Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer

Du, Dongyang; Gu, Jianyou; Chen, Xiaohui; Lv, Wenbing; Feng, Qianjin; Rahmim, Arman; Wu, Hubing; Lu, Lijun

doi:10.1007/s11307-020-01550-4

Cited by 53 publications

(40 citation statements)

References 37 publications

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“…We collected 71 articles evaluating lung cancer [ 37 , 57 , 58 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 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 ,…”

Section: Resultsunclassified

“…Especially relevantly, Kirienko et al [ 79 ] compared the ability of PET and CT radiomics for differentiating primary and metastatic lung lesions in a pool of 482 patients, finding that only PET features were predictive. Other studies that focused on diagnosis demonstrated the ability of PET radiomics to distinguish between malign and benign FDG-avid lesions [ 67 , 71 , 74 ] or between tuberculosis and lung cancer [ 75 , 80 ]. Regarding characterization, in a study including 867 patients, Han et al [ 129 ] showed that PET radiomics in combination with deep learning was able to differentiate histological subtypes of cancer, particularly, adenocarcinoma and squamous cell carcinoma.…”

Section: Resultsmentioning

confidence: 99%

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A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

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Section: Resultsmentioning

confidence: 99%

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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“…PET/CT provides more precise location information and more detailed surrounding structures than PET alone, meaning that radiomics based on PET/CT is able to extract both functional and structural feature and has a wider application prospect than PET or CT alone. One of the applications of diagnosis is using radiomics to distinguish pulmonary tuberculosis, lymphoma, and other benign lesions from lung cancer [99][100][101]. Another application of diagnosis based on PET/CT radiomics is to distinguish lung adenocarcinoma from SCC as well as primary from metastatic lung cancer [102,103].…”

Section: Applications Of Functional Radiomic Features In Lung Cancermentioning

confidence: 99%

Structural and functional radiomics for lung cancer

Jochems

Ibrahim

et al. 2021

Eur J Nucl Med Mol Imaging

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Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”

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“…Conventional radiomics workflows involve extraction of so-called radiomics features (hand-crated or explicit features) from segmented regions of interest (ROI) [ 15 , 16 , 17 , 18 , 19 , 20 ]. An alternative approach to this is deep leaning (DL), in which the features are implicitly derived utilizing neural networks [ 21 , 22 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-driven assessment of radiological images for COVID-19

Bouchareb

Khaniabadi

Kindi

et al. 2021

Computers in Biology and Medicine

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Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.

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Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer

Cited by 53 publications

References 37 publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Structural and functional radiomics for lung cancer

Artificial intelligence-driven assessment of radiological images for COVID-19

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