Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival
| Autor: | Elin Trägårdh, Måns Larsson, Poul Flemming Høilund-Carlsen, Mike Allan Mortensen, Lars Edenbrandt, Pablo Borrelli, Olof Enqvist, Johannes Ulén, Henrik Kjölhede, Mads Hvid Poulsen |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male PCa Prognostic factor Physiology 030204 cardiovascular system & hematology 03 medical and health sciences Prostate cancer 0302 clinical medicine Artificial Intelligence Predictive Value of Tests Positron Emission Tomography Computed Tomography Physiology (medical) fluorocholine Image Interpretation Computer-Assisted medicine Humans Lymph node Aged Aged 80 and over PET-CT business.industry Prostatic Neoplasms 030229 sport sciences General Medicine Middle Aged artificial intelligence medicine.disease Survival Analysis PET medicine.anatomical_structure Curative treatment Lymphatic Metastasis Feasibility Studies Lymph Nodes Artificial intelligence business lymph node metastases |
| Zdroj: | Borrelli, P, Larsson, M, Ulén, J, Enqvist, O, Trägårdh, E, Poulsen, M H, Mortensen, M A, Kjölhede, H, Høilund-Carlsen, P F & Edenbrandt, L 2021, ' Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival ', Clinical Physiology and Functional Imaging, vol. 41, no. 1, pp. 62-67 . https://doi.org/10.1111/cpf.12666 |
| ISSN: | 1475-097X 1475-0961 |
| Popis: | Introduction: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions. Methods: A group of 399 patients with biopsy-proven PCa who had undergone 18F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated. Results: The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117; p =.045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111; p =.63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival. Conclusion: This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients. |
| Databáze: | OpenAIRE |
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