Combining multimodal diffusion-weighted imaging and morphological parameters for detecting lymph node metastasis in cervical cancer.

BACKGROUND: Accurate detection of lymph node metastasis (LNM) is crucial for determining the tumor stage, selecting optimal treatment, and estimating the prognosis for cervical cancer. This study aimed to assess the diagnostic efficacy of multimodal diffusion-weighted imaging (DWI) and morphological parameters alone or in combination, for detecting LNM in cervical cancer. METHODS: In this prospective study, we enrolled consecutive cervical cancer patients who received multimodal DWI (conventional DWI, intravoxel incoherent motion DWI, and diffusion kurtosis imaging) before treatment from June 2022 to June 2023. The largest lymph node (LN) observed on each side on imaging was matched with that detected on pathology to improve the accuracy of LN matching. Comparison of the diffusion and morphological parameters of LNs and the primary tumor between the positive and negative LN groups. A combined diagnostic model was constructed using multivariate logistic regression, and the diagnostic performance was evaluated using receiver operating characteristic curves. RESULTS: A total of 93 cervical cancer patients were enrolled: 35 with LNM (48 positive LNs were collected), and 58 without LNM (116 negative LNs were collected). The area under the curve (AUC) values for the apparent diffusion coefficient, diffusion coefficient, mean diffusivity, mean kurtosis, long-axis diameter, short-axis diameter of LNs, and the largest primary tumor diameter were 0.716, 0.720, 0.716, 0.723, 0.726, 0.798, and 0.744, respectively. Independent risk factors included the diffusion coefficient, mean kurtosis, short-axis diameter of LNs, and the largest primary tumor diameter. The AUC value of the combined model based on the independent risk factors was 0.920, superior to the AUC values of all the parameters mentioned above. CONCLUSION: Combining multimodal DWI and morphological parameters improved the diagnostic efficacy for detecting cervical cancer LNM than using either alone.

Habitat Radiomics Based on MRI for Predicting Platinum Resistance in Patients with High-Grade Serous Ovarian Carcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of MRI-based habitat radiomics for predicting response of platinum-based chemotherapy in patients with high-grade serous ovarian carcinoma (HGSOC), and compared to conventional radiomics and deep learning models. MATERIALS AND METHODS: A retrospective study was conducted on HGSOC patients from three hospitals. K-means algorithm was used to perform clustering on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (CE-T1WI), and apparent diffusion coefficient (ADC) maps. After feature extraction and selection, the radiomics model, habitat model, and deep learning model were constructed respectively to identify platinum-resistant and platinum-sensitive patients. A nomogram was developed by integrating the optimal model and clinical independent predictors. The model performance and benefit was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: A total of 394 eligible patients were incorporated. Three habitats were clustered, a significant difference in habitat 2 (weak enhancement, high ADC values, and moderate T2WI signal) was found between the platinum-resistant and platinum-sensitive groups (P < 0.05). Compared to the radiomics model (0.640) and deep learning model (0.603), the habitat model had a higher AUC (0.710). The nomogram, combining habitat signatures with a clinical independent predictor (neoadjuvant chemotherapy), yielded a highest AUC (0.721) among four models, with positive NRI and IDI. CONCLUSION: MRI-based habitat radiomics had the potential to predict response of platinum-based chemotherapy in patients with HGSOC. The nomogram combining with habitat signature had a best performance and good model gains for identifying platinum-resistant patients.

A nomogram-based optimized Radscore for preoperative prediction of lymph node metastasis in patients with cervical cancer after neoadjuvant chemotherapy.

Purpose: To construct a superior single-sequence radiomics signature to assess lymphatic metastasis in patients with cervical cancer after neoadjuvant chemotherapy (NACT). Methods: The first half of the study was retrospectively conducted in our hospital between October 2012 and December 2021. Based on the history of NACT before surgery, all pathologies were divided into the NACT and surgery groups. The incidence rate of lymphatic metastasis in the two groups was determined based on the results of pathological examination following lymphadenectomy. Patients from the primary and secondary centers who received NACT were enrolled for radiomics analysis in the second half of the study. The patient cohorts from the primary center were randomly divided into training and test cohorts at a ratio of 7:3. All patients underwent magnetic resonance imaging after NACT. Segmentation was performed on T1-weighted imaging (T1WI), T2-weighted imaging, contrast-enhanced T1WI (CET1WI), and diffusion-weighted imaging. Results: The rate of lymphatic metastasis in the NACT group (33.2%) was significantly lower than that in the surgery group (58.7%, P=0.007). The area under the receiver operating characteristic curve values of Radscore_CET1WI for predicting lymph node metastasis and non-lymphatic metastasis were 0.800 and 0.797 in the training and test cohorts, respectively, exhibiting superior diagnostic performance. After combining the clinical variables, the tumor diameter on magnetic resonance imaging was incorporated into the Rad_clin model constructed using Radscore_CET1WI. The Hosmer-Lemeshow test of the Rad_clin model revealed no significant differences in the goodness of fit in the training (P=0.594) or test cohort (P=0.748). Conclusions: The Radscore provided by CET1WI may achieve a higher diagnostic performance in predicting lymph node metastasis. Superior performance was observed with the Rad_clin model.

Are CT and MRI useful tools to distinguish between micropapillary type and typical type of ovarian serous borderline tumors?

PURPOSE: To investigate the computed tomography (CT) and magnetic resonance imaging (MRI) characteristics of ovarian serous borderline tumors (SBTs), and evaluate whether CT and MRI can be used to distinguish micropapillary from typical subtypes. MATERIALS AND METHODS: We retrospectively reviewed the clinical features and CT and MR imaging findings of 47 patients with SBTs encountered at our institute from September 2013 to December 2019. 30 patients with 58 histologically proven typical SBT and 17 patients with 26 micropapillary SBT were reviewed. Preoperative CT and MR images were evaluated, by two observers in consensus for the laterality, maximum diameter (MD), morphology patterns, internal architecture, attenuation or signal intensity, ADC value, enhancement patterns of solid portions (SP), and extra-ovarian imaging features. RESULTS: The median age were similar between typical SBT and SBT-MP (32.5 years, 36 years, respectively, P>0.05). Morphology patterns between two subtypes were significantly different on CT and MR images (P < 0.001). Irregular solid tumor (21/37, 56.76%) was the major morphology pattern of typical SBT tumor, while unilocular cyst with mural nodules (14/20, 70%) was the major morphology pattern of SBT-MP on CT images. Similarly, papillary architecture with internal branching (PA&IB) (17/21, 80.95%) was the major morphology pattern of typical SBT tumor, while unilocular cyst with mural nodules (4/6, 66.67%) was the major pattern of SBT-MP on MR images. PA&IB all showed slightly hyperintense papillary architecture with hypointense internal branching on T2-weighted MRI. More calcifications were found in typical SBT (24/37, 64.86%) than SBT-MP mass lesion (6/20, 30%) (P < 0.05). Hemorrhage was less frequently visible in (20/37, 54.05%) typical SBT lessons than SBT-MP mass lesion (18/20, 90%) (P < 0.05). The ovarian preservation is more seen in typical SBT (38/58, 65.52%) than SBT-MP (12/28, 42.86%) in our series (P < 0.05). Mean ADC value of solid portions (papillary architecture and mural nodules) was 1.68 (range from 1.44 to 1.85) × 10-3 mm2/s for typical SBT and 1.62 (range from 1.45 to 1.7) × 10-3 mm2/s for that of SBT-MP. The solid components of the two SBT subtypes showed wash-in appearance enhancements after contrast injection both in CT and MR images except 2 of SBT-MP with no enhancement as complete focal hemorrhage on MR images. CONCLUSION: Morphology and internal architecture are two major imaging features that can help to distinguish between SBT-MP and typical SBT.

Assessing CT imaging features combined with CEA and CA125 levels to identify endometriosis-associated ovarian cancer.

PURPOSE: To improve the diagnosis and identification of ovarian clear cell carcinoma (CCC) and ovarian endometrioid carcinoma (EC), we evaluated CT imaging findings and cut-off values for CEA and CA125. METHODS: The CT features and tumour markers (tumour size, location, morphology, composition, number of cysts, growth pattern of the mural nodules, mural nodule HWR, enhancement of the mural nodules, ascites, complications, CEA level, CA125 level) of 55 tumours in 52 patients with CCC, confirmed by surgery and pathology at the Yunnan Cancer Hospital from January 1, 2012 to December 30, 2018, were compared with those of 41 tumours in 36 patients with EC. All patients had a long history of endometriosis. Statistical analysis was performed using t test, chi-square test, Mann-Whitney U test, univariate analysis, multivariate logistic regression analysis and receiver-operating characteristic (ROC) curves. RESULTS: CCC and EC presented as large oval or irregular mixed cystic-solid masses in the pelvic region, with moderately delayed enhancement of the solid components. There was a statistically significant difference between the number of cysts, the growth pattern of the mural nodules, the presence/absence of ascites, and the levels of CEA and CA125 (P < 0.05). Most CCCs had unilocular cysts, mural nodules that were polypoid structures, and no ascites (46/55, 33/55, 42/55); most ECs had multilocular cysts and broad-based nodular structures and were ascites positive (28/41, 31/41, 21/41). The CEA positive rate was lower in the CCC group than in the EC group (2/52, 3.8% versus 11/36, 30.6%, P < 0.05), and the CA125 positive rate was high in both the CCC and EC groups (44/52, 84.6% versus EC = 35/36, 97.2%, P = 0.118). The ROC curves revealed that when the values of CEA and CA125 were higher than the cut-off values (CEA = 3.270 µg/L, CA125 = 589.400 kU/L), the diagnostic efficiency of CEA was 0.723, and the diagnostic specificity of CEA was as high as 0.903. CONCLUSIONS: The number of cysts, growth pattern of the mural nodules, presence/absence of ascites, and levels of CEA and CA125 were useful factors for distinguishing CCC from EC; the best cut-off values of CEA and CA125 for distinguishing CCC from EC were 3.270 and 589.40, respectively. These findings may be helpful for correctly diagnosing and identifying CCC and EC.

Combined dynamic DCE-MRI and diffusion-weighted imaging to evaluate the effect of neoadjuvant chemotherapy in cervical cancer.

PURPOSE: To prospectively investigate changes in quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and the apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) in patients with cervical cancer before and after neoadjuvant chemotherapy (NACT). METHODS: Thirty-eight patients with cervical cancer underwent DCE-MRI and DWI 1 week before and 4 weeks after NACT. The patients were classified into 2 groups: significant reaction (sCR) group and the non-sCR group. DCE-MRI parameters and ADC values were measured and compared between the 2 groups. RESULTS: Before NACT, the mean Ktrans value was higher, but the mean Ve was lower, in the sCR group compared with the non-sCR group; these differences were statistically significant (p<0.05). After NACT, the mean Ktrans value and the delta (i.e., changed) value of Ktrans were significantly lower in the sCR group compared with the non-sCR group (p<0.05). However, the mean ADC and the delta value of the mean ADC between the 2 groups were slightly higher in the sCR group compared with the non-sCR group (p<0.05). The area under the curve of pre-mean Ktrans, DKtrans, and pre-mean Ktrans combined with post-mean ADC values were 0.801, 0.955, and 0.878, respectively (p<0.05). The optimal cutoff values for distinguishing sCR from non-sCR were pretreatment Ktrans (0.7020 min-1) and DKtrans (0.0437 min-1). CONCLUSIONS: Quantitative parameters (pre-mean Ktrans, DKtrans, and pre-mean Ktrans) combined with post-mean ADC could predict treatment efficacy more precisely. However, quantitative DCE-MRI combined with DWI could not significantly improve prognostic efficacy.