Prognostic factors in patients with uterine sarcoma: the SARCUT study

The value of multiparametric MRI-based habitat imaging for differentiating uterine sarcomas from atypical leiomyomas: a multicentre study

PURPOSE

To explore the feasibility of multiparametric MRI-based habitat imaging for distinguishing uterine sarcoma (US) from atypical leiomyoma (ALM).

METHODS

This retrospective study included the clinical and preoperative MRI data of 69 patients with US and 225 patients with ALM from three hospitals. At both the individual and cohort levels, the K-means and Gaussian mixture model (GMM) algorithms were utilized to perform habitat imaging on MR images, respectively. Specifically, T2-weighted images (T2WI) and contrast-enhanced T1-weighted images (CE-T1WI) were clustered to generate structural habitats, while apparent diffusion coefficient (ADC) maps and CE-T1WI were clustered to create functional habitats. Parameters of each habitat subregion were extracted to construct distinct habitat models. The integrated models were constructed by combining habitat and clinical independent predictors. Model performance was assessed using the area under the curve (AUC).

RESULTS

Abnormal vaginal bleeding, lactate dehydrogenase (LDH), and white blood cell (WBC) counts can serve as clinical independent predictors of US. The GMM-based functional habitat model at the cohort level had the highest mean AUC (0.766) in both the training and validation cohorts, followed by the GMM-based structural habitat model at the cohort level (AUC = 0.760). Within the integrated models, the K-means functional habitat model based on the cohort level achieved the highest mean AUC (0.905) in both the training and validation cohorts.

CONCLUSION

Habitat imaging based on multiparametric MRI has the potential to distinguish US from ALM. The combination of clinical independent predictors with the habitat models can effectively improve the performance.

Uterine smooth muscle tumors: a multicenter, retrospective, comparative study of clinical and ultrasound features

OBJECTIVE

To evaluate a wide range of clinical and ultrasound characteristics of different uterine smooth muscle tumors to identify features capable of discriminating between these types.

METHODS

This was a retrospective, multicenter study that included 285 patients diagnosed with uterine smooth muscle tumors (50 leiomyosarcomas, 35 smooth muscle tumors of uncertain malignant potential, and 200 leiomyomas). The patients were divided into three groups based on the histological type of their tumors, and the groups were compared according to the variables collected.

RESULTS

Leiomyosarcomas were more common in older and post-menopausal women. Compared with leiomyomas, smooth muscle tumors of uncertain malignant potential and leiomyosarcomas had similar ultrasound features such as absence of normal myometrium, multilocular appearance, hyper-echogenicity in case of uniform echogenicity, absence of posterior shadows, echogenic areas, and hyperechoic rim. Leiomyosarcomas were larger, had more cystic areas, and were associated with a higher prevalence of pelvic free fluid. Smooth muscle tumors of uncertain malignant potential were characterized by a higher frequency of International Federation of Gynecology and Obstetrics (FIGO) type 6-7, the absence of internal shadows, and, in the case of cystic area, the presence of a regular internal wall. Tumor outline varied among the three histological types. A color score of 1 was typical of leiomyoma, a color score 2 was mainly observed in leiomyomas and smooth muscle tumors of uncertain malignant potential, a color score 3 did not differ among the tumors, while a color of score 4 was related to leiomyosarcomas. When combining color scores 3 and 4, leiomyosarcomas and smooth muscle tumors of uncertain malignant potential showed a high percentage of both circumferential and intra-lesional vascularization. A cooked appearance was not statistically different among the tumors.

CONCLUSIONS

Based on our findings, specific ultrasonographic features as well as age and menopausal status are associated with different uterine smooth muscle tumor types. Integration of these data can help the pre-operative assessment of these lesions for proper management.