Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Yan, Ming; Wang, Weidong

doi:10.3389/fonc.2020.555514

Cited by 16 publications

(11 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Radiomics provides a quantitative method to mine useful data as much as possible from medical images and can be applied to clinical decision support systems [3][4][5][6][7][8][9][10]. And CT-based radiomics can quantify tumor phenotypic differences in CT images using radiomic features.…”

Section: Introductionmentioning

confidence: 99%

Radiomic Analysis of CT Predicts Tumor Response in Human Lung Cancer with Radiotherapy

Yan

Wei-dong

2020

J Digit Imaging

Self Cite

View full text Add to dashboard Cite

Purpose Radiomics features can be positioned to monitor changes throughout treatment. In this study, we evaluated machine learning for predicting tumor response by analyzing CT images of lung cancer patients treated with radiotherapy. Experimental Design For this retrospective study, screening or standard diagnostic CT images were collected for 100 patients (mean age, 67 years; range, 55–82 years; 64 men [mean age, 68 years; range, 55–82 years] and 36 women [mean age, 65 years; range, 60–72 years]) from two institutions between 2013 and 2017. Radiomics analysis was available for each patient. Features were pruned to train machine learning classifiers with 50 patients, then trained in the test dataset. Result A support vector machine classifier with 2 radiomic features (flatness and coefficient of variation) achieved an area under the receiver operating characteristic curve (AUC) of 0.91 on the test set. Conclusion The 2 radiomic features, flatness, and coefficient of variation, from the volume of interest of lung tumor, can be the biomarkers for predicting tumor response at CT.

show abstract

Section: Introductionmentioning

confidence: 99%

Radiomic Analysis of CT Predicts Tumor Response in Human Lung Cancer with Radiotherapy

Yan

Wei-dong

2020

J Digit Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…At present, there have been a number of studies on 18 F-FDG PET/CT radiomics to assist in the differentiation of pulmonary space-occupying lesions ( 20 – 25 ), indicating the excellent value of radiomics in nuclear medicine imaging. It was generally considered that the 18 F-FDG PET & CT combined model performed better than the PET feature model or CT feature model, and the PET feature model was better than the CT feature model ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images of 68Ga-NOTA-PRGD2 and 18F-FDG PET/CT for Differentiating Benign and Malignant Pulmonary Space-Occupying Lesions

et al. 2022

View full text Add to dashboard Cite

BackgroundThis is a pilot study of radiomics based on 68Ga-NOTA-PRGD2 [NOTA-PEG4-E[c(RGDfK)]2)] and 18F-FDG PET/CT to (i) evaluate the diagnostic efficacy of radiomics features of 68Ga-NOTA-PRGD2 PET in the differential diagnosis of benign and malignant pulmonary space-occupying lesions and (ii) compare the diagnostic efficacy of multi-modality and multi-probe images.MethodsWe utilized a dataset of 48 patients who participated in 68Ga-NOTA-PRGD2 PET/CT and 18F-FDG PET/CT clinical trials to extract image features and evaluate their diagnostic efficacy in the differentiation of benign and malignant lesions by the Mann-Whitney U test. After feature selection with sequential forward selection, random forest models were developed with tenfold cross-validation. The diagnostic performance of models based on different image features was visualized by receiver operating characteristic (ROC) curves and compared by permutation tests.ResultsFourteen of the 68Ga-NOTA-PRGD2 PET features between benign and malignant pulmonary space-occupying lesions had significant differences (P<0.05, Mann-Whitney U test). Eighteen of the 68Ga-NOTA-PRGD2 PET features demonstrated higher AUC values than all CT features in the differential diagnosis of pulmonary lesions. The AUC value (0.908) of the three-modal feature model was significantly higher (P<0.05, permutation test) than those of the single- and dual-modal models.Conclusion68Ga-NOTA-PRGD2 PET features have better diagnostic capacity than CT features for pulmonary space-occupying lesions. The combination of multi-modality and multi-probe images can improve the diagnostic efficiency of models. Our preliminary clinical hypothesis of using radiomics based on 68Ga-NOTA-PRGD2 PET images and multimodal images as a diagnostic tool warrants further validation in a larger multicenter sample size.

show abstract

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Cited by 16 publications

References 36 publications

Radiomic Analysis of CT Predicts Tumor Response in Human Lung Cancer with Radiotherapy

Radiomic Analysis of CT Predicts Tumor Response in Human Lung Cancer with Radiotherapy

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

A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images of 68Ga-NOTA-PRGD2 and 18F-FDG PET/CT for Differentiating Benign and Malignant Pulmonary Space-Occupying Lesions

Contact Info

Product

Resources

About