Gallium-68-somatostatin receptor PET/CT parameters as potential prognosticators for clinical time to progression after peptide receptor radionuclide therapy: a cohort study

Prediction of 177Lu-DOTATATE Therapy Outcomes in Neuroendocrine Tumor Patients Using Semi-Automatic Tumor Delineation on 68Ga-DOTATATE PET/CT

BACKGROUND

Treatment of metastatic neuroendocrine tumors (NET) with 177Lu-DOTATATE peptide receptor radionuclide therapy (PRRT) results in favorable response only in a subset of patients. We investigated the prognostic value of quantitative pre-treatment semi-automatic 68Ga-DOTATATE PET/CT analysis in NET patients treated with PRRT.

METHODS

The medical records of 94 NET patients who received at least one cycle of PRRT at a single institution were retrospectively reviewed. On each pre-treatment 68Ga-DOTATATE PET/CT, the total tumor volume (TTV), maximum tumor standardized uptake value for the patient (SUVmax), and average uptake in the lesion with the lowest radiotracer uptake (SUVmin) were determined with a semi-automatic tumor delineation method. Progression-free survival (PFS) and overall survival (OS) among the patients were compared based on optimal cutoff values for the imaging parameters.

RESULTS

On Kaplan-Meier analysis and univariate Cox regression, significantly shorter PFS was observed in patients with lower SUVmax, lower SUVmin, and higher TTV. On multivariate Cox regression, lower SUVmin and higher TTV remained predictive of shorter PFS. Only higher TTV was found to be predictive of shorter OS on Kaplan-Meier and Cox regression analyses. In a post hoc Kaplan-Meier analysis, patients with at least one high-risk feature (low SUVmin or high TTV) showed shorter PFS and OS, which may be the most convenient parameter to measure in clinical practice.

CONCLUSIONS

The tumor volume and lowest lesion uptake on 68Ga-DOTATATE PET/CT can predict disease progression following PRRT in NET patients, with the former also predictive of overall survival. NET patients at risk for poor outcomes following PRRT can be identified with semi-automated quantitative analysis of 68Ga-DOTATATE PET/CT.

AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT

BACKGROUND

Segmenting the whole-body somatostatin receptor-expressing tumour volume (SRETVwb) on positron emission tomography/computed tomography (PET/CT) images is highly time-consuming but has shown value as an independent prognostic factor for survival. An automatic method to measure SRETVwb could improve disease status assessment and provide a tool for prognostication. This study aimed to develop an artificial intelligence (AI)-based method to detect and quantify SRETVwb and total lesion somatostatin receptor expression (TLSREwb) from [68Ga]Ga-DOTA-TOC/TATE PET/CT images.

METHODS

A UNet3D convolutional neural network (CNN) was used to train an AI model with [68Ga]Ga-DOTA-TOC/TATE PET/CT images, where all tumours were manually segmented with a semi-automatic method. The training set consisted of 148 patients, of which 108 had PET-positive tumours. The test group consisted of 30 patients, of which 25 had PET-positive tumours. Two physicians segmented tumours in the test group for comparison with the AI model.

RESULTS

There were good correlations between the segmented SRETVwb and TLSREwb by the AI model and the physicians, with Spearman rank correlation coefficients of r = 0.78 and r = 0.73, respectively, for SRETVwb and r = 0.83 and r = 0.81, respectively, for TLSREwb. The sensitivity on a lesion detection level was 80% and 79%, and the positive predictive value was 83% and 84% when comparing the AI model with the two physicians.

CONCLUSION

It was possible to develop an AI model to segment SRETVwb and TLSREwb with high performance. A fully automated method makes quantification of tumour burden achievable and has the potential to be more widely used when assessing PET/CT images.

Prognostic Implications of PET-Derived Tumor Volume and Uptake in Patients with Neuroendocrine Tumors

Historically, molecular imaging of somatostatin receptor (SSTR) expression in patients with neuroendocrine tumors (NET) was performed using SSTR scintigraphy (SRS). Sustained advances in medical imaging have led to its gradual replacement with SSTR positron-emission tomography (SSTR-PET). The higher sensitivity in comparison to SRS on the one hand and conventional cross-sectional imaging, on the other hand, enables more accurate staging and allows for image quantification. In addition, in recent years, a growing body of evidence has assessed the prognostic implications of SSTR-PET-derived prognostic biomarkers for NET patients, with the aim of risk stratification, outcome prognostication, and prediction of response to peptide receptor radionuclide therapy. In this narrative review, we give an overview of studies examining the prognostic value of advanced SSTR-PET-derived (semi-)quantitative metrics like tumor volume, uptake, and composite metrics. Complementing this analysis, a discussion of the current trends, clinical implications, and future directions is provided.

PET/CT and PET/MRI in neuroendocrine neoplasms

Advanced molecular imaging has come to play an integral role in the management of gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NENs). Somatostatin receptor (SSTR) PET has now emerged as the reference standard for the evaluation of NENs and is particularly critical in the context of peptide receptor radionuclide therapy (PRRT) eligibility. SSTR PET/MRI with liver-specific contrast agent has a strong potential for one-stop-shop multiparametric evaluation of GEP-NENs. 18F-FDG is a complementary radiotracer to SSTR, especially in the context of high-grade neuroendocrine neoplasms. Knowledge gaps in quantitative evaluation of molecular imaging studies and their role in assessment of response to PRRT and combination therapies are active research areas. Novel radiotracers have the potential to overcome existing limitations in the molecular imaging of GEP-NENs. The purpose of this article is to provide an overview of the current trends, pitfalls, and recent advancements of molecular imaging for GEP-NENs.