ArticleEuropean journal of nuclear medicine and molecular imaging2021
Just another "Clever Hans"? Neural networks and FDG PET-CT to predict the outcome of patients with breast cancer.
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer.NPJ precision oncology · 2026Article
- Deep learning [Clinical and translational radiation oncology · 2026Article
- Deep learning in nuclear medicine: from imaging to therapy.Annals of nuclear medicine · 2025Review
- Full-Body Tumor Response Heterogeneity of Metastatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radiopharmaceutical Therapy.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Article
- Role of Radiology in the Diagnosis and Treatment of Breast Cancer in Women: A Comprehensive Review.Cureus · 2024Review
- Uncertainty quantification via localized gradients for deep learning-based medical image assessments.Physics in medicine and biology · 2024Article
- Is Automatic Tumor Segmentation on Whole-BodyJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Theranostics and artificial intelligence: new frontiers in personalized medicine.Theranostics · 2024Review
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- A Prognostic Risk Score for Prostate Cancer Based on PSMA PET-derived Organ-specific Tumor Volumes.Radiology · 2023Article
- Artificial intelligence guided enhancement of digital PET: scans as fast as CT?European journal of nuclear medicine and molecular imaging · 2022Article
- The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.Cancers · 2022Review
- Automated classification of PET-CT lesions in lung cancer: An independent validation study.Clinical physiology and functional imaging · 2022Article
- Review
- Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs.Diagnostics (Basel, Switzerland) · 2022Article
- Explaining a XX century horse behaviour.European journal of nuclear medicine and molecular imaging · 2021Article
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundManual quantification of the metabolic tumor volume (MTV) from whole-body
methodsFifty consecutive breast cancer patients that underwent
resultsIf PERCIST measurable foci were regarded, the neural network displayed high per patient sensitivity and specificity in detecting suspicious
conclusionAlthough trained on lymphoma and lung cancer, PARS showed good accuracy in the detection of PERCIST measurable lesions. Therefore, the neural network seems not prone to the clever Hans effect. However, the network has poor accuracy if all manually segmented lesions were used as reference standard. Both the whole body and organ-wise MTV were significant prognosticators of overall survival in advanced breast 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.