ArticleEuropean journal of nuclear medicine and molecular imaging2025
Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [
Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Head-to-head comparison of [¹⁸F]F-PSMA-1007 PET/CT and [⁶⁸Ga]Ga-PSMA-11 PET/CT for intraprostatic tumour detection and localisation using histopathology as reference: A prospective single-centre diagnostic accuracy study.European journal of nuclear medicine and molecular imaging · 2026Article
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- The value of [Quantitative imaging in medicine and surgery · 2026Article
- Transformer-based prediction of radiotherapy couch shift risk in prostate cancer based on rectal volume.Scientific reports · 2026Article
- A deep learning-based prognostic model for diffuse large B-cell lymphoma incorporating PET/CT imaging features.Frontiers in oncology · 2026Article
- Construction and validation of a multimodal MRI-based deep learning model for early differential diagnosis of prostate cancer in the PSA gray zone: a retrospective cohort study.Frontiers in oncology · 2026Article
- Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.American journal of clinical and experimental urology · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
purposeAccurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [
methods341 PCa patients who underwent radical prostatectomy (RP) with mpMRI and PET/CT scans were retrospectively analyzed. We generated deep learning signature from mpMRI and PET/CT with a multimodal deep learning model (MPC) based on convolutional neural networks and transformer, which was subsequently incorporated with clinical characteristics to construct an integrated model (MPCC). These models were compared with clinical models and single mpMRI or PET/CT models.
resultsThe MPCC model showed the best performance in predicting AP (AUC, 0.955 [95% CI: 0.932-0.975]), which is higher than MPC model (AUC, 0.930 [95% CI: 0.901-0.955]). The performance of the MPC model is better than that of single PET/CT (AUC, 0.813 [95% CI: 0.780-0.845]) or mpMRI (AUC, 0.865 [95% CI: 0.829-0.901]). Additionally, MPCC model is also effective in predicting single adverse pathological features.
conclusionThe deep learning model that integrates mpMRI and [
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Registered trials
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.