Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis

Tumor specific protein 70 targeted tumor cell isolation technology can improve the accuracy of cytopathological examination

OBJECTIVES

Although existing cytopathological examination is considered essential for the diagnosis of malignant serous effusions, its accuracy is pretty low. Tumor specific protein 70 (SP70), which is highly expressed on human tumor cell membrane, was identified in our previous study. This study aimed to explore whether SP70 targeted tumor cell isolation technology with immunomagnetic beads can improve the accuracy of cytopathological examination.

METHODS

Cytopathological analysis with SP70 targeted tumor cell isolation technology was used in this study. In total, 255 cases were enrolled. Serous effusions were analyzed by both existing cytopathological examination and the new cytopathological analysis concurrently.

RESULTS

The sensitivities of existing cytopathological examination and the new cytopathological analysis were 51.26 % and 85.43 %, respectively, while the specificities were 100 % for both. This new cytopathological analysis demonstrated a higher interobserver agreement with malignant diagnosis than the existing cytopathological examination (kappa coefficient: 0.720 vs. 0.316, p<0.001). In addition, it achieved superior diagnostic efficacy for malignancy differentiation compared to existing cytopathological examination (AUC: 0.927 vs. 0.756, p<0.001). The follow-up results showed that 74 malignant cases with final clinical diagnosis were positive only with the new cytopathological analysis. Among these cases, there were 58 negative and 16 atypical by the existing cytopathological examination. In these malignant cases, 74.3 % (55/74) had been confirmed to have serosa metastasis based on radiographic evidence, and 73.7 % (28/38) harbored tumor hotspot mutations.

CONCLUSIONS

As illustrated in this work, cytopathological analysis with SP70 targeted tumor cell isolation technology can improve the accuracy of existing cytopathological examination prominently.

Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer

PURPOSE

To establish a nomogram for predicting brain metastasis (BM) in primary lung cancer at 12, 18, and 24 months after initial diagnosis.

METHODS

In this study, we included 428 patients who were diagnosed with primary lung cancer at Harbin Medical University Cancer Hospital between January 2020 and January 2022. The endpoint event was BM. The patients were randomly categorized into two groups in a 7:3 ratio: training (n = 299) and validation (n = 129) sets. Least absolute shrinkage and selection operator was utilized to analyze the laboratory test results in the training set. Furthermore, clinlabomics-score was determined using regression coefficients. Then, clinlabomics-score was combined with clinical data to construct a nomogram using random survival forest (RSF) and Cox multivariate regression. Then, various methods were used to evaluate the performance of the nomogram.

RESULTS

Five independent predictive factors (pathological type, diameter, lymph node metastasis, non-lymph node metastasis and clinlabomics-score) were used to construct the nomogram. In the validation set, the bootstrap C-index was 0.7672 (95% CI 0.7092-0.8037), 12-month AUC was 0.787 (95% CI 0.708-0.865), 18-month AUC was 0.809 (95% CI 0.735-0.884), and 24-month AUC was 0.858 (95% CI 0.792-0.924). In addition, the calibration curve, decision curve analysis and Kaplan-Meier curves revealed a good performance of the nomogram.

CONCLUSIONS

Finally, we constructed and validated a nomogram to predict BM risk in primary lung cancer. Our nomogram can identify patients at high risk of BM and provide a reference for clinical decision-making at different disease time points.

Development and Validation of Chinese Version of Dental Pain Screening Questionnaire

INTRODUCTION

Dental pain is one of the most prevalent oral complaints. This study aimed to establish a Chinese version of Dental Pain Screening Questionnaire (DePaQ) to help classify patients into three groups of dental pain diseases (group 1, reversible pulpitis and dentine hypersensitivity; group 2, acute periapical periodontitis and irreversible pulpitis; and group 3, pericoronitis).

METHODS

The DePaQ was translated from English to Chinese. The clinical subjects (CS, n = 290) and nonclinical subjects (NS, n = 100) with dental pain were collected. The CS were randomly divided into two subsamples: CS1 (n = 203) and CS2 (n = 87). The Fisher discriminant functions of the final 13-item Chinese version of the DePaQ were obtained from the CS1 group, and discriminant validity was further verified in the CS2 and NS groups.

RESULTS

The discriminant functions of the final 13-item DePaQ obtained from the CS1 group were capable of correctly distinguishing 93.1% and 89.0% cases of the CS2 and NS groups, respectively. In the CS2 group, the sensitivity for groups 1, 2, and 3 was 88.0%, 80.0%, and 83.0%, respectively, and the specificity was 95.0%, 95.0%, and 86.0%, respectively. In the NS group, the sensitivity for groups 1, 2, and 3 was 82.0%, 80.0%, and 86.0%, respectively, and the specificity was 91.0%, 97.0%, and 90.0%, respectively.

CONCLUSIONS

The Chinese version of DePaQ could help classify the three groups of dental pain diseases and guide medical treatment.

Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models

Objectives: Lung cancer is one of the most prevalent cancers worldwide. Accurately determining lung cancer subtypes and identifying high-risk patients are helpful for individualized treatment and follow-up. Our study aimed to establish an effective model for subtype classification and overall survival (OS) prediction in patients with lung cancer. Methods: Histopathological images, clinical data, and genetic information of lung adenocarcinoma and lung squamous cell carcinoma cases were downloaded from The Cancer Genome Atlas. An influencing factor system was optimized based on the nuclear, clinical, and genetic features. Four machine-learning models-light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), random forest (RF), and adaptive boosting (AdaBoost)-and three deep-learning models-multilayer perceptron (MLP), TabNet, and convolutional neural network (CNN)-were employed for subtype classification and OS prediction. The performance of the models was comprehensively evaluated. Results: XGBoost exhibited the highest area under the curve (AUC) value of 0.9821 in subtype classification, whereas RF exhibited the highest AUC values of 0.9134, 0.8706, and 0.8765 in predicting OS at 1, 2, and 3 years, respectively. Conclusions: Our study was the first to incorporate the characteristics of nuclei and the genetic information of patients to predict the subtypes and OS of patients with lung cancer. The combination of different factors and the usage of artificial intelligence methods achieved a small breakthrough in the results of previous studies regarding AUC values.

Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model

Objective

To assess the effectiveness of a feature self-recognition machine learning model in screening for pulmonary nodule risk in a physical examination population and to evaluate the constructed visualization system.

Methods

We analyzed data from 4,861 individuals who underwent chest CT exams during their physical examinations at the Western Theater General Hospital of the People's Liberation Army from January 2023 to November 2023. Among them, 1,168 had positive CT reports for pulmonary nodules, while 3,693 had negative findings. We developed a machine learning model using the XGBoost algorithm and employed an improved sooty tern optimization algorithm (ISTOA) for feature selection. The significance of the selected features was evaluated through univariate analysis and multivariable logistic stepwise regression analysis. A visualization system was created to estimate the risk of developing pulmonary nodules.

Results

Multivariable analysis identified older age, smoking or passive smoking, high psychological stress within the past year, occupational exposure (e.g., air pollution at the workplace), presence of chronic lung diseases, and elevated carcinoembryonic antigen levels as significant risk factors for pulmonary nodules. The feature self-recognition machine learning model further highlighted age, smoking or passive smoking, high psychological stress, occupational exposure, chronic lung diseases, family history of lung cancer, decreased albumin levels, and elevated carcinoembryonic antigen as key predictors for early pulmonary nodule risk, demonstrating superior performance.

Conclusion

The feature self-recognition machine learning model effectively aids in the early prediction and clinical identification of pulmonary nodule risk, facilitating timely intervention and improving patient prognosis.

A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer

According to data from the World Health Organization (WHO), lung cancer is becoming a global epidemic. It is particularly high in the list of the leading causes of death not only in developed countries, but also worldwide; furthermore, it holds the leading place in terms of cancer-related mortality. Nevertheless, many breakthroughs have been made the last two decades regarding its management, with one of the most prominent being the implementation of artificial intelligence (AI) in various aspects of disease management. We included 473 papers in this thorough review, most of which have been published during the last 5-10 years, in order to describe these breakthroughs. In screening programs, AI is capable of not only detecting suspicious lung nodules in different imaging modalities-such as chest X-rays, computed tomography (CT), and positron emission tomography (PET) scans-but also discriminating between benign and malignant nodules as well, with success rates comparable to or even better than those of experienced radiologists. Furthermore, AI seems to be able to recognize biomarkers that appear in patients who may develop lung cancer, even years before this event. Moreover, it can also assist pathologists and cytologists in recognizing the type of lung tumor, as well as specific histologic or genetic markers that play a key role in treating the disease. Finally, in the treatment field, AI can guide in the development of personalized options for lung cancer patients, possibly improving their prognosis.

Performance and efficiency of machine learning models in analyzing capillary serum protein electrophoresis

BACKGROUND AND OBJECTIVE

Serum protein electrophoresis (SPEP) plays a critical role in diagnosing diseases associated with M-proteins. However, its clinical application is limited by a heavy reliance on experienced experts.

METHODS

A dataset comprising 85,026 SPEP outcomes was utilized to develop artificial intelligence diagnostic models for the classification and localization of M-proteins. These models were trained and validated using three data features, and their performance was evaluated using comprehensive metrics, including sensitivity, positive predictive value (PPV), specificity, negative predictive value (NPV), F1 score, accuracy, area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), and Intersection over Union (IoU). The best-performing machine learning (ML) and deep learning (DL) models were further tested on a separate dataset of 1,079 samples. The localization ability of the DL model was compared against three clinical experts.

RESULTS

Among the four ML models, the extreme gradient boosting (XGB) model achieved the best performance, with MCC, AUC, F1 score, sensitivity, specificity, accuracy, PPV, and NPV of 0.847, 0.903, 0.875, 0.822, 0.985, 0.951, 0.934, and 0.955, respectively. Different feature extraction methods significantly influenced model performance. The DL models outperformed the ML models in comprehensive performance. The U-Net combined with Transformer model demonstrated localization ability comparable to that of clinical experts, achieving sensitivity, specificity, accuracy, PPV, NPV, F1 score, AUC, MCC, and IoU of 0.947, 0.984, 0.976, 0.938, 0.986, 0.942, 0.966, 0.927, and 0.877, respectively.

CONCLUSION

The U-Net combined with the Transformer model demonstrated expert-level performance in M-protein classification and localization, achieving an accuracy of 0.976 and an IoU of 0.877. This exceptional performance highlights the potential of this combined model for automating clinical SPEP workflows.

Multi-omics model is an effective means to diagnose benign and malignant pulmonary nodules

In response to the high false positive rate of traditional Low-Dose Computed Tomography (LDCT) in diagnosing pulmonary malignant nodules, this study aimed to investigate the effectiveness of scoring of blood-based non-invasive biological metabolite detection combined with artificial intelligent scoring of non-invasive imaging in the clinical diagnosis of Pulmonary Nodules (PNs). In this retrospective study, risk scoring was performed in patients positive for pulmonary nodules and subsequently, PNs were sampled by invasive procedures for pathological examinations. The pathological classification was used as the gold standard, and statistical and machine learning methods showed, that in 210 patients (23 benign PN and 187 malignant PN), the risk score of Metabonomics, radiomics, and multi-omics had different levels of performance in different risk groups based on various predictive models. The Area Under the receiver operating Characteristic Curve (AUC) of the multi-omics model was 0.823. The present results indicate that a multi-omics model is more effective than a single model in the non-invasive diagnosis of pulmonary malignant nodules.

Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook

Background

Artificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications in chemotherapy development, cancer diagnosis, and predicting chemotherapy response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) and deep learning (DL).

Objective

This review aims to explore the role of AI in forecasting outcomes related to chemotherapy development, cancer diagnosis, and treatment response, synthesizing current advancements and identifying critical gaps in the field.

Methods

A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and Cochrane databases up to 2023. Keywords included "Artificial Intelligence (AI)," "Machine Learning (ML)," and "Deep Learning (DL)" combined with "chemotherapy development," "cancer diagnosis," and "cancer treatment." Articles published within the last four years and written in English were included. The Prediction Model Risk of Bias Assessment tool was utilized to assess the risk of bias in the selected studies.

Conclusion

This review underscores the substantial impact of AI, including ML and DL, on cancer diagnosis, chemotherapy innovation, and treatment response for both solid and hematological tumors. Evidence from recent studies highlights AI's potential to reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing treatment plans, and improving therapeutic outcomes. Future research should focus on addressing challenges in clinical implementation, ethical considerations, and scalability to enhance AI's integration into oncology care.

Artificial intelligence for diagnosis and predictive biomarkers in Non-Small cell lung cancer Patients: New promises but also new hurdles for the pathologist

The rapid development of artificial intelligence (AI) based tools in pathology laboratories has brought forward unlimited opportunities for pathologists. Promising AI applications used for accomplishing diagnostic, prognostic and predictive tasks are being developed at a high pace. This is notably true in thoracic oncology, given the significant and rapid therapeutic progress made recently for lung cancer patients. Advances have been based on drugs targeting molecular alterations, immunotherapies, and, more recently antibody-drug conjugates which are soon to be introduced. For over a decade, many proof-of-concept studies have explored the use of AI algorithms in thoracic oncology to improve lung cancer patient care. However, despite the enthusiasm in this domain, the set-up and use of AI algorithms in daily practice of thoracic pathologists has not been operative until now, due to several constraints. The purpose of this review is to describe the potential but also the current barriers of AI applications in routine thoracic pathology for non-small cell lung cancer patient care and to suggest practical solutions for rapid future implementation.

Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review

Artificial intelligence (AI) is revolutionizing early cancer detection by enhancing the sensitivity, efficiency, and precision of screening programs for breast, colorectal, and lung cancers. Deep learning algorithms, such as convolutional neural networks, are pivotal in improving diagnostic accuracy by identifying patterns in imaging data that may elude human radiologists. AI has shown remarkable advancements in breast cancer detection, including risk stratification and treatment planning, with models achieving high specificity and precision in identifying invasive ductal carcinoma. In colorectal cancer screening, AI-powered systems significantly enhance polyp detection rates during colonoscopies, optimizing the adenoma detection rate and improving diagnostic workflows. Similarly, low-dose CT scans integrated with AI algorithms are transforming lung cancer screening by increasing the sensitivity and specificity of early-stage cancer detection, while aiding in accurate lesion segmentation and classification. This review highlights the potential of AI to streamline cancer diagnosis and treatment by analyzing vast datasets and reducing diagnostic variability. Despite these advancements, challenges such as data standardization, model generalization, and integration into clinical workflows remain. Addressing these issues through collaborative research, enhanced dataset diversity, and improved explainability of AI models will be critical for widespread adoption. The findings underscore AI's potential to significantly impact patient outcomes and reduce cancer-related mortality, emphasizing the need for further validation and optimization in diverse healthcare settings.

Revolutionizing public health through digital health technology

The aging population and increasing chronic diseases strain public health systems. Advancements in digital health promise to tackle these challenges and enhance public health outcomes. Digital health integrates digital health technology (DHT) across healthcare, including smart consumer devices. This article examines the application of DHT in public health and its significant impact on revolutionizing the field. Historically, DHT has not only enhanced the efficiency of disease prevention, diagnosis, and treatment but also facilitated the equitable distribution of global health resources. Looking ahead, DHT holds vast potential in areas such as personalized medicine, telemedicine, and intelligent health management. However, it also encounters challenges such as ethics, privacy, and data security. To further advance DHT, concerted efforts are essential, including policy support, investment in research and development, involvement of medical institutions, and improvement of public digital health literacy.

Computer-aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review

Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.

Diagnosis of clear cell renal cell carcinoma via a deep learning model with whole-slide images

Background

Traditional pathological diagnosis methods have limitations in terms of interobserver variability and the time consumption of evaluations. In this study, we explored the feasibility of using whole-slide images (WSIs) to establish a deep learning model for the diagnosis of clear cell renal cell carcinoma (ccRCC).

Methods

We retrospectively collected pathological data from 95 patients with ccRCC from January 2023 to December 2023. All pathological slices conforming to the standards of the model were manually annotated first. The WSIs were preprocessed to extract the region of interest. The WSIs were divided into a training set and a test set, and the ratio of tumor slices to normal tissue slices in the training set to the test set was 3:1. Positive and negative samples were randomly extracted. Model training was based on a convolutional neural network (CNN) and a random forest model. The accuracy of the model was evaluated by generating a receiver operating characteristic (ROC) curve.

Results

A total of 663 pathological slices from 95 patients with ccRCC were collected. The mean number of slices per patient was 7.6 ± 2.7 (range: 3-17), with 506 tumor slices and 157 normal tissue slices. There were 200 tumor slices and 74 normal slices in the training set, and a total of 200,870 small images were extracted. There were 250 tumor slices and 63 normal slices in the test set, and a total of 39,211 small images were extracted. According to the CNN model and random forest model trained with the training set, 11 pathological slices in the test set were identified as false normal slices, and six pathological slices were identified as false tumor slices. The total accuracy was 94.6% (296/313), the precision rate was 97.6% (239/245), and the recall rate was 95.6% (239/250). The generated probabilistic heatmaps were consistent with the manually annotated pathological images. The ROC curve results revealed that the area under curve (AUC) reached 0.9658 (95% confidence interval: 0.9603-0.9713), the specificity was 90.5%, and the sensitivity was 95.6%.

Conclusion

The use of a deep learning method for the diagnosis of ccRCC is feasible. The ccRCC model established in this study achieved high accuracy. AI-based diagnostic methods for ccRCC may improve diagnostic efficiency.

Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis

Background

The increasing body of evidence has been stimulating the application of artificial intelligence (AI) in precision medicine research for lung cancer. This trend necessitates a comprehensive overview of the growing number of publications to facilitate researchers' understanding of this field.

Method

The bibliometric data for the current analysis was extracted from the Web of Science Core Collection database, CiteSpace, VOSviewer ,and an online website were applied to the analysis.

Results

After the data were filtered, this search yielded 4062 manuscripts. And 92.27% of the papers were published from 2014 onwards. The main contributing countries were China, the United States, India, Japan, and Korea. These publications were mainly published in the following scientific disciplines, including Radiology Nuclear Medicine, Medical Imaging, Oncology, and Computer Science Notably, Li Weimin and Aerts Hugo J. W. L. stand out as leading authorities in this domain. In the keyword co-occurrence and co-citation cluster analysis of the publication, the knowledge base was divided into four clusters that are more easily understood, including screening, diagnosis, treatment, and prognosis.

Conclusion

This bibliometric study reveals deep learning frameworks and AI-based radiomics are receiving attention. High-quality and standardized data have the potential to revolutionize lung cancer screening and diagnosis in the era of precision medicine. However, the importance of high-quality clinical datasets, the development of new and combined AI models, and their consistent assessment for advancing research on AI applications in lung cancer are highlighted before current research can be effectively applied in clinical practice.

Single-Cell and Bulk Transcriptomics Reveal the Immunosenescence Signature for Prognosis and Immunotherapy in Lung Cancer

BACKGROUND

Immunosenescence is the aging of the immune system, which is closely related to the development and prognosis of lung cancer. Targeting immunosenescence is considered a promising therapeutic approach.

METHODS

We defined an immunosenescence gene set (ISGS) and examined it across 33 TCGA tumor types and 29 GTEx normal tissues. We explored the 46,993 single cells of two lung cancer datasets. The immunosenescence risk model (ISRM) was constructed in TCGA LUAD by network analysis, immune infiltration analysis, and lasso regression and validated by survival analysis, cox regression, and nomogram in four lung cancer cohorts. The predictive ability of ISRM for drug response and immunotherapy was detected by the oncopredict algorithm and XGBoost model.

RESULTS

We found that senescent lung tissues were significantly enriched in ISGS and revealed the heterogeneity of immunosenescence in pan-cancer. Single-cell and bulk transcriptomics characterized the distinct immune microenvironment between old and young lung cancer. The ISGS network revealed the crucial function modules and transcription factors. Multiplatform analysis revealed specific associations between immunosenescence and the tumor progression of lung cancer. The ISRM consisted of five risk genes (CD40LG, IL7, CX3CR1, TLR3, and TLR2), which improved the prognostic stratification of lung cancer across multiple datasets. The ISRM showed robustness in immunotherapy and anti-tumor therapy. We found that lung cancer patients with a high-risk score showed worse survival and lower expression of immune checkpoints, which were resistant to immunotherapy.

CONCLUSIONS

Our study performed a comprehensive framework for assessing immunosenescence levels and provided insights into the role of immunosenescence in cancer prognosis and biomarker discovery.

Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification

MicroRNAs (miRNAs) are pivotal biomarkers for cancer screening. Identifying distinctive expression patterns of miRNAs in specific cancer types can serve as an effective strategy for classification and characterization. However, the development of a minimal signature of miRNAs for accurate cancer classification remains challenging, hindered by the lack of integrated approaches that systematically analyse miRNA expression levels of miRNAs alongside their associated biological pathways. In this study, we present a comprehensive integrative approach that utilizes transcriptomic data from lung, breast, and melanoma cancer cell lines to identify specific expression patterns. By combining bioinformatics, dimensionality reduction techniques, machine learning, and experimental validation, we pinpoint miRNAs linked to critical biological pathways. Our results demonstrate a highly significant differentiation of cancer types, achieving 100 % classification accuracy with minimal training time using a streamlined miRNA signature. Validation of the miRNA profile confirms that each of the three identified miRNAs regulates distinct biological pathways with minimal overlap. This specificity highlights their unique roles in tumour biology and set the stage for further exploration of miRNAs interactions and their contributions to tumourigenesis across diverse cancer types. Our work paves the way for multi-cancer classification, emphasizing the transformative potential of miRNA research in oncology. Beyond advancing the understanding of tumour biology, our step-by-step guide offers a robust tool for a wide range of users to investigate precise diagnostics and promising therapeutic strategies.

Pinpointing the integration of artificial intelligence in liver cancer immune microenvironment

Liver cancer remains one of the most formidable challenges in modern medicine, characterized by its high incidence and mortality rate. Emerging evidence underscores the critical roles of the immune microenvironment in tumor initiation, development, prognosis, and therapeutic responsiveness. However, the composition of the immune microenvironment of liver cancer (LC-IME) and its association with clinicopathological significance remain unelucidated. In this review, we present the recent developments related to the use of artificial intelligence (AI) for studying the immune microenvironment of liver cancer, focusing on the deciphering of complex high-throughput data. Additionally, we discussed the current challenges of data harmonization and algorithm interpretability for studying LC-IME.

Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity

BACKGROUND

Cytopathological examination serves as a tool for diagnosing solid tumors and hematologic malignancies. Artificial intelligence (AI)-assisted methods have been widely discussed in the literature for increasing sensitivity, specificity and accuracy in the diagnosis of cytopathological clinical samples. Many of these tools are also used in clinical practice. There is a growing body of literature describing the role of AI in clinical settings, particularly in improving diagnostic accuracy and providing predictive and prognostic insights.

METHODS

A comprehensive search for this systematic review was conducted using databases Google, PUBMED (n = 450) and Google Scholar (n = 1067) with the keywords "Artificial Intelligence" AND "cytopathological" and "fine needle aspiration" AND "Deep Learning" AND "Machine Learning" AND "Hematologic Disorders" AND "Lung Cancer" AND "Pap Smear" and "cervical cancer screening" AND "Thyroid Cancer" AND "Breast Cancer" and "Sensitivity" and "Specificity". The search focused on literature reviews and systematic reviews published in English language between 2020 and 2024. PRISMA guidelines were adhered to with studies included and excluded as depicted in a flowchart. 417 results were screened with 34 studies were chosen for this review.

RESULTS

In the screening of patients with cervical cancer, bone marrow and peripheral blood smears and benign and malignant lesions in the lung, AI-assisted methods, particularly machine learning and deep learning (a subset of machine learning) methods, were applied to cytopathological data. These methods yielded greater diagnostic accuracy, specificity and sensitivity and decreased interobserver variability. Data sets were collected for both training and validation. Human machine combined performance was also found to be comparable to standalone performance in comparison with medical performance as well.

CONCLUSIONS

The use of AI in the analysis of cytopathological samples in research and clinical settings is increasing, and the involvement of pathologists in AI workflows is becoming increasingly important.

Prediction of iodine-125 seed implantation efficacy in lung cancer using an enhanced CT-based nomogram model

BACKGROUND

Lung cancer, a leading cause of death, sees variable outcomes with iodine-125 seed implantation. Predictive tools are lacking, complicating clinical decisions. This study integrates radiomics and clinical features to develop a predictive model, advancing personalized treatment.

OBJECTIVE

To construct a nomogram model combining enhanced CT image features and general clinical characteristics to evaluate the efficacy of radioactive iodine-125 seed implantation in lung cancer treatment.

METHODS

Patients who underwent lung iodine-125 seed implantation at the Nuclear Medicine Department of Xiling Campus, Yichang Central People's Hospital from January 1, 2018, to January 31, 2024, were randomly divided into a training set (73 cases) and a test set (31 cases). Radiomic features were extracted from the enhanced CT images, and optimal clinical factors were analyzed to construct clinical, radiomics, and combined models. The best model was selected and validated for its role in assessing the efficacy of iodine-125 seed implantation in lung cancer patients.

RESULTS

Three clinical features and five significant radiomic features were successfully selected, and a combined nomogram model was constructed to evaluate the efficacy of iodine-125 seed implantation in lung cancer patients. The AUC values of the model in the training and test sets were 0.95 (95% CI: 0.91-0.99) and 0.83 (95% CI: 0.69-0.98), respectively. The calibration curve demonstrated good agreement between predicted and observed values, and the decision curve indicated that the combined model outperformed the clinical or radiomics model across the majority of threshold ranges.

CONCLUSION

A combined nomogram model was successfully developed to assess the efficacy of iodine-125 seed implantation in lung cancer patients, demonstrating good clinical predictive performance and high clinical value.

Prognostic Value of miR-10a-3p in Non-Small Cell Lung Cancer Patients

Purpose

Poor lung cancer patients' outcomes and survival rates demand the discovery of new biomarkers for the specific, significant, and less invasive detection of non-small cell lung cancer (NSCLC) progression. The present study aimed to investigate the potential of miRNA expression as biomarkers in NSCLC utilizing a preclinical cell culture setup based on screening of miRNAs in NSCLC cells grown in 3D cell culture.

Patients and Methods

The study was performed using lung cancer cell lines, varying in different levels of aggressiveness: NCI-H1299, A549, Calu-1, and NCI-H23, as well as noncancerous bronchial epithelial cell line HBEC3, which were grown in 3D cell culture. Total RNA from all cell lines was extracted and small RNA libraries were prepared and sequenced using the Illumina NGS platform. The expression of 8 differentially expressed miRNAs was further validated in 89 paired tissue specimens and plasma samples obtained from NSCLC patients. Statistical analysis was performed to determine whether miRNA expression and clinicopathological characteristics of NSCLC patients could be considered as independent factors significantly influencing PFS or OS.

Results

Differentially expressed miRNAs, including let-7d-3p, miR-10a-3p, miR-28-3p, miR-28-5p, miR-100-3p, miR-182-5p, miR-190a-5p, and miR-340-5p, were identified through next-generation sequencing in NSCLC cell lines with varying levels of aggressiveness. Validation of patient samples, including tumor and plasma specimens, revealed that out of the 8 investigated miRNAs, only plasma miR-10a-3p showed a significant increase, which was associated with significantly extended progression-free survival (PFS) (p=0.009). Furthermore, miR-10a-3p in plasma emerged as a statistically significant prognostic variable for NSCLC patients' PFS (HR: 0.5, 95% CI: 0.3-0.9, p=0.029).

Conclusion

Our findings of screening miRNA expression patterns in NSCLC cells grown in 3D cell culture indicated that the expression level of circulating miR-10a-3p has the potential as a novel non-invasive biomarker to reflect the short-term prognosis of NSCLC patients.

A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images

Background/Objectives: Despite recent advances in research, cancer remains a significant public health concern and a leading cause of death. Among all cancer types, lung cancer is the most common cause of cancer-related deaths, with most cases linked to non-small cell lung cancer (NSCLC). Accurate classification of NSCLC subtypes is essential for developing treatment strategies. Medical professionals regard tissue biopsy as the gold standard for the identification of lung cancer subtypes. However, since biopsy images have very high resolutions, manual examination is time-consuming and depends on the pathologist's expertise. Methods: In this study, we propose a hybrid model to assist pathologists in the classification of NSCLC subtypes from histopathological images. This model processes deep, textural and contextual features obtained by using EfficientNet-B0, local binary pattern (LBP) and vision transformer (ViT) encoder as feature extractors, respectively. In the proposed method, each feature matrix is flattened separately and then combined to form a comprehensive feature vector. The feature vector is given as input to machine learning classifiers to identify the NSCLC subtype. Results: We set up 13 different training scenarios to test 4 different classifiers: support vector machine (SVM), logistic regression (LR), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost). Among these scenarios, we obtained the highest classification accuracy (99.87%) with the combination of EfficientNet-B0 + LBP + ViT Encoder + SVM. The proposed hybrid model significantly enhanced the classification accuracy of NSCLC subtypes. Conclusions: The integration of deep, textural, and contextual features assisted the model in capturing subtle information from the images, thereby reducing the risk of misdiagnosis and facilitating more effective treatment planning.

Lightweight Advanced Deep Neural Network (DNN) Model for Early-Stage Lung Cancer Detection

Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an early stage), but these still have various issues, such as low accuracy, high noise, low contrast, poor recognition rates, and a high false-positive rate, etc. Thus, in this research effort, we have proposed an advanced algorithm and combined two different types of deep neural networks to make it easier to spot lung melanoma in the early phases. Methods: We have used WDSI (weakly supervised dense instance-level lung segmentation) for laborious pixel-level annotations. In addition, we suggested an SS-CL (deep continuous learning-based deep neural network) that can be applied to the labeled and unlabeled data to improve efficiency. This work intends to evaluate potential lightweight, low-memory deep neural net (DNN) designs for image processing. Results: Our experimental results show that, by combining WDSI and LSO segmentation, we can achieve super-sensitive, specific, and accurate early detection of lung cancer. For experiments, we used the lung nodule (LUNA16) dataset, which consists of the patients' 3D CT scan images. We confirmed that our proposed model is lightweight because it uses less memory. We have compared them with state-of-the-art models named PSNR and SSIM. The efficiency is 32.8% and 0.97, respectively. The proposed lightweight deep neural network (DNN) model archives a high accuracy of 98.2% and also removes noise more effectively. Conclusions: Our proposed approach has a lot of potential to help medical image analysis to help improve the accuracy of test results, and it may also prove helpful in saving patients' lives.

Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment

BACKGROUND

Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient's unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options.

METHODOLOGY

We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models' recent innovations and implementations to aid in molecular diagnosis and treatment planning.

RESULTS

The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target's likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs.

CONCLUSIONS

Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient's demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.

Cepharanthine triggers ferroptosis through inhibition of NRF2 for robust ER stress against lung cancer

BACKGROUND

Severe endoplasmic reticulum (ER) stress elicits apoptosis to suppress lung cancer. Our previous research identified that Cepharanthine (CEP), a kind of phytomedicine, possessed powerful anti-cancer efficacy, for which the underlying mechanism was still uncovered. Herein, we investigated how CEP induced ER stress and worked against lung cancer.

METHODS

The differential expression genes (DEGs) and enrichment were detected by RNA-sequence. The affinity of CEP and NRF2 was analyzed by cellular thermal shift assay (CETSA) and molecular docking. The function assay of lung cancer cells was measured by western blots, flow cytometry, immunofluorescence staining, and ferroptosis inhibitors.

RESULTS

CEP treatment enriched DEGs in ferroptosis and ER stress. Further analysis demonstrated the target was NRF2. In vitro and in vivo experiments showed that CEP induced obvious ferroptosis, as characterized by the elevated iron ions, ROS, COX-2 expression, down-regulation of GPX4, and atrophic mitochondria. Moreover, enhanced Grp78, CHOP expression, β-amyloid mass, and disappearing parallel stacked structures of ER were observed in CEP group, suggesting ER stress was aroused. CEP exhibited excellent anti-lung cancer efficacy, as evidenced by the increased apoptosis, reduced proliferation, diminished cell stemness, and prominent inhibition of tumor grafts in animal models. Furthermore, the addition of ferroptosis inhibitors weakened CEP-induced ER stress and apoptosis.

CONCLUSION

In summary, our findings proved CEP drives ferroptosis through inhibition of NRF2 for induction of robust ER stress, thereby leading to apoptosis and attenuated stemness of lung cancer cells. The current work presents a novel mechanism for the anti-tumor efficacy of the natural compound CEP.

Targeted degradation of KRAS protein in non-small cell lung cancer: Therapeutic strategies using liposomal PROTACs with enhanced cellular uptake and pharmacokinetic profiles

The role of KRAS mutation in non-small cell lung cancer (NSCLC) initiation and progression is well-established. However, "undruggable" KRAS protein poses the research of small molecule inhibitors a significant challenge. Addressing this, proteolysis-targeting chimeras (PROTACs) have become a cutting-edge treatment method, emphasizing protein degradation. A modified ethanol injection method was employed in this study to formulate liposomes encapsulating PROTAC drug LC-2 (LC-2 LPs). Precise surface modifications using cell-penetrating peptide R8 yielded R8-LC-2 liposomes (R8-LC-2 LPs). Comprehensive cellular uptake and cytotoxicity studies unveiled that R8-LC-2 LPs depended on concentration and time, showcasing the superior performance of R8-LC-2 LPs compared to normal liposomes. In vivo pharmacokinetic profiles demonstrated the capacity of DSPE-PEG2000 to prolong the circulation time of LC-2, leading to higher plasma concentrations compared to free LC-2. In vivo antitumor efficacy research underscored the remarkable ability of R8-LC-2 LPs to effectively suppress tumor growth. This study contributed to the exploration of enhanced therapeutic strategies for NSCLC, specifically focusing on the development of liposomal PROTACs targeting the "undruggable" KRAS protein. The findings provide valuable insights into the potential of this innovative approach, offering prospects for improved drug delivery and heightened antitumor efficacy.

An Artificial Intelligence (AI)-Integrated Approach to Enhance Early Detection and Personalized Treatment Strategies in Lung Cancer Among Smokers: A Literature Review

Lung cancer (LC) is a significant global health issue, particularly among smokers, and is characterized by high rates of incidence and mortality. This comprehensive review offers detailed insights into the potential of artificial intelligence (AI) to revolutionize early detection and personalized treatment strategies for LC. By critically evaluating the limitations of conventional methodologies, we emphasize the innovative potential of AI-driven risk prediction models and imaging analyses to enhance diagnostic precision and improve patient outcomes. Our in-depth analysis of the current state of AI integration in LC care highlights the achievements and challenges encountered in real-world applications, thereby shedding light on practical implementation. Furthermore, we examined the profound implications of AI on treatment response, survival rates, and patient satisfaction, addressing ethical considerations to ensure responsible deployment. In the future, we will outline emerging technologies, anticipate potential barriers to their adoption, and identify areas for further research, emphasizing the importance of collaborative efforts to fully harness the transformative potential of AI in reshaping LC therapy. Ultimately, this review underscores the transformative impact of AI on LC care and advocates for a collective commitment to innovation, collaboration, and ethical stewardship in healthcare.

The Advent of Artificial Intelligence into Cardiac Surgery: A Systematic Review of Our Understanding

When faced with questions about artificial intelligence (AI), many surgeons respond with scepticism and rejection. However, in the realm of cardiac surgery, it is imperative that we embrace the potential of AI and adopt a proactive mindset. This systematic review utilizes PubMed® to explore the intersection of AI and cardiac surgery since 2017. AI has found applications in various aspects of cardiac surgery, including teaching aids, diagnostics, predictive outcomes, surgical assistance, and expertise. Nevertheless, challenges such as data computation errors, vulnerabilities to malware, and privacy concerns persist. While AI has limitations, its restricted capabilities without cognitive and emotional intelligence should lead us to cautiously and partially embrace this advancing technology to enhance patient care.

Machine Learning-Assisted Decision Making in Orthopaedic Oncology

» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.

A Systematic Review of the Application of Computational Technology in Microtia

Microtia is a congenital and morphological anomaly of one or both ears, which results from a confluence of genetic and external environmental factors. Up to now, extensive research has explored the potential utilization of computational methodologies in microtia and has obtained promising results. Thus, the authors reviewed the achievements and shortcomings of the research mentioned previously, from the aspects of artificial intelligence, computer-aided design and surgery, computed tomography, medical and biological data mining, and reality-related technology, including virtual reality and augmented reality. Hoping to offer novel concepts and inspire further studies within this field.

Worldwide research landscape of artificial intelligence in lung disease: A scientometric study

Purpose

To perform a comprehensive bibliometric analysis of the application of artificial intelligence (AI) in lung disease to understand the current status and emerging trends of this field.

Materials and methods

AI-based lung disease research publications were selected from the Web of Science Core Collection. Citespace, VOS viewer and Excel were used to analyze and visualize co-authorship, co-citation, and co-occurrence analysis of authors, keywords, countries/regions, references and institutions in this field.

Results

Our study included a total of 5210 papers. The number of publications on AI in lung disease showed explosive growth since 2017. China and the United States lead in publication numbers. The most productive author were Li, Weimin and Qian Wei, with Shanghai Jiaotong University as the most productive institution. Radiology was the most co-cited journal. Lung cancer and COVID-19 emerged as the most studied diseases. Deep learning, convolutional neural network, lung cancer, radiomics will be the focus of future research.

Conclusions

AI-based diagnosis and treatment of lung disease has become a research hotspot in recent years, yielding significant results. Future work should focus on establishing multimodal AI models that incorporate clinical, imaging and laboratory information. Enhanced visualization of deep learning, AI-driven differential diagnosis model for lung disease and the creation of international large-scale lung disease databases should also be considered.

Artificial intelligence facial recognition system for diagnosis of endocrine and metabolic syndromes based on a facial image database

AIM

To build a facial image database and to explore the diagnostic efficacy and influencing factors of the artificial intelligence-based facial recognition (AI-FR) system for multiple endocrine and metabolic syndromes.

METHODS

Individuals with multiple endocrine and metabolic syndromes and healthy controls were included from public literature and databases. In this facial image database, facial images and clinical data were collected for each participant and dFRI (disease facial recognition intensity) was calculated to quantify facial complexity of each syndrome. AI-FR diagnosis models were trained for each disease using three algorithms: support vector machine (SVM), principal component analysis k-nearest neighbor (PCA-KNN), and adaptive boosting (AdaBoost). Diagnostic performance was evaluated. Optimal efficacy was achieved as the best index among the three models. Effect factors of AI-FR diagnosis were explored with regression analysis.

RESULTS

462 cases of 10 endocrine and metabolic syndromes and 2310 controls were included into the facial image database. The AI-FR diagnostic models showed diagnostic accuracies of 0.827-0.920 with SVM, 0.766-0.890 with PCA-KNN, and 0.818-0.935 with AdaBoost. Higher dFRI was associated with higher optimal area under the curve (AUC) (P = 0.035). No significant correlation was observed between the sample size of the training set and diagnostic performance.

CONCLUSIONS

A multi-ethnic, multi-regional, and multi-disease facial database for 10 endocrine and metabolic syndromes was built. AI-FR models displayed ideal diagnostic performance. dFRI proved associated with the diagnostic performance, suggesting inherent facial features might contribute to the performance of AI-FR models.

Predictors of disease recurrence after radical resection and adjuvant chemotherapy in patients with stage IIb-IIIa squamous cell lung cancer: A retrospective analysis

BACKGROUND

Lung cancer (LC) is a global medical, social and economic problem and is one of the most common cancers and the leading cause of mortality from malignant neoplasms. LC is characterized by an aggressive course, and in the presence of disease recurrence risk factors, patients, even at an early stage, may be indicated for adjuvant therapy to improve survival. However, combined treatment does not always guarantee a favorable prognosis. In this regard, establishing predictors of LC recurrence is highly important both for determining the optimal treatment plan for the patients and for evaluating its effectiveness.

AIM

To establish predictors of disease recurrence after radical resection and adjuvant chemotherapy in patients with stage IIb-IIIa lung squamous cell carcinoma (LSCC).

METHODS

A retrospective case-control cohort study included 69 patients with LSCC who underwent radical surgery at the Orenburg Regional Clinical Oncology Center from 2009 to 2018. Postoperatively, all patients received adjuvant chemotherapy. Histological samples of the resected lung were stained with Mayer's hematoxylin and eosin and examined under a light microscope. Univariate and multivariate analyses were used to identify predictors associated with the risk of disease recurrence. Receiver operating characteristic curves were constructed to discriminate between patients with a high risk of disease recurrence and those with a low risk of disease recurrence. Survival was analyzed using the Kaplan-Meier method. The log-rank test was used to compare survival curves between patient subgroups. Differences were considered to be significant at P < 0.05.

RESULTS

The following predictors of a high risk of disease recurrence in patients with stage IIb-IIa LSCC were established: a low degree of tumor differentiation [odds ratio (OR) = 7.94, 95%CI = 1.08-135.81, P = 0.049]; metastases in regional lymph nodes (OR = 5.67, 95%CI = 1.09-36.54, P = 0.048); the presence of loose, fine-fiber connective tissue in the tumor stroma (OR = 21.70, 95%CI = 4.27-110.38, P = 0.0002); and fragmentation of the tumor solid component (OR = 2.53, 95%CI = 1.01-12.23, P = 0.049). The area under the curve of the predictive model was 0.846 (95%CI = 0.73-0.96, P < 0.0001). The sensitivity, accuracy and specificity of the method were 91.8%, 86.9% and 75.0%, respectively. In the group of patients with a low risk of LSCC recurrence, the 1-, 2- and 5-year disease-free survival (DFS) rates were 84.2%, 84.2% and 75.8%, respectively, while in the group with a high risk of LSCC recurrence the DFS rates were 71.7%, 40.1% and 8.2%, respectively (P < 0.00001). Accordingly, in the first group of patients, the 1-, 2- and 5-year overall survival (OS) rates were 94.7%, 82.5% and 82.5%, respectively, while in the second group of patients, the OS rates were 89.8%, 80.1% and 10.3%, respectively (P < 0.00001).

CONCLUSION

The developed method allows us to identify a group of patients at high risk of disease recurrence and to adjust to ongoing treatment.

Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques

Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.

The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review

OBJECTIVES

To present a comprehensive review of the current state of artificial intelligence (AI) applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases.

METHODS

A review of the literature was conducted using PubMed, EMBASE and Cochrane, including relevant studies between 2002 and 2023 to identify the latest research on artificial intelligence and lung cancer.

CONCLUSION

While AI holds promise in managing lung cancer, challenges exist. In the preoperative phase, AI can improve diagnostics and predict biomarkers, particularly in cases with limited biopsy materials. During surgery, AI provides real-time guidance. Postoperatively, AI assists in pathology assessment and predictive modeling. Challenges include interpretability issues, training limitations affecting model use and AI's ineffectiveness beyond classification. Overfitting and global generalization, along with high computational costs and ethical frameworks, pose hurdles. Addressing these challenges requires a careful approach, considering ethical, technical, and regulatory factors. Rigorous analysis, external validation, and a robust regulatory framework are crucial for responsible AI implementation in lung surgery, reflecting the evolving synergy between human expertise and technology.

Bibliometric analysis and research trends of artificial intelligence in lung cancer

Background

Due to the rapid advancement of technology, artificial intelligence (AI) has become extensively used for the diagnosis and prognosis of various diseases, such as lung cancer. Research in the field of literature has demonstrated that artificial intelligence (AI) can be valuable in the timely detection of lung cancer and the formulation of an effective treatment plan. This study aims to conduct a bibliometric analysis to examine and illustrate the specific areas of focus, research frontiers, evolutionary processes, and trends in existing research on artificial intelligence in the context of lung cancer.

Methods

Publications on AI in lung cancer were selected from the SCIE and ESCI indexes on September 19, 2023. The examination of nations, academic publications, organizations, writers, citations, and terms in this domain was visually analyzed with InCites and VOSviewer.

Results

In this study, a total of 4275 publications were selected and analyzed. Artificial intelligence-related lung cancer publications have increased significantly in the last 5 years. China and the USA have contributed the most to the literature in this field (1418 publications with 13.92 citation impacts and 1117 publications with 37.34 citation impacts, respectively). The institution with the highest contribution was "Chinese Academy of Sciences," with 118 publications and 29.09 citation impacts. Among the research categories, "Radiology, Nuclear Medicine & Imaging", "Oncology", and "Engineering, Biomedical" were in first place.

Conclusion

The USA and China have always been leaders in this field and will continue to be for some time. Research in countries such as the Netherlands is increasing. However, research collaboration has to be strengthened in developing countries.

A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders

This systematic review examines the transformative impact of artificial intelligence (AI) in managing lung disorders through a comprehensive analysis of articles spanning 2014 to 2023. Evaluating AI's multifaceted roles in radiological imaging, disease burden prediction, detection, diagnosis, and molecular mechanisms, this review presents a critical synthesis of key insights from select articles. The findings underscore AI's significant strides in bolstering diagnostic accuracy, interpreting radiological imaging, predicting disease burdens, and deepening the understanding of tuberculosis (TB), chronic obstructive pulmonary disease (COPD), silicosis, pneumoconiosis, and lung fibrosis. The synthesis positions AI as a revolutionary tool within the healthcare system, offering vital implications for healthcare workers, policymakers, and researchers in comprehending and leveraging AI's pivotal role in lung disease management.

Opportunities and challenges of 5G network technology toward precision medicine

Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.

Application of InceptionV3, SqueezeNet, and VGG16 Convoluted Neural Networks in the Image Classification of Oral Squamous Cell Carcinoma: A Cross-Sectional Study

Background Artificial intelligence (AI) is a rapidly emerging field in medicine and has applications in diagnostics, therapeutics, and prognostication in various malignancies. The present study was conducted to analyze and compare the accuracy of three deep learning neural networks for oral squamous cell carcinoma (OSCC) images. Materials and methods Three hundred and twenty-five cases of OSCC were included and graded histologically by two grading systems. The images were then analyzed using the Orange data mining tool. Three neural networks, viz., InceptionV3, SqueezeNet, and VGG16, were used for further analysis and classification. Positive predictive value, negative predictive value, specificity, sensitivity, area under curve (AUC), and accuracy were estimated for each neural network. Results Histological grading by Bryne's yielded significantly stronger inter-observer agreement. The highest accuracy was found for the classification of poorly differentiated squamous cell carcinoma images irrespective of the network used. Other values were variegated. Conclusion AI could serve as an adjunct for improvement in theragnostics. Further research is required to achieve the modification of mining tools for greater predictive values, sensitivity, specificity, AUC, accuracy, and security. Bryne's grading system is warranted for the better application of AI in OSCC image analytics.

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare's path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis

BACKGROUND

The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large-scale meta-analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine-learning classifier.

METHODS

In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony-related functions.

RESULTS

The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top-ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota-associated functions between cancer-affected and healthy individuals that were primarily associated with metabolic disturbances.

CONCLUSIONS

GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC-specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.

Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors

Artificial intelligence (AI), also known as machine intelligence, is widely utilized in the medical field, promoting medical advances. Malignant tumors are the critical focus of medical research and improvement of clinical diagnosis and treatment. Mediastinal malignancy is an important tumor that attracts increasing attention today due to the difficulties in treatment. Combined with artificial intelligence, challenges from drug discovery to survival improvement are constantly being overcome. This article reviews the progress of the use of AI in the diagnosis, treatment, and prognostic prospects of mediastinal malignant tumors based on current literature findings.

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.

An improved AdaBoost algorithm for identification of lung cancer based on electronic nose

The research developed an improved intelligent enhancement learning algorithm based on AdaBoost, that can be applied for lung cancer breath detection by the electronic nose (eNose). First, collected the breath signals from volunteers by eNose, including healthy individuals and people who had lung cancer. Additionally, the signals' features were extracted and optimized. Then, multi sub-classifiers were obtained, and their coefficients were derived from the training error. To improve generalization performance, K-fold cross-validation was used when constructing each sub-classifier. The prediction results of a sub-classifier on the test set were then achieved by the voting method. Thus, an improved AdaBoost classifier would be built through heterogeneous integration. The results shows that the average precision of the improved algorithm classifier for distinguishing between people with lung cancer and healthy individuals could reach 98.47%, with 98.33% sensitivity and 97% specificity. And in 100 independent and randomized tests, the coefficient of variation of the classifier's performance hardly exceeded 4%. Compared with other integrated algorithms, the generalization and stability of the improved algorithm classifier are more superior. It is clear that the improved AdaBoost algorithm may help screen out lung cancer more comprehensively. Additionally, it will significantly advance the use of eNose in the early identification of lung cancer.

Biomarkers in the management of lung cancer: changing the practice of thoracic oncology

Lung cancer currently represents a leading cause of cancer death. Substantial progress achieved in the medical therapy of lung cancer during the last decade has been associated with the advent of targeted therapy, including immunotherapy. The targeted therapy has gradually shifted from drugs suppressing general mechanisms of tumor growth and progression to agents aiming at transforming mechanisms like driver mutations in a particular tumor. Knowledge of the molecular characteristics of a tumor has become an essential component of the more targeted therapeutic approach. There are specific challenges for biomarker determination in lung cancer, in particular a commonly limited size of tumor sample. Liquid biopsy is therefore of particular importance in the management of lung cancer. Laboratory medicine is an indispensable part of multidisciplinary management of lung cancer. Clinical Chemistry and Laboratory Medicine (CCLM) has played and will continue playing a major role in updating and spreading the knowledge in the field.