Monitoring and Predicting Treatment Response of Extraocular Muscles in Grave's Orbitopathy by 99mTc-DTPA SPECT/CT

Jiang, Chengzhi; Deng, Zilong; Huang, Jin; Deng, Hongbin; Tan, Jia; Li, Xinhui; Zhao, Min

doi:10.3389/fmed.2021.791131

Cited by 3 publications

(4 citation statements)

References 19 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in a schematic 3D segmentation of the EOMs from CT and EOMs from SPECT (Supplemental Fig. 3), the EOMs in the active GO group was thicker and darker than those in the inactive GO group, consistent with our previous ndings that signi cantly higher DPTA uptake and thickening of EOMs in patients with active GO over inactive GO assessed by orbital SPECT/CT [7].…”

Section: Visualizationsupporting

confidence: 89%

“…99m Tc-DTPA SPECT/CT scan acquisition After intravenous administration of 99m Tc-DTPA(Chinese Atomic Energy Institute, Beijing, China) 555 MBq (15 mCi) for 20 min, orbital SPECT/CT was performed using a 16-layer SPECT/CT scanner (Preference 16, SPECT/CT, Philips Medical Systems, The Netherlands) as described in our previous report [7]. Brie y, subjects were stabilized in a supine position and were asked to keep their eyes closed and stationary to reduce eye movements throughout the scan [7]. The CT scan (140 kV, 100 mA,1 mm slice thickness, 1:1 pitch ratio) was rstly performed.…”

Section: Introductionmentioning

confidence: 99%

“…SPECT/CT is a kind of hybrid imaging consisting of SPECT for functional images and CT for anatomical images, thereby increasing the diagnostic accuracy of the test [5,6]. We recently reported that hybrid diagnostic CT and SPECT imaging has the promise to assess in ammatory activity through semi-quantitative image analysis of EOMs in patients with GO [7]. Moreover, 99m Tc-DPTA uptake of EOMs from SPECT/CT might be superior to the CAS in predicting treatment response with periocular glucocorticoid therapy in GO patients, even if the CAS was below 3 points [5,7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Diagnosis of Disease Activity in Patients with Graves’ Orbitopathy Using Orbital SPECT/CT

Zhao

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Purpose Orbital 99mTc-DTPA SPECT/CT is an important new method for the assessment of inflammatory activity in patients with Graves' Orbitopathy (GO), but it consumes a heavy workload for physicians for interpretation. We aim to propose an automated method, called GO-Net, to detect the activity of GO to assist physicians for diagnosis. Materials and methods GO-Net had two stages: a semantic V-Net segmentation network (SV-Net) to extract extraocular muscles (EOMs) on orbital CT images; a three-channel convolutional neural network (CNN), including SPECT/CT images and segmentation results, to classify inflammatory activity. Manual corrections were applied when the segmentation results were not accurate. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) from Xiangya Hospital of Central South University were enrolled. For the segmentation, five-fold cross-validation with 194 eyes were used for training and internal validation. For the classification, 80% of eyes were trained and internally validated by five-fold cross-validation, and 20% of eyes were used for testing. The contours of the EOMs were drawn manually by an experienced physicians and used as the ground truth. The criteria for the diagnosis of GO activity were determined by the physician through the clinical activity score(CAS) and 99mTc-DTPA uptake. Results Our GO-Net method achieved an accuracy of 84.25%, a precision of 83.35%, a sensitivity of 84.63%, a specificity of 83.87%, an F1 score of 0.83, and an area under the receiver (AUC) of 0.89. For EOMs segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82. Contours of EOMs in 47 eyes (4.91%) were manually corrected and the average correction time was 5 mins for each eye. Conclusion Our proposed Go-Net model could accurately detect GO activity, which has great potential for the diagnosis of GO.

show abstract

Section: Visualizationsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Diagnosis of Disease Activity in Patients with Graves’ Orbitopathy Using Orbital SPECT/CT

Zhao

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…( 76 ) discovered orbital gallium-67 scintigraphy as a predictive marker (OR=1.74, P<0.001), that subsequent studies developed various independent predictive nuclide biomarkers. These predictive markers include baseline 99mTc-anti-TNF-α scintigraphy uptake region of interest (ROI)-ratios on SPECT/CT (P=0.036) ( 79 ), as well as the maximum uptake ratio of four EOMs (U max ) obtained by 99mTc-DTPA SPECT/CT (OR=2.08, P=0.001) ( 80 ), which outperformed CAS among patients with CAS≥3 (AUC=0.850 vs 0.780). For response prediction to ORT, Ujhelyi et al.…”

Section: Imaging Biomarkersmentioning

confidence: 99%

Predictive markers for anti-inflammatory treatment response in thyroid eye disease

Zhang,

Fan,

et al. 2023

Front. Endocrinol.

View full text Add to dashboard Cite

Anti-inflammatory treatment is the primary and vital therapeutic approach for active, moderate-to-severe thyroid eye disease (TED). Accurate pretreatment prediction of treatment response is of paramount importance for the prognosis of patients. However, relying solely on the clinical activity score asa determinant of activity has led to unsatisfactory treatment outcomes. In recent years, significant advancements have been made in identifying predictive markers for anti-inflammatory treatment response in TED, clinical markers, body fluid biomarkers and imaging biomarkers. Several clinical studies have developed prediction models based on these markers. However, there is still a lack of comprehensive elucidation or comparison between the different markers. Therefore, this review aims to provide a detailed analysis of the definition, characteristics, and application of predictive markers for anti-inflammatory treatment response in TED. Through detailed literature search, 26 articles applying anti-inflammatory treatment effect prediction with a total of 1948 TED patients were used for analysis and discussion. By gaining a better understanding of the current research on predictive markers, we can accelerate and guide the exploration of treatment prediction strategies, leading us towards an era of precise therapy for TED.

show abstract

Deep learning-based diagnosis of disease activity in patients with Graves’ orbitopathy using orbital SPECT/CT

Zhao

et al. 2023

Eur J Nucl Med Mol Imaging

Self Cite

View full text Add to dashboard Cite

Purpose Orbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves’ orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO. Materials and methods GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) at Xiangya Hospital of Central South University were investigated. For the segmentation task, five-fold cross-validation with 194 eyes was used for training and internal validation. For the classification task, 80% of the eye data were used for training and internal fivefold cross-validation, and the remaining 20% of the eye data were used for testing. The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. Furthermore, results are interpreted and visualized using gradient-weighted class activation mapping (Grad-CAM). Results The GO-Net model combining CT, SPECT, and EOM masks achieved a sensitivity of 84.63%, a specificity of 83.87%, and an area under the receiver operating curve (AUC) of 0.89 (p < 0.01) on the test set for distinguishing active and inactive GO. Compared with the CT-only model, the GO-Net model showed superior diagnostic performance. Moreover, Grad-CAM demonstrated that the GO-Net model placed focus on the GO-active regions. For EOM segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82. Conclusion The proposed Go-Net model accurately detected GO activity and has great potential in the diagnosis of GO.

show abstract

Monitoring and Predicting Treatment Response of Extraocular Muscles in Grave's Orbitopathy by 99mTc-DTPA SPECT/CT

Cited by 3 publications

References 19 publications

Deep Learning-Based Diagnosis of Disease Activity in Patients with Graves’ Orbitopathy Using Orbital SPECT/CT

Deep Learning-Based Diagnosis of Disease Activity in Patients with Graves’ Orbitopathy Using Orbital SPECT/CT

Predictive markers for anti-inflammatory treatment response in thyroid eye disease

Deep learning-based diagnosis of disease activity in patients with Graves’ orbitopathy using orbital SPECT/CT

Contact Info

Product

Resources

About