The future of radiomics in lung cancer

Thawani, Rajat; Mustafa, S.

doi:10.1016/s2589-7500(20)30022-4

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Diagnostics 2022, 12, 1064 2 of 15 in oncology assessment and diagnosis as well as in survival outcome and tumor response assessment [15]. In the field of early lung cancer diagnosis research, several studies have demonstrated that radiomics has great clinical impacts in terms of classifying benign or malignant pulmonary nodules, histopathologic lung cancer phenotypes, and invasiveness in lung adenocarcinoma spectrum lesions based on quantitative CT images [16,17]. In this paper, we aim to describe current radiomics applications for early lung cancer diagnosis research and their future clinical applications and potential limitations.…”

Section: Introductionmentioning

confidence: 99%

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Yang

et al. 2022

Diagnostics

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Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making.

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Section: Introductionmentioning

confidence: 99%

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Yang

et al. 2022

Diagnostics

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“…In this setting, specific initiatives (such as the Image Biomarker Standardization Initiative, IBSI) have been undertaken to promote the adoption of a standardized approach for the definition, nomenclature, and calculation of radiomics features, resulting in improved statistical reliability as long as calculation settings are harmonized too. A greater standardization of radiomics methods and their validation on larger patient samples obtained from multicenter studies (possibly based on standardized image acquisition protocols), as well as a better integration of radiomics software in real world clinical and radiological environments [ 121 , 122 , 123 , 124 , 125 , 126 , 127 ], could be key to increase the role of radiomics in diagnosis, treatment planning and individual outcome prediction in patients with LM.…”

Section: Discussionmentioning

confidence: 99%

Radiomics in Lung Metastases: A Systematic Review

Gabelloni

Faggioni

Fusco

et al. 2023

JPM

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Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature.

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“…The potential role of radiomics and radiogenomics Radiomics are defined as the extraction of quantitative and qualitative data from radiological images to create databases than can help diagnose and predict prognosis and the response to treatment of different malignancies, including non-small cell lung cancer (NSCLC) (24,25). Radiomics have been applied to lung cancer screening in order to increase its sensitivity and specificity (22,26). Several radiomic-based predictive models have been already proposed for CT-based early detection of lung cancer.…”

Section: Radiological Challengesmentioning

confidence: 99%

Molecular biomarkers in early stage lung cancer

Rodríguez¹,

Ajona²,

Seijo³

et al. 2021

Transl Lung Cancer Res

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Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival.Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs.Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians' capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease's mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

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The future of radiomics in lung cancer

Abstract: CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction.

Cited by 7 publications

References 4 publications

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Radiomics in Lung Metastases: A Systematic Review

Molecular biomarkers in early stage lung cancer

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