MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer

• Published in: BMC medical imaging Vol. 24; no. 1; pp. 262 - 13
• Main Authors: Ma, Jiaqi, Nie, Xinsheng, Kong, Xiangjiang, Xiao, Lingqing, Liu, Han, Shi, Shengming, Wu, Yupeng, Li, Na, Hu, Linlin, Li, Xiaofu
• Format: Journal Article
• Language: English
• Published: London BioMed Central 04.10.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Ablation; Adenocarcinoma; Adenocarcinoma - diagnostic imaging; Adenocarcinoma - genetics; Adenocarcinoma - secondary; Adult; Aged; Algorithms; Cancer; Cancer therapies; Clinical decision making; Colorectal cancer; Decision making; Deep learning; Female; Gene mutations; Genetic aspects; Health aspects; Humans; Imaging; K-Ras protein; KRAS mutation; Liver; Liver cancer; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - genetics; Liver Neoplasms - secondary; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical prognosis; Medicine; Medicine & Public Health; Metastases; Metastasis; Middle Aged; MRI; Mutation; Performance evaluation; Performance prediction; Point mutation; Prediction models; Predictive Value of Tests; Proto-Oncogene Proteins p21(ras) - genetics; Radiology; Radiomics; RCLM; Rectal Neoplasms - diagnostic imaging; Rectal Neoplasms - genetics; Rectal Neoplasms - pathology; Rectum; Regression analysis; Retrospective Studies; RC; KRAS mutation; RCLM; MRI; Radiomics
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-024-01439-6

Background The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. Methods A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set ( n  = 136) and validation set ( n  = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared. Results KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471–19.830; p  < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778–0.907) and the validation set (AUC: 0.805; 95%CI: 0.692–0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models. Conclusions Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis. Clinical Trial Number Not applicable.