RESUMEN
RATIONALE AND OBJECTIVES: Clear cell renal cell carcinoma (ccRCC) is the most common malignant neoplasm affecting the kidney, exhibiting a dismal prognosis in metastatic instances. Elucidating the composition of ccRCC holds promise for the discovery of highly sensitive biomarkers. Our objective was to utilize habitat imaging techniques and integrate multimodal data to precisely predict the risk of metastasis, ultimately enabling early intervention and enhancing patient survival rates. MATERIAL AND METHODS: A retrospective analysis was performed on a cohort of 263 patients with ccRCC from three hospitals between April 2013 and March 2021. Preoperative CT images, ultrasound images, and clinical data were comprehensively analyzed. Patients from two campuses of Qilu Hospital of Shandong University were assigned to the training dataset, while the third hospital served as the independent testing dataset. A robust consensus clustering method was used to classify the primary tumor space into distinct sub-regions (i.e., habitats) using contrast-enhanced CT images. Radiomic features were extracted from these tumor sub-regions and subsequently reduced to identify meaningful features for constructing a predictive model for ccRCC metastasis risk assessment. In addition, the potential value of radiomics in predicting ccRCC metastasis risk was explored by integrating ultrasound image features and clinical data to construct and compare alternative models. RESULTS: In this study, we performed k-means clustering within the tumor region to generate three distinct tumor subregions. We quantified the Hounsfiled Unit (HU) value, volume fraction, and distribution of high- and low-risk groups in each subregion. Our investigation focused on 252 patients with Habitat1 + Habitat3 to assess the discriminative power of these two subregions. We then developed a risk prediction model for ccRCC metastasis risk classification based on radiomic features extracted from CT and ultrasound images, and clinical data. The Combined model and the CT_Habitat3 model showed AUC values of 0.935 [95%CI: 0.902-0.968] and 0.934 [95%CI: 0.902-0.966], respectively, in the training dataset, while in the independent testing dataset, they achieved AUC values of 0.891 [95%CI: 0.794-0.988] and 0.903 [95%CI: 0.819-0.987], respectively. CONCLUSION: We have identified a non-invasive imaging predictor and the proposed sub-regional radiomics model can accurately predict the risk of metastasis in ccRCC. This predictive tool has potential for clinical application to refine individualized treatment strategies for patients with ccRCC.
RESUMEN
OBJECTIVE: To apply diffusion tensor imaging (DTI) for investigating the correlation between leukoaraiosis (LA) lesion's fraction anisotropy (FA) as well as average diffusion coefficient (DCavg) and LA severity, so as to explore DTI changes in microstructure of white marrow with normal ordinary MRI and its correlation with cognitive function. METHODS: Sixty LA patients and 30 healthy elderly people accepted DTI examination to detect the value of DCavg and FA of LA lesion and normal white marrow. The Mini-Mental State Examination (MMSE) was used for assessing cognitive function. RESULTS: LA severity (0 grade to 3 grade) was positively associated with DCavg, i.e. the more severe was LA, the higher DCavg was (0.66 +/- 0.05 to 1.09 +/- 0.06, P < 0.05); and it was negatively associated with FA, i.e. the more severe was LA, the lower FA was (0.42 +/- 0.04 to 0.26 +/- 0.03, P < 0.05). Neuropsychology tests (Mini-Mental State Examination, MMSE) had a significant relationship with DCavg and FA of normal appearing white matter (NAWM) in LA patients (P < 0.05), especially in anterior horn (Pearson Correlation Coefficient 0.422, P < 0.05) and in centrum semiovale (Pearson Correlation Coefficient -0.495, P < 0.01). CONCLUSIONS: In DTI examination, DCavg and FA of LA displays characteristic changes. Therefore, DTI can detect the macrostructaral changes of white marrow with normal MRI and these changes are related to cognitive function.