Evidence map›Paper›PMID 41111086›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

Fully automated volumetric assessment of tumor burden using artificial intelligence on

Shiming Zang, Qingle Meng, Xiaoyuan Li, Tiantian Guo, Lele Zhang, Zhenyu Zhao, Fei Yu, Pengjun Zhang, Wenyu Wu, Yudan Ni and 8 more

Abstract read
PubMed Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Prognostic value and diagnostic performance of nectin-4-targeted [European journal of nuclear medicine and molecular imaging · 2026
    Article
  2. Dosimetry-Driven Personalization of Radiopharmaceutical Therapies: Evidence and Opportunities.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026
    Review
  3. Baseline PSMA PET/CT Biomarkers for Patient Selection in [Pharmaceuticals (Basel, Switzerland) · 2026
    Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. FDG/PSMA discordance in [Frontiers in oncology · 2026
    Review
  9. Review
  10. Review
  11. Review
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 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 Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
Tiantian GuoUnited Imaging Healthcare, Shanghai, China.
Lele ZhangDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
Zhenyu ZhaoDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
Fei YuDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
Pengjun ZhangDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
Wenyu WuDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
Yudan NiDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
Yuhang ShiUnited Imaging Healthcare, Shanghai, China.
Guoqiang ShaoDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
Youdan FengUnited Imaging Healthcare, Shanghai, China.
Lingzhi HuUnited Imaging Healthcare, Shanghai, China.
Ruipeng JiaDepartment of Urology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
A Cahid CivelekDepartment of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, 21287, MD, USA. accivelek@gmail.com.
Hongqian GuoDepartment of Urology, Drum Tower Hospital, Medical School of Nanjing University, 321 Zhongshan Rd, Nanjing, China. dr.ghq@nju.edu.cn.
Feng WangDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China. fengwangcn@hotmail.com.

Funding

National Natural Science Foundation of China 82272038
6 · The paper itself

Abstract

purposeDespite 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.

methodsA 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

resultsUnivariate analysis revealed that higher PSMA

conclusionAI-based volumetric analysis of tumor burden on PSMA PET has prognostic significance for survival in ¹⁷⁷Lu-PSMA-treated mCRPC patients. The nomogram integrating PSMA

Indexed as

Artificial IntelligenceDipeptidesEdetic AcidLutetiumOligopeptidesPositron Emission Tomography Computed TomographyProstatic Neoplasms, Castration-ResistantTumor BurdenAgedAged, 80 and overAutomationGallium IsotopesGallium RadioisotopesHumansMaleMiddle AgedDipeptidesEdetic Acidgallium 68 PSMA-11Gallium IsotopesGallium RadioisotopesLutetiumLutetium-177OligopeptidesPluvictoProstate-Specific AntigenRadioisotopes177Lu-PSMA therapyArtificial intelligenceMetastatic castration-resistant prostate cancerPSMA PET/CTTumor volume

Identifiers

PMID41111086

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.