Proton-density fat fraction measurement: A viable quantitative biomarker for differentiating adrenal adenomas from nonadenomas

Quantitative imaging biomarkers in the assessment of adrenal nodules

Incidental adrenal nodules provide a diagnostic conundrum. Current imaging techniques have demonstrated success in identifying lipid-rich adenomas, however, are limited in detecting malignancy and assessing for functionality. Imaging biomarkers are objective characteristics derived from imaging that may measure normal or pathological processes or assess response to therapy. Recent attempts have been made to standardize the measurement of the most common imaging biomarkers used in assessing the adrenal gland, offering a path to more uniform research in this area. The aim of this review is to describe the imaging biomarkers used in adrenal imaging and assess the evidence supporting their use.

Fat quantification: Imaging methods and clinical applications in cancer

Recently, the study of the relationship between lipid metabolism and cancer has evolved. The characteristics of intratumoral and peritumoral fat are distinct and changeable during cancer development. Subcutaneous and visceral adipose tissue are also associated with cancer prognosis. In non-invasive imaging, fat quantification parameters such as controlled attenuation parameter, fat volume fraction, and proton density fat fraction from different imaging methods complement conventional images by providing concrete fat information. Therefore, measuring the changes of fat content for further understanding of cancer characteristics has been applied in both research and clinical settings. In this review, the authors summarize imaging advances in fat quantification and highlight their clinical applications in cancer precaution, auxiliary diagnosis and classification, therapy response monitoring, and prognosis.

Adrenal metastases: early biphasic contrast-enhanced CT findings with emphasis on differentiation from lipid-poor adrenal adenomas

AIM

To evaluate the accuracy of unenhanced attenuation and early biphasic contrast-enhanced computed tomography (CT) in differentiating adrenal metastases (AMs) from lipid-poor adrenal adenomas (AAs).

MATERIALS AND METHODS

This retrospective study included 37 patients with 50 AMs and 86 patients with 89 lipid-poor AAs. Quantitative data including the longest diameter (LD), the shortest diameter (SD), LD/SD ratio, CT attenuation values (CTu, CTa, CTv), degree of enhancement (DEAP, DEPP, DEpeak, APW, RPW), and peak enhanced/unenhanced (PE/U) CT attenuation ratio were obtained. Qualitative data including enhancement pattern, location, shape, the presence of calcification or haemorrhage, and intra-lesion necrosis were analysed. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated.

RESULTS

The PE/U ratio (≤1.25), CTu (≥32.2 HU), DEpeak (≤43.15 HU), DEPP (≤37.65 HU), presence of intralesional necrosis, location (bilateral adrenal glands), and irregular shape were significant variables for differentiating AMs from lipid-poor AAs (p<0.05). Among them, PE/U ratio (≤1.25) was of greater value in differentiating the two adrenal diseases, with sensitivity, specificity, area under the receiver operating curve (ROC) curve (AUC) of 92%, 84%, 0.933, respectively. When at least any three of above criteria were combined, the sensitivity, specificity, PPV, and NPV for diagnosing AMs were 88%, 93%, 88%, and 88%, respectively.

CONCLUSIONS

These seven CT criteria are conducive to differentiate AMs from lipid-poor AAs. Early biphasic contrast-enhanced CT is a high-efficient and practical imaging tool in differentiating them.

Robust water-fat separation based on deep learning model exploring multi-echo nature of mGRE

PURPOSE

To design a new deep learning network for fast and accurate water-fat separation by exploring the correlations between multiple echoes in multi-echo gradient-recalled echo (mGRE) sequence and evaluate the generalization capabilities of the network for different echo times, field inhomogeneities, and imaging regions.

METHODS

A new multi-echo bidirectional convolutional residual network (MEBCRN) was designed to separate water and fat images in a fast and accurate manner for the mGRE data. This new MEBCRN network contains 2 main modules, the first 1 is the feature extraction module, which learns the correlations between consecutive echoes, and the other one is the water-fat separation module that processes the feature information extracted from the feature extraction module. The multi-layer feature fusion (MLFF) mechanism and residual structure were adopted in the water-fat separation module to increase separation accuracy and robustness. Moreover, we trained the network using in vivo abdomen images and tested it on the abdomen, knee, and wrist images.

RESULTS

The results showed that the proposed network could separate water and fat images accurately. The comparison of the proposed network and other deep learning methods shows the advantage in both quantitative metrics and robustness for different TEs, field inhomogeneities, and images acquired for various imaging regions.

CONCLUSION

The proposed network could learn the correlations between consecutive echoes and separate water and fat images effectively. The deep learning method has certain generalization capabilities for TEs and field inhomogeneity. Although the network was trained only in vivo abdomen images, it could be applied for different imaging regions.

Using the modified Dixon technique to evaluate incidental adrenal lesions on 3T MRI

OBJECTIVES

To use the mDIXON-Quant sequence to quantify the fat fraction of adrenal lesions discovered incidentally on CT studies. To analyze the relation between the signal loss between in-phase and out-of-phase T1-weighted sequences and the fat fraction in mDIXON-Quant. To compare the sensitivity and specificity of the two methods for characterizing adrenal lesions.

MATERIAL AND METHODS

This prospective descriptive study included 31 patients with incidentally discovered adrenal lesions evaluated with 3T MRI using in-phase and out-of-phase T1-weighted sequences and mDIXON-Quant; the fat fraction of the adrenal lesions was measured by mDIXON-Quant and by calculating the percentage of signal loss between in-phase and out-of-phase T1-weighted sequences.

RESULTS

The percentage of signal loss was significantly higher in the group of patients with adenoma (61.3% ± 20.4% vs. 5.1% ± 5.8% in the group without adenoma, p<0.005). The mean fat fraction measured by mDIXON-Quant was also higher for the adenomas (26.9% ±10.8% vs. 3.4% ± 3.0%, p<0.005).The area under the ROC curve was 0.99 (0.96 - 1.00) for the percentage of signal loss and 0.98 (0.94 - 1.00) for the fat fraction measured by mDIXON-Quant. The cutoffs obtained were 24.42% for the percentage of signal loss and 9.2% for the fat fraction measured by mDIXON-Quant. The two techniques had the same values for diagnostic accuracy: sensitivity 96% (79.6 - 99.9), specificity 100% (39.8 - 100.0), positive predictive value 100% (85.8 - 100.0), and negative predictive value 80% (28.4 - 99.5).

CONCLUSION

The fat fraction measured by the modified Dixon technique can differentiate between adenomas and other adrenal lesions with the same sensitivity and specificity as the percentage of signal loss between in-phase and out-of-phase T1-weighted sequences.