# Multicentric Validation of Early Quantitative 177Lu-PSMA SPECT/CT for mCRPC Response Assessment

Eduardo Rios Sanchez, Jean-Noël Badel, Thomas Baudier, Arnaud

Dieudonné, H. Gröhn, Laëtitia Imbert, J. Labour, V. Nuttens, A. Terro, L.

Vergnaud, et al.

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To cite this version:

Eduardo Rios Sanchez, Jean-Noël Badel, Thomas Baudier, Arnaud Dieudonné, H. Gröhn, et al. Multicentric Validation of Early Quantitative 177Lu-PSMA SPECT/CT for mCRPC Response Assessment. EFOMP SIG 2025, Nov 2025, Athenes, Greece. hal-05373122

HAL Id: hal-05373122

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Submitted on 19 Nov 2025

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# Multicentric Validation of Early Quantitative <sup>177</sup>Lu-PSMA SPECT/CT for mCRPC Response Assessment

Eduardo Rios Sanchez¹,², J.N. Badel¹, T. Baudier¹, A. Dieudonné⁷, H. Gröhn⁶, L. Imbert⁴, J. Labour³, V. Nuttens⁵, A. Terro⁷, L. Vergnaud⁸, T. Zaragori⁹,¹⁰, and D. Sarrut¹

## INTRODUCTION

Metastatic castration-resistant prostate cancer (mCRPC) remains a major clinical challenge. While <sup>177</sup>Lu-PSMA radioligand therapy (RLT) improves outcomes, patient responses vary widely. Conventional monitoring tools such as PSA kinetics and standard imaging often fail to provide early, reliable indicators of treatment efficacy. Advances in cadmium-zinc-telluride (CZT) SPECT/CT technology enable fast, quantitative post-therapy imaging, offering a potential biomarker for early response assessment.

## AIM

To evaluate whether quantitative <sup>177</sup>Lu-PSMA SPECT/CT metrics acquired 24 hours post-injection after the first RLT cycle predict biochemical response (PSA50) in mCRPC patients, and to compare quantitative versus diagnostic reconstruction protocols.

## METHOD

**Design:** Multicenter retrospective study across 4 European centers (n = 130 patients).

**Imaging:** All patients underwent whole-body CZT SPECT/CT (VERITON-CT) 24 h post-injection after the first cycle of <sup>177</sup>Lu-PSMA therapy.

**Segmentation:** Total Tumor Volume (TTV) segmented using adaptive liver-based SUV threshold after masking physiological uptake.

**Features extracted:** SUVmean, SUVmax, SUVmin, SUVstdev, TTV volume, and total lesion activity.

**Analysis:**

Univariate ROC analysis (Welch t-test, AUC, Youden threshold).

Multivariate machine-learning models (Logistic Regression, Random Forest, Gradient Boosting, SVM) trained on 92 patients and tested on 38 independent cases.

**Comparison:** Quantitative vs diagnostic reconstruction protocols. Set of 38 patients with both reconstructions.

## RESULTS

**Predictive biomarkers:**

* TTV SUVstdev: AUC = 0.88, accuracy = 0.82 (test set).

* SUVmax: AUC = 0.84; SUVmean: AUC = 0.77.

* Tumor volume alone was not predictive (AUC ≈ 0.59).

**Novel finding:** SUV variability (standard deviation) within TTV emerged as the strongest single predictor.

**Machine learning:** Logistic Regression achieved AUC = 0.87, accuracy = 0.76, F1 = 0.69 on test set.

**Reconstruction comparison:** Quantitative protocols significantly outperformed diagnostic reconstructions (p < 0.001 for SUVstdev vs p = 0.067).

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## DISCUSSION AND CONCLUSIONS

Early quantitative SPECT/CT imaging after the first RLT cycle provides actionable biomarkers for predicting treatment response. SUV heterogeneity within TTV may reflect biological complexity and treatment sensitivity, making it a promising candidate for clinical decision-making. The superiority of quantitative reconstruction underscores the need for standardized protocols. Our federated, multicentric design enhances robustness and generalizability, though prospective validation is required.

TL;DR

* 24-h quantitative CZT SPECT/CT after the first <sup>177</sup>Lu-PSMA cycle predicts PSA50 response.

* SUVstdev within TTV is a novel, strong biomarker.

* Quantitative reconstruction should be adopted for reliable response assessment.

* Findings support early treatment stratification and personalized care in mCRPC.

## REFERENCES

Gafita, A et al. (2019). QPSMA: Semiautomatic software for whole-body tumor burden assessment in prostate cancer using 68Ga-PSMA11 PET/CT. Journal of Nuclear Medicine

Wasserthal, J. et al. (2023). TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images • Content codes. Radiology: Artificial Intelligence.

Rios-Sanchez et al. (2025) Predicting PSA50 response to 177Lu-PSMA therapy using machine learning and automated total tumor volume. EJNMMI Phys

## ACKNOWLEDGEMENTS

We thank the Spectrum Dynamics team, especially Justine McQuillan, for their support.

## CONTACT INFORMATION

eduardo.rios-sanchez@creatis.insa-lyon.fr

rioseduardolfm@gmail.com

<footnote>(1)Université de Lyon, CREATIS; CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1; Centre Léon Bérard, France. (2)Siemens Healthcare SAS, Courbevoie, France. (3)Nuclear Medicine, Hospices Civiles de Lyon, Lyon, FRANCE. (4)Nuclear Medicine, Centre hospitalier régional et universitaire de Nancy, Nancy, FRANCE. (5)Nuclear Medicine, AZORG Aalst, Aalst, Belgium. (6)Nuclear Medicine, Kuopio University Hospital, Kuopio, FINLAND.</footnote>

<footnote>(7)Nuclear Medicine, Centre Henri Becquerel, Rouen, FRANCE. (8)Autorité de Sûreté Nucléaire et de Radioprotection (ASNR), PSE-SANTE/SDOS/LEDI, F-92260, Fontenay-aux-Roses - Service de recherche en dosimétrie, FRANCE. (9)CHRU-Nancy, Inserm, Université de Lorraine, CIC 1433, Innovation Technologique, Nancy, FRANCE. (10)JADI, U1254, Inserm, Université de Lorraine, Nancy, France.</footnote>