A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes

Shen, Hui; Chen, Ling; Liu, Kanfeng; Zhao, Kui; Li, Jingsong; Yu, Lijuan; Ye, Hongwei; Zhu, Wentao

doi:10.21037/qims-20-1182

Cited by 16 publications

(13 citation statements)

References 43 publications

(75 reference statements)

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“…PET radiomics in pulmonary oncology gathered 107 articles [ 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ,…”

Section: Resultsunclassified

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

et al. 2022

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Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

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Section: Resultsunclassified

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

et al. 2022

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“…It retrieves quantitative evaluation indicators, such as transport rate and blood volume, through kinetic modeling to distinguish between ADC, SCC, and benign or malignant FDG hypermetabolic lymph nodes, providing objective imaging evidence for clinical treatment plans and prognostic evaluation. In addition, we included the texture parameter (23,24) as an indicator based on the quantitative evaluation of dynamic PET/CT. Combining the two, 18 F-FDG PET/CT kinetic modeling yielded the firstorder texture parameters based on transport rate and blood volume, thereby suggesting a theoretical basis and technical support for early, accurate, and individualized treatment.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value of dynamic 18F-FDG PET/CT imaging in non-small cell lung cancer and FDG hypermetabolic lymph nodes

Sun¹,

Xiao²,

Wang³

et al. 2023

Quant Imaging Med Surg

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Background: We aimed to investigate the diagnostic value of dynamic 2-deoxy-2-[ 18 F]-fluoro-D-glucose positron emission tomography-computed tomography in non-small cell lung cancer and fluoro-D-glucose hypermetabolic lymph nodes. Methods: Patients who made an active appointment for positron emission tomography-computed tomography were randomly enrolled by referring to previous imaging data and clinical information. Finally, 34 histopathologically confirmed non-small cell lung cancers (18 adenocarcinoma and 16 squamous cell carcinoma cases) were prospectively studied using dynamic and static 2-deoxy-2-[ 18 F]-fluoro-D-glucose positron emission tomography-computed tomography imaging (the diagnostic study has not yet been registered on a clinical trial platform). In dynamic positron emission tomography images, a volume of interest, defined by the thoracic aorta, was selected for estimating the arterial input function. Patlak and irreversible two-tissue compartment model analyses were performed based on the pixel points to obtain first-order characteristic kinetic parameters for each lesion and hypermetabolic lymph node. The firstorder characteristic kinetic parameters were obtained based on the basic data of dynamic positron emission tomography images in the corresponding model and the lesion delineation of region-of-interest based on computed tomography images, such as V_Median (the median gray intensity of V), k3_Entropy, VB_ Entropy, K1_Uniformity, and ki_Uniformity. The first-order characteristic kinetic parameters were also modeled by logistic regression for the differential diagnosis of non-small cell lung cancer and hypermetabolic lymph nodes. Maximum and mean standard uptake values (SUVmax and SUVmean, respectively) were obtained from static positron emission tomography images. The diagnostic efficacy of the parameters was evaluated using the receiver operating characteristic curve and the DeLong test.Results: There was a significant difference in the V_Median values of adenocarcinoma and squamous cell carcinoma. The regression models for K1, k3, and V provided good predictions of adenocarcinoma and squamous cell carcinoma typology. Significant differences were observed in k3_Entropy, VB_Entropy, K1_ Uniformity, and ki_Uniformity between benign and malignant lymph nodes. The regression model of Ki, VB, and k3 could make a good prediction for identifying benign and malignant lymph nodes. Conclusions: Dynamic 2-deoxy-2-[ 18 F]-fluoro-D-glucose positron emission tomography-computed tomography imaging showed high diagnostic value in the staging of non-small cell lung cancer and fluoro-D-glucose hypermetabolic lymph nodes, and can be of great use in non-small cell lung cancer lymph node staging and surgical decision-making.

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“…Thus, in a 3D environment, they can provide a comprehensive characterization of the tumor phenotype. Radiomic signatures, such as nodule diameter, CT value, and lesion size, play important roles in the prediction of pathological classification (13)(14)(15)(16), pathological staging (17), tumor diagnosis (benign/malignant) (18), treatment response (19), and patient survival (20). Radiomics has been used in the prediction and prognosis of lung cancer (21,22).…”

Section: Introductionmentioning

confidence: 99%

Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma

Ren¹,

Xiao²,

Chen³

et al. 2023

Quant Imaging Med Surg

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Background: Lung cancer is one of the most serious cancers in the world. Subtypes of lung adenocarcinoma can be quickly distinguished by analyzing 3D radiomic signatures and radiological features.Methods: This study included 493 patients from 3 hospitals with a total of 506 lesions confirmed as minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), or invasive adenocarcinoma (IAC). After segmenting the lesion area, 3D radiomic signatures were extracted using the PyRadiomics package v. 3.0.1 implemented in Python (https://pyradiomics.readthedocs.io/en/latest/index.html), and the corresponding radiological features were collected. Subsequently, the top 100 features were identified by feature screening methods, including the Spearman rank correlation and minimum redundancy maximum relevance (mRMR) feature selection, and the top 10 features were determined by the least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating 3D radiomic signatures and radiological features in the prediction system. The nomogram was evaluated from multiple perspectives and tested on the validation cohort. Results: The model combined 3 radiological features and seven 3D radiomic signatures. The area under the curve (AUC) of the model was 0.877 (95% CI: 0.829-0.925) in the training cohort, 0.864 (95% CI: 0.789-0.940) in the testing cohort, and 0.836 (95% CI: 0.749-0.924) in the validation cohort. The nomogram applied in all 3 cohorts showed reliable accuracy and calibration. The decision curve also demonstrated the clinical effectiveness of the nomogram. Conclusions: In this study, a nomogram-based model combining 3D radiomic signatures and radiological features was developed. Its performance in identifying IAC and MIA/AIS was satisfactory and had clinical value.

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A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes

Abstract: Background: This study classifies lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using subregion-based radiomics features extracted from positron emission tomography/computed tomography (PET/CT) images.

Cited by 16 publications

References 43 publications

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Diagnostic value of dynamic 18F-FDG PET/CT imaging in non-small cell lung cancer and FDG hypermetabolic lymph nodes

Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma

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