Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer.
Qiang WangJianhua XuAnrong WangYi ChenTian WangDanyu ChenJia-Xing ZhangTorkel B BrismarPublished in: La Radiologia medica (2023)
This study aimed to systematically summarize the performance of the machine learning-based radiomics models in the prediction of microsatellite instability (MSI) in patients with colorectal cancer (CRC). It was conducted according to the preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guideline and was registered at the PROSPERO website with an identifier CRD42022295787. Systematic literature searching was conducted in databases of PubMed, Embase, Web of Science, and Cochrane Library up to November 10, 2022. Research which applied radiomics analysis on preoperative CT/MRI/PET-CT images for predicting the MSI status in CRC patients with no history of anti-tumor therapies was eligible. The radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) were applied to evaluate the research quality (full score 100%). Twelve studies with 4,320 patients were included. All studies were retrospective, and only four had an external validation cohort. The median incidence of MSI was 19% (range 8-34%). The area under the receiver operator curve of the models ranged from 0.78 to 0.96 (median 0.83) in the external validation cohort. The median sensitivity was 0.76 (range 0.32-1.00), and the median specificity was 0.87 (range 0.69-1.00). The median RQS score was 38% (range 14-50%), and half of the studies showed high risk in patient selection as evaluated by QUADAS-2. In conclusion, while radiomics based on pretreatment imaging modalities had a high performance in the prediction of MSI status in CRC, so far it does not appear to be ready for clinical use due to insufficient methodological quality.
Keyphrases
- contrast enhanced
- systematic review
- machine learning
- lymph node metastasis
- pet ct
- case control
- magnetic resonance imaging
- end stage renal disease
- computed tomography
- meta analyses
- chronic kidney disease
- public health
- magnetic resonance
- patients undergoing
- squamous cell carcinoma
- quality improvement
- newly diagnosed
- positron emission tomography
- risk factors
- artificial intelligence
- convolutional neural network
- peritoneal dialysis
- adverse drug
- prognostic factors
- cross sectional
- electronic health record
- data analysis
- drug induced