Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3-Dimensional Tumor Volume Assessment.

BACKGROUND: The volume doubling time (VDT) of breast cancer was most frequently calculated using the two-dimensional (2D) diameter, which is not reliable for irregular tumors. It was rarely investigated using three-dimensional (3D) imaging with tumor volume on serial magnetic resonance imaging (MRI). PURPOSE: To investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs. STUDY TYPE: Retrospective. SUBJECTS: Sixty women (age at diagnosis: 57 ± 10 years) with breast cancer, assessed by two or more breast MRI examinations. The median interval time was 791 days (range: 70-3654 days). FIELD STRENGTH/SEQUENCE: 3-T, fast spin-echo T2-weighted imaging (T2WI), single-shot echo-planar diffusion-weighted imaging (DWI), and gradient echo dynamic contrast-enhanced imaging. ASSESSMENT: Three radiologists independently reviewed the morphological, DWI, and T2WI features of lesions. The whole tumor was segmented to measure the volume on contrast-enhanced images. The exponential growth model was fitted in the 11 patients with at least three MRI examinations. The VDT of breast cancer was calculated using the modified Schwartz equation. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis test, Chi-squared test, intraclass correlation coefficients, and Fleiss kappa coefficients. A P-value <0.05 was considered statistically significant. The exponential growth model was evaluated using the adjusted R2 and root mean square error (RMSE). RESULTS: The median tumor diameter was 9.7 mm and 15.2 mm on the initial and final MRI, respectively. The median adjusted R2 and RMSE of the 11 exponential models were 0.97 and 15.8, respectively. The median VDT was 540 days (range: 68-2424 days). For invasive ductal carcinoma (N = 33), the median VDT of the non-luminal type was shorter than that of the luminal type (178 days vs. 478 days). On initial MRI, breast cancer manifesting as a focus or mass lesion showed a shorter VDT than that of a non-mass enhancement (NME) lesion (median VDT: 426 days vs. 665 days). DATA CONCLUSION: A shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis.

BACKGROUND: Radiomics-based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI-invisible EC to date. PURPOSE: To develop and validate radiomics models based on sagittal T2-weighted images (T2WI) and T1-weighted contrast-enhanced images (T1CE) for the preoperative assessment of MRI-invisible early-stage EC and myometrial invasion (MI). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-eight consecutive patients (mean age 50.7 years) with MRI-invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3. FIELD STRENGTH/SEQUENCE: 3-T, T2WI, and T1CE sequences, turbo spin echo. ASSESSMENT: Two radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad-score 1 (RS1) and rad-score 2 (RS2) for evaluating MRI-invisible EC and MI. STATISTICAL TESTS: The least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann-Whitney U tests and Chi-square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P-value <0.05 was considered statistically significant. RESULTS: Model 1 had good performance for preoperative detecting of MRI-invisible early-stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI-invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834). DATA CONCLUSION: MRI-based radiomics models may provide good performance for detecting MRI-invisible EC and MI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists.

BACKGROUND: Differentiating benign from malignant renal tumors is important for selection of the most effective treatment. PURPOSE: To develop magnetic resonance imaging (MRI)-based deep learning (DL) models for differentiation of benign and malignant renal tumors and to compare their discrimination performance with the performance of radiomics models and assessment by radiologists. STUDY TYPE: Retrospective. POPULATION: A total of 217 patients were randomly assigned to a training cohort (N = 173) or a testing cohort (N = 44). FIELD STRENGTH/SEQUENCE: Diffusion-weighted imaging (DWI) and fast spin-echo sequence T2-weighted imaging (T2WI) at 3.0T. ASSESSMENT: A radiologist manually labeled the region of interest (ROI) on each image. Three DL models using ResNet-18 architecture and three radiomics models using random forest were developed using T2WI alone, DWI alone, and a combination of the two image sets to discriminate between benign and malignant renal tumors. The diagnostic performance of two radiologists was assessed based on professional experience. We also compared the performance of each model and the radiologists. STATISTICAL TESTS: The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of each model and the radiologists. P < 0.05 indicated statistical significance. RESULTS: The AUC of the DL models based on T2WI, DWI, and the combination was 0.906, 0.846, and 0.925 in the testing cohorts, respectively. The AUC of the combination DL model was significantly better than that of the models based on individual sequences (0.925 > 0.906, 0.925 > 0.846). The AUC of the radiomics models based on T2WI, DWI, and the combination was 0.824, 0.742, and 0.826 in the testing cohorts, respectively. The AUC of two radiologists was 0.724 and 0.667 in the testing cohorts. CONCLUSION: Thus, the MRI-based DL model is useful for differentiating benign from malignant renal tumors in clinic, and the DL model based on T2WI + DWI had the best performance. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Assessment of optic nerve and optic tract alterations in patients with orbital space-occupying lesions using probabilistic diffusion tractography.

AIM: To investigate the diffusion changes in both the optic nerve and optic tract in orbital space-occupying lesion patients with decreased visual acuity, and its clinical significance using probabilistic diffusion tractography (PDT). METHODS: Twenty patients with orbital space-occupying lesions and 25 age- and gender-matched healthy persons were included. All patients and controls underwent routine orbital magnetic resonance imaging and diffusion tensor imaging (DTI), using a 3.0T magnetic resonance scanner (Trio Tim Siemens). After the image data were preprocessed, each DTI parameters of the optic nerve and optic tract was obtained by PDT, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). The asymmetry index (AI) of each parameter was calculated. Compared the parameters of the affected side optic nerve and ipsilateral optic tract with the contralateral side by paired sample t-test; compared AI of parameters of optic nerve and optic tract between the patient group and the control group by independent sample t-test. Patients were divided into three subgroups according to the low vision grade standard of WHO, compared the FA and AI of FA between the three subgroups by single factor variance analysis. RESULTS: The affected side optic nerve presented significantly decreased FA, increased MD, AD, and RD values compared to the unaffected side (P<0.05). The AI of FA, MD, AD, and RD of optic nerve in the patients was significantly higher than that of the controls (P<0.05). The comparison results of the optic tract showed that there was no significant difference between the patient group and control group in terms of the bilateral optic tracts in patients (P>0.05). The AIs of the FA value of the optic nerve in the eyesight <0.1 subgroup was significantly higher than that in the other groups (P<0.05). CONCLUSION: FA, MD, AD, and RD of the affected side optic nerve of the orbital space-occupying lesions have significantly changed, the FA value is the most sensitive. The PDT could be a useful tool to provide valid quantitative markers of optic nerve injuries and evaluate the severity of orbital diseases, which other examinations cannot be acquired.

Simultaneous determination of seven flavonoids, two phenolic acids and two cholesterines in Tanreqing injection by UHPLC-MS/MS.

A new ultra-high performance liquid chromatography combined with triple quadrupole mass spectrometry was developed to evaluate the quality of Tanreqing injection. Seven flavonoids (Rutin, Baicalin, Scutellarin, Chrysin-7-O-Beta-d-glucoronide, Oroxylin A-7-O-ß-d-glucoronide, Wogonin, Luteolin-7-O-glucoside), two phenolic acids (Chlorogenic acid, Caffeic acid) and two cholesterines (Ursodeoxycholic acid, Chenodeoxycholic acid) in Tanreqing injection could be measured simultaneously. For the determination of the eleven compounds, the conditions were set as follows: The mobile phase was a gradient of 0.1% aqueous formic acid solution (A) and acetonitrile (B); the flow rate was 0.2 mL min-1, the column was Acquity UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 µm); and the multiple-reaction monitoring (MRM) with a negative electro spray ionization interface (ESI-) was selected. Within the test ranges, all the standard regression curves showed excellent linear regression (r > 0.99). In terms of (relative standard deviation) RSDs, the precision, repeatability and stability of the eleven compounds were all lower than 3%. The recovery rates of Tanreqing injection and the RSD were 97.8-103.7% and 0.4%-2.0%, respectively. The RSD value was in accordance with the requirements of less than 3.0%. This method has been successfully used in the analysis of Tanreqing injection. In summary, a fast, accurate and reliable UPLC-ESI--MS/MS method was successfully developed for the simultaneous detection of the eleven major active ingredients with different chemical structures in Tanreqing injection, and can be used for the quality control of Tanreqing injection as well.