World journal of radiology · 2025
Magnetic resonance imaging-based radiomics signature for predicting preoperative staging of esophageal cancer.
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Yang Ri-Hui, Lin Zhi-Ping, Dong Ting, Fan Wei-Xiong, Qin Hao-Dong, Jiang Gui-Hua, Dai Hai-Yang
Аннотация
BACKGROUND: Esophageal cancer (EC) is one of the most prevalent malignant gastrointestinal tumors; accurate prediction of EC staging has high significance before treatment.
AIM: To explore a rational radiomic approach for predicting preoperative staging of EC based on magnetic resonance imaging (MRI).
METHODS: This retrospective study included 210 patients with pathologically confirmed EC, randomly divided into a primary cohort (n = 147) and a validation cohort (n = 63) in a ratio of 7:3. All patients underwent a preoperative MRI scan from the neck to the abdomen. High-throughput and quantitative radiomics features were extracted from T2-weighted imaging (T2WI) and gadolinium contrast-enhanced T1-weighted imaging (T1WI)-Gd images. Radiomics signatures were selected using minimal redundancy maximal relevance and the least absolute shrinkage and selection operator. Then a logistic regression model was built to predict the EC stages. The diagnostic performance of the radiomics model for discriminating between stages I-II and III-IV was evaluated using the area under the curve (AUC), sensitivity (SEN), and specificity (SPE).
RESULTS: A total of 214 radiomics features were extracted. Following feature dimension reduction, the T1WI and T2WI sequences were retained, and 14 features from the T1WI sequence and 3 features from the T2WI sequence were selected to construct radiomics signatures. The radiomics signature combining T2WI with T1WI-Gd demonstrated superior discrimination of stages in the validation cohort (AUC: 0.851; SEN: 0.697; SPE: 0.793), which outperformed single-sequence models (AUC: 0.779, 0.844; SEN: 0.667, 0.636; SPE: 0.8, 0.8).
CONCLUSION: MRI-based radiomics signatures could identify EC stages before treatment, which could serve as a noninvasive and quantitative approach aiding personalized treatment planning.
AIM: To explore a rational radiomic approach for predicting preoperative staging of EC based on magnetic resonance imaging (MRI).
METHODS: This retrospective study included 210 patients with pathologically confirmed EC, randomly divided into a primary cohort (n = 147) and a validation cohort (n = 63) in a ratio of 7:3. All patients underwent a preoperative MRI scan from the neck to the abdomen. High-throughput and quantitative radiomics features were extracted from T2-weighted imaging (T2WI) and gadolinium contrast-enhanced T1-weighted imaging (T1WI)-Gd images. Radiomics signatures were selected using minimal redundancy maximal relevance and the least absolute shrinkage and selection operator. Then a logistic regression model was built to predict the EC stages. The diagnostic performance of the radiomics model for discriminating between stages I-II and III-IV was evaluated using the area under the curve (AUC), sensitivity (SEN), and specificity (SPE).
RESULTS: A total of 214 radiomics features were extracted. Following feature dimension reduction, the T1WI and T2WI sequences were retained, and 14 features from the T1WI sequence and 3 features from the T2WI sequence were selected to construct radiomics signatures. The radiomics signature combining T2WI with T1WI-Gd demonstrated superior discrimination of stages in the validation cohort (AUC: 0.851; SEN: 0.697; SPE: 0.793), which outperformed single-sequence models (AUC: 0.779, 0.844; SEN: 0.667, 0.636; SPE: 0.8, 0.8).
CONCLUSION: MRI-based radiomics signatures could identify EC stages before treatment, which could serve as a noninvasive and quantitative approach aiding personalized treatment planning.