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Theranostics 2024; 14(9):3708-3718. doi:10.7150/thno.98053 This issue Cite

Research Paper

Enhancing precision: A predictive model for ¹⁷⁷Lu-DOTATATE treatment response in neuroendocrine tumors using quantitative ⁶⁸Ga-DOTATATE PET and clinicopathological biomarkers

Azadeh Akhavanallaf^1✉, Sonal Joshi¹, Arathi Mohan^2,3, Francis P Worden^2,3, John C Krauss^2,3, Habib Zaidi^5,6,7,8, Kirk Frey¹, Krithika Suresh⁴, Yuni K Dewaraja¹, Ka Kit Wong¹

1. Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
2. Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
3. Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
4. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
5. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.
6. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, Netherlands.
7. Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark.
8. University Research and Innovation Center, Óbuda University, Budapest, Hungary.

✉ Corresponding author: Azadeh Akhavanallaf, 1301 Catherine, 2276 Medical Science I/5610, Ann Arbor, MI 48109. Email: a.akhavan88com; ORCID: 0000-0002-1486-4702; Phone: 7345960267.

Abstract

Purpose: This study aims to elucidate the role of quantitative SSTR-PET metrics and clinicopathological biomarkers in the progression-free survival (PFS) and overall survival (OS) of neuroendocrine tumors (NETs) treated with peptide receptor radionuclide therapy (PRRT).

Methods: A retrospective analysis including 91 NET patients (M47/F44; age 66 years, range 34-90 years) who completed four cycles of standard ¹⁷⁷Lu-DOTATATE was conducted. SSTR-avid tumors were segmented from pretherapy SSTR-PET images using a semiautomatic workflow with the tumors labeled based on the anatomical regions. Multiple image-based features including total and organ-specific tumor volume and SSTR density along with clinicopathological biomarkers including Ki-67, chromogranin A (CgA) and alkaline phosphatase (ALP) were analyzed with respect to the PRRT response.

Results: The median OS was 39.4 months (95% CI: 33.1-NA months), while the median PFS was 23.9 months (95% CI: 19.3-32.4 months). Total SSTR-avid tumor volume (HR = 3.6; P = 0.07) and bone tumor volume (HR = 1.5; P = 0.003) were associated with shorter OS. Also, total tumor volume (HR = 4.3; P = 0.01), liver tumor volume (HR = 1.8; P = 0.05) and bone tumor volume (HR = 1.4; P = 0.01) were associated with shorter PFS. Furthermore, the presence of large lesion volume with low SSTR uptake was correlated with worse OS (HR = 1.4; P = 0.03) and PFS (HR = 1.5; P = 0.003). Among the biomarkers, elevated baseline CgA and ALP showed a negative association with both OS (CgA: HR = 4.9; P = 0.003, ALP: HR = 52.6; P = 0.004) and PFS (CgA: HR = 4.2; P = 0.002, ALP: HR = 9.4; P = 0.06). Similarly, number of prior systemic treatments was associated with shorter OS (HR = 1.4; P = 0.003) and PFS (HR = 1.2; P = 0.05). Additionally, tumors originating from the midgut primary site demonstrated longer PFS, compared to the pancreas (HR = 1.6; P = 0.16), and those categorized as unknown primary (HR = 3.0; P = 0.002).

Conclusion: Image-based features such as SSTR-avid tumor volume, bone tumor involvement, and the presence of large tumors with low SSTR expression demonstrated significant predictive value for PFS, suggesting potential clinical utility in NETs management. Moreover, elevated CgA and ALP, along with an increased number of prior systemic treatments, emerged as significant factors associated with worse PRRT outcomes.

Keywords: SSTR-PET, images-based features, NET, PRRT, outcome prediction

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