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.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Oct 20, 2025

Abstract

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.

Authors

  • Shiming Zang
    Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
  • Qingle Meng
    Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
  • Xiaoyuan Li
    Department of Traditional Chinese Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
  • Tiantian Guo
    College of Chemical Engineering, Department of Pharmaceutical Engineering, Northwest University, Taibai North Road 229, Xi'an 710069, Shaanxi, China.
  • Lele Zhang
  • Zhenyu Zhao
    Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Fei Yu
    Department of Nutrition and food hygiene, College of Public Health of Zhengzhou University, Zhengzhou, China, 450001. Electronic address: [email protected].
  • Pengjun Zhang
    Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
  • Wenyu Wu
    Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
  • Yudan Ni
    Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
  • Yuhang Shi
    Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China.
  • Guoqiang Shao
    Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
  • Youdan Feng
  • Lingzhi Hu
    UIH America Inc., Houston 77054, United States of America.
  • RuiPeng Jia
    Department of Urology Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • A Cahid Civelek
    Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, 21287, MD, USA. [email protected].
  • Hongqian Guo
    Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Institute of Urology, Nanjing University, No. 321 Zhongshan Rd, Nanjing, 210008, Jiangsu Province, China. [email protected].
  • Feng Wang
    Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals