Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective

Huang, Shigao; Yang, Jie; Shen, Na; Xu, Qingsong; Zhao, Qi

doi:10.1016/j.semcancer.2023.01.006

Cited by 77 publications

(47 citation statements)

References 75 publications

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“…Deep learning methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been widely used in medical imaging applications, due to their ability to detect and classify patterns in data more accurately and efficiently than traditional methods [ 4 , 5 ]. This is especially important for medical images, where time is of essence [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

Gugulothu

Balaji

2023

Multimed Tools Appl

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Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. Using hybrid deep learning techniques to detect nodules will improve the sensitivity of lung cancer screening and the interpretation speed of lung scans. Accurate detection of lung nodes is an important step in computed tomography (CT) imaging to detect lung cancer. However, it is very difficult to identify strong nodes due to the diversity of lung nodes and the complexity of the surrounding environment. Here, we proposed lung nodule detection and classification with CT images based on hybrid deep learning (LNDC-HDL) techniques. First, we introduce a chaotic bird swarm optimization (CBSO) algorithm for lung nodule segmentation using statistical information. Second, we illustrate an improved Fish Bee (IFB) algorithm for feature extraction and selection. Third, we develop a hybrid classifier i.e. hybrid differential evolution-based neural network (HDE-NN) for tumor prediction and classification. Experimental results have shown that the use of computed tomography, which demonstrates the efficiency and importance of the HDE-NN specific structure for detecting lung nodes on CT scans, increases sensitivity and reduces the number of false positives. The proposed method shows that the benefits of HDE-NN node detection can be reaped by combining clinical practice.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

Gugulothu

Balaji

2023

Multimed Tools Appl

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“…At present, there are a few studies that combined deep learning models with reinforcement learning algorithms to improve and optimize algorithms, using 3D reconstructed images to accurately locate, segment, and classify tumors [ 72 ]. Perhaps in the future, AI-based radiomics can not only use images of diagnosed lung cancer patients for curative effect evaluation and survival prediction but also analyze image abnormalities in physical examination populations, thus making a better function in lung cancer’s early screening and early diagnosis [ 73 ].…”

Section: Discussion and Prospectmentioning

confidence: 99%

The artificial intelligence and machine learning in lung cancer immunotherapy

Gao

Yang

et al. 2023

J Hematol Oncol

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Since the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy. It is imperative to accurately and intelligently select appropriate patients for immunotherapy or predict the immunotherapy efficacy. In recent years, machine learning (ML)-based artificial intelligence (AI) was developed in the area of medical-industrial convergence. AI can help model and predict medical information. A growing number of studies have combined radiology, pathology, genomics, proteomics data in order to predict the expression levels of programmed death-ligand 1 (PD-L1), tumor mutation burden (TMB) and tumor microenvironment (TME) in cancer patients or predict the likelihood of immunotherapy benefits and side effects. Finally, with the advancement of AI and ML, it is believed that "digital biopsy" can replace the traditional single assessment method to benefit more cancer patients and help clinical decision-making in the future. In this review, the applications of AI in PD-L1/TMB prediction, TME prediction and lung cancer immunotherapy are discussed.

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“…In the future, it would still be necessary to study the optimal decision algorithms for selecting the best compounds and provide personalized drug therapy to LC patients 78 . In addition to traditional ML algorithms, deep learning can help build deep networks continuously and learn and approximate real models 79 . This particular ML technology would help us achieve powerful learning and diagnostic capabilities in the future.…”

Section: Discussionmentioning

confidence: 99%

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

Han,

Wang,

Han

et al. 2023

Cancer Medicine

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BackgroundThe 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.MethodsIn 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.ResultsThe 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.ConclusionsGM 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.

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Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective

Cited by 77 publications

References 75 publications

RETRACTED ARTICLE: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

RETRACTED ARTICLE: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

The artificial intelligence and machine learning in lung cancer immunotherapy

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

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