A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [18F]FDG-PET Imaging

Soufi, Motahare; Kamali-Asl, Alireza; Geramifar, Parham; Rahmim, Arman

doi:10.1007/s11307-016-1015-0

Cited by 11 publications

(8 citation statements)

References 52 publications

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“…Advanced radiographic metrics that quantify heterogeneity in shape and uptake have been explored, primarily in the field of oncology, in order to improve diagnosis as well as prediction of treatment response and survival outcome in different cancers ( Eary et al, 2008 , El Naqa et al, 2009 , Hatt et al, 2015 , Rahmim et al, 2016b , Soufi et al, 2017 , Tixier et al, 2014 , Tixier et al, 2011 , van Velden et al, 2011 , Vriens et al, 2012 ). Overall, the field of radiomics aims to extract a large number of quantitative features from radiological images, aiming to uncover correlates of disease characteristics that are ordinarily not visually observed or quantitatively measured ( Aerts et al, 2014 , Asselin et al, 2012 , Chicklore et al, 2013 , Kumar et al, 2012 , Lambin et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images

Rahmim

Huang

Shenkov

et al. 2017

NeuroImage: Clinical

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No disease modifying therapies for Parkinson's disease (PD) have been found effective to date. To properly power clinical trials for discovery of such therapies, the ability to predict outcome in PD is critical, and there is a significant need for discovery of prognostic biomarkers of PD. Dopamine transporter (DAT) SPECT imaging is widely used for diagnostic purposes in PD. In the present work, we aimed to evaluate whether longitudinal DAT SPECT imaging can significantly improve prediction of outcome in PD patients. In particular, we investigated whether radiomics analysis of DAT SPECT images, in addition to use of conventional non-imaging and imaging measures, could be used to predict motor severity at year 4 in PD subjects. We selected 64 PD subjects (38 male, 26 female; age at baseline (year 0): 61.9 ± 7.3, range [46,78]) from the Parkinson's Progressive Marker Initiative (PPMI) database. Inclusion criteria included (i) having had at least 2 SPECT scans at years 0 and 1 acquired on a similar scanner, (ii) having undergone a high-resolution 3 T MRI scan, and (iii) having motor assessment (MDS-UPDRS-III) available in year 4 used as outcome measure. Image analysis included automatic region-of-interest (ROI) extraction on MRI images, registration of SPECT images onto the corresponding MRI images, and extraction of radiomic features. Non-imaging predictors included demographics, disease duration as well as motor and non-motor clinical measures in years 0 and 1. The image predictors included 92 radiomic features extracted from the caudate, putamen, and ventral striatum of DAT SPECT images at years 0 and 1 to quantify heterogeneity and texture in uptake. Random forest (RF) analysis with 5000 trees was used to combine both non-imaging and imaging variables to predict motor outcome (UPDRS-III: 27.3 ± 14.7, range [3,77]). The RF prediction was evaluated using leave-one-out cross-validation. Our results demonstrated that addition of radiomic features to conventional measures significantly improved (p < 0.001) prediction of outcome, reducing the absolute error of predicting MDS-UPDRS-III from 9.00 ± 0.88 to 4.12 ± 0.43. This shows that radiomics analysis of DAT SPECT images has a significant potential towards development of effective prognostic biomarkers in PD.

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Section: Introductionmentioning

confidence: 99%

Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images

Rahmim

Huang

Shenkov

et al. 2017

NeuroImage: Clinical

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“…They used a novel fuzzy random walk algorithm, which showed a significantly improved performance relative to conventional random walk segmentation. 6 In the present study, we utilized PET feature maps to segment highly heterogeneous intratumoral regions quantitatively. In accordance with other studies, entropy, as the most popular textural feature in local heterogeneity tumor studies, measures the intratumoral heterogeneity relative to changes in the FDG uptake between voxels.…”

Section: Discussionmentioning

confidence: 99%

“…Intratumoral heterogeneity refers to the differences within the tumor and provides vital information for the clinical prognosis, and personalized treatment of cancer patients 5 . Thus an accurate delineation of tumor volume and intratumoral heterogeneity can potentially increase the efficacy of radiotherapy by dose escalation of the heterogeneous areas 6,7 . In modern radiotherapy, dose escalation can be applied to administer tailored booster doses to heterogeneous areas using techniques such as intensity‐modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT), and also help the patient's treatment response 8 …”

Section: Introductionmentioning

confidence: 99%

“…5 Thus an accurate delineation of tumor volume and intratumoral heterogeneity can potentially increase the efficacy of radiotherapy by dose escalation of the heterogeneous areas. 6,7 In modern radiotherapy, dose escalation can be applied to administer tailored booster doses to heterogeneous areas using techniques such as intensity-modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT), and also help the patient's treatment response. 8 To improve the delineation of target volume, fluorodeoxyglucose (FDG) positron emission tomography (PET) has been combined with treatment planning computed tomography (CT).…”

Section: Introductionmentioning

confidence: 99%

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Potential advantages of FDG‐PET radiomic feature map for target volume delineation in lung cancer radiotherapy

Falahatpour

Geramifar

Mahdavi

et al. 2022

J Applied Clin Med Phys

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To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy. Methods: Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTV CT , CTV CT ), PET (GTV PET40 , CTV PET40 ), and RFMs (GTV RFM , CTV RFM ,). Intratumoral heterogeneity areas were segmented as GTV PET50-Boost and radiomic boost target volume (RTV Boost ) on PET and RFMs, respectively. GTV CT in homogenous tumors and GTV PET40 in heterogeneous tumors were considered as GTV gold standard (GTV GS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTV RFM with GTV GS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes. Results: Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTV RFM-entropy , GTV RFM-contrast , and GTV RFM-H-correlation with GTV GS , respectively. The linear regression results showed a positive correlation between GTV GS and GTV RFM-entropy (r = 0.98, p < 0.001), between GTV GS and GTV RFM-contrast (r = 0.93, p < 0.001), and between GTV GS and GTV RFM-H-correlation (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTV RFM-entropy and CTV RFM-contrast with CTV GS , respectively. Moreover, we used RFM to determine RTV Boost in the heterogeneous tumors. Comparison of RTV Boost with GTV PET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTV Boost-entropy , RTV Boost-contrast , and RTV Boost-H-correlation , respectively. Conclusions: FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.

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“…Fully automated segmentation methods in PET have been proposed, using fuzzy random walk (Soufi et al 2017) or mutual information of CT and PET to identify NSCLC (Weikert et al 2019;Bug et al 2019). U-Net, one of the mostly used CNN architectures for image contouring, has shown to be able to segment pulmonary parenchyma (Ait Skourt et al 2018), and relatively small tumors (1.83 cm 2 ) resulting reproducible across different scanners (dice scores of 74%), relatively uninfluenced by the partial volume effect, and effectively trained with limited data (30 patients yielded a dices score of 70%) (Leung et al 2020).…”

Section: Tumor Detection and Segmentationmentioning

confidence: 99%

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Sollini

Bartoli

Marciano

et al. 2020

European J Hybrid Imaging

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Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer—being one of the most extensively malignancy studied by hybrid medical imaging—has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.

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A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [18F]FDG-PET Imaging

Abstract: We proposed and demonstrated an automatic framework for significantly improved segmentation and labeling of homogeneous vs. heterogeneous tumors in lung [F]FDG-PET images.

Cited by 11 publications

References 52 publications

Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images

Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images

Potential advantages of FDG‐PET radiomic feature map for target volume delineation in lung cancer radiotherapy

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

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