Quantitative imaging in medicine and surgery · 2026
Multi-b-value diffusion-weighted imaging-based habitat imaging for preoperative prediction of histological grade and prognosis in soft tissue sarcoma: focusing on intratumoral heterogeneity.
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Chen Chunlian, Zhou Jie, Zhang Zesheng, Zhang Kai, Wang Xu, Shi Lei, Pan Yuxin, Zhang Kexin, Ye Huimin, Pan Zi-Ang, Liu Yixin, Wang Wenjia, Wang Shaowu
Аннотация
BACKGROUND: Marked intratumoral heterogeneity (ITH) is a critical biological characteristic of soft tissue sarcoma (STS). This study aimed to develop a novel multi-b-value diffusion-weighted imaging (DWI)-based habitat imaging approach to quantify and visualize ITH for the preoperative prediction of the histological grade and prognosis of STS.
METHODS: A total of 101 patients were prospectively enrolled in this study and underwent multi-b-value DWI examinations. The true diffusion coefficient (D), perfusion fraction (f), and mean kurtosis coefficient (MK) parameter maps were clustered using the K-means algorithm. The elbow method and silhouette coefficient were employed to determine the appropriate number of habitats. A modified histological grading system incorporating Ki‑67 expression was adopted to stratify the patients into low‑ and high‑grade groups. Differences in clinical features, conventional magnetic resonance imaging (MRI) features, and habitat features between the two groups were compared using the Chi‑squared test, independent‑samples t‑test, or Mann-Whitney U test, as appropriate. Predictive models were developed using univariate and multivariable logistic regression. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. Cox proportional hazards regression and Kaplan-Meier analyses were performed to identify risk factors for overall survival (OS) and metastasis‑free survival (MFS).
RESULTS: Four habitats were identified. Several features derived from Habitats 1 and 3 differed significantly between the low- and high-grade groups (P<0.05). Based on the grading predictors identified by logistic regression, both the combined model [area under the curve (AUC) =0.896, accuracy =87.28%, sensitivity =79.70%, specificity =80.95%, 95% confidence interval (CI): 0.837-0.916] and habitat model (AUC =0.841, accuracy =76.75%, sensitivity =69.50%, specificity =85.71%, 95% CI: 0.765-0.916) achieved significantly higher AUCs and greater diagnostic accuracy than the conventional MRI model (AUC =0.771, accuracy =70.52%, sensitivity =62.50%, specificity =88.09%, 95% CI: 0.680-0.861). Survival analyses revealed that high T2-weighted imaging (T2WI) heterogeneity, a multilobulated or other tumor shape, pVol_Habitat 3 ≥26.25% and MK_Mean_Habitat 3 ≥0.913 were significantly associated with poor outcomes (all P<0.05).
CONCLUSIONS: Multi-b-value DWI-based habitat imaging effectively characterizes ITH in STS and demonstrates significant clinical potential for the noninvasive preoperative prediction of histological grade and prognosis.
METHODS: A total of 101 patients were prospectively enrolled in this study and underwent multi-b-value DWI examinations. The true diffusion coefficient (D), perfusion fraction (f), and mean kurtosis coefficient (MK) parameter maps were clustered using the K-means algorithm. The elbow method and silhouette coefficient were employed to determine the appropriate number of habitats. A modified histological grading system incorporating Ki‑67 expression was adopted to stratify the patients into low‑ and high‑grade groups. Differences in clinical features, conventional magnetic resonance imaging (MRI) features, and habitat features between the two groups were compared using the Chi‑squared test, independent‑samples t‑test, or Mann-Whitney U test, as appropriate. Predictive models were developed using univariate and multivariable logistic regression. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. Cox proportional hazards regression and Kaplan-Meier analyses were performed to identify risk factors for overall survival (OS) and metastasis‑free survival (MFS).
RESULTS: Four habitats were identified. Several features derived from Habitats 1 and 3 differed significantly between the low- and high-grade groups (P<0.05). Based on the grading predictors identified by logistic regression, both the combined model [area under the curve (AUC) =0.896, accuracy =87.28%, sensitivity =79.70%, specificity =80.95%, 95% confidence interval (CI): 0.837-0.916] and habitat model (AUC =0.841, accuracy =76.75%, sensitivity =69.50%, specificity =85.71%, 95% CI: 0.765-0.916) achieved significantly higher AUCs and greater diagnostic accuracy than the conventional MRI model (AUC =0.771, accuracy =70.52%, sensitivity =62.50%, specificity =88.09%, 95% CI: 0.680-0.861). Survival analyses revealed that high T2-weighted imaging (T2WI) heterogeneity, a multilobulated or other tumor shape, pVol_Habitat 3 ≥26.25% and MK_Mean_Habitat 3 ≥0.913 were significantly associated with poor outcomes (all P<0.05).
CONCLUSIONS: Multi-b-value DWI-based habitat imaging effectively characterizes ITH in STS and demonstrates significant clinical potential for the noninvasive preoperative prediction of histological grade and prognosis.