MRI-based radiomics features for the non-invasive prediction of FIGO stage in cervical carcinoma: A multi-center study.

PURPOSE: To develop and validate a model based on MRI radiomics modals for predicting surgical high FIGO(IB3 and ≥ IIA2) and low FIGO(IB1, IB2, and IIA1) stages in patients with cervical carcinoma (CC) . METHODS: A total of 296 early-stage patients with CC (preoperative FIGO stages IB-IIA) confirmed by surgery and pathology were included in this retrospective study from two institutions For each patient,we extracted radiomics features from spectral attenuated inversion-recovery T2-weighted (SPAIR-T2W) and contrast-enhanced T1-weighted (CE-T1W) images.Manual segmentation was performed using the 3D Slicer software, while radiomics features were extracted, screened using the R software. A 2-stage feature extraction strategy involving univariate analysis and the Least Absolute Shrinkage Selection Operator technique was performed. A support vector machine-based model was eventually constructed. Predictive accuracy of the training and validation datasets was assessed in terms of area under the ROC curve (AUC). RESULTS: A total of 1130 features were extracted from SPAIR-T2WI and CET1WI images respectively, in which 8 and 7 features significantly were associated with FIGO staging. AUCs of the SPAIR-T2W and CE-T1W models were were 0.803 and 0.790, respectively, in the internal validation group. In the external validation group, the AUCs were 0.767 and 0.749, respectively, which increased to 0.771 in the combined model. CONCLUSION: Our study demonstrated the feasibility of radiomics features from SPAIR-T2W and CE-T1W images for the prediction of surgical FIGO stage in CC. Our proposed model thereby carries the potential as a non-invasive tool for the staging and treatment planning of this disease. ADVANCES IN KNOWLEDGE: A radiomics model provide a non-invasive and objective method for the detection of FIGO staging in patients with cervical cancer before surgery, thus providing a reference for the selection of treatment options for patients.

MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study.

BACKGROUND: Preoperative prediction of clinical pathological indicators of cervical cancer (CC) is of great significance to the formulation of personalized treatment plans for CC. PURPOSE: To investigate magnetic resonance imaging (MRI) radiomics analysis for the evaluation of pathological types, tumor grade, FIGO stage, and lymph node metastasis (LNM) of CC. MATERIAL AND METHODS: A total of 235 patients with CC from three institutes were enrolled in the study. All patients underwent T2/SPAIR and contrast-enhanced T1-weighted (CE-T1WI) imaging scans before radical hysterectomy by pelvic lymph node dissection surgery. Radiomics features extracted from T2/SPAIR and CE-T1WI imaging were selected by the least absolute shrinkage and selection operator (LASSO) methods for further radiomics signature calculation. These radiomic features were used to construct regression and decision tree models to evaluate the performance of radiomic features in distinguishing clinicopathological indicators. RESULTS: The area under the curve (AUC) of T2/SPAIR and CE-T1WI imaging were 0.777 and 0.750, respectively, for differentiating between adenocarcinoma and squamous cell carcinoma. From the two sequences, the AUC of the verification group that distinguished low FIGO stage from high FIGO stage was 0.716 and 0.676, respectively. The AUC for moderately well and poorly differentiated tumors were 0.729 on T2/SPAIR and 0.749 on CE-T1WI imaging. The AUC of the verification groups for LNM was 0.730 and 0.618 on T2/SPAIR and CE-T1WI imaging, respectively. CONCLUSION: MRI radiomics features can be used as a non-invasive method to evaluate the clinicopathological indexes of CC and provide an important auxiliary examination method for patients to determine individualized treatment plans before operation.

Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning.

Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.

The Predictive Value of Magnetic Resonance Imaging-based Texture Analysis in Evaluating Histopathological Grades of Breast Phyllodes Tumor.

PURPOSE: Knowing the distinction between benign and borderline/malignant phyllodes tumors (PTs) can help in the surgical treatment course. Herein, we investigated the value of magnetic resonance imaging-based texture analysis (MRI-TA) in differentiating between benign and borderline/malignant PTs. METHODS: Forty-three women with 44 histologically proven PTs underwent breast MRI before surgery and were classified into benign (n = 26) and borderline/malignant groups (n = 18 [15 borderline, 3 malignant]). Clinical and routine MRI parameters (CRMP) and MRI-TA were used to distinguish benign from borderline/malignant PT. In total, 298 texture parameters were extracted from fat-suppression (FS) T2-weighted, FS unenhanced T1-weighted, and FS first-enhanced T1-weighted sequences. To evaluate the diagnostic performance, receiver operating characteristic curve analysis was performed for the K-nearest neighbor classifier trained with significantly different parameters of CRMP, MRI sequence-based TA, and the combination strategy. RESULTS: Compared with benign PTs, borderline/malignant ones presented a higher local recurrence (p = 0.045); larger size (p < 0.001); different time-intensity curve pattern (p = 0.010); and higher frequency of strong lobulation (p = 0.024), septation enhancement (p = 0.048), cystic component (p = 0.023), and irregular cystic wall (p = 0.045). TA of FS T2-weighted images (0.86) showed a significantly higher area under the curve (AUC) than that of FS unenhanced T1-weighted (0.65, p = 0.010) or first-enhanced phase (0.72, p = 0.049) images. The texture parameters of FS T2-weighted sequences tended to have a higher AUC than CRMP (0.79, p = 0.404). Additionally, the combination strategy exhibited a similar AUC (0.89, p = 0.622) in comparison with the texture parameters of FS T2-weighted sequences. CONCLUSION: MRI-TA demonstrated good predictive performance for breast PT pathological grading and could provide surgical planning guidance. Clinical data and routine MRI features were also valuable for grading PTs.

Clinical and MRI features of sacral insufficiency fractures after radiotherapy in patients with cervical cancer.

BACKGROUND: To determine the incidence, clinical and MRI features of sacral insufficiency fracture (SIF) after radiotherapy (RT) in patients with cervical cancer. METHODS: Our study included 167 patients with cervical cancer after radiotherapy that underwent pelvic MRI for follow-up. MRIs included pre-enhanced T1-weighted, coronal fat-Suppressed T2-weighted (FS-T2W) and enhanced T1-weighted imaging. The clinical and MRI dates were reviewed. The gold standard of SIF was based on radiologic findings, clinical data and follow-up at least 12 months. RESULTS: 28 patients (10.8%) with 47 sites were diagnosed with SIFs, including 9 patients with unilateral SIF and 19 patients with bilateral SIFs. The median age was 60 years (range 41-72 years), and 89.3% (25/28) of patients were postmenopausal. 64.3% (18/28) of patients were symptomatic, and 53.6% of patients (15/28) had concomitant pelvic fractures. The median interval time from RT to SIFs was 10 months (range 3-34 months). For the lesion-wise analysis based on all MR images, all lesions were detected by visualizing bone marrow edema patterns, and fracture lines were detected in 64.6% (31/47) of SIFs. No soft-tissue tumors were founded. For each MRI sequence analysis, coronal FS-T2WI detected the most bone marrow edema pattern and fracture line than T1WI or enhanced T1WI. CONCLUSION: SIF is a common complication in cervical cancer after radiotherapy, which has some certain clinical and MRI features. Coronal FS-T2WI may be more useful to detect and characterize these fractures than other imaging sequences.

MRI characteristics of brain edema in preeclampsia/eclampsia patients with posterior reversible encephalopathy syndrome.

BACKGROUND: The neuroimaging manifestations of eclampsia and preeclampsia often overlap, mainly presenting as posterior reversible encephalopathy syndrome (PRES). The purpose of this retrospective study was to compare the extent and nature of brain edema in eclampsia and preeclampsia patients with PRES based on MRI characteristics. METHODS: One hundred fifty women diagnosed with preeclampsia-eclampsia and undergoing cranial MRI were enrolled; 24 of these were diagnosed as having eclampsia. According to clinicoradiologic diagnosis of PRES, eligible patients were classified as having eclampsia with PRES (group E-PRES) and preeclampsia with PRES (group P-PRES). A scale on T2W FLAIR-SPIR images was established to evaluate the extent of brain edema, and the score of brain edema (SBE) of both groups was compared. In patients of the two groups who also underwent DWI sequence, the presence or absence of hyperintensity on DWI and hypointensity on ADC maps were determined to compare the nature of brain edema. Furthermore, clinical and biochemical data of the two groups were compared. RESULTS: The incidence of PRES in eclampsia patients was significantly higher than that in preeclampsia patients (87.50% vs. 46.03%, P<0.001). The SBE of all regions and typical regions in group E-PRES patients were significantly higher than those in group P-PRES patients (15.88±8.72 vs. 10.90±10.21, P=0.021; 8.52±3.87 vs. 5.01±4.19, P=0.002; respectively). The presence of hyperintensity on DWI was determined more frequently in group E-PRES patients than group P-PRES patients (71.43% vs. 32.00%, P=0.024). Age, systolic blood pressure, white blood cell count, neutrophil count and percentage of neutrophils were significantly different between the two groups (P<0.05). CONCLUSIONS: Certain MRI characteristics that reflect the extent and nature of brain edema were different between eclampsia and preeclampsia patients with PRES. Additional prospective studies are still required to explore whether these MRI characteristics of brain edema may further become a potential predictor for eclamptic seizures in preeclampsia patients with PRES.

Pelvic insufficiency fracture or bone metastasis after radiotherapy for cervical cancer? The added value of DWI for characterization.

OBJECTIVES: We sought to determine the added value of diffusion-weighted magnetic resonance imaging (DWI) in the differentiation of pelvic insufficiency fracture (PIF) from bone metastasis after radiotherapy in cervical cancer patients. METHODS: In the present study, 42 cervical cancer patients after radiotherapy with 61 bone lesions (n = 40, PIFs; n = 21, bone metastasis) were included. Conventional MRI and DWI were performed in all patients. For qualitative imaging diagnosis, two sets of images were reviewed independently by three observers, including a conventional MRI set (unenhanced T1-weighted, T2-weighted, and enhanced T1-weighted images) and a DWI set (conventional MRIs, DW images, and ADC maps). The mean ADC value of each lesson was measured on ADC maps. The diagnostic performance was assessed by using the area under the receiver operating characteristic curve (Az), and sensitivity and specificity were determined. RESULTS: For all observers, the Az value and sensitivity of the DWI set showed improvement compared with the conventional MRI set. The observer who had the least experience (3 years) demonstrated significant improvement in diagnostic performance with the addition of DWI; Az value increased from 0.804 to 0.915 (p = 0.042) and sensitivity increased from 75.0 to 92.5% (p = 0.035). The mean ADCs of the PIFs were significantly higher than the bone metastases (p < 0.001); ADC values > 0.97 × 10-3 mm2/s yielded an Az of 0.887, a sensitivity of 92.5%, and a specificity of 76.2%. CONCLUSIONS: The addition of DWI to conventional MRI improved the differentiation of PIF from bone metastasis after RT in patients with cervical cancer. KEY POINTS: • DWI showed additive value to conventional MRI in the differentiation of PIF from bone metastasis after RT. • For qualitative diagnosis, the addition of DWI can improve diagnostic performance compared with conventional MRI alone and can particularly improve the sensitivity. • Quantitative ADC assessment showed potential value for identifying PIF from bone metastasis.

The Utility of Texture Analysis Based on Breast Magnetic Resonance Imaging in Differentiating Phyllodes Tumors From Fibroadenomas.

Background: The preoperative diagnosis of phyllodes tumors (PTs) of the breast is critical to appropriate surgical treatment. However, reliable differentiation between PT and fibroadenoma (FA) remains difficult in daily clinical practice. The purpose of this study was to investigate the utility of breast MRI texture analysis for differentiating PTs from FAs. Materials and Methods: Forty-two PTs and 42 FAs were enrolled in this retrospective study. Clinical and conventional MRI features (CCMF) and MRI texture analysis were used to distinguish between PT and FA. Texture features were extracted from the axial short TI inversion recovery T2-weighted (T2W-STIR), T1-weighted pre-contrast, and two contrast-enhanced series (first contrast and third contrast). The Mann-Whitney U test was used to select statistically significant features of texture analysis and CCMF. Using a linear discriminant analysis, the most discriminative features were determined from statistically significant features. The K-nearest neighbor classifier and ROC curve were applied to evaluate the diagnostic performance. Results: With a higher classification accuracy (89.3%) and an AUC of 0.89, the texture features on T2W-STIR outperformed the texture features on other MRI sequences and CCMF. The AUC of the combination of CCMF with texture features on T2W-STIR was significantly higher than that of CCMF or texture features on T2W-STIR alone (p < 0.05). Based on the result of the classification accuracy (95.2%) and AUC (0.95), the diagnostic performance of the combination strategy performed better than texture features on T2W-STIR or CCMF separately. Conclusions: Texture features on T2W-STIR showed better diagnostic performance compared to CCMF for the distinction between PTs and FAs. After further validation of multi-institutional large datasets, MRI-based texture features may become a potential biomarker and be a useful medical decision tool in clinical trials having patients with breast fibroepithelial neoplasms.

Adoptive Transfer of NKG2D CAR mRNA-Engineered Natural Killer Cells in Colorectal Cancer Patients.

By fusing the extracellular domain of the natural killer (NK) cell receptor NKG2D to DAP12, we constructed a chimeric antigen receptor (CAR) to improve NK cell tumor responses. An RNA electroporation approach that provides transient expression of the CAR was adopted as a risk mitigation strategy. Expression of the NKG2D RNA CAR significantly augmented the cytolytic activity of NK cells against several solid tumor cell lines in vitro and provided a clear therapeutic benefit to mice with established solid tumors. Three patients with metastatic colorectal cancer were then treated with local infusion of the CAR-NK cells. Reduction of ascites generation and a marked decrease in number of tumor cells in ascites samples were observed in the first two patients treated with intraperitoneal infusion of low doses of the CAR-NK cells. The third patient with metastatic tumor sites in the liver was treated with ultrasound-guided percutaneous injection, followed by intraperitoneal infusion of the CAR-NK cells. Rapid tumor regression in the liver region was observed with Doppler ultrasound imaging and complete metabolic response in the treated liver lesions was confirmed by positron emission tomography (PET)- computed tomographic (CT) scanning. Our results highlight a promising therapeutic potential of using RNA CAR-modified NK cells to treat metastatic colorectal cancer.

Diffusion tensor imaging in the cervical spinal cord.

There are discrepancy between MR findings and clinical presentations. The compressed cervical cord in patients of the spondylotic myelopathy may be normal on conventional MRI when it is at the earlier stage or even if patients had severe symptoms. Therefore, it is necessary to take a developed MR technique--diffusion tensor imaging (DTI)--to detect the intramedullary lesions. Prospective MR and DTI were performed in 53 patients with cervical compressive myelopathy and twenty healthy volunteers. DTI was performed along six non-collinear directions with single-shot spin echo echo-planar imaging (EPI) sequence. Intramedullary apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values were measured in four segments (C2/3, C3/4, C4/5, C5/6) for volunteers, in lesions (or the compressed cord) and normal cord for patients. DTI original images were processed to produce color DTI maps. In the volunteers' group, cervical cord exhibited blue on the color DTI map. FA values between four segments had a significant difference (P < 0.01), with the highest FA value (0.85 ± 0.03) at C2/3 level. However, ADC value between them had no significant difference (P > 0.05). For patients, only 24 cases showed hyperintense on T2-weighted image, while 39 cases shown patchy green signal on color DTI maps. ADC and FA values between lesions or the compressed cord and normal spinal cord of patients had a significant difference (both P < 0.01). FA value at C2/3 cord is the highest of other segments and it gradually decreases towards the caudal direction. Using single-shot spin echo EPI sequence and six non-collinear diffusion directions with b value of 400 s mm(-2), DTI can clearly show the intramedullary microstructure and more lesions than conventional MRI.