Intratumoral heterogeneity in 18F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate

Molina-García, David; García-Vicente, Ana María; Pérez-Beteta, Julián; Amo-Salas, Mariano; Martínez-González, Alicia; Tello-Galán, María Jesús; Soriano-Castrejón, Ángel; Pérez-Garcı́a, Vı́ctor M.

doi:10.1007/s12149-018-1253-0

Cited by 42 publications

(29 citation statements)

References 43 publications

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“…This feature was mainly found in oncology research (61)(62)(63)(64). For example, a study on breast cancer showed that LRHGE was associated with risk categories as well as the neoadjuvant chemotherapy response (61). Another study on cervical cancer found that LRHGE was correlated with the differentiation (62).…”

Section: A B Cmentioning

confidence: 94%

Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls

Wu¹,

Jiang²,

Chen³

et al. 2019

Ann Transl Med

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Background: Parkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract highorder features by using radiomics approach and achieve acceptable diagnosis accuracy in PD. Methods: In this retrospective multicohort study, we collected 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) images and clinical scale [the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn & Yahr scale (H&Y)] from two cohorts. One cohort from Huashan Hospital had 91 normal controls (NC) and 91 PD patients (UPDRS: 22.7±11.7, H&Y: 1.8±0.8), and the other cohort from Wuxi 904 Hospital had 26 NC and 22 PD patients (UPDRS: 20.9±11.6, H&Y: 1.7±0.9). The Huashan cohort was used as the training and test sets by 5-fold cross-validation and the Wuxi cohort was used as another separate testset. After identifying regions of interests (ROIs) based on the atlas-based method, radiomic features were extracted and selected by using autocorrelation and fisher score algorithm. A support vector machine (SVM) was trained to classify PD and NC based on selected radiomic features. In the comparative experiment, we compared our method with the traditional voxel values method. To guarantee the robustness, above processes were repeated in 500 times.Results: Twenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively.Conclusions: This study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.

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Section: A B Cmentioning

confidence: 94%

Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls

Wu¹,

Jiang²,

Chen³

et al. 2019

Ann Transl Med

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“…Similar to breast density in mammography, background parenchymal enhancement and background parenchymal uptake may be regarded as potential imaging biomarkers for breast cancer incidence [85]. Texture analysis and radiomics could also play a role in the evaluation of tumor aggressiveness as well in the prediction of response to neoadjuvant chemotherapy [86][87][88]. However, only few single center studies are available on PET MRI-derived imaging biomarkers for local breast cancer staging.…”

Section: Local Stagingmentioning

confidence: 99%

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Dietzel

Clauser

Kapetas

et al. 2021

Rofo

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Background Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology “imaging biomarker”, “radiomics”, and “artificial intelligence” are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. Methods and Results This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. Conclusion Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. Key Points: Citation Format

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“…Non-invasive image-derived biomarkers can also be generated from PET radiomics according to the intensity of the pixels, their associated parameters, and their positions ( 138 ). Based on the clinical application of PET-based techniques, several studies have focused on the clinical and technical feasibility of applying PET radiomics to diagnosis ( 139 , 140 ), staging ( 141 – 143 ), pathological characterization ( 17 , 18 ), NAC response ( 18 , 144 – 146 ), and outcome prediction ( 147 , 148 ) in breast cancer. However, the difficulty in detecting the edges of breast lesions using CT limits the PET/CT in identifying the breast tumor and the alignment of the imaging modalities ( 141 ).…”

Section: Recent Advances In Pet-based Strategiesmentioning

confidence: 99%

Progress and Future Trends in PET/CT and PET/MRI Molecular Imaging Approaches for Breast Cancer

Ming

Qian

et al. 2020

Front. Oncol.

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Breast cancer is a major disease with high morbidity and mortality in women worldwide. Increased use of imaging biomarkers has been shown to add more information with clinical utility in the detection and evaluation of breast cancer. To date, numerous studies related to PET-based imaging in breast cancer have been published. Here, we review available studies on the clinical utility of different PET-based molecular imaging methods in breast cancer diagnosis, staging, distant-metastasis detection, therapeutic and prognostic prediction, and evaluation of therapeutic responses. For primary breast cancer, PET/MRI performed similarly to MRI but better than PET/CT. PET/CT and PET/MRI both have higher sensitivity than MRI in the detection of axillary and extra-axillary nodal metastases. For distant metastases, PET/CT has better performance in the detection of lung metastasis, while PET/MRI performs better in the liver and bone. Additionally, PET/CT is superior in terms of monitoring local recurrence. The progress in novel radiotracers and PET radiomics presents opportunities to reclassify tumors by combining their fine anatomical features with molecular characteristics and develop a beneficial pathway from bench to bedside to predict the treatment response and prognosis of breast cancer. However, further investigation is still needed before application of these modalities in clinical practice. In conclusion, PET-based imaging is not suitable for early-stage breast cancer, but it adds value in identifying regional nodal disease and distant metastases as an adjuvant to standard diagnostic imaging. Recent advances in imaging techniques would further widen the comprehensive and convergent applications of PET approaches in the clinical management of breast cancer.

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Intratumoral heterogeneity in 18F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate

Abstract: Textural variables obtained with F-FDG PET/CT were predictors of neoadjuvant chemotherapy response and prognosis, being as relevant as PET stage at diagnosis for OS and DFS prediction.

Cited by 42 publications

References 43 publications

Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls

Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Progress and Future Trends in PET/CT and PET/MRI Molecular Imaging Approaches for Breast Cancer

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