Emerging non‑invasive detection methodologies for lung cancer (Review)

Li, Zhen; Shu, Jinian; Yang, Bo; Zhang, Zuojian; Huang, Jingyun; Yang, Chen

doi:10.3892/ol.2020.11460

Cited by 7 publications

(5 citation statements)

References 123 publications

(113 reference statements)

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“…Although traditional diagnostic methods used to identify mutated sequences in tumor cells have contributed to the advancement of targeted therapies, they are highly invasive and time intensive [9]. Therefore, new noninvasive screening methods should be urgently developed to overcome the drawbacks of invasive traditional approaches [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer

Kha

Nguyen

et al. 2021

IJMS

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Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machine learning-based model for feature selection and prediction of EGFR and KRAS mutations in patients with NSCLC by including the least number of the most semantic radiomics features. We included a cohort of 161 patients from 211 patients with NSCLC from The Cancer Imaging Archive (TCIA) and analyzed 161 low-dose computed tomography (LDCT) images for detecting EGFR and KRAS mutations. A total of 851 radiomics features, which were classified into 9 categories, were obtained through manual segmentation and radiomics feature extraction from LDCT. We evaluated our models using a validation set consisting of 18 patients derived from the same TCIA dataset. The results showed that the genetic algorithm plus XGBoost classifier exhibited the most favorable performance, with an accuracy of 0.836 and 0.86 for detecting EGFR and KRAS mutations, respectively. We demonstrated that a noninvasive machine learning-based model including the least number of the most semantic radiomics signatures could robustly predict EGFR and KRAS mutations in patients with NSCLC.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer

Kha

Nguyen

et al. 2021

IJMS

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“…Endogenous VOCs are gaseous organic molecules which are products of different metabolic pathways in the body. Since diseases such as cancer have the ability to alter some of these pathways, it holds true that the associated VOC profile would be affected as well and could serve as a diagnostic biomarker (52). Additionally, biomarkers are being explored specifically for use in precision medicine.…”

Section: Future Directionsmentioning

confidence: 99%

Biomarkers in lung cancer screening: a narrative review

Marmor

Zorn

Deppen

et al. 2023

Curr Chall Thorac Surg

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Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA (ctDNA), and DNA methylation. Finally, we will describe future directions in the field of biomarker development.

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“…A CNN classifier [21]- [23] is used to determine if the patient is normal or abnormal, and this is done using HE. Following that, the survival rate of the patient is projected based on the attributes that were retrieved [24], [25]. with practical methodologies to detect malignant lung growth early and screen the seriousness.…”

Section: Literature Surveymentioning

confidence: 99%

Intelligent deep learning algorithm for lung cancer detection and classification

Reddy

Khanaa

2023

Bulletin EEI

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Lung cancer is one of the leading causes of cancer mortality. The overlapping of cancer cells makes early diagnosis difficult. When lung cancer is found early, many therapy choices are reduced, the danger of invasive surgery is reduced, and the chance of survival increases. The primary goal of this study work is to identify early-stage lung cancer and categories using an intelligent deep learning algorithm. Following a thorough review of the literature, we discovered that certain classifiers are ineffective while others are almost perfect. In general, several different kinds of images are employed, but computer tomography scanned images are preferable due to their reduced noise. Intelligent deep learning algorithm is one such approach that employs convolutional neural network techniques and has been shown to be the most effective way for medical image processing, lung nodule identification, classification, feature extraction, and lung cancer prediction. The characteristics are taken from the segmented images and classified using intelligent deep learning algorithm. The suggested techniques' performances are assessed based on their accuracy, sensitivity, specificity, recall, and precision.

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Emerging non‑invasive detection methodologies for lung cancer (Review)

Cited by 7 publications

References 123 publications

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer

Biomarkers in lung cancer screening: a narrative review

Intelligent deep learning algorithm for lung cancer detection and classification

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