ArticleJournal of medical systems2009
Artificial neural network to predict skeletal metastasis in patients with prostate cancer.
Article in Journal of medical systems, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for bone metastases: a systematic review and meta-analysis of diagnostic and prognostic performance.Frontiers in oncology · 2026Pooled it
- A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.BMC medical informatics and decision making · 2021Pooled it
- AI-Driven Quantitative Metabolomics for Early and Precise HIE Diagnosis: Challenges and Solutions.ACS omega · 2025Review
- Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.Journal of bone oncology · 2025Review
- Artificial Intelligence and Radiomics Applied to Prostate Cancer Bone Metastasis Imaging: A Review.iRadiology · 2024Article
- Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review.Cancers · 2024Review
- Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.International journal of computer assisted radiology and surgery · 2023Article
- Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients.BioMed research international · 2022Article
- Deep Learning for the Automatic Diagnosis and Analysis of Bone Metastasis on Bone Scintigrams.Cancer management and research · 2022Article
- Digital Natives' Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study.JMIR mHealth and uHealth · 2021Article
- Diagnosis system for hepatocellular carcinoma based on fractal dimension of morphometric elements integrated in an artificial neural network.BioMed research international · 2014Article
- Artificial neural networks and prostate cancer--tools for diagnosis and management.Nature reviews. Urology · 2013Review
- Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors.World journal of gastroenterology · 2012Article
- The use of fuzzy backpropagation neural networks for the early diagnosis of hypoxic ischemic encephalopathy in newborns.Journal of biomedicine & biotechnology · 2011Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of an artificial neural network (ANN) in prediction of outcomes using clinical data is being increasingly used. The aim of this study was to assess whether an ANN model is a useful tool for predicting skeletal metastasis in patients with prostate cancer. Consecutive patients with prostate cancer who underwent the technetium-99m methylene diphosphate (Tc-99m MDP) whole body bone scintigraphies were retrospectively analyzed between 2001 and 2005. The predictors were the patient's age and radioimmunometric serum PSA concentration. The outcome variable was dichotomous, either skeletal metastasis or non-skeletal metastasis, based on the results of Tc-99m MDP whole body bone scintigraphy. To assess the performance for classification model in clinical study, the discrimination and calibration of an ANN model was calculated. The enrolled subjects consisted of 111 consecutive male patients aged 72.41 +/- 7.69 years with prostate cancer. Sixty-seven patients (60.4%) had skeletal metastasis based on the scintigraphic diagnosis. The final best architecture of neural network model was four-layered perceptrons. The area under the receiver-operating characteristics curve (0.88 +/- 0.07) revealed excellent discriminatory power (p < 0.001) with the best simultaneous sensitivity (87.5%) and specificity (83.3%). The Hosmer-Lemeshow statistic was 6.74 (p = 0.08 > 0.05), which represented a good-fit calibration. These results suggest that an ANN, which is based on limited clinical parameters, appears to be a promising method in forecasting of the skeletal metastasis in patients with prostate cancer.
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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.