Feasibility of Patlak parametric imaging in the diagnosis of liver cancer

doi:10.21203/rs.3.rs-2959478/v1

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Feasibility of Patlak parametric imaging in the diagnosis of liver cancer

https://doi.org/10.21203/rs.3.rs-2959478/v1

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Conclusion Whole-body dynamic ¹⁸F-FDG PET/CT parameters are of great value in the differential diagnosis of benign and malignant liver lesions. Whole-body dynamic ¹⁸F-FDG PET/CT is more sensitive than whole-body static ¹⁸F-FDG PET/CT in the differential diagnosis of liver malignancy.Using reliable Patlak graphical analysis, whole-body dynamic PET/CT parametric imaging can achieve comparable or potentially better lesion detection capabilities than static SUV imaging.

Background To investigate the value of whole-body dynamic ¹⁸F-FDG PET/CT parametric imaging in the differential diagnosis of liver-occupying lesions.

Methods Eighty-one patients with hepatic occupancies were selected for whole-body dynamic PET/CT examinations. The imaging characteristics of Patlak parameter images of patients in the benign and malignant liver groups and patients with different pathological types of hepatic malignancies were analysed and compared, and the results were statistically analysed.

Results Among 81 patients, SUV_max,Ki_max,TBR_SUV and TBR_Ki showed statistically significant differences in the benign liver groups and the malignant liver groups. (SUV_max: t =-13.020, P<0.001; Ki_max: t =-12.984, P<0.001; TBR_SUV: t =-10.541, P<0.001; TBR_Ki: t=-7.536, P<0.001). Among 67 cases in liver malignancy, the TBR_Ki values were higher than TBR_SUV according to the results of the box plot analysis. Pearson correlation analysis showed that SUVmax was positively correlated with Ki_max (r=0.882, P<0.01) and TBR_SUVwas positively correlated with TBR_Ki (r=0.848, P<0.01),respectively.The sensitivity and specificity of SUV_max, Ki_max, TBR_SUV and TBR_Ki in the diagnosis of primary liver cancer and liver metastases were 84.0% 65.7%, 68.0% 88.6%, 64.0% 85.1% ,92.0% and 68.6%, respectively.

Patlak

Hepatocellular carcinoma

SUVmax

Kimax

TBRSUV

TBRKi

Liver is the largest solid organ in the body with a dual blood supply, 75–80% from the portal vein and 20–25% from the hepatic artery[1], and is susceptible to various primary and secondary tumours. The best treatment option for liver-occupying lesions depends on accurate diagnosis. Positron emission tomography (PET/CT) is an imaging technique that has been shown valuable in diagnosing tumours.¹⁸F-deoxyglucose (FDG) is the most widely used positron imaging agent,and ¹⁸F-FDG PET/CT offers rapid, accurate and practical advantages in the staging, recurrence, treatment response and prognosis of many malignant diseases[2]. Currently, whole-body static ¹⁸F-FDG PET/CT parametric imaging is commonly used clinically for parametric analysis using standardised uptake value (SUV), which is a semi-quantitative index, and factors such as timing of tracer injection, glucose metabolism, scanner calibration and injection imperfections can affect the final image[3][4][5].In essence, tracer distribution is a dynamic process, therefore such a process could be described by Patlak parameter imaging[6]. It is capable of providing multi-parameter whole-body PET images by tracking the spatial and temporal distribution of ¹⁸F-FDG uptake across multiple beds[7]. Quantitative Ki parameter maps have been reported to be more sensitive than SUV in normal sized tumours[8]. As a result, whole-body dynamic PET/CT offers enhanced dynamic reconstruction and improved image quality compared to conventional PET/CT. We use dynamic PET imaging to precisely quantify radiotracer uptake by kinetic analysis of FDG accumulation over time. Whole-body dynamic ¹⁸F-FDG PET imaging protocols are beginning to attract interest in clinical oncology for their ability to provide highly quantitative parametric whole-body dynamic PET/CT images that go beyond conventional standardised uptake value (SUV) metrics by tracking the spatial and temporal distribution of ¹⁸F-FDG uptake across multiple beds[9][10]. However, dynamic imaging is able to accurately quantify FDG uptake and is not affected by these biases.This is the reason why dynamic PET has been introduced in this specific indication, allowing early assessment of small metabolic changes in tumours, more direct and effective reflection of tracer uptake and absolute quantification[11][12].

In this study, we investigated the value of whole-body dynamic ¹⁸F-FDG PET/CT parametric imaging in the diagnosis of liver-occupying lesions by comparing and analyzing the imaging data, including the results of parametric imaging,81 patients with primary and metastatic hepatocellular carcinoma.

Research object

Among these 81 patients, there were 15 cases of HCC, 20 cases of cholangiocarcinoma, 25 cases of hepatic metastases, 7 cases of hepatic lymphoma and 14 cases of benign liver lesions including 9 cases of hepatic hemangioma, 1 case of liver abscess, 3 cases of cirrhotic regenerative nodules and 1 case of focal hepatic nodular hyperplasia. There were 56 male and 25 female cases with a mean age of (59.52 ± 10.64 years)(Table 1). All enrolled patients underwent whole-body dynamic ¹⁸F-FDG PET/CT. Inclusion criteria:Patients who have voluntarily signed an informed consent form; those without severe cardiopulmonary impairment; those with an expected survival of more than three months.Exclusion criteria: fasting blood glucose greater than 8.1 mmol/L; those who could not lie down for a long time; those who withdrew from the study. The study was approved by the hospital's ethics committee and all patients signed an informed consent form before the examination.

Table 1

Patients characteristics(n = 81)
Characteristics	n = 81
Age,years old/a(rang)	59(38–83)
Gender Male Female	56(69.1%) 25(30.8%)
Benign Liver Lesions Group Hepatic hemangioma Heoatophyma Liver abscess Regenerative nodules in cirrhosis Liver Focal nodular hyperplasia Malingnancy Primary hepatocellular carcinoma (subtotal)	14(17.3%) 9 1 3 1 67(82.7%) 35
Hepatocellular carcinoma group Cholangiocarcinoma Group	15 20
Hepatic metastases group	25
Hepatic lymphoma group	7

Experimental materials and apparatus

A Siemens Biograph Vision PET/CT scanner was used. The ¹⁸F-FDG imaging agent was purchased from Nanjing Jiangyuan Dike Positron Research and Development Co. Before the scan, patients were asked to fast for more than 6 hours, rest for 15 minutes. Explain the procedure and purpose of the scan to the patient and their family, and perform the whole-body dynamic 18F-FDG PET/CT scan after the patient has signed informed consent. A low-dose CT acquisition for PET data attenuation correction (slice thickness 1.0 mm, tube voltage 100 KV, tube current 43 Ma/s) was performed before the examination bed was moved into the PET field of view, and the patient received a bullet injection of ¹⁸F-FDG imaging agent (3.71 ± 1.05) MBq/kg by intravenous infusion on a body weight basis while a 6-minute dynamic single-slice cardiac-centred PET acquisition was started, and the patient rested for 55–60 min before undergoing 3 consecutive PASS of whole-body dynamic PET/CT scans from the superior orbital rim to the superior femur.

Image reconstruction method

After completion of the whole-body dynamic ¹⁸F-FDG PET/CT scan, Patlak multiparametric reconstruction was performed using the input function, and SUVmax, MR_FDG images were obtained by Patlak reconstruction of the whole-body dynamic PET scan data using the descending aortic time-radiation curve (TAC) as the input function (IDIF) directly on the Siemens workstation. Image reconstruction was performed using the ordered subsets expectation maximisation (OSEM) iterative method (4 iterations, 5 subsets, 220 matrices). After acquisition, the original dynamic PET data were reconstructed. The left ventricular volume of interest (VOI) was delineated in the reconstructed PET images, and the delineated left ventricular VOI was copied to all frames in the same tomography to generate the left ventricular tumour time-activity curve (TAC). The left ventricular TAC curve was used as an input function for Patlak model analysis and MR_FDG images were obtained. The last frame of the dynamic PET image was used as the measured SUV image for static analysis, and the resulting images were transferred to a workstation for image processing.

¹⁸ F-FDG PET/CT imaging and image analysis.

PET images, CT images and MR_FDG images were analyzed by 2 experienced nuclear medicine physicians and experts were asked to assess the discordance. On the Patlak image, the plane with the highest radioactive concentration of the lesion was selected to measure SUV_max and Ki_max, and the region of interest (ROI) of the lesion was outlined in the SUV image, and the ROI was copied to the MR_FDG map to measure the SUV_max and Ki_max of the lesion, and the TBR_SUV and TBR_Ki values were calculated from the ratio of the SUV_max and Ki_max ROI to the background ROI of the liver as an index of statistical analysis.

Statistical methods

All data were expressed as mean ± standard deviation (SD). Statistical analysis was performed using SPSS 26.0 (Armonk, USA) and GraphPad Prism 8.3 Software (San Diego, USA).and the comparison of SUV_max and Ki_max, TBR_SUV and TBR_Ki between the benign liver group and the malignant liver group was performed by t-test of two independent samples mean, and P < 0.05 was considered a statistically significant difference. 67 cases of liver malignancy between SUV_max and Ki_max, TBR_SUV and TBR_Ki were statistically analysed using Pearson correlation analysis method. ROC was used to analyse the sensitivity and specificity of SUV_max, Ki_max, TBR_SUV and TBR_Ki for the differential diagnosis of primary liver cancer and liver metastases.

Comparison of SUV _max, Ki_max, TBR_SUV and TBR_Ki in 81 patients with liver occupancy(Fig. 1).

The difference in SUV_max between the benign and malignant liver groups was statistically significant (t=-13.020, P < 0.001). The difference in Ki_max between the hepatic malignancy group and the hepatic benign lesion group was statistically significant (t=-12.984, P < 0.001). The difference in Ki_max between the hepatic malignancy group and the hepatic benign lesion group was statistically significant (t=-10.541, P < 0.001). The difference in TBR_Ki between the malignant liver group and the benign liver lesion group was statistically significant (t=-7.536, P < 0.001) (Table 2).

Table 2 Comparison of SUV_max values, Ki_max values, TBR_SUV and TBR_Kiin 81 patients with hepatic occupancy

Classification

SUV_max

Ki_max

TBR_SUV

TBR_Ki

Benign Liver Lesions Group

Malingnancy

Hepatocellular carcinoma group

Cholangiocarcinoma Group

Hepatic metastases group

Hepatic lymphoma group

5.28±1.33

12.73±3.67

8.36±1.30

12.51±2.39

14.14±2.52

17.73±3.87

0.08±0.02

0.23±0.84

0.15±0.04

0.20±0.07

0.26±0.06

0.35±0.10

1.67±0.49

3.55±0.99

2.34±0.37

3.45±0.56

4.00±0.74

4.88±0.84

1.63±0.77

4.22±1.23

3.19±0.98

3.77±0.79

4.58±0.58

6.43±1.24

Results of analysis of 67 cases of liver malignancy

In 67 cases of liver malignancy, SUV_max vs. Ki_max box plots showed TBR (Fig. 2). TBR_Ki values were higher than TBR_SUV values. Correlation analysis showed a positive correlation between SUV_max and Ki_max (r = 0.882, P < 0.01),TBR_SUV and TBR_Ki (r = 0.848, P < 0.01) (Fig. 3).

Results of ROC curve analysis of primary liver cancer and liver metastases

The differential diagnostic efficacy of ROC curves with SUV_max and Ki_max, TBR_SUV and TBR_Ki for primary liver cancer versus liver metastases (Table 3).The area under the curve was 0.805, with a 95% confidence interval of 0.697–0.913. Using SUV_max 11.75 as the diagnostic threshold, the Jorden index was the highest (0.497), with a sensitivity and specificity of 84.0% and 65.7%, respectively.The area under the Ki_max curve was 0.853 and the 95% confidence interval was 0.757–0.949. Using Ki_max 0.225 µmol/min/ml as the diagnostic threshold, the Jorden index was maximum (0.566), and the sensitivity and specificity were 68.0% and 88.6%, respectively.The ROC curve for the differential diagnosis of primary hepatocellular carcinoma and liver metastases was plotted, and the area under the curve was 0.834, with a 95% confidence interval of 0.733–0.934(Fig. 4). Using TBR_SUV 3.770 as the diagnostic threshold, the Jorden index was the highest (0.497) and the sensitivity and specificity were 64.0% and 85.7%, respectively.The area under the Ki_max curve was 0.843 with a 95% confidence interval of 0.742–0.945. Using TBR_Ki3.830 as the diagnostic cut-off, the Jorden index was the highest (0.606), and the sensitivity and specificity were 92.0% and 68.6%, respectively.

Table 3

ROC curve results of SUV_max, Ki_max, TBR_Ki and TBR_SUV for the differential diagnosis of primary liver cancer and liver metastases
Methods	Area	Cut-off-value	Sensitivity	Specificity
SUV_max	0.805	11.75	0.840	0.657
Ki_max TBR_SUV TBR_Ki	0.853 0.834 0.843	0.225 3.770 3.830	0.680 0.640 0.920	0.886 0.857 0.686

Liver is usually the nidus where malignant tumours are more likely to metastasise and is a good site for metastatic cancer, cause malignant problems. Liver is also a target organ for many malignant diseases and is associated with a large number of benign lesions[13]. Early diagnosis is therefore very important. Currently, the diagnosis of liver-occupying lesions is mainly based on imaging modalities such as ultrasound, CT and MRI. Conventional imaging modalities have their limitations and are easily influenced by anatomical location and cannot assess systemic tumour invasion and biological characteristics[14]. PET/CT can provide functional imaging from a molecular biology perspective, and the organic combination of metabolic imaging and anatomical imaging can provide in vivo metabolic information non-invasively[15].¹⁸F-FDG indirectly reflects intracellular glucose metabolism by monitoring deoxyglucose and has become an important tool for the qualitative diagnosis, staging, prognosis and efficacy assessment of tumours[16][17]. ¹⁸F-FDG PET/CT reflects the glucose metabolism of tumour tissue, and the differential diagnosis between benign and malignant tumours is based on the difference in glucose metabolism activity between tumour cells and normal tissue cells. ¹⁸F-FDG is an derivative of glucose and is involved in the process of glucose metabolism. Because it is deoxygenated, it cannot form hexose diphosphate, preventing the next step of metabolism and remaining in the cell. In tumour cells, ¹⁸F-FDG uptake is increased by a combination of high expression of glucose transporter mRNA, increased levels of glucose transporter proteins Glut-1 and Glut-3, increased expression of hexokinase and downregulation of glucose-6-phosphatase levels.In highly and moderately differentiated HCC, ¹⁸F-FDG uptake levels are similar to normal liver parenchyma due to low GLUT-1 and GLUT-2 expression and high glucose-6-phosphatase activity, whereas in hypofractionated HCC, ¹⁸F-FDG affinity is usually higher[18].

In this study, SUV_max and Ki_max, TBR_SUV and TBR_Ki were significantly different between the benign liver group and the malignant liver group, and the difference was statistically significant (P < 0.05), and SUV_max and Ki_max, TBR_SUV and TBR_Ki were higher in the liver malignancy group than in the benign liver lesion group. The results show that dynamic whole-body ¹⁸F-FDG PET/CT parametric imaging provides highly sensitive imaging of whole-body uptake changes and that ¹⁸F-FDG uptake in benign liver lesions can be distinguished from uptake in malignant liver lesions by dynamic imaging to assess uptake changes. Therefore, whole-body dynamic PET/CT has a high diagnostic value in the differential diagnosis of benign and malignant liver lesions.In the case of focal nodular hyperplastic lesions where ¹⁸F-FDG uptake levels are identical to normal liver parenchyma, visual analysis of metabolic imaging findings may be confused with malignant lesions of highly differentiated hepatocellular carcinoma (HCC), and further analysis requires quantitative assessment of ¹⁸F-FDG uptake to differentiate between malignant and benign lesions. It has been suggested that this quantitative analysis may have similar value to conventional early delayed PET scans[19]. During the delineation of the HCC, some lesions showed no ¹⁸F-FDG uptake and only hypointense shadows on the CT images (Fig. 5), which may be due to the fact that the better differentiated HCC behaved negatively on images similar to the surrounding normal liver tissue. Since the degree of ¹⁸F-FDG uptake in HCC is related to the degree of differentiation of the tumour tissue, the higher the degree of differentiation, the lower the FDG uptake. A proportion of more differentiated hepatocellular carcinomas have higher levels of dephosphorylation, allowing lower FDG uptake by cancer cells and predisposing to false-negative results.

In this study, by analysing box plots, it was found that TBR_Ki was higher than TBR_SUV for different pathological types of liver malignancy. MR_FDG images were found to have better tumour-liver contrast than SUV images[20]. On visual analysis, most of the different pathological types of liver malignancy were detected in both modalities, with the exception of one HCC nodule, which showed non-hypermetabolism in both modalities. Thus, TBR_SUV was largely sufficient to detect lesions in most cases. Correlation analysis showed positive correlations between SUV_max and Ki_max (r = 0.882, P < 0.01) and TBR_SUV and TBR_Ki (r = 0.848, P < 0.01), which were stronger when different pathological types of liver malignancies were considered separately.This suggests that MR_FDG images can identify hypermetabolic lesions[21]. TBR may demonstrate the ability to differentiate focal lesions from background and map appropriate regions of interest for further quantitative evaluation. On visual analysis, TBR_SUV value of2.34 and TBR_Ki value of 3.20 in a liver lesion that was negative on both SUV and MR_FDG images(Fig. 5).TBR_SUV value of 0.33 and TBR_Ki value of 0.51 in a liver lesion that was negative on both SUV and MR_FDG images(Fig. 6).The ratio of TBR in both cases had a greater TBR_Ki value than TBR_SUV, suggesting that the Patlak dynamic parameter Ki_max is slightly more sensitive in detecting hypermetabolic lesions than the static parameter SUV_max. In addition, most malignant lesions can show a gradual increase in FDG uptake, indicating higher metabolism at the lesion site, while benign lesions and areas of physiological uptake show little change or decrease. Recent reports suggest that the TBR ratio of Ki in Patlak analysis is higher than the commonly used SUV values, indicating that the contrast of parametric PET images is higher than that of static images[22][23][24]. In addition, there are relevant findings suggesting that Patlak parametric imaging can provide improved TBR as well as an additional set of highly quantitative tumour features beyond the static features currently supported by the respective SUV image metrics[25][26].

In this study, the sensitivity and specificity of whole-body static PET/CT SUV_max parametric imaging for the diagnosis of primary liver cancer (including hepatocellular carcinoma and intrahepatic cholangiocarcinoma) with liver metastases were 84.0% and 65.7%, respectively, with an area under the curve of 0.805. The sensitivity and specificity of TBR_SUV in the differential diagnosis of primary liver cancer and liver metastases were 64.0% and 85.7%, respectively, with an area under the curve of 0.834. The sensitivity and specificity of Ki_max parameters in the differential diagnosis of primary liver cancer and liver metastases were 68.0% and 88.6%, respectively, and the area under the curve was 0.853. The sensitivity and specificity of TBR_Ki for the differential diagnosis of primary liver cancer and liver metastases were 92.0% and 68.6%, respectively. The area under the curve was 0.843. The ROC curve fit of Patlak parametric imaging was better than that of static whole-body PET/CT parametric imaging in the differential diagnosis of primary liver cancer versus liver metastases. The use of dynamic imaging to assess temporal changes in FDG uptake has several clinical advantages over static imaging. Continuous assessment of changes in FDG uptake, even over short periods of time, can help distinguish pathological uptake, particularly in the abdominal region[27].

MR_FDG images have been shown to have good visual quality and lesion contrast. In addition, MR_FDG images complement standard SUV images by providing better quantification and improve image reading, which also reduces the number of false positives. Patlak parametric imaging has been shown to provide better quantitation and specificity for the detection of hepatocellular carcinoma lesions[28]. It has been suggested that Patlak parametric images can complement conventional standardised uptake value (SUV) images and improve current applications or enable new ones. The high sensitivity of our PET/CT system in continuous bed motion mode is capable of providing image quality for continuous short-interval whole body dynamic scanning for routine image interpretation[29][30].

The main limitation of this study is the relatively small number of patients. Nevertheless, we also found the value of whole-body dynamic Ki-parametric imaging in the differential diagnosis of benign and malignant liver lesions and in the differential diagnosis of different pathological types of liver malignancy to provide useful conclusions for the clinical performance assessment of whole-body dynamic 18F-FDG PET Ki-parametric imaging.In addition, manual ROI mapping for IDIF calculation is subjective to the outliner's subjective factors and may be affected by variability. We plan to continue this work in a larger cohort of prospective controlled studies for specific tumour types. Therefore, future studies should increase sample size and minimise selection bias to better assess the diagnostic value of whole-body dynamic ¹⁸F-FDG PET/CT for liver occupancy.

Whole-body dynamic ¹⁸F-FDG PET/CT with dynamic parameters Ki is of high value in identifying benign and malignant liver lesions. Whole-body dynamic PET/CT is more sensitive than whole-body static PET/CT in the differential diagnosis of hepatic malignant tumors. A full understanding of the advantages and limitations of PET/CT in liver tumors is beneficial to the clinical science and rational treatment of liver tumors.

¹⁸F-FDG ¹⁸F-Fluorodeoxyglucose

CT Computed tomography

IDIF Image-derived input function

PET Positron emission tomography

PET/CT Positron emission tomography/computed tomography

ROI Region of interest

SUV Standardized uptake value

Ki Maximum focal net tracer inflow rate constant

SUV_maxMaximum standardised uptake value

Ki_maxMaximum focal net tracer uptake rate constant

TBR_SUVTumour to background SUV ratio

TBR_KiTumour to background Ki ratio

VOI Volume of interest

ROC Receiver Operating Characteristic

OSEM Ordered subsets expectation maximisation

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University, Grant No. PJ2023-04-45. In accordance with the ethical principles of the Ethical Review of Biomedical Research Involving Human Beings, the WMA Declaration of Helsinki and the CIOMS International Ethical Guidelines for Human Biomedical Research, it was reviewed and agreed by the presiding member and the results of the review were communicated to the Ethics Committee, which agreed to carry out This study was conducted and the research results were published publicly. Informed consent has been obtained from all the subjects and/or their legal guardian(s).

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated during and analyzed during the current study are not publicly available due to protection of study participant privacy, but are available from the corresponding author on reasonable request.

Competing interests

The authors report no conflicts of interest in this work.

Funding

This work was supported by Natural Science Foundation of Anhui Province (No. 2108085QH355),the Scientific Research Foundation of the Higher Education Institutions of Anhui Province (No. 2022AH051165) and Cultivate Youth Project of The First Affiliated Hospital of Anhui Medical University (No. 2021kj05).

Authors' contributions

Xin Wang conceived the study and designed the trial. Material preparation, data collection and analysis were performed by Xin Wang, Huimin Shi and Yue Xu. The first draft of the manuscript was written by Xin Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

Authors' information

Authors and Affiliations

Xin Wang &Huimin Shi &Yue Xu &Gan Zhu &Hui Wang &Peng-fei Dai

Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.

Corresponding author

Correspondence to Peng-fei Dai.

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Feasibility of Patlak parametric imaging in the diagnosis of liver cancer

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Abstract

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Introduction

Materials and Methods

Research object

Experimental materials and apparatus

Image reconstruction method

Statistical methods

Result

Results of analysis of 67 cases of liver malignancy

Results of ROC curve analysis of primary liver cancer and liver metastases

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