Integration of artificial intelligence in lung cancer: Rise of the machine

Liquid biopsy in cancer management: Integrating diagnostics and clinical applications

Liquid biopsy is an innovative, minimally invasive diagnostic tool revolutionizing cancer management by enabling the detection and analysis of cancer-related biomarkers from bodily fluids such as blood, urine, or cerebrospinal fluid. Unlike traditional tissue biopsies, which require invasive procedures, liquid biopsy offers a more accessible and repeatable method for tracking cancer progression, detecting early-stage cancers, and monitoring therapeutic responses. The technology primarily focuses on analyzing circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and other cancer-derived genetic materials. These biomarkers provide critical information on tumor heterogeneity, mutation profiles, and potential drug resistance. In clinical practice, liquid biopsy has demonstrated its utility in identifying actionable mutations, guiding personalized treatment strategies, and assessing minimal residual disease (MRD). While liquid biopsy holds immense promise, challenges related to its sensitivity, specificity, and standardization remain. Efforts to optimize pre-analytical and analytical processes, along with the establishment of robust regulatory frameworks, are crucial for its widespread clinical adoption. This abstract highlights the transformative potential of liquid biopsy in cancer diagnosis, prognosis, and treatment monitoring, emphasizing its role in advancing personalized oncology. Further research, clinical trials, and regulatory harmonization will be vital in realizing its full potential in precision cancer care.

RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data

Many diseases are the end outcomes of multifactorial risks that interact and increment over months or years. Timeseries AI methods have attracted increasing interest given their ability to operate on native timeseries data to predict disease outcomes. Instantiating such models in risk stratification tools has proceeded more slowly, in part limited by factors such as structural complexity, model size and explainability. Here, we present RiskPath, an explainable AI toolbox that offers advanced timeseries methods and additional functionality relevant to risk stratification use cases in classic and emerging longitudinal cohorts. Theoretically-informed optimization is integrated in prediction to specify optimal model topology or explore performance-complexity tradeoffs. Accompanying modules allow the user to map the changing importance of predictors over the disease course, visualize the most important antecedent time epochs contributing to disease risk or remove predictors to construct compact models for clinical applications with minimal performance impact.

Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives

Lung cancer is still a leading cause of cancer-related deaths worldwide. Vital to ameliorating patient survival rates are early detection, precise evaluation, and personalized treatments. Recent years have witnessed a profound transformation in the field, marked by intricate diagnostic processes and intricate therapeutic protocols that integrate diverse omics domains, heralding a paradigm shift towards personalized and preventive healthcare. This dynamic landscape has embraced the incorporation of advanced machine learning and deep learning techniques, particularly artificial intelligence (AI), into the realm of precision medicine. These groundbreaking innovations create fertile ground for the development of AI-based models adept at extracting valuable insights to inform clinical decisions, with the potential to quantitatively interpret patient data and impact overall patient outcomes significantly. In this comprehensive narrative review, a synthesis of various studies is presented, with a specific focus on three core areas aimed at providing clinicians with a practical understanding of AI-based technologies' potential applications in the diagnosis and management of non-small cell lung cancer (NSCLC). The emphasis is placed on methods for diagnosing malignancy in lung lesions, approaches to predicting histology and other pathological characteristics, and methods for predicting NSCLC gene mutations. The review culminates in a discussion of current trends and future perspectives within the domain of AI-based models, all directed toward enhancing patient care and outcomes in NSCLC. Furthermore, the review underscores the synthesis of diverse studies, accentuating AI applications in NSCLC diagnosis and management. It concludes with a forward-looking discussion on current trends and future perspectives, highlighting the LANTERN Study as a pioneering force set to elevate patient care and outcomes to unprecedented levels.

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.

AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging

Purpose

Segmentation of ovarian/adnexal masses from surrounding tissue on ultrasound images is a challenging task. The separation of masses into different components may also be important for radiomic feature extraction. Our study aimed to develop an artificial intelligence-based automatic segmentation method for transvaginal ultrasound images that (1) outlines the exterior boundary of adnexal masses and (2) separates internal components.

Approach

A retrospective ultrasound imaging database of adnexal masses was reviewed for exclusion criteria at the patient, mass, and image levels, with one image per mass. The resulting 54 adnexal masses (36 benign/18 malignant) from 53 patients were separated by patient into training (26 benign/12 malignant) and independent test (10 benign/6 malignant) sets. U-net segmentation performance on test images compared to expert detailed outlines was measured using the Dice similarity coefficient (DSC) and the ratio of the Hausdorff distance to the effective diameter of the outline (R HD - D ) for each mass. Subsequently, in discovery mode, a two-level fuzzy c-means (FCM) unsupervised clustering approach was used to separate the pixels within masses belonging to hypoechoic or hyperechoic components.

Results

The DSC (median [95% confidence interval]) was 0.91 [0.78, 0.96], andR HD - D was 0.04 [0.01, 0.12], indicating strong agreement with expert outlines. Clinical review of the internal separation of masses into echogenic components demonstrated a strong association with mass characteristics.

Conclusion

A combined U-net and FCM algorithm for automatic segmentation of adnexal masses and their internal components achieved excellent results compared with expert outlines and review, supporting future radiomic feature-based classification of the masses by components.

Recommendations for reporting tissue and circulating tumour (ct)DNA next-generation sequencing results in non-small cell lung cancer

Non-small cell lung cancer is a heterogeneous disease and molecular characterisation plays an important role in its clinical management. Next-generation sequencing-based panel testing enables many molecular alterations to be interrogated simultaneously, allowing for comprehensive identification of actionable oncogenic drivers (and co-mutations) and appropriate matching of patients with targeted therapies. Despite consensus in international guidelines on the importance of broad molecular profiling, adoption of next-generation sequencing varies globally. One of the barriers to its successful implementation is a lack of accepted standards and guidelines specifically for the reporting and clinical annotation of next-generation sequencing results. Based on roundtable discussions between pathologists and oncologists, we provide best practice recommendations for the reporting of next-generation sequencing results in non-small cell lung cancer to facilitate its use and enable easy interpretation for physicians. These are intended to complement existing guidelines related to the use of next-generation sequencing (solid and liquid). Here, we discuss next-generation sequencing workflows, the structure of next-generation sequencing reports, and our recommendations for best practice thereof. The aim of these recommendations and considerations is ultimately to ensure that reports are fully interpretable, and that the most appropriate treatment options are selected based on robust molecular profiles in well-defined reports.

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images

The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.

How will lung cancer screening and lung nodule management change the diagnostic and surgical lung cancer landscape?

INTRODUCTION

Implementation of lung cancer screening, with its subsequent findings, is anticipated to change the current diagnostic and surgical lung cancer landscape. This review aimed to identify and present the most updated expert opinion and discuss relevant evidence regarding the impact of lung cancer screening and lung nodule management on the diagnostic and surgical landscape of lung cancer, as well as summarise points for clinical practice.

METHODS

This article is based on relevant lectures and talks delivered during the European Society of Thoracic Surgeons-European Respiratory Society Collaborative Course on Thoracic Oncology (February 2023). Original lectures and talks and their relevant references were included. An additional literature search was conducted and peer-reviewed studies in English (December 2022 to June 2023) from the PubMed/Medline databases were evaluated with regards to immediate affinity of the published papers to the original talks presented at the course. An updated literature search was conducted (June 2023 to December 2023) to ensure that updated literature is included within this article.

RESULTS

Lung cancer screening suspicious findings are expected to increase the number of diagnostic investigations required therefore impacting on current capacity and resources. Healthcare systems already face a shortage of imaging and diagnostic slots and they are also challenged by the shortage of interventional radiologists. Thoracic surgery will be impacted by the wider lung cancer screening implementation with increased volume and earlier stages of lung cancer. Nonsuspicious findings reported at lung cancer screening will need attention and subsequent referrals where required to ensure participants are appropriately diagnosed and managed and that they are not lost within healthcare systems.

CONCLUSIONS

Implementation of lung cancer screening requires appropriate mapping of existing resources and infrastructure to ensure a tailored restructuring strategy to ensure that healthcare systems can meet the new needs.

DIEANet: an attention model for histopathological image grading of lung adenocarcinoma based on dimensional information embedding

Efficient and rapid auxiliary diagnosis of different grades of lung adenocarcinoma is conducive to helping doctors accelerate individualized diagnosis and treatment processes, thus improving patient prognosis. Currently, there is often a problem of large intra-class differences and small inter-class differences between pathological images of lung adenocarcinoma tissues under different grades. If attention mechanisms such as Coordinate Attention (CA) are directly used for lung adenocarcinoma grading tasks, it is prone to excessive compression of feature information and overlooking the issue of information dependency within the same dimension. Therefore, we propose a Dimension Information Embedding Attention Network (DIEANet) for the task of lung adenocarcinoma grading. Specifically, we combine different pooling methods to automatically select local regions of key growth patterns such as lung adenocarcinoma cells, enhancing the model's focus on local information. Additionally, we employ an interactive fusion approach to concentrate feature information within the same dimension and across dimensions, thereby improving model performance. Extensive experiments have shown that under the condition of maintaining equal computational expenses, the accuracy of DIEANet with ResNet34 as the backbone reaches 88.19%, with an AUC of 96.61%, MCC of 81.71%, and Kappa of 81.16%. Compared to seven other attention mechanisms, it achieves state-of-the-art objective metrics. Additionally, it aligns more closely with the visual attention of pathology experts under subjective visual assessment.

Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.

Artificial Intelligence and Anticancer Drug Development-Keep a Cool Head

Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology.

A method combining LDA and neural networks for antitumor drug efficacy prediction

Background

Personalized medicine has gained more attention for cancer precision treatment due to patient genetic heterogeneity in recent years. However, predicting the efficacy of antitumor drugs in advance remains a significant challenge to achieve this task.

Objective

This study aims to predict the efficacy of antitumor drugs in individual cancer patients based on clinical data.

Methods

This paper proposes to predict personalized antitumor drug efficacy based on clinical data. Specifically, we encode the clinical text of cancer patients as a probability distribution vector in hidden topics space using the Latent Dirichlet Allocation (LDA) model, named LDA representation. Then, a neural network is designed, and the LDA representation is input into the neural network to predict drug response in cancer patients treated with platinum drugs. To evaluate the effectiveness of the proposed method, we gathered and organized clinical records of lung and bowel cancer patients who underwent platinum-based treatment. The prediction performance is assessed using the following metrics: Precision, Recall, F1-score, Accuracy, and Area Under the ROC Curve (AUC).

Results

The study analyzed a dataset of 958 patients with non-small cell cancer treated with antitumor drugs. The proposed method achieved a stratified 5-fold cross-validation average Precision of 0.81, Recall of 0.89, F1-score of 0.85, Accuracy of 0.77, and AUC of 0.81 for cisplatin efficacy prediction on the data, which most are better than those of previous methods. Of these, the AUC value is at least 4% higher than those of the previous. At the same time, the superior result over the previous method persisted on an independent dataset of 266 bowel cancer patients, showing the generalizability of the proposed method. These results demonstrate the potential value of precise tumor treatment in clinical practice.

Conclusions

Combining LDA and neural networks can help predict the efficacy of antitumor drugs based on clinical text. Our approach outperforms previous methods in predicting drug clinical efficacy.

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.

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.

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

New Frontiers in Oncological Imaging

The more that advances in the medical field are capable of targeted treatments, the more imaging should be tailored to patients [...].