Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [18F]FDG PET/CT: A Comparison between Two PET/CT Scanners

Dondi, Francesco; Gatta, Roberto; Albano, Domenico; Bellini, Pietro; Camoni, Luca; Treglia, Giorgio; Bertagna, Francesco

doi:10.3390/jcm12010255

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Axial CT in lung window a, PET b, PET/CT fusion c and PET maximum intensity projection d are shown.they did not include CT tumor size and included fewer patients with a shorter follow up time (mean 28.7 months). In another recent study, Dondi et al[33] confirmed the prognostic value of baseline [ 18 F]FDG-PET/CT parameters, such as SUV max and SUVmean , in 296 patients with stage I and II NSCLC. Therefore, the combined analysis of the well-established predictive value of SUV max with the CT volume can be additionally helpful to stratify OS in low grade NSCLC patients using baseline [18 F]FDG-PET/CT scans.…”

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confidence: 86%

Combined morphologic-metabolic biomarkers from [18F]FDG-PET/CT stratify prognostic groups in low-risk NSCLC

Deininger,

Raacke,

Yousefzadeh-Nowshahr

et al. 2023

Nuklearmedizin

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Aim The aim of this study was to derive prognostic parameters from 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG-PET/CT) in patients with low-risk NSCLC and determine their prognostic value. Methods 81 (21 female, mean age 66 a) therapy-naive patients that underwent [18F]FDG-PET/CT before histologic confirmation of NSCLC with stadium I and II between 2008–2016 were included. A mean follow-up time of 58 months (13–176), overall and progression free survival (OS, PFS) were registered. A volume of interest for the primary tumor was defined on PET and CT images. Parameters SUVmax, PET-solidity, PET-circularity, and CT-volume were analyzed. To evaluate the prognostic value of each parameter for OS, a minimum p-value approach was used to define cutoff values, survival analysis, and log-rank tests were performed, including subgroup analysis for combinations of parameters. Results Mean OS was 58±28 months. Poor OS was associated with a tumor CT-volume >14.3 cm3 (p=0.02, HR=7.0, CI 2.7–17.7), higher SUVmax values >12.2 (p=0.003; HR=3.0, CI 1.3–6.7) and PET-solidity >0.919 (p=0.004; HR=3.0, CI 1.0–8.9). Combined parameter analysis revealed worse prognosis in larger volume/high SUVmax tumors compared to larger volume/lower SUVmax (p=0.028; HR=2.5, CI 1.1–5.5), high PET-solidity/low volume (p=0.01; HR=2.4, CI 0.8–6.6) and low SUVmax/high PET-solidity (p=0.02, HR=4.0, CI 0.8–19.0). Conclusion Even in this group of low-risk NSCLC patients, we identified a subgroup with a significantly worse prognosis by combining morphologic-metabolic biomarkers from [18F]FDG-PET/CT. The combination of SUVmax and CT-volume performed best. Based on these preliminary data, future prospective studies to validate this combined morphologic-metabolic imaging biomarker for potential therapeutic decisions seem promising.

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confidence: 86%

Combined morphologic-metabolic biomarkers from [18F]FDG-PET/CT stratify prognostic groups in low-risk NSCLC

Deininger,

Raacke,

Yousefzadeh-Nowshahr

et al. 2023

Nuklearmedizin

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“…It was found that the VGG16 DL algorithm was better than other models. Other scholars 84 used LR, kNN, DT, and RF to further analyze the PET/CT image characteristics of patients with Phase I and II NSCLC. They found that the model built by kNN and LR had better performance than other methods, because it was in the nonlinear cutting space of kNN, but it had the risk of overfitting, and LR could reduce the risk of overfitting.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Yuan,

An,

Zhu

et al. 2024

CMAR

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As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

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“…Recently, an increase in the extraction of specific quantitative features from PET and scintigraphic images, called radiomics or texture analysis, is being experienced and researches in this field are focusing on its diagnostic and prognostic role in a wide range of pathological conditions, and the thyroid does not make any exception [13][14]. Similarly, machine learning (ML) is a hot topic of recent clinical research and focuses on the development of algorithms that can use different combinations of features in order to predict a specific target [15][16].…”

Section: Introductionmentioning

confidence: 99%

Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review

Dondi

Gatta

Treglia

et al. 2023

Rev Endocr Metab Disord

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Background: In the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting. Methods: A wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases. Results: Seventeen studies were included in the systematic review. Radiomics and ML were applied for assessment of thyroid incidentalomas at 18 F-FDG PET, evaluation of cytologically indeterminate thyroid nodules, assessment of thyroid cancer and classification of thyroid diseases using nuclear medicine techniques. Conclusion: Despite some intrinsic limitations of radiomics and ML may have affect the results of this review, these technologies seem to have a promising role in the assessment of thyroid diseases. Validation of preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.

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Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [18F]FDG PET/CT: A Comparison between Two PET/CT Scanners

Cited by 3 publications

References 28 publications

Combined morphologic-metabolic biomarkers from [18F]FDG-PET/CT stratify prognostic groups in low-risk NSCLC

Combined morphologic-metabolic biomarkers from [18F]FDG-PET/CT stratify prognostic groups in low-risk NSCLC

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review

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