Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis

Huang, Shih‐Yu; Franc, Benjamin L.; Harnish, Roy; Liu, Gengbo; Mitra, Debasis; Copeland, Thomas E.; Arasu, Vignesh A.; Kornak, John; Jones, Ella F.; Behr, Spencer C.; Hylton, Nola M.; Price, Elissa R.; Esserman, Laura J.; Seo, Youngho

doi:10.1038/s41523-018-0078-2

Cited by 85 publications

(67 citation statements)

References 37 publications

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“…Other studies have focused on building radiomic models predictive of a clinical endpoint, such as survival or response to therapy 46,47 . To date, only a few studies have investigated the functional links between radiomics and other omics data 26,27,48 . In 2007, Segal et al have suggested that gene expression profiles could be reconstructed using "image traits" derived from CT scans in hepatocarcinoma 26 .…”

Section: Discussionmentioning

confidence: 99%

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Tixier

Cheze-Le-Rest

Schick

et al. 2020

Sci Rep

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Metabolic images from Positron Emission Tomography (PET) are used routinely for diagnosis, follow-up or treatment planning purposes of cancer patients. in this study we aimed at determining if radiomic features extracted from 18 F-Fluoro Deoxy Glucose (FDG) PET images could mirror tumor transcriptomics. In this study we analyzed 45 patients with locally advanced head and neck cancer (H&N) that underwent FDG-PET scans at the time of diagnosis and transcriptome analysis using RnAs from both cancer and healthy tissues on microarrays. Association between pet radiomics and transcriptomics was carried out with the Genomica software and a functional annotation was used to associate PET radiomics, gene expression and altered biological pathways. We identified relationships between PET radiomics and genes involved in cell-cycle, disease, DNA repair, extracellular matrix organization, immune system, metabolism or signal transduction pathways, according to the Reactome classification. Our results suggest that these FDG PET radiomic features could be used to infer tissue gene expression and cellular pathway activity in H&n cancers. these observations strengthen the value of radiomics as a promising approach to personalize treatments through targeting tumor-specific molecular processes. 18 F-FDG Positron emission tomography (PET) imaging is largely used for diagnostic purposes in several cancer types, allowing accurate disease staging 1,2 , but it is also gaining ground for therapy applications, including monitoring treatment response and planning in the field of external beam radiotherapy 3. Although visual interpretation may be sufficient for diagnosis, a (semi)quantitative analysis of PET data for image guided therapy applications is most frequently necessary. The simplest parameter usually extracted from PET images used in clinical practice is the maximum standardized uptake value (SUV max) corresponding to the highest single voxel intensity within a region of interest. On the one hand, SUV max has often been reported as a biomarker with potential for improving overall patient management, including the prediction of response to therapy and survival in several cancers 4-6. On the other hand, numerous studies have shown that the predictive/prognostic value of SUV max can be limited, particularly when using images to characterize tumors on baseline PET images 7-9. For this reason, there has been an increasing interest in extracting additional parameters from 18 F-FDG PET images with the objective of more fully characterizing the entire tumor uptake. Most studies initially focused on the delineation of the metabolically active tumor volume (MATV) and associated SUV measurements (such as the mean value or total lesion glycolysis) 10,11 , while more recent studies have concentrated on parameters characterizing the shape or activity distribution within the tumor. These parameters, today known as radiomic features 14 , include 1 st-order image features such as (cumulative) intensity histograms, geometrical tumor shape descriptors...

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Section: Discussionmentioning

confidence: 99%

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Tixier

Cheze-Le-Rest

Schick

et al. 2020

Sci Rep

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“…The next step is to specify (or segment) the ROI, from which radiomics features are computed. ROIs are specified to limit the spatial extents of the analysis and can be specified manually, semi-automatically, or automatically (16,17). Among the methods of tumor segmentation, automated or semi-automated methods have been reported to be superior to manual methods for segmenting the tumor (18,19).…”

Section: Specification Of Roismentioning

confidence: 99%

“…To rationalize the number of bins, a statistical formula such as the Freedman-Diaconis rule could be applied. Existing breast imaging studies reported the entropy, mean, minimum, and maximum as important features (17). These features do not consider the spatial neighborhood information of the voxels.…”

Section: Image Acquisitionmentioning

confidence: 99%

Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review

Lee

Park

2020

Korean J Radiol

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Recent advances in computer technology have generated a new area of research known as radiomics. Radiomics is defined as the high throughput extraction and analysis of quantitative features from imaging data. Radiomic features provide information on the gray-scale patterns, inter-pixel relationships, as well as shape and spectral properties of radiological images. Moreover, these features can be used to develop computational models that may serve as a tool for personalized diagnosis and treatment guidance. Although radiomics is becoming popular and widely used in oncology, many problems such as overfitting and reproducibility issues remain unresolved. In this review, we will outline the steps of radiomics used for oncology, specifically addressing applications for breast cancer patients and focusing on technical issues.

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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)(142)(143), pathological characterization (17,18), NAC response (18,(144)(145)(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: Pet Radiomicsmentioning

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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Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis

Cited by 85 publications

References 37 publications

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review

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

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