Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI

Diagnostic Value of Dynamic 18F-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (18F-FDG PET-CT) in Cervical Lymph Node Metastasis of Nasopharyngeal Cancer

BACKGROUND AND PURPOSE

Dynamic 18F-FDG PET-CT scanning can accurately quantify 18F-FDG uptake and has been successfully applied in diagnosing and evaluating therapeutic effects in various malignant tumors. There is no conclusion as to whether it can accurately distinguish benign and malignant lymph nodes in nasopharyngeal cancer. The main purpose of this study is to reveal the diagnostic value of dynamic PET-CT in cervical lymph node metastasis of nasopharyngeal cancer through analysis.

METHOD

We first searched for cervical lymph nodes interested in static PET-CT, measured their SUV-Max values, and found the corresponding lymph nodes in magnetic resonance images before and after treatment. The valid or invalid groups were included according to the changes in lymph node size before and after treatment. If the change in the product of the maximum diameter and maximum vertical transverse diameter of the lymph node before and after treatment was greater than or equal to 50%, they would be included in the valid group. If the change was less than 50%, they would be included in the invalid group. Their Ki values were measured on dynamic PET-CT and compared under different conditions. Then, we conducted a correlation analysis between various factors and Ki values. Finally, diagnostic tests were conducted to compare the sensitivity and specificity of Ki and SUV-Max.

RESULT

We included 67 cervical lymph nodes from different regions of 51 nasopharyngeal cancer patients and divided them into valid and invalid groups based on changes before treatment. The valid group included 50 lymph nodes, while the invalid group included 17. There wer significant differences (p < 0.001) between the valid and the invalid groups in SUV-Max, Ki-Mean, and Ki-Max values. When the SUV-Max was ≤4.5, there was no significant difference in the Ki-Mean and Ki-Max between the two groups (p > 0.05). When the SUV-Max was ≤4.5 and pre-treatment lymph nodes were <1.0 cm, the valid group had significantly higher Ki-Mean (0.00910) and Ki-Maximum (0.01004) values than the invalid group (Ki-Mean = 0.00716, Ki-Max = 0.00767) (p < 0.05). When the SUV-Max was ≤4.5, the pre-treatment lymph nodes < 1.0 cm, and the EBV DNA replication normal, Ki-Mean (0.01060) and Ki-Max (0.01149) in the valid group were still significantly higher than the invalid group (Ki-Mean = 0.00670, Ki-Max = 0.00719) (p < 0.05). The correlation analysis between different factors (SUV-Max, T-stage, normal EB virus DNA replication, age, and pre-treatment lymph node < 1.0 cm) and the Ki value showed that SUV-Max and a pre-treatment lymph node < 1.0 cm were related to Ki-Mean and Ki-Max. Diagnostic testing was conducted; the AUC value of the SUV-Max value was 0.8259 (95% confidence interval: 0.7296-0.9222), the AUC value of the Ki-Mean was 0.8759 (95% confidence interval: 0.7950-0.9567), and the AUC value of the Ki-Max was 0.8859 (95% confidence interval: 0.8089-0.9629). After comparison, it was found that there was no significant difference in AUC values between Ki-Mean and SUV-Max (p = 0.220 > 0.05), and there was also no significant difference in AUC values between Ki max and SUV-Max (p = 0.159 > 0.05). By calculating the Youden index, we identified the optimal cut-off value. It was found that the sensitivity of SUV-Max was 100% and the specificity was 66%, the sensitivity of Ki-Mean was 100% and the specificity was 70%, and the sensitivity of Ki-Max was 100% and the specificity was 72%. After Chi-Square analysis, it was found that there was no significant difference in specificity between Ki-Mean and SUV-Max (p = 0.712), and there was also no significant difference in specificity between Ki-Max and SUV-Max (p = 0.755).

CONCLUSION

Dynamic PET-CT has shown a significant diagnostic value in diagnosing cervical lymph node metastasis of nasopharyngeal cancer, especially for the small SUV value, and lymph nodes do not meet the metastasis criteria before treatment, and EBV DNA replication is normal. Although the diagnostic accuracy, sensitivity, and specificity of dynamic PET-CT were not significantly different from traditional static PET-CT, the dynamic PET-CT had a more accurate tendency.

Does consensus contours improve robustness and accuracy on [Formula: see text]F-FDG PET imaging tumor delineation?

PURPOSE

The aim of this study is to explore the robustness and accuracy of consensus contours with 225 nasopharyngeal carcinoma (NPC) clinical cases and 13 extended cardio-torso simulated lung tumors (XCAT) based on 2-deoxy-2-[[Formula: see text]F]fluoro-D-glucose ([Formula: see text]F-FDG) PET imaging.

METHODS

Primary tumor segmentation was performed with two different initial masks on 225 NPC [Formula: see text]F-FDG PET datasets and 13 XCAT simulations using methods of automatic segmentation with active contour, affinity propagation (AP), contrast-oriented thresholding (ST), and 41% maximum tumor value (41MAX), respectively. Consensus contours (ConSeg) were subsequently generated based on the majority vote rule. The metabolically active tumor volume (MATV), relative volume error (RE), Dice similarity coefficient (DSC) and their respective test-retest (TRT) metrics between different masks were adopted to analyze the results quantitatively. The nonparametric Friedman and post hoc Wilcoxon tests with Bonferroni adjustment for multiple comparisons were performed with [Formula: see text] 0.05 considered to be significant.

RESULTS

AP presented the highest variability for MATV in different masks, and ConSeg presented much better TRT performances in MATV compared with AP, and slightly poorer TRT in MATV compared with ST or 41MAXin most cases. Similar trends were also found in RE and DSC with the simulated data. The average of four segmentation results (AveSeg) showed better or comparable results in accuracy for most cases with respect to ConSeg. AP, AveSeg and ConSeg presented better RE and DSC in irregular masks as compared with rectangle masks. Additionally, all methods underestimated the tumour boundaries in relation to the ground truth for XCAT including respiratory motion.

CONCLUSIONS

The consensus method could be a robust approach to alleviate segmentation variabilities, but did not seem to improve the accuracy of segmentation results on average. Irregular initial masks might be at least in some cases attributable to mitigate the segmentation variability as well.

The valuable role of dynamic 18F FDG PET/CT-derived kinetic parameter Ki in patients with nasopharyngeal carcinoma prior to radiotherapy: A prospective study

BACKGROUND AND PURPOSE

Dynamic positron emission tomography/computed tomography (PET/CT) served the potential role of characterizing malignant foci. The main objective of this prospective study was to explore the advantage of dynamic PET/CT imaging in characterizing nasopharyngeal carcinoma (NPC).

METHODS AND MATERIALS

Patients with probable head and neck disease underwent a local dynamic PET/CT scan followed by a whole-body static scan. Patlak analysis was used to generate parametric influx rate constant (K_i) images from 48 frames obtained from a dynamic PET/CT scan. By delineating the volumes-of-interest (VOIs) of: primary tumor (PT), lymph node (LN), and normal nasopharyngeal tissues (N), we acquired the corresponding K_i mean and SUVmean of each site respectively to perform the quantitative statistical analysis.

RESULTS

Qualified images of 71 patients with newly diagnosed NPC and 8 without nasopharyngeal malignant lesions were finally included. We found the correlations between K_i mean-PT and critical clinical features, including clinical stage (r = 0.368), T category (r = 0.643) and EBV-DNA copy status (r = 0.351), and K_i mean-PT differed within the group. SUVmean-PT showed correlations with clinical stage (r = 0.280) and T category (r = 0.472), but could hardly differ systematically within group of clinical features except T category. K_i mean-LN offered the positive correlations with N category (r = 0.294), M category (r = 0.238) and EBV-DNA copy status (r = 0.446), and differed within the group. In addition, K_i mean represented a sensitivity of 94.4 % and a specificity of 100 %, in distinguishing NPC from the non-NPC, when the cut-off was defined as 0.0106. When the cut-off of SUV being defined as 2.03, the sensitivity and specificity were both 100 %.

CONCLUSION

Our research confirmed K_i compared favorably to SUV in characterizing NPC and found that K_i can serve as an effective imaging marker of NPC.

Parametric Imaging With Dynamic PET for Oncological Applications: Protocols, Interpretation, Current Applications and Limitations for Clinical Use

Nuclear medicine imaging modalities, and in particular positron emission tomography (PET), provide functional images that demonstrate the mean radioactivity distribution at a defined point in time. With the help of mathematical model's, it is possible to depict isolated parameters of the radiotracers' pharmacokinetics and to visualize them. These so called parametric images add a new dimension to the existing conventional PET images and provide more detailed information about the tracer distribution over time and space. Prerequisite for the calculation of parametric images, which reflect specific pharmacokinetic parameters, is the dynamic PET (dPET) data acquisition. Hitherto, PET parametric imaging has mainly found use for research purposes. However, it has not been yet implemented into clinical routine, since it is more time-consuming, it requires a complicated analysis and still lacks a clear benefit over conventional PET imaging. However, the recent introduction of new PET-CT scanners with an ultralong field of view, which allow a faster data acquisition and are associated with higher sensitivity, as well as the development of more sophisticated evaluation software packages will probably lead to a renaissance of dPET and parametric maps even of the whole body. The implementation of dPET imaging in daily routine with appropriate acquisition protocols, as well as the calculation, interpretation and potential clinical applications of parametric images will be discussed in this review article.

Feasibility of Deep Learning-Guided Attenuation and Scatter Correction of Whole-Body 68Ga-PSMA PET Studies in the Image Domain

OBJECTIVE

This study evaluates the feasibility of direct scatter and attenuation correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning.

METHODS

Whole-body 68Ga-PSMA PET images of 399 subjects were used to train a residual deep learning model, taking PET non-attenuation-corrected images (PET-nonAC) as input and CT-based attenuation-corrected PET images (PET-CTAC) as target (reference). Forty-six whole-body 68Ga-PSMA PET images were used as an independent validation dataset. For validation, synthetic deep learning-based attenuation-corrected PET images were assessed considering the corresponding PET-CTAC images as reference. The evaluation metrics included the mean absolute error (MAE) of the SUV, peak signal-to-noise ratio, and structural similarity index (SSIM) in the whole body, as well as in different regions of the body, namely, head and neck, chest, and abdomen and pelvis.

RESULTS

The deep learning-guided direct attenuation and scatter correction produced images of comparable visual quality to PET-CTAC images. It achieved an MAE, relative error (RE%), SSIM, and peak signal-to-noise ratio of 0.91 ± 0.29 (SUV), -2.46% ± 10.10%, 0.973 ± 0.034, and 48.171 ± 2.964, respectively, within whole-body images of the independent external validation dataset. The largest RE% was observed in the head and neck region (-5.62% ± 11.73%), although this region exhibited the highest value of SSIM metric (0.982 ± 0.024). The MAE (SUV) and RE% within the different regions of the body were less than 2.0% and 6%, respectively, indicating acceptable performance of the deep learning model.

CONCLUSIONS

This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning with clinically tolerable errors. The technique has the potential of performing attenuation correction on stand-alone PET or PET/MRI systems.