Estimation of proton density fat fraction of the salivary gland

Development and accuracy validation of a fat fraction imaging biomarker for sialadenitis in the parotid gland

BACKGROUND

The diagnosis of sialadenitis, the most frequent disease of the salivary glands, is challenging when the symptoms are mild. In such cases, biomarkers can be used as definitive diagnostic indicators. Recently, biomarkers have been developed by extracting and analyzing pathological and morphological features from medical imaging. This study aimed to establish a diagnostic reference for sialadenitis based on the quantitative magnetic resonance imaging (MRI) biomarker IDEAL-IQ and assess its accuracy.

METHODS

Patients with sialadenitis (n = 46) and control subjects (n = 90) that underwent MRI were selected. Considering that the IDEAL-IQ value is a sensitive fat fractional marker to the body mass index (BMI), all subjects were also categorized as under-, normal-, and overweight. The fat fraction of parotid gland in the control and sialadenitis groups were obtained using IDEAL-IQ map. The values from the subjects in the control and sialadenitis groups were compared in each BMI category. For comparison, t-tests and receiver operating characteristic (ROC) curve analyses were performed.

RESULTS

The IDEAL-IQ fat faction of the control and sialadenitis glands were 38.57% and 23.69%, respectively, and the differences were significant. The values were significantly lower in the sialadenitis group (P), regardless of the BMI types. The area under the ROC curve (AUC) was 0.83 (cut-off value: 28.72) in patients with sialadenitis. The AUC for under-, normal-, and overweight individuals were 0.78, 0.81, and 0.92, respectively.

CONCLUSIONS

The fat fraction marker based on the IDEAL-IQ method was useful as an objective indicator for diagnosing sialadenitis. This marker would aid less-experienced clinicians in diagnosing sialadenitis.

Impact of physiological parameters on the parotid gland fat fraction in a normal population

Quantifying physiological fat tissue in the organs is important to further assess the organ's pathologic status. This study aimed to investigate the impact of body mass index (BMI), age, and sex on the fat fraction of normal parotid glands. Patients undergoing magnetic resonance imaging (MRI) of iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL-IQ) due to non-salivary gland-related disease were reviewed. Clinical information of individual patients was categorized into groups based on BMI (under/normal/overweight), age (age I/age II/age III), and sex (female/male) and an inter-group comparison of the fat fraction values of both parotid glands was conducted. Overall, in the 626 parotid glands analyzed, the fat fraction of the gland was 35.80%. The mean fat fraction value increased with BMI (30.23%, 35.74%, and 46.61% in the underweight, normal and overweight groups, respectively [p < 0.01]) and age (32.42%, 36.20%, and 41.94% in the age I, II, and III groups, respectively [p < 0.01]). The fat content of normal parotid glands varies significantly depending on the body mass and age regardless of sex. Therefore, the patient's age and body mass should be considered when evaluating fatty change in the parotid glands in imaging results.

Quantification of penile fat infiltration using the mDIXON Quant sequence: a pilot study on the correlation with penis hardness and erectile dysfunction

OBJECTIVE

The purpose of this study was to determine fat/water signal ratios using the mDIXON Quant sequence, quantitatively assess fat infiltration in the penis, and explore its possible relationship with penile hardness and erectile dysfunction.

METHODS

Routine pelvic MRI with the mDIXON Quant sequence was performed in 62 subjects, including 22 people in the normal group, 20 people in the normal erectile hardness group, and 20 people in the erectile dysfunction (ED) group. The fat/water signal ratio in the penis was measured using the mDIXON Quant sequence. Shear wave elastography was used to evaluate the hardness of the corpus cavernosa of the penis.

RESULTS

The fat/water signal ratio of the corpus spongiosum was significantly lower than that of the corpus cavernosa in the normal group (p = 0.03) and ED group (p < 0.01). There was no significant difference in the fat/water signal ratios between the normal group and the normal erectile hardness group. Fat infiltration was significantly lower, and erectile hardness was significantly higher in the normal erectile hardness group than in the ED group, and the fat infiltration in the left and right corpus cavernosa was inversely proportional to the erectile hardness of the penis.

CONCLUSION

This study suggests that mDIXON Quant can be used as a non-invasive, quantitative, and objective method for evaluating penile fat infiltration. This method could help diagnose penile fat infiltration in patients with erectile dysfunction and varying body mass indexes. Our results could also allow for a more accurate diagnosis and monitoring of erectile hardness function by quantitatively measuring penile fat infiltration.

ADVANCES IN KNOWLEDGE

(1) The proton density fat fraction technology is a new tool for the objective, quantitative and non-invasive evaluation of penile fat infiltration. (2) The quantitative measurement of fat infiltration in the corpora cavernosa might help diagnose and monitor penile erection hardness and its function more accurately.

Advanced imaging for quantification of abnormalities in the salivary glands of patients with primary Sjögren's syndrome

OBJECTIVES

To assess non-invasive imaging for detection and quantification of gland structure, inflammation and function in patients with primary Sjogren's syndrome (pSS) using PET-CT with 11C-Methionine (11C-MET; radiolabelled amino acid), and 18F-fluorodeoxyglucose (18F-FDG; glucose uptake marker), to assess protein synthesis and inflammation, respectively; multiparametric MRI evaluated salivary gland structural and physiological changes.

METHODS

In this imaging/clinical/histology comparative study (GSK study 203818; NCT02899377) patients with pSS and age- and sex-matched healthy volunteers underwent MRI of the salivary glands and 11C-MET PET-CT. Patients also underwent 18F-FDG PET-CT and labial salivary gland biopsies. Clinical and biomarker assessments were performed. Primary endpoints were semi-quantitative parameters of 11C-MET and 18F-FDG uptake in submandibular and parotid salivary glands and quantitative MRI measures of structure and inflammation. Clinical and minor salivary gland histological parameter correlations were explored.

RESULTS

Twelve patients with pSS and 13 healthy volunteers were included. Lower 11C-MET uptake in parotid, submandibular and lacrimal glands, lower submandibular gland volume, higher MRI fat fraction, and lower pure diffusion in parotid and submandibular glands were observed in patients vs healthy volunteer, consistent with reduced synthetic function. Disease duration correlated positively with fat fraction and negatively with 11C-MET and 18F-FDG uptake, consistent with impaired function, inflammation and fatty replacement over time. Lacrimal gland 11C-MET uptake positively correlated with tear flow in patients, and parotid gland 18F-FDG uptake positively correlated with salivary gland CD20+ B-cell infiltration.

CONCLUSION

Molecular imaging and MRI may be useful tools to non-invasively assess loss of glandular function, increased glandular inflammation and fat accumulation in pSS.

Usefulness of a deep learning system for diagnosing Sjögren's syndrome using ultrasonography images

OBJECTIVES

We evaluated the diagnostic performance of a deep learning system for the detection of Sjögren's syndrome (SjS) in ultrasonography (US) images, and compared it with the performance of inexperienced radiologists.

METHODS

100 patients with a confirmed diagnosis of SjS according to both the Japanese criteria and American-European Consensus Group criteria and 100 non-SjS patients that had a dry mouth and suspected SjS but were definitively diagnosed as non-SjS were enrolled in this study. All the patients underwent US scans of both the parotid glands (PG) and submandibular glands (SMG). The training group consisted of 80 SjS patients and 80 non-SjS patients, whereas the test group consisted of 20 SjS patients and 20 non-SjS patients for deep learning analysis. The performance of the deep learning system for diagnosing SjS from the US images was compared with the diagnoses made by three inexperienced radiologists.

RESULTS

The accuracy, sensitivity and specificity of the deep learning system for the PG were 89.5, 90.0 and 89.0%, respectively, and those for the inexperienced radiologists were 76.7, 67.0 and 86.3%, respectively. The deep learning system results for the SMG were 84.0, 81.0 and 87.0%, respectively, and those for the inexperienced radiologists were 72.0, 78.0 and 66.0%, respectively. The AUC for the inexperienced radiologists was significantly different from that of the deep learning system.

CONCLUSIONS

The deep learning system had a high diagnostic ability for SjS. This suggests that deep learning could be used for diagnostic support when interpreting US images.

Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images

OBJECTIVES

This study estimated the diagnostic performance of a deep learning system for detection of Sjögren's syndrome (SjS) on CT, and compared it with the performance of radiologists.

METHODS

CT images were assessed from 25 patients confirmed to have SjS based on the both Japanese criteria and American-European Consensus Group criteria and 25 control subjects with no parotid gland abnormalities who were examined for other diseases. 10 CT slices were obtained for each patient. From among the total of 500 CT images, 400 images (200 from 20 SjS patients and 200 from 20 control subjects) were employed as the training data set and 100 images (50 from 5 SjS patients and 50 from 5 control subjects) were used as the test data set. The performance of a deep learning system for diagnosing SjS from the CT images was compared with the diagnoses made by six radiologists (three experienced and three inexperienced radiologists).

RESULTS

The accuracy, sensitivity, and specificity of the deep learning system were 96.0%, 100% and 92.0%, respectively. The corresponding values of experienced radiologists were 98.3%, 99.3% and 97.3% being equivalent to the deep learning, while those of inexperienced radiologists were 83.5%, 77.9% and 89.2%. The area under the curve of inexperienced radiologists were significantly different from those of the deep learning system and the experienced radiologists.

CONCLUSIONS

The deep learning system showed a high diagnostic performance for SjS, suggesting that it could possibly be used for diagnostic support when interpreting CT images.