In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: n = 125; validation dataset, n = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.
Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods A retrospective analysis was conducted on clinical data of 261 patients with IAC treated at Yantaishan Hospital from October 2018 to May 2023. Among them, there were 101 males and 160 females, aged 27 to 88 years at a mean age of (61.96±9.17) years. Six patients had dual primary lesions, with each lesion analyzed as an independent sample. According to the 2021 WHO classification criteria for lung adenocarcinoma, 267 IACs were classified as gradeⅠ(48 patients), grade Ⅱ (89 patients), and grade Ⅲ (130 patients). Differences in parameters among groups were compared, and logistic regression analysis was used to evaluate the predictive value of AI quantitative parameters for grade Ⅲ IAC. LASSO regression analysis was employed to select parameters with non-zero coefficients, and three machine learning models were constructed and internally verified based on the joint parameters to predict grade Ⅲ IAC efficacy, which were visualized by the Nomogram. Results(1) There were statistical differences between the two groups in parameters such as solid component proportion, long diameter, short diameter, malignancy probability, CT average value, CT maximum value, CT minimum value, CT median value, CT standard deviation, kurtosis, skewness, and entropy (P<0.05). (2) Comparison between two groups: gradeⅠand gradeⅡwere combined for single-factor analysis against grade Ⅲ, indicating differences in all variables except age (P<0.05). Multi-factor analysis identified CTR and CT standard deviation as independent risk factors for distinguishing grade Ⅲ IAC, with a negative correlation between them. (3) Pathological comparisons: gradeⅠhad no lymph node metastasis, gradeⅡhad 2 patients of lymph node metastasis with micro-papillary components, and Grade Ⅲ had 19 patients of lymph node metastasis. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. (4) Correlation analysis: CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade Ⅱ. (5) CTR and CT median value were selected by using LASSO regression, and logistic regression, random forest, and XGBoost models were constructed and validated. Among them, the XGBoost model demonstrated the best predictive performance. Conclusions Cautious consideration should be given to grade Ⅲ IAC when CTR is more than 39.48% and CT standard deviation is less than 122.75 Hu. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.