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Purpose <p>Despite the rapid development of artificial intelligence (AI)-powered automated segmentation tools for PET/CT imaging, their prognostic value in predicting survival outcomes remains inadequately assessed. Our objective was to explore the prognostic significance of tumor burden quantification derived from PSMA PET/CT using AI for metastatic castration-resistant prostate cancer (mCRPC) patients receiving Lutetium-177 (¹⁷⁷Lu) PSMA therapy.</p> Methods <p>A retrospective cohort of 107 consecutive patients with mCRPC treated with ¹⁷⁷Lu-PSMA therapy were analyzed. Utilizing a deep learning algorithm, PSMA-positive lesions were automatically delineated on baseline 68Ga-PSMA-11 PET/CT scans. Key metrics were derived from the segmented lesions: total tumor volume (PSMA<sub>TV</sub>), total tumor load (PSMA<sub>TU</sub> = PSMA<sub>TV</sub> × SUV<sub>mean</sub>), and total tumor quotient (PSMA<sub>TQ</sub> = PSMA<sub>TV</sub> / SUV<sub>mean</sub>). A prognostic nomogram was developed through Cox regression analysis, incorporating LASSO regularization for variable selection.</p> Results <p>Univariate analysis revealed that higher PSMA<sub>TV</sub> (HR 1.26), PSMA<sub>TU</sub> (HR 1.18), and PSMA<sub>TQ</sub> (HR 1.29) were significantly associated with shorter overall survival (OS). A prognostic nomogram that integrated PSMA<sub>TQ</sub> alongside chemotherapy history, hemoglobin levels, alkaline phosphatase, and prostate-specific antigen demonstrated a bootstrap-corrected C-index of 0.71 (95% CI 0.64–0.78). Risk stratification using the nomogram showed significantly prolonged OS in low-risk vs. high-risk groups (median OS 30.9 vs. 7.9 months; HR 0.25, 95% CI 0.13–0.45, <i>P</i> &lt; 0.001). The retrospective design is a study limitation.</p> Conclusion <p>AI-based volumetric analysis of tumor burden on PSMA PET has prognostic significance for survival in ¹⁷⁷Lu-PSMA-treated mCRPC patients. The nomogram integrating PSMA<sub>TQ</sub> with clinical factors might help in personalized risk stratification, facilitating AI-aided therapeutic decision-making.</p>

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Fully automated volumetric assessment of tumor burden using artificial intelligence on 68Ga-PSMA-11 PET predicts survival after 177Lu-PSMA therapy in metastatic Castration-resistant prostate cancer

  • 期刊论文

  • 发表日期：

    2025年10月20日

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  • Shiming Zang,
  • Qingle Meng,
  • Xiaoyuan Li,
  • Tiantian Guo,
  • Lele Zhang,
  • Zhenyu Zhao,
  • Fei Yu,
  • Pengjun Zhang,
  • Wenyu Wu,
  • Yudan Ni,
  • Yuhang Shi,
  • Guoqiang Shao,
  • Youdan Feng,
  • Lingzhi Hu,
  • Ruipeng Jia,
  • A. Cahid Civelek,
  • Hongqian Guo,
  • Feng Wang

摘要

Purpose

Despite the rapid development of artificial intelligence (AI)-powered automated segmentation tools for PET/CT imaging, their prognostic value in predicting survival outcomes remains inadequately assessed. Our objective was to explore the prognostic significance of tumor burden quantification derived from PSMA PET/CT using AI for metastatic castration-resistant prostate cancer (mCRPC) patients receiving Lutetium-177 (¹⁷⁷Lu) PSMA therapy.

Methods

A retrospective cohort of 107 consecutive patients with mCRPC treated with ¹⁷⁷Lu-PSMA therapy were analyzed. Utilizing a deep learning algorithm, PSMA-positive lesions were automatically delineated on baseline 68Ga-PSMA-11 PET/CT scans. Key metrics were derived from the segmented lesions: total tumor volume (PSMATV), total tumor load (PSMATU = PSMATV × SUVmean), and total tumor quotient (PSMATQ = PSMATV / SUVmean). A prognostic nomogram was developed through Cox regression analysis, incorporating LASSO regularization for variable selection.

Results

Univariate analysis revealed that higher PSMATV (HR 1.26), PSMATU (HR 1.18), and PSMATQ (HR 1.29) were significantly associated with shorter overall survival (OS). A prognostic nomogram that integrated PSMATQ alongside chemotherapy history, hemoglobin levels, alkaline phosphatase, and prostate-specific antigen demonstrated a bootstrap-corrected C-index of 0.71 (95% CI 0.64–0.78). Risk stratification using the nomogram showed significantly prolonged OS in low-risk vs. high-risk groups (median OS 30.9 vs. 7.9 months; HR 0.25, 95% CI 0.13–0.45, P < 0.001). The retrospective design is a study limitation.

Conclusion

AI-based volumetric analysis of tumor burden on PSMA PET has prognostic significance for survival in ¹⁷⁷Lu-PSMA-treated mCRPC patients. The nomogram integrating PSMATQ with clinical factors might help in personalized risk stratification, facilitating AI-aided therapeutic decision-making.