Artificial Intelligence and radiomics models for the diagnosis and prognosis of peritoneal metastases on imaging: a systematic review and meta-analysis.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Department of Surgical Oncology, Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, the Netherlands. Electronic address: [email protected].
- Department of Medical Oncology, Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, the Netherlands.
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
- Department of Obstetrics and Gynecology, Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, the Netherlands.
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Reproduction, Maastricht University, Maastricht, the Netherlands.
- Department of Surgical Oncology, Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, the Netherlands; GROW School for Oncology and Developmental Reproduction, Maastricht University, Maastricht, the Netherlands.
- Department of Surgical Oncology, Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, the Netherlands.
Abstract
Peritoneal metastases (PM) significantly impact treatment options and prognosis of patients with cancer. Early detection and accurate evaluation are essential for guiding clinical decisions. This systematic review and meta-analysis aimed to provide a comprehensive overview and evaluate the performance of Artificial Intelligence (AI) and radiomics models for diagnosis and prognosis of PM on imaging. A systematic search of PubMed, Embase, and the Cochrane Library was conducted for studies published up to July 2024 that evaluated AI or radiomics models analyzing imaging data for diagnosing or predicting prognosis in PM. Data were extracted, and if more than 3 studies evaluated the same endpoint and reported true/false positive and negative values, a meta-analysis was conducted to obtain pooled area under the curve (AUC), sensitivity, and specificity. Bias was assessed using the PROBAST + AI tool. This review included 24 studies, of which 18 evaluated PM presence, 2 assessed PM severity (low versus high Peritoneal Cancer Index (PCI)), and 4 focused on prognosis or treatment efficacy. Meta-analysis of 13 studies evaluating PM presence revealed a pooled AUC of 0.84, sensitivity of 0.75, and specificity of 0.80. Subgroup analysis indicated comparable performance for 2D and 3D imaging data, and lower performance for models detecting occult PM compared to all PM presentations. Incorporating clinical factors into AI and radiomics models improved performance. AI and radiomics models demonstrated promising performance outcomes for PM evaluation on imaging, showing potential to aid in diagnosis and prognosis prediction. However, large validation studies are needed to evaluate their effects in clinical practice.