers (n = 7). Rule-based algorithms were the most frequently used algorithms for developing these programs. In most articles, the criteria of accuracy (n = 14) and sensitivity (n = 12) were used to evaluate the algorithms. In addition, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) and Unified Medical Language System (UMLS) were the most commonly used terminologies to identify concepts. Most studies focused on breast cancer (n = 4, 19%) and lung cancer (n = 4, 19%).

CONCLUSION

The use of NLP for extracting the concepts and symptoms of cancer has increased in recent years. The rule-based algorithms are well-liked algorithms by developers. Due to these algorithms' high accuracy and sensitivity in identifying and extracting cancer concepts, we suggested that future studies use these algorithms to extract the concepts of other diseases as well.

Predicting lymph node metastasis in colorectal cancer: An analysis of influencing factors to develop a risk model

BACKGROUND

Colorectal cancer (CRC) is a significant global health issue, and lymph node metastasis (LNM) is a crucial prognostic factor. Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC. However, the prediction of LNM is challenging and depends on various factors such as tumor histology, clinicopathological features, and molecular characteristics. The most reliable method to detect LNM is the histopathological examination of surgically resected specimens; however, this method is invasive, time-consuming, and subject to sampling errors and interobserver variability.

AIM

To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.

METHODS

This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021. A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.

RESULTS

The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables. The model achieved high accuracy (0.86), sensitivity (0.81), specificity (0.87), positive predictive value (0.66), negative predictive value (0.94), area under the curve for the receiver operating characteristic (0.91), and a low Brier score (0.10). The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds, indicating good calibration and clinical utility.

CONCLUSION

The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC. This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables, demonstrating superior performance and clinical applicability compared to existing models. The study provides new insights into the potential of deep learning to extract valuable information from tumor histology, in turn, improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.

Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application

WHO grade II and III gliomas demonstrate diverse biological behaviors resulting in variable survival outcomes. In the context of glioma prognosis, machine learning (ML) approaches could facilitate the navigation through the maze of factors influencing survival, aiding clinicians in generating more precise and personalized survival predictions. Here we report the utilization of ML models in predicting survival at 12, 24, 36, and 60 months following grade II and III glioma diagnosis. From the National Cancer Database, we analyze 10,001 WHO grade II and 11,456 grade III cranial gliomas. Using the area under the receiver operating characteristic (AUROC) values, we deploy the top-performing models in a web application for individualized predictions. SHapley Additive exPlanations (SHAP) enhance the interpretability of the models. Top-performing predictive models are the ones built with LightGBM and Random Forest algorithms. For grade II gliomas, the models yield AUROC values ranging from 0.813 to 0.896 for predicting mortality across different timeframes, and for grade III gliomas, the models yield AUROCs ranging from 0.855 to 0.878. ML models provide individualized survival forecasts for grade II and III glioma patients across multiple clinically relevant time points. The user-friendly web application represents a pioneering digital tool to potentially integrate predictive analytics into neuro-oncology clinical practice, to empower prognostication and personalize clinical decision-making.

Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature

Aim

Artificial intelligence (AI) is a new field of science in which computers will provide decisions-supporting tools to help doctors make difficult clinical choices. Recent AI applications in otolaryngology include head and neck oncology, rhinology, neurotology, and laryngology. The aim of this systematic review is to describe the potential uses of AI in head and neck oncology with a special focus on the surgical field.

Methods

The authors performed a systematic review, in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, in the main medical databases, including PubMed, Scopus, and Cochrane Library, considering all original studies published until February 2023 about the role of AI in head and neck cancer surgery. The search strategy included a combination of the following terms: "artificial intelligence" or "machine learning" and "head and neck cancer".

Results

Overall, 303 papers were identified and after duplicate removal (12 papers) and excluding papers not written in English (1 paper) and off-topic (4 papers), papers were assessed for eligibility; finally, only 12 papers were included. Three main fields of clinical interest were identified: the most widely investigated included the role of AI in surgical margins assessment (7 papers); the second most frequently evaluated topic was complications assessment (4 papers); finally, only one paper dealt with the indication of salvage laryngectomy after primary radiotherapy.

Conclusions

The authors report the first systematic review in the literature concerning the role of AI in head and neck cancer surgery. An increasing influx of AI applications to clinical problems in otolaryngology is expected, so specialists should be increasingly prepared to manage the constant changes. It will always remain critical for clinicians to use their skills and knowledge to critically evaluate the additional information provided by AI and make the final decisions on each patient.

Novel immune-related gene signature for risk stratification and prognosis prediction in ovarian cancer

BACKGROUND

The immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction.

METHODS

Differently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed.

RESULTS

Five DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets.

CONCLUSIONS

The prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.

Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) account for 80% of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). GEP-NETs are well-differentiated tumors, highly heterogeneous in biology and origin, and are often diagnosed at the metastatic stage. Diagnosis is commonly through clinical symptoms, histopathology, and PET-CT imaging, while molecular markers for metastasis and the primary site are unknown. Here, we report the identification of multi-gene signatures for hepatic metastasis and primary sites through analyses on RNA-SEQ datasets of pancreatic and small intestinal NETs tissue samples. Relevant gene features, identified from the normalized RNA-SEQ data using the mRMRe algorithm, were used to develop seven Machine Learning models (LDA, RF, CART, k-NN, SVM, XGBOOST, GBM). Two multi-gene random forest (RF) models classified primary and metastatic samples with 100% accuracy in training and test cohorts and >90% accuracy in an independent validation cohort. Similarly, three multi-gene RF models identified the pancreas or small intestine as the primary site with 100% accuracy in training and test cohorts, and >95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned >98.42% and >87.42% accuracies on training and test cohorts, respectively. A robust molecular signature to predict liver metastasis or the primary site for GEP-NETs is reported for the first time and could complement the clinical management of GEP-NETs.

Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study

BACKGROUND

The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, progression-free survival after six months, as well as overall survival after six and twelve months in patients with stage IV malignant melanoma undergoing first-line targeted therapy.

METHODS

A baseline machine-learning model using clinical variables (demographic parameters and tumor markers) was compared with an extended model using clinical variables and radiomic features of the whole tumor burden, utilizing repeated five-fold cross-validation. Baseline CTs of 91 stage IV malignant melanoma patients, all treated in the same university hospital, were identified in the Central Malignant Melanoma Registry and all metastases were volumetrically segmented (n = 4727).

RESULTS

Compared with the baseline model, the extended radiomics model did not add significantly more information to the best-response prediction (AUC [95% CI] 0.548 (0.188, 0.808) vs. 0.487 (0.139, 0.743)), the prediction of PFS after six months (AUC [95% CI] 0.699 (0.436, 0.958) vs. 0.604 (0.373, 0.867)), or the overall survival prediction after six and twelve months (AUC [95% CI] 0.685 (0.188, 0.967) vs. 0.766 (0.433, 1.000) and AUC [95% CI] 0.554 (0.163, 0.781) vs. 0.616 (0.271, 1.000), respectively).

CONCLUSIONS

The results showed no additional value of baseline whole-body CT radiomics for best-response prediction, progression-free survival prediction for six months, or six-month and twelve-month overall survival prediction for stage IV melanoma patients receiving first-line targeted therapy. These results need to be validated in a larger cohort.

A different way to diagnosis acute appendicitis: machine learning

<b><br>Indroduction:</b> Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.</br> <b><br>Aim:</b> Our aim is to predict acute appendicitis, which is the most common indication for emergency surgery, using machine learning algorithms with an easy and inexpensive method.</br> <b><br>Materials and methods:</b> Patients who were treated surgically with a prediagnosis of acute appendicitis in a single center between 2011 and 2021 were analyzed. Patients with right lower quadrant pain were selected. A total of 189 positive and 156 negative appendectomies were found. Gender and hemogram were used as features. Machine learning algorithms and data analysis were made in Python (3.7) programming language.</br> <b><br>Results:</b> Negative appendectomies were found in 62% (n = 97) of the women and in 38% (n = 59) of the men. Positive appendectomies were present in 38% (n = 72) of the women and 62% (n = 117) of the men. The accuracy in the test data was 82.7% in logistic regression, 68.9% in support vector machines, 78.1% in k-nearest neighbors, and 83.9% in neural networks. The accuracy in the voting classifier created with logistic regression, k-nearest neighbor, support vector machines, and artificial neural networks was 86.2%. In the voting classifier, the sensitivity was 83.7% and the specificity was 88.6%.</br> <b><br>Conclusions:</b> The results of our study show that machine learning is an effective method for diagnosing acute appendicitis. This study presents a practical, easy, fast, and inexpensive method to predict the diagnosis of acute appendicitis.</br>.

Construction and effect evaluation of prediction model for red blood cell transfusion requirement in cesarean section based on artificial intelligence

OBJECTIVES

This study intends to build an artificial intelligence model for obstetric cesarean section surgery to evaluate the intraoperative blood transfusion volume before operation, and compare the model prediction results with the actual results to evaluate the accuracy of the artificial intelligence prediction model for intraoperative red blood cell transfusion in obstetrics. The advantages and disadvantages of intraoperative blood demand and identification of high-risk groups for blood transfusion provide data support and improvement suggestions for the realization of accurate blood management of obstetric cesarean section patients during the perioperative period.

METHODS

Using a machine learning algorithm, an intraoperative blood transfusion prediction model was trained. The differences between the predicted results and the actual results were compared by means of blood transfusion or not, blood transfusion volume, and blood transfusion volume targeting postoperative hemoglobin (Hb).

RESULTS

Area under curve of the model is 0.89. The accuracy of the model for blood transfusion was 96.85%. The statistical standard for the accuracy of the model blood transfusion volume is the calculation of 1U absolute error, the accuracy rate is 86.56%, and the accuracy rate of the blood transfusion population is 45.00%. In the simulation prediction results, 93.67% of the predicted and actual cases in no blood transfusion surgery; 63.45% of the same predicted blood transfusion in blood transfusion surgery, and only 20.00% of the blood transfusion volume is the same.

CONCLUSIONS

In conclusion, this study used machine learning algorithm to process, analyze and predict the results of a large sample of cesarean section clinical data, and found that the important predictors of blood transfusion during cesarean section included preoperative RBC, surgical method, the site of surgery, coagulation-related indicators, and other factors. At the same time, it was found that the overall accuracy of the AI model was higher than actual blood using. Although the prediction of blood transfusion volume was not well matched with the actual blood using, the model provided a perspective of preoperative identification of high blood transfusion risks. The results can provide good auxiliary decision support for preoperative evaluation of obstetric cesarean section, and then promote the realization of accurate perioperative blood management for obstetric cesarean section patients.

A prognostic mathematical model based on tumor microenvironment-related genes expression for breast cancer patients

Background

Tumor microenvironment (TME) status is closely related to breast cancer (BC) prognosis and systemic therapeutic effects. However, to date studies have not considered the interactions of immune and stromal cells at the gene expression level in BC as a whole. Herein, we constructed a predictive model, for adjuvant decision-making, by mining TME molecular expression information related to BC patient prognosis and drug treatment sensitivity.

Methods

Clinical information and gene expression profiles were extracted from The Cancer Genome Atlas (TCGA), with patients divided into high- and low-score groups according to immune/stromal scores. TME-related prognostic genes were identified using Kaplan-Meier analysis, functional enrichment analysis, and protein-protein interaction (PPI) networks, and validated in the Gene Expression Omnibus (GEO) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct and verify a prognostic model based on TME-related genes. In addition, the patients' response to chemotherapy and immunotherapy was assessed by survival outcome and immunohistochemistry (IPS). Immunohistochemistry (IHC) staining laid a solid foundation for exploring the value of novel therapeutic target genes.

Results

By dividing patients into low- and high-risk groups, a significant distinction in overall survival was found (p < 0.05). The risk model was independent of multiple clinicopathological parameters and accurately predicted prognosis in BC patients (p < 0.05). The nomogram-integrated risk score had high prediction accuracy and applicability, when compared with simple clinicopathological features. As predicted by the risk model, regardless of the chemotherapy regimen, the survival advantage of the low-risk group was evident in those patients receiving chemotherapy (p < 0.05). However, in patients receiving anthracycline (A) therapy, outcomes were not significantly different when compared with those receiving no-A therapy (p = 0.24), suggesting these patients may omit from A-containing adjuvant chemotherapy. Our risk model also effectively predicted tumor mutation burden (TMB) and immunotherapy efficacy in BC patients (p < 0.05).

Conclusion

The prognostic score model based on TME-related genes effectively predicted prognosis and chemotherapy effects in BC patients. The model provides a theoretical basis for novel driver-gene discover in BC and guides the decision-making for the adjuvant treatment of early breast cancer (eBC).

Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms

Objective

The cause and mechanism of non-obstructive azoospermia (NOA) is complicated; therefore, an effective therapy strategy is yet to be developed. This study aimed to analyse the pathogenesis of NOA at the molecular biological level and to identify the core regulatory genes, which could be utilised as potential biomarkers.

Methods

Three NOA microarray datasets (GSE45885, GSE108886, and GSE145467) were collected from the GEO database and merged into training sets; a further dataset (GSE45887) was then defined as the validation set. Differential gene analysis, consensus cluster analysis, and WGCNA were used to identify preliminary signature genes; then, enrichment analysis was applied to these previously screened signature genes. Next, 4 machine learning algorithms (RF, SVM, GLM, and XGB) were used to detect potential biomarkers that are most closely associated with NOA. Finally, a diagnostic model was constructed from these potential biomarkers and visualised as a nomogram. The differential expression and predictive reliability of the biomarkers were confirmed using the validation set. Furthermore, the competing endogenous RNA network was constructed to identify the regulatory mechanisms of potential biomarkers; further, the CIBERSORT algorithm was used to calculate immune infiltration status among the samples.

Results

A total of 215 differentially expressed genes (DEGs) were identified between NOA and control groups (27 upregulated and 188 downregulated genes). The WGCNA results identified 1123 genes in the MEblue module as target genes that are highly correlated with NOA positivity. The NOA samples were divided into 2 clusters using consensus clustering; further, 1027 genes in the MEblue module, which were screened by WGCNA, were considered to be target genes that are highly correlated with NOA classification. The 129 overlapping genes were then established as signature genes. The XGB algorithm that had the maximum AUC value (AUC=0.946) and the minimum residual value was used to further screen the signature genes. IL20RB, C9orf117, HILS1, PAOX, and DZIP1 were identified as potential NOA biomarkers. This 5 biomarker model had the highest AUC value, of up to 0.982, compared to other single biomarker models; additionally, the results of this biomarker model were verified in the validation set.

Conclusions

As IL20RB, C9orf117, HILS1, PAOX, and DZIP1 have been determined to possess the strongest association with NOA, these five genes could be used as potential therapeutic targets for NOA patients. Furthermore, the model constructed using these five genes, which possessed the highest diagnostic accuracy, may be an effective biomarker model that warrants further experimental validation.

Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms

OBJECTIVE

Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive surveys have been presented to briefly analyze methods for detecting breast cancer with mammograms. In this article, our objective is to give an overview of recent advancements in machine learning (ML) and deep learning (DL)-based breast cancer detection systems.

METHODS

We give a structured framework to categorize mammogram-based breast cancer detection techniques. Several publicly available mammogram databases and different performance measures are also mentioned.

RESULTS

After deliberate investigation, we find most of the works classify breast tumors either as normal-abnormal or malignant-benign rather than classifying them into three classes. Furthermore, DL-based features are more significant than hand-crafted features. However, transfer learning is preferred over others as it yields better performance in small datasets, unlike classical DL techniques.

SIGNIFICANCE AND CONCLUSION

In this article, we have made an attempt to give recent advancements in artificial intelligence (AI)-based breast cancer detection systems. Furthermore, a number of challenging issues and possible research directions are mentioned, which will help researchers in further scopes of research in this field.

Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective

Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.

Nd-Mn Molecular Cluster with Searched Targets for Oral Cancer Imaging

In this research, we designed a novel NIR II luminescence imaging probe with targeting effect to accurately track oral squamous cell carcinoma (OSCC) cells. Massive gene expression data were processed by weighted gene co-expression network analysis to establish a network of relationships between genes. After clustering, correlation of clinical information, and gene functional enrichment analysis, MMP1 was predicted to be a biomarker/therapeutic target for OSCC cells. To obtain rare-earth probes with better luminescence in the NIR II region, we adjusted the doping ratio of the rare-earth element (Nd, Gd, Er, and Yb) fraction of the Nd-Mn molecular cluster to optimize its luminescence properties. The results of in vitro targeting experiments showed that Nd-Mn-MMP1Ab can target Cal-27 cells, demonstrating at the cellular level that the MMP1 gene is a biomarker for oral cancer, which also proves that the cancer targets predicted by the bioinformatics approach are correct.

Translation of AI into oncology clinical practice

Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.

A clinical decision support system to predict the efficacy for EGFR-TKIs based on artificial neural network

BACKGROUND

The efficacy of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) was affected by numerous factors. In the study, we developed and validated an artificial neural network (ANN) system based on clinical characteristics and next-generation sequencing (NGS) to support clinical decisions.

METHODS

A multicenter retrospective non-interventional study was conducted. 240 patients from three hospitals with advanced non-small cell lung cancer (NSCLC) and EGFR mutation were tested by NGS before the first treatment. All patients received formal EGFR-TKIs treatment. Five different models were individually trained to predict the efficacy of EGFR-TKIs based on one medical center with 188 patients. Two independent cohorts from other medical centers were collected for external validation.

RESULTS

Compared with logistic regression, four machine learning methods showed better predicting abilities for EGFR-TKIs. The inclusion of NGS tests improved the predictive power of models. ANN performed best on the dataset with mutations TP53, RB1, PIK3CA, EGFR mutation sites, and tumor mutation burden (TMB). The prediction accuracy, recall and AUC were 0.82, 0.82, and 0.82, respectively in our final model. In the external validation set, ANN still showed good performance and differentiated patients with poor outcomes. Finally, a clinical decision support software based on ANN was developed and provided a visualization interface for clinicians.

CONCLUSION

This study provides an approach to assess the efficacy of NSCLC patients with first-line EGFR-TKI treatment. Software is developed to support clinical decisions.

Early prediction of response to palliative chemotherapy in patients with stage-IV gastric and esophageal cancer

BACKGROUND

The goal of therapy for many patients with advanced stage malignancies, including those with metastatic gastric and esophageal cancers, is to extend overall survival while also maintaining quality of life. After weighing the risks and benefits of treatment with palliative chemotherapy (PC) with non-curative intent, many patients decide to pursue treatment. It is known that a subset of patients who are treated with PC experience significant side effects without clinically significant survival benefits from PC.

METHODS

We use data from 150 patients with stage-IV gastric and esophageal cancers to train machine learning models that predict whether a patient with stage-IV gastric or esophageal cancers would benefit from PC, in terms of increased survival duration, at very early stages of the treatment.

RESULTS

Our findings show that machine learning can predict with high accuracy whether a patient will benefit from PC at the time of diagnosis. More accurate predictions can be obtained after only two cycles of PC (i.e., about 4 weeks after diagnosis). The results from this study are promising with regard to potential improvements in quality of life for patients near the end of life and a potential overall survival benefit by optimizing systemic therapy earlier in the treatment course of patients.

A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection

The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system's overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.

A screened predictive model for esophageal squamous cell carcinoma based on salivary flora data

Esophageal squamous cell carcinoma (ESCC) is a malignant tumor of the digestive system in the esophageal squamous epithelium. Many studies have linked esophageal cancer (EC) to the imbalance of oral microecology. In this work, different machine learning (ML) models including Random Forest (RF), Gaussian mixture model (GMM), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM) and extreme gradient boosting (XGBoost) based on Genetic Algorithm (GA) optimization was developed to predict the relationship between salivary flora and ESCC by combining the relative abundance data of Bacteroides, Firmicutes, Proteobacteria, Fusobacteria and Actinobacteria in the saliva of patients with ESCC and healthy control. The results showed that the XGBoost model without parameter optimization performed best on the entire dataset for ESCC diagnosis by cross-validation (Accuracy = 73.50%). Accuracy and the other evaluation indicators, including Precision, Recall, F1-score and the area under curve (AUC) of the receiver operating characteristic (ROC), revealed XGBoost optimized by the GA (GA-XGBoost) achieved the best outcome on the testing set (Accuracy = 89.88%, Precision = 89.43%, Recall = 90.75%, F1-score = 90.09%, AUC = 0.97). The predictive ability of GA-XGBoost was validated in phylum-level salivary microbiota data from ESCC patients and controls in an external cohort. The results obtained in this validation (Accuracy = 70.60%, Precision = 46.00%, Recall = 90.55%, F1-score = 61.01%) illustrate the reliability of the predictive performance of the model. The feature importance rankings obtained by XGBoost indicate that Bacteroides and Actinobacteria are the two most important factors in predicting ESCC. Based on these results, GA-XGBoost can predict and diagnose ESCC according to the relative abundance of salivary flora, providing an effective tool for the non-invasive prediction of esophageal malignancies.

On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders

This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository. Our findings highlight the ability of machine learning algorithms to not only streamline diagnostic procedures but also reduce diagnostic errors and the dependency on extensive clinical testing. Three key features-mean arterial pressure, pulsatile blood pressure index, and resistance-compliance indicator-were found to significantly improve the accuracy of machine learning algorithms in binary heart disease classification. Logistic regression achieved the highest average accuracy among the examined classifiers when utilizing these features. While such novel indicators contribute substantially to the classification process, they should be integrated into a broader diagnostic framework that includes comprehensive patient evaluations and medical expertise. Therefore, the present study offers valuable insights for leveraging data science techniques in the diagnosis and management of cardiovascular diseases.

Explainable AI for Bioinformatics: Methods, Tools and Applications

Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that are often perceived as opaque and black-box methods make it difficult to understand the reasoning behind their decisions. This lack of transparency can be a challenge for both end-users and decision-makers, as well as AI developers. In sensitive areas such as healthcare, explainability and accountability are not only desirable properties but also legally required for AI systems that can have a significant impact on human lives. Fairness is another growing concern, as algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes. Explainable AI (XAI) aims to overcome the opaqueness of black-box models and to provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and identify factors that influence their outcomes. However, the majority of the state-of-the-art interpretable ML methods are domain-agnostic and have evolved from fields such as computer vision, automated reasoning or statistics, making direct application to bioinformatics problems challenging without customization and domain adaptation. In this paper, we discuss the importance of explainability and algorithmic transparency in the context of bioinformatics. We provide an overview of model-specific and model-agnostic interpretable ML methods and tools and outline their potential limitations. We discuss how existing interpretable ML methods can be customized and fit to bioinformatics research problems. Further, through case studies in bioimaging, cancer genomics and text mining, we demonstrate how XAI methods can improve transparency and decision fairness. Our review aims at providing valuable insights and serving as a starting point for researchers wanting to enhance explainability and decision transparency while solving bioinformatics problems. GitHub: https://github.com/rezacsedu/XAI-for-bioinformatics.

Combination of deep learning and ensemble machine learning using intraoperative video images strongly predicts recovery of urinary continence after robot-assisted radical prostatectomy

BACKGROUND

We recently reported the importance of deep learning (DL) of pelvic magnetic resonance imaging in predicting the degree of urinary incontinence (UI) following robot-assisted radical prostatectomy (RARP). However, our results were limited because the prediction accuracy was approximately 70%.

AIM

To develop a more precise prediction model that can inform patients about UI recovery post-RARP surgery using a DL model based on intraoperative video images.

METHODS AND RESULTS

The study cohort comprised of 101 patients with localized prostate cancer undergoing RARP. Three snapshots from intraoperative video recordings showing the pelvic cavity (prior to bladder neck incision, immediately following prostate removal, and after vesicourethral anastomosis) were evaluated, including pre- and intraoperative parameters. We evaluated the DL model plus simple or ensemble machine learning (ML), and the area under the receiver operating characteristic curve (AUC) was analyzed through sensitivity and specificity. Of 101, 64 and 37 patients demonstrated "early continence (using 0 or 1 safety pad at 3 months post-RARP)" and "late continence (others)," respectively, at 3 months postoperatively. The combination of DL and simple ML using intraoperative video snapshots with clinicopathological parameters had a notably high performance (AUC, 0.683-0.749) to predict early recovery from UI after surgery. Furthermore, combining DL with ensemble artificial neural network using intraoperative video snapshots had the highest performance (AUC, 0.882; sensitivity, 92.2%; specificity, 78.4%; overall accuracy, 85.3%) to predict early recovery from post-RARP incontinence, with similar results by internal validation. The addition of clinicopathological parameters showed no additive effects for each analysis using DL, EL and simple ML.

CONCLUSION

Our findings suggest that the DL algorithm with intraoperative video imaging is a reliable method for informing patients about the severity of their recovery from UI after RARP, although it is not clear if our methods are reproducible for predicting long-term UI and pad-free continence.

Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models

PURPOSE

The primary purpose of this study was to utilize machine learning (ML) models to create a web application that can predict survival outcomes for patients diagnosed with atypical and anaplastic meningiomas.

METHODS

In this retrospective cohort study, patients diagnosed with WHO grade II and III meningiomas were selected from the National Cancer Database (NCDB) to analyze survival outcomes at 12, 36, and 60 months. Five machine learning algorithms - TabPFN, TabNet, XGBoost, LightGBM, and Random Forest were employed and optimized using the Optuna library for hyperparameter tuning. The top-performing models were then deployed into our web-based application.

RESULTS

From the NCDB, 12,197 adult patients diagnosed with histologically confirmed WHO grade II and III meningiomas were retrieved. The mean age was 61 (± 20), and 6,847 (56.1%) of these were females. Performance evaluation indicated that the top-performing models for each outcome were the models built with the TabPFN algorithm. The TabPFN models yielded area under the receiver operating characteristic (AUROC) values of 0.805, 0.781, and 0.815 in predicting 12-, 36-, and 60-month mortality, respectively.

CONCLUSION

With the continuous growth of neuro-oncology data, ML algorithms act as key tools in predicting survival outcomes for WHO grade II and III meningioma patients. By incorporating these interpretable models into a web application, we can practically utilize them to improve risk evaluation and prognosis for meningioma patients.

Survival Prediction After Transarterial Chemoembolization for Hepatocellular Carcinoma: a Deep Multitask Survival Analysis Approach

The accurate prediction of postoperative survival time of patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) is important for postoperative health care. Survival analysis is a common method used to predict the occurrence time of events of interest in the medical field. At present, the mainstream survival analysis models, such as the Cox proportional risk model, should make strict assumptions about the potential random process to solve the censored data, thus potentially limiting their application in clinical practice. In this paper, we propose a novel deep multitask survival model (DMSM) to analyze HCC survival data. Specifically, DMSM transforms the traditional survival time prediction problem of patients with HCC into a survival probability prediction problem at multiple time points and applies entropy regularization and ranking loss to optimize a multitask neural network. Compared with the traditional methods of deleting censored data and strong hypothesis, DMSM makes full use of all the information in the censored data but does not need to make any assumption. In addition, we identify the risk factors affecting the prognosis of patients with HCC and visualize the importance of ranking these factors. On the basis of the analysis of a real dataset of patients with BCLC stage B HCC, experimental results on three different validation datasets show that the DMSM achieves competitive performance with concordance index of 0.779, 0.727, and 0.780 and integrated Brier score (IBS) of 0.172, 0.138, and 0.135, respectively. Our DMSM has a comparatively small standard deviation (0.002, 0.002, and 0.003) for IBS of bootstrapping 100 times. The DMSM we proposed can be utilized as an effective survival analysis model and provide an important means for the accurate prediction of postoperative survival time of patients with BCLC stage B HCC.

Prediction of Five-Year Survival Rate for Rectal Cancer Using Markov Models of Convolutional Features of RhoB Expression on Tissue Microarray

The ability to predict survival in cancer is clinically important because the finding can help patients and physicians make optimal treatment decisions. Artificial intelligence in the context of deep learning has been increasingly realized by the informatics-oriented medical community as a powerful machine-learning technology for cancer research, diagnosis, prediction, and treatment. This paper presents the combination of deep learning, data coding, and probabilistic modeling for predicting five-year survival in a cohort of patients with rectal cancer using images of RhoB expression on biopsies. Using about one-third of the patients' data for testing, the proposed approach achieved 90% prediction accuracy, which is much higher than the direct use of the best pretrained convolutional neural network (70%) and the best coupling of a pretrained model and support vector machines (70%).

Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer.

Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach

Cardiovascular disease (CVD) in cancer patients can affect the risk of unplanned readmissions, which have been reported to be costly and associated with worse mortality and prognosis. We aimed to demonstrate the feasibility of using machine learning techniques in predicting the risk of unplanned 180-day readmission attributable to CVD among hospitalized cancer patients using the 2017-2018 Nationwide Readmissions Database. We included hospitalized cancer patients, and the outcome was unplanned hospital readmission due to any CVD within 180 days after discharge. CVD included atrial fibrillation, coronary artery disease, heart failure, stroke, peripheral artery disease, cardiomegaly, and cardiomyopathy. Decision tree (DT), random forest, extreme gradient boost (XGBoost), and AdaBoost were implemented. Accuracy, precision, recall, F2 score, and receiver operating characteristic curve (AUC) were used to assess the model's performance. Among 358,629 hospitalized patients with cancer, 5.86% (n = 21,021) experienced unplanned readmission due to any CVD. The three ensemble algorithms outperformed the DT, with the XGBoost displaying the best performance. We found length of stay, age, and cancer surgery were important predictors of CVD-related unplanned hospitalization in cancer patients. Machine learning models can predict the risk of unplanned readmission due to CVD among hospitalized cancer patients.

Accuracy of deep neural learning models in the imaging prediction of pathological complete response after neoadjuvant chemoradiotherapy for locally advanced rectal cancer: a systematic review

PURPOSE

Up to 15-27% of patients achieve pathologic complete response (pCR) following neoadjuvant chemoradiotherapy (CRT) for locally advanced rectal cancer (LARC). Deep neural learning (DL) algorithms have been suggested to be a useful adjunct to allow accurate prediction of pCR and to identify patients who could potentially avoid surgery. This systematic review aims to interrogate the accuracy of DL algorithms at predicting pCR.

METHODS

Embase (PubMed, MEDLINE) databases and Google Scholar were searched to identify eligible English-language studies, with the search concluding in July 2022. Studies reporting on the accuracy of DL models in predicting pCR were selected for review and information pertaining to study characteristics and diagnostic measures was extracted from relevant studies. Risk of bias was evaluated using the Newcastle-Ottawa scale (NOS).

RESULTS

Our search yielded 85 potential publications. Nineteen full texts were reviewed, and a total of 12 articles were included in this systematic review. There were six retrospective and six prospective cohort studies. The most common DL algorithm used was the Convolutional Neural Network (CNN). Performance comparison was carried out via single modality comparison. The median performance for each best-performing algorithm was an AUC of 0.845 (range 0.71-0.99) and Accuracy of 0.85 (0.83-0.98).

CONCLUSIONS

There is a promising role for DL models in the prediction of pCR following neoadjuvant-CRT for LARC. Further studies are needed to provide a standardised comparison in order to allow for large-scale clinical application.

PROPERO REGISTRATION

PROSPERO 2021 CRD42021269904 Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021269904 .

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

Cyclin-dependent kinase 2 (CDK2) is a promising target for cancer treatment, developing new effective CDK2 inhibitors is of great significance in anticancer therapy. The involvement of CDK2 in tumorigenesis has been debated, but recent evidence suggests that specifically inhibiting CDK2 could be beneficial in treating certain tumors. This approach remains attractive in the development of anticancer drugs. Several small-molecule inhibitors targeting CDK2 have reached clinical trials, but a selective inhibitor for CDK2 is yet to be discovered. In this study, we conducted machine learning-based drug designing to search for a drug candidate for CDK2. Machine learning models, including k-NN, SVM, RF, and GNB, were created to detect active and inactive inhibitors for a CDK2 drug target. The models were assessed using 10-fold cross-validation to ensure their accuracy and reliability. These methods are highly suitable for classifying compounds as either active or inactive through the virtual screening of extensive compound libraries. Subsequently, machine learning techniques were employed to analyze the test dataset obtained from the zinc database. A total of 25 compounds with 98% accuracy were predicted as active against CDK2. These compounds were docked into CDK2's active site. Finally, three compounds were selected based on good docking score, and, along with a reference compound, underwent MD simulation. The Gaussian naïve Bayes model yielded superior results compared to other models. The top three hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as CDK2 inhibitors.

Microglial morphometric analysis: so many options, so little consistency

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist's toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community.