Qualitative Heterogeneous Signal Drop on Chemical Shift (CS) MR Imaging: Correlative Quantitative Analysis between CS Signal Intensity Index and Contrast Washout Parameters Using T1-Weighted Sequences

Stanzione, Arnaldo; Verde, Francesco; Galatola, Roberta; Romeo, Valeria; Liuzzi, Raffaele; Mainenti, Pier Paolo; Aprea, Giovanni; Klain, Michele; Guadagno, Elia; De, Marialaura Del Basso; Maurea, Simone

doi:10.3390/tomography7040079

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Different ratios have been described; however, a meta-analysis found no significant differences between visual evaluations of the chemical shift images and quantitative analyses [59]. Moreover, the addition of dynamic contrast-enhanced MRI sequences to chemical shift imaging improves the diagnostic capabilities of chemical shift imaging alone, with 94% sensitivity and 98% specificity versus 87% sensitivity and 95% specificity for standalone chemical shift imaging for the diagnosis of AA [59][60][61]. DWI has limited capacity for characterizing adrenal lesions and differentiating between benign and adrenal lesions [62].…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art

Barat

Gaujoux

Tenenbaum

et al. 2022

Cancers

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Detection and characterization of adrenal lesions have evolved during the past two decades. Although the role of imaging in adrenal lesions associated with hormonal secretion is usually straightforward, characterization of non-functioning adrenal lesions may be challenging to confidently identify those that need to be resected. Although many adrenal lesions can be readily diagnosed when they display typical imaging features, the diagnosis may be challenging for atypical lesions. Computed tomography (CT) remains the cornerstone of adrenal imaging, but other morphological or functional modalities can be used in combination to reach a diagnosis and avoid useless biopsy or surgery. Early- and delayed-phase contrast-enhanced CT images are essential for diagnosing lipid-poor adenoma. Ongoing studies are evaluating the capabilities of dual-energy CT to provide valid virtual non-contrast attenuation and iodine density measurements from contrast-enhanced examinations. Adrenal lesions with attenuation values between 10 and 30 Hounsfield units (HU) on unenhanced CT can be characterized by MRI when iodinated contrast material injection cannot be performed. 18F-FDG PET/CT helps differentiate between atypical benign and malignant adrenal lesions, with the adrenal-to-liver maximum standardized uptake value ratio being the most discriminative variable. Recent studies evaluating the capabilities of radiomics and artificial intelligence have shown encouraging results.

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Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art

Barat

Gaujoux

Tenenbaum

et al. 2022

Cancers

View full text Add to dashboard Cite

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Development and validation of a model predicting adrenal lipid-poor adenoma based on the minimum attenuation value from non-contrast CT: a dual-center retrospective study

Zhu,

Wu,

Feng

et al. 2024

BMC Med Imaging

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Objective The early differentiation of adrenal lipid-poor adenomas from non-adenomas is a crucial step in reducing excessive examinations and treatments. This study seeks to construct an eXtreme Gradient Boosting (XGBoost) predictive model utilizing the minimum attenuation values (minAVs) from non-contrast CT (NCCT) scans to identify lipid-poor adenomas. Materials and methods Retrospective analysis encompassed clinical data, minAVs, CT histogram (CTh), mean attenuation values (meanAVs), and lesion diameter from patients with pathologically or clinically confirmed adrenal lipid-poor adenomas across two medical institutions, juxtaposed with non-adenomas. Variable selection transpired in Institution A (training set), with XGBoost models established based on minAVs and CTh separately. Institution B (validation set) corroborated the diagnostic efficacy of the two models. Receiver operator characteristic (ROC) curve analysis, calibration curves, and Brier scores assessed the diagnostic performance and calibration of the models, with the Delong test gauging differences in the area under the curve (AUC) between models. SHapley Additive exPlanations (SHAP) values elucidated and visualized the models. Results The training set comprised 136 adrenal lipid-poor adenomas and 126 non-adenomas, while the validation set included 46 and 40 instances, respectively. In the training set, there were substantial inter-group differences in minAVs, CTh, meanAVs, diameter, and body mass index (BMI) ( p < 0.05 for all). The AUC for the minAV and CTh models were 0.912 (95% confidence interval [CI]: 0.866–0.957) and 0.916 (95% CI: 0.873–0.958), respectively. Both models exhibited good calibration, with Brier scores of 0.141 and 0.136. In the validation set, the AUCs were 0.871 (95% CI: 0.792–0.951) and 0.878 (95% CI: 0.794–0.962), with Brier scores of 0.156 and 0.165, respectively. The Delong test revealed no statistically significant differences in AUC between the models ( p > 0.05 for both). SHAP value analysis for the minAV model suggested that minAVs had the highest absolute weight (AW) and negative contribution. Conclusion The XGBoost predictive model based on minAVs demonstrates effective discrimination between adrenal lipid-poor adenomas and non-adenomas. The minAV variable is easily obtainable, and its diagnostic performance is comparable to that of the CTh model. This provides a basis for patient diagnosis and treatment plan selection.

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Qualitative Heterogeneous Signal Drop on Chemical Shift (CS) MR Imaging: Correlative Quantitative Analysis between CS Signal Intensity Index and Contrast Washout Parameters Using T1-Weighted Sequences

Cited by 2 publications

References 32 publications

Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art

Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art

Development and validation of a model predicting adrenal lipid-poor adenoma based on the minimum attenuation value from non-contrast CT: a dual-center retrospective study

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