Radiomics-based Management of Indeterminate Lung Nodules? Are We There Yet?

Peikert, Tobias; Bartholmai, Brian J.; Maldonado, Fabien

doi:10.1164/rccm.202004-1279ed

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…In the NLST study, all enrolled patients had pulmonary nodules of 4 mm or larger in diameter, and the false-positive rate was over 96.4% after 3 rounds of LDCT screening (6). This is particularly impactful for nodules between 6 and 20 mm, i.e., indeterminate nodules (pCA 5%-65%), which account for the majority of the nodules identified by LDCT (50%-76%) and for which the risk of malignancy is hard to determine with current clinical risk assessment models (19). Current guidelines suggest further evaluation with PET-CT scan, endobronchial ultrasound-guided transbronchial forceps biopsy (EBUS-TBB), or TTNA.…”

Section: Discussionmentioning

confidence: 99%

Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test

Liang¹,

Chen²,

Li³

et al. 2021

Journal of Clinical Investigation

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BACKGROUND. Current clinical management of patients with pulmonary nodules involves either repeated low-dose CT (LDCT)/CT scans or invasive procedures, yet causes significant patient misclassification. An accurate noninvasive test is needed to identify malignant nodules and reduce unnecessary invasive tests. METHOD.We developed a diagnostic model based on targeted DNA methylation sequencing of 389 pulmonary nodule patients' plasma samples and then validation in 140 plasma samples independently. We tested the model in different stages and subtypes of pulmonary nodules. RESULTS.A 100-feature model was developed and validated for pulmonary nodule diagnosis; the model achieved a receiver operating characteristic curve-AUC (ROC-AUC) of 0.843 on 140 independent validation samples, with an accuracy of 0.800. The performance was well maintained in (a) a 6 to 20 mm size subgroup (n = 100), with a sensitivity of 1.000 and adjusted negative predictive value (NPV) of 1.000 at 10% prevalence; (b) stage I malignancy (n = 90), with a sensitivity of 0.971; (c) different nodule types: solid nodules (n = 78) with a sensitivity of 1.000 and adjusted NPV of 1.000, part-solid nodules (n = 75) with a sensitivity of 0.947 and adjusted NPV of 0.983, and ground-glass nodules (n = 67) with a sensitivity of 0.964 and adjusted NPV of 0.989 at 10% prevalence. This methylation test, called PulmoSeek, outperformed PET-CT and 2 clinical prediction models (Mayo Clinic and Veterans Affairs) in discriminating malignant pulmonary nodules from benign ones. CONCLUSION.This study suggests that the blood-based DNA methylation model may provide a better test for classifying pulmonary nodules, which could help facilitate the accurate diagnosis of early stage lung cancer from pulmonary nodule patients and guide clinical decisions.

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Section: Discussionmentioning

confidence: 99%

Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test

Liang¹,

Chen²,

Li³

et al. 2021

Journal of Clinical Investigation

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“…In recent years, there have also been studies on the use of radiomics to predict the invasiveness of LUAD (33)(34)(35)(36)(37). The 15 radiomics features selected in the study of Zhao et al combined with the average value of CT, were used to construct a prediction model for sub-centimeter LUAD nodule invasion.…”

Section: Discussionmentioning

confidence: 99%

The predictive accuracy of CT radiomics combined with machine learning in predicting the invasiveness of small nodular lung adenocarcinoma

Liu¹,

Jia²,

Yu³

et al. 2023

Transl Lung Cancer Res

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Background: Conventionally, the judgment of whether small pulmonary nodules are invasive is mainly made by thoracic surgeons according to the chest computed tomography (CT) features of patients.However, there are limits to how much useful information can be obtained from this approach. A large number of feature information was extracted from CT images by CT radiomics. The machine learning algorithm was used to construct models based on radiomic characteristics to predict the invasiveness of lung adenocarcinoma (LUAD) with a good prediction accuracy.Methods: A total of 416 patients with pathologically confirmed preinvasive lesions and LUAD after videoassisted thoracoscopic surgery (VATS) in the Department of Thoracic Surgery of the First People's Hospital of Yunnan Province from February 2020 to February 2022 were retrospectively analyzed. According to random classification, patients were divided into 2 groups. The RadCloud platform was used to extract radiomics features, and the most relevant radiomics features were selected by continuous dimension reduction method. Then, 6 machine learning algorithms were used to establish and verify the prediction model of small lung nodular adenocarcinoma invasiveness. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to evaluate the predictive performance.Results: There were 78 cases of pre-invasive lesions and 226 cases of invasive lesions in the training group, and 34 cases of pre-invasive lesions and 78 cases of invasive lesions in the validation group. In the training group, the AUC values of the 6 models were all more than 0.914, the 95% confidence interval (CI) was 0.857-1.00, the sensitivity was equal or more than 0.87, and the specificity was equal or more than 0.85. In the validation group, the AUC values of the 6 models were all equal or more than 0.732, the 95% CI was 0.651-1.00, the sensitivity was equal or more than 0.7, and the specificity was more than 0.77. Conclusions:Machine learning algorithms were used to construct models to predict the invasiveness of small nodular LUAD based on radiomics features, which it could provide more evidence for doctors to make ^ ORCID: 0000-0002-0596-2685.

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“…In addition, tumours may be assessed at multiple timepoints, with assessment of changes in radiomics features over time termed 'deltaradiomics', 21 rather than information derived from a single biopsy performed at a single timepoint alone. The utility of radiomics to enhance our understanding of cancer diagnosis, prognosis and response to treatment, as well as better understand tumour heterogeneity and the tumour microenvironment, has been investigated, 22,23 with studies relating to immunotherapy emerging over recent years. This review aims to summarise the current literature relating to the utility of radiomics in patients with cancer undergoing immunotherapy treatment, including its role in predicting treatment response and toxicity, as well as correlates to relevant tumour biology.…”

Section: Radiomicsmentioning

confidence: 99%

Role of radiomics in predicting immunotherapy response

Kothari

2022

J Med Imag Rad Onc

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Summary Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue‐based biomarkers. Quantitative image analysis or radiomics, which involves the high‐throughput extraction of imaging features, has the potential to non‐invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune‐related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high‐quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well‐defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune‐related adverse effects and less well‐studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.

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Radiomics-based Management of Indeterminate Lung Nodules? Are We There Yet?

Cited by 10 publications

References 18 publications

Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test

Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test

The predictive accuracy of CT radiomics combined with machine learning in predicting the invasiveness of small nodular lung adenocarcinoma

Role of radiomics in predicting immunotherapy response

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