Quantitative Whole-Body Diffusion-Weighted MR Imaging

Donners, Ricardo; Blackledge, Matthew; Tunariu, Nina; Messiou, Christina; Merkle, Elmar M.; Koh, Dow‐Mu

doi:10.1016/j.mric.2018.06.002

Cited by 21 publications

(19 citation statements)

References 62 publications

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“…11,105,[107][108][109][110] The proportion of patients with significant radiologic findings increased with age. 111 The rate of malignancy in incidentalomas also varied substantially between organs. 112 11.…”

Section: Correlate With the Clinical And Serological Data Includingmentioning

confidence: 99%

“…The application of machine learning algorithms to MSK oncologic imaging offers faster, more accurate, and more efficient patient pathways for cancer diagnosis and management. 6,104,[109][110][111][112][113][114][115] Radiologists could have access to artificial intelligence (AI) algorithms to offer computerassisted diagnosis as a double reader that may minimize the false-reading margins and expedite reporting times. Volumetric evaluation of lesions may offer rapidly acquired information on disease progress and/or response to treatment.…”

Section: Technology Powered By Artificial Intelligence Including Machmentioning

confidence: 99%

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Detection and Characterization of Musculoskeletal Cancer Using Whole-Body Magnetic Resonance Imaging

Isaac

Lecouvet

Dalili

et al. 2020

Semin Musculoskelet Radiol

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Whole-body magnetic resonance imaging (WB-MRI) is gradually being integrated into clinical pathways for the detection, characterization, and staging of malignant tumors including those arising in the musculoskeletal (MSK) system. Although further developments and research are needed, it is now recognized that WB-MRI enables reliable, sensitive, and specific detection and quantification of disease burden, with clinical applications for a variety of disease types and a particular application for skeletal involvement. Advances in imaging techniques now allow the reliable incorporation of WB-MRI into clinical pathways, and guidelines recommending its use are emerging. This review assesses the benefits, clinical applications, limitations, and future capabilities of WB-MRI in the context of other next-generation imaging modalities, as a qualitative and quantitative tool for the detection and characterization of skeletal and soft tissue MSK malignancies.

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Section: Correlate With the Clinical And Serological Data Includingmentioning

confidence: 99%

Section: Technology Powered By Artificial Intelligence Including Machmentioning

confidence: 99%

Detection and Characterization of Musculoskeletal Cancer Using Whole-Body Magnetic Resonance Imaging

Isaac

Lecouvet

Dalili

et al. 2020

Semin Musculoskelet Radiol

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“…However, there are some limitations to qualitative assessment on DWI. In particular, the signal intensity (SI) depends on both water mobility and T2-relaxation time of tissue, so a lesion with very long T2 may demonstrate high SI on the DWI (T2-shining thought artefact); therefore, SI of different solid tumor may be similar [ 11 ]. In the clinical practice, a definite confirmation of real hyperintense signal DWI areas is conveyed by the apparent diffusion coefficient (ADC) maps.…”

Section: Introductionmentioning

confidence: 99%

The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

et al. 2021

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Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWIb2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWIb2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWIb2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWIb2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWIb2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

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“…6,7 There is increasing awareness that non-invasive imaging may have a role in assessing tumour heterogeneity. 8 Imaging is already used widely in the management of a cancer patient including diagnosis, staging, assessment of treatment response, and surveillance. Computed tomography (CT) is well placed for tumour heterogeneity assessment as it is routinely used in clinical practice as the standard of care for cancer staging and response assessment and thus is cost effective.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Spatial Heterogeneity of Breast Cancers: Associations Between Computed Tomography and Immunohistochemistry

Woolf

Detre

et al. 2019

Biomark�Cancer

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Background: Tumour heterogeneity is considered an important mechanism of treatment failure. Imaging-based assessment of tumour heterogeneity is showing promise but the relationship between these mathematically derived measures and accepted ‘gold standards’ of tumour biology such as immunohistochemical measures is not established. Methods: A total of 20 women with primary breast cancer underwent a research dynamic contrast-enhanced computed tomography prior to treatment with data being available for 15 of these. Texture analysis was performed of the primary tumours to extract 13 locoregional and global parameters. Immunohistochemical analysis associations were assessed by the Spearman rank correlation. Results: Hypoxia-inducible factor-1α was correlated with first-order kurtosis ( r = −0.533, P = .041) and higher order neighbourhood grey-tone difference matrix coarseness ( r = 0.54, P = .038). Vascular maturity-related smooth muscle actin was correlated with higher order grey-level run-length long-run emphasis ( r = −0.52, P = .047), fractal dimension ( r = 0.613, P = .015), and lacunarity ( r = −0.634, P = .011). Micro-vessel density, reflecting angiogenesis, was also associated with lacunarity ( r = 0.547, P = .035). Conclusions: The associations suggest a biological basis for these image-based heterogeneity features and support the use of imaging, already part of standard care, for assessing intratumoural heterogeneity.

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Quantitative Whole-Body Diffusion-Weighted MR Imaging

Cited by 21 publications

References 62 publications

Detection and Characterization of Musculoskeletal Cancer Using Whole-Body Magnetic Resonance Imaging

Detection and Characterization of Musculoskeletal Cancer Using Whole-Body Magnetic Resonance Imaging

The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

Assessment of the Spatial Heterogeneity of Breast Cancers: Associations Between Computed Tomography and Immunohistochemistry

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