Improved Personalised Neuroendocrine Tumours’ Diagnosis Predictive Power by New Receptor Somatostatin Image Processing Quantification

Although neuroendocrine tumours (NETs) are intensively studied, their diagnosis and consequently personalised therapy management is still puzzling due to their tumoral heterogeneity. In their theragnosis algorithm, receptor somatostatin scintigraphy takes the central place, the diagnosis receptor somatostatin analogue (RSA) choice depending on laboratory experience and accessibility. However, in all cases, the results depend decisively on correct radiotracer tumoral uptake quantification, where unfortunately there are still unrevealed clues and lack of standardization. We propose an improved method to quantify the biodistribution of gamma-emitting RSA, using tissular corrected uptake indices. We conducted a bi-centric retrospective study on 101 patients with different types of NETs. Three uptake indices obtained after applying new corrections to areas of interest drawn for the tumour and for three reference organs (liver, spleen and lung) were statistically analysed. For the corrected pathological uptake indices, the results showed a significant decrease in the error of estimating the occurrence of errors and an increase in the diagnostic predictive power for NETs, especially in the case of lung-referring corrected index. In conclusion, these results support the importance of corrected uptake indices use in the analysis of 99mTcRSA biodistribution for a better personalised diagnostic accuracy of NETs patients.

Accuracy comparison of various quantitative [99mTc]Tc-DPD SPECT/CT reconstruction techniques in patients with symptomatic hip and knee joint prostheses

Background There is a need for better diagnostic tools that identify loose total hip and knee arthroplasties. Here, we present the accuracy of different 99mTc-dicarboxypropandiphosphate ([99mTc]Tc-DPD) SPECT/CT quantification tools for the detection of loose prostheses in patients with painful hip and knee arthroplasties. Methods Quantitative reconstruction of mineral phase SPECT data was performed using Siemens xSPECT-Quant and xSPECT-Bone, with and without metal artefact reduction (iMAR) of CT-data. Quantitative data (SUVmax values) were compared to intraoperative diagnosis or clinical outcome after at least 1 year as standard of comparison. Cut-off values and accuracies were calculated using receiver operator characteristics. Accuracy of uptake quantification was compared to the accuracy of visual SPECT/CT readings, blinded for the quantitative data and clinical outcome. Results In this prospective study, 30 consecutive patients with 33 symptomatic hip and knee prostheses underwent [99mTc]Tc-DPD SPECT/CT. Ten arthroplasties were diagnosed loose and 23 stable. Mean-SUVmax was significantly higher around loose prostheses compared to stable prostheses, regardless of the quantification method (P = 0.0025–0.0001). Quantification with xSPECT-Bone-iMAR showed the highest accuracy (93.9% [95% CI 79.6–100%]) which was significantly higher compared to xSPECT-Quant-iMAR (81.8% [67.5–96.1%], P = 0.04) and xSPECT-Quant without iMAR (77.4% [62.4–92.4%], P = 0.02). Accuracies of clinical reading were non-significantly lower compared to quantitative measures (84.8% [70.6–99.1%] (senior) and 81.5% [67.5–96.1%] (trainee)). Conclusion Quantification with [99mTc]Tc-DPD xSPECT-Bone-iMAR discriminates best between loose and stable prostheses of all evaluated methods. The overall high accuracy of different quantitative measures underlines the potential of [99mTc]Tc-DPD-quantification as a biomarker and demands further prospective evaluation in a larger number of prosthesis.

Group-level Regional Cerebral Uptake Quantification in Micro PET-CT

The lack of soft tissue CT contrast is an impediment to accurate quantification of regional cerebral uptake on micro PET-CT. Co-registration to an atlas can aid with quantitative analysis, particularly when MRI is not available. Methods: A subject CT is cropped to the skull, the brain void is then filled to create a “skull cavity mask”. An MRI mouse brain volumetric template is then co-registered to the skull cavity mask via affine transformation. Each subsequent subject CT is then skull cropped and co-registered to the first, these transformations are then applied to each corresponding PET for group level co-registration. Results: The method was tested on eight mice with each possible registration permutation; resulting in small variations in regional cerebral PET counts. Conclusion: We present a semi-automated method that allows for quantification of regional cerebral PET uptake from micro PET-CT images without subject MRI data.