T1-weighted Dynamic Contrast-enhanced Imaging Research Articles

Radiomics analysis for prediction of lymph node metastasis after neoadjuvant chemotherapy based on pretreatment MRI in patients with locally advanced cervical cancer.

This study aims to develop and validate a pretreatment MRI-based radiomics model to predict lymph node metastasis (LNM) following neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Patients with LACC who underwent NACT from two centers between 2013 and 2022 were enrolled retrospectively. Based on the lymph node (LN) status determined in the pathology reports after radical hysterectomy, patients were categorized as LN positive or negative. The patients from center 1 were assigned as the training set while those from center 2 formed the validation set. Radiomics features were extracted from pretreatment sagittal T2-weighted imaging (Sag-T2WI), axial diffusion-weighted imaging (Ax-DWI), and the delayed phase of dynamic contrast-enhanced sagittal T1-weighted imaging (Sag-T1C) for each patient. The K-best and least absolute shrinkage and selection operator (LASSO) methods were employed to reduce dimensionality, and the radiomics features strongly associated with LNM were selected and used to construct three single-sequence models. Furthermore, clinical variables were incorporated through multivariate regression analysis and fused with the selected radiomics features to construct the clinical-radiomics combined model. The diagnostic performance of the models was assessed using receiver operating characteristic (ROC) curve analysis. The clinical utility of the models was evaluated by the area under the ROC curve (AUC) and decision curve analysis (DCA). A total of 282 patients were included, comprising 171 patients in the training set, and 111 patients in the validation set. Compared to the Sag-T2WI model (AUC, 95%CI, training set, 0.797, 0.722-0.782; validation set, 0.648, 0.521-0.776) and the Sag-T1C model (AUC, 95%CI, training set, 0.802, 0.723-0.882; validation set, 0.630, 0.505-0.756), the Ax-DWI model exhibited the highest diagnostic performance with AUCs of 0.855 (95%CI, 0.791-0.919) in training set, and 0.753 (95%CI, 0.638-0.867) in validation set, respectively. The combined model, integrating selected features from three sequences and FIGO stage, surpassed predictive ability compared to the single-sequence models, with AUC of 0.889 (95%CI, 0.833-0.945) and 0.859 (95%CI, 0.781-0.936) in the training and validation sets, respectively. The pretreatment MRI-based radiomics model, integrating radiomics features from three sequences and clinical variables, exhibited superior performance in predicting LNM following NACT in patients with LACC.

Utility of Prostate Health Index Density for Biopsy Strategy in Biopsy-Naïve Patients With PI-RADS v2.1 Category 3 Lesions.

Category 3 lesions in PI-RADSv2.1 pose diagnostic challenges, complicating biopsy decisions. Recent biomarkers like prostate health index (PHI) have shown higher specificity in detecting clinically significant prostate cancer (csPCa) than prostate-specific antigen (PSA). Yet their integration with MRI remains understudied. To evaluate the utility of PSA and PHI with its derivatives for detecting csPCa in biopsy-naïve patients with category 3 lesion on initial prostate MRI scan. Retrospective. One hundred ninety-three biopsy-naïve patients who underwent MRI, PSA, and PHI testing, followed by both targeted and systematic biopsies. Turbo spin-echo T2-weighted imaging, diffusion-weighted single-shot echo-planar imaging, and dynamic contrast-enhanced T1-weighted fast field echo sequence imaging in 3 T. PHI density (PHID) and PSA density (PSAD) derived by dividing serum PHI and PSA with prostate volume (MRI based methodology suggested by PI-RADSv2.1). Risk-stratified models to evaluate the utility of markers in triaging patients for biopsy, including low-, intermediate-, and high-risk groups. Independent t-test, Mann-Whitney U test, Mantel-Haenszel test, generalized estimating equation, and receiver operating characteristic (ROC) curve analysis were used. Statistical significance defined as P < 0.05. CsPCa was found in 16.6% (32/193) of patients. PHID had the highest area under the ROC curve (AUROC) of 0.793, followed by PHI of 0.752, PSAD of 0.750, and PSA of 0.654. PHID with two cut-off points (0.88/mL and 1.82/mL) showed the highest potential biopsy avoidance of 47.7% (92/193) with 5% missing csPCa, and the lowest intermediate-risk group (borderline decision group) at 38.9% (75/193), compared to PSA and PHI. PHID demonstrated better potential in triaging patients with category 3 lesions, possibly aiding more selective and confident biopsy decisions for csPCa detection, than traditional markers. 4 TECHNICAL EFFICACY: Stage 5.

Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer

To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

Multiparametric MRI and Radiomics for the Prediction of HER2-Zero, -Low, and -Positive Breast Cancers.

Background Half of breast cancers exhibit low expression levels of human epidermal growth factor receptor 2 (HER2) and can be targeted by new antibody-drug conjugates. The imaging differences between HER2-zero (immunohistochemistry [IHC] score of 0), HER2-low (IHC score of 1+ or 2+ with negative findings at fluorescence in situ hybridization [FISH]), and HER2-positive (IHC score of 2+ with positive findings at FISH or IHC score of 3+) breast cancers were unknown. Purpose To assess whether multiparametric dynamic contrast-enhanced MRI-based radiomic features can help distinguish HER2 expressions in breast cancer. Materials and Methods This study included women with breast cancer who underwent MRI at two different centers between December 2020 and December 2022. Tumor segmentation and radiomic feature extraction were performed on T2-weighted and dynamic contrast-enhanced T1-weighted images. Unsupervised correlation analysis of reproducible features and least absolute shrinkage and selector operation were used for the selection of features to build a radiomics signature. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the radiomic signature. Multivariable logistic regression was used to identify independent predictors for distinguishing HER2 expressions in both the training and prospectively acquired external data set. Results The training set included 208 patients from center 1 (mean age, 53 years ± 14 [SD]), and the external test set included 131 patients from center 2 (mean age, 54 years ± 13). In the external test data set, the radiomic signature achieved an AUC of 0.80 (95% CI: 0.71, 0.89) for distinguishing HER2-low and -positive tumors versus HER2-zero tumors and was a significant predictive factor for distinguishing these two groups (odds ratio = 7.6; 95% CI: 2.9, 19.8; P < .001). Among HER2-low or -positive breast cancers, histology type, associated nonmass enhancement, and multiple lesions at MRI had an AUC of 0.77 (95% CI: 0.68, 0.86) in the external test set for the prediction of HER2-positive versus HER2-low cancers. Conclusion The radiomic signature and tumor descriptors from multiparametric breast MRI may predict distinct HER2 expressions of breast cancers with therapeutic implications. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kataoka and Honda in this issue.

Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer.

Tumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC. One hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI). In total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754-0.920), 0.875 (95% CI:0.797-0.952), and 0.923 (95% CI: 0.869-0.977) for the training set; 0.857 (95% CI: 0.741-0.973), 0.785 (95% CI: 0.592-0.979), and 0.914 (95% CI: 0.827-0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214-1.061) and 0.657 (0.079-1.394), and IDIs were 0.115 (0.077-0.306) and 0.053 (0.027-0.357) in the training set and validation set, respectively. The radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage.

Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors.

Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. Retrospective. A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). 1.5T and 3T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. 4 TECHNICAL EFFICACY: Stage 2.

MRI-derived cardiac washout is slowed in the left ventricle and associated with left ventricular non-compaction in young patients with cryptogenic ischemic stroke

To elucidate underlying disease mechanisms, we compared transition of gadolinium-based contrast agent bolus in cardiac chambers in magnetic resonance imaging between young patents with cryptogenic ischemic stroke and stroke-free controls. We included 30 patients aged 18–50 years with cryptogenic ischemic stroke from the prospective Searching for Explanations for Cryptogenic Stroke in the Young: Revealing the Etiology, Triggers and Outcome (NCT01934725) study and 30 age- and gender-matched stroke-free controls. Dynamic contrast-enhanced T1-weighted first-pass perfusion images were acquired at 1.5 T and analyzed for transit time variables, area under curves, relative blood flow, and maximum and minimum enhancement rates in left atrial appendage, left atrium, and left ventricle. These data were compared with previously published left ventricular non-compaction data of the same study population. Arrival time of contrast agent bolus in superior vena cava was similar in patients and controls (6.7[2.0] vs. 7.1[2.5] cardiac cycles, P = 0.626). Arrival and peak times showed comparable characteristics in patients and controls (P > 0.535). The minimum enhancement rate of the left ventricle was lower in patients than in controls (− 28 ± 11 vs. − 36 ± 13 1/(cardiac cycle), P = 0.012). Area under curves, relative blood flow, and other enhancement rates showed no significant differences between patients and controls (P > 0.107). Relative blood flow of cardiac chambers correlated with non-compacted left ventricular volume ratio (P < 0.011). Our results indicate slower washout of contrast agent and blood flow stagnation in the left ventricle of young patients with cryptogenic ischemic stroke. The washout was associated with left ventricular non-compaction, suggesting conditions favoring formation of intraventricular thrombosis.

Quantitative Changes in Intratumoral Habitats on MRI Correlate With Pathologic Response in Early-stage ER/PR+ HER2- Breast Cancer Treated With Preoperative Stereotactic Ablative Body Radiotherapy.

To quantitatively evaluate intratumoral habitats on dynamic contrast-enhanced (DCE) breast MRI to predict pathologic breast cancer response to stereotactic ablative body radiotherapy (SABR). Participants underwent SABR treatment (28.5 Gy x3), baseline and post-SABR MRI, and breast-conserving surgery for ER/PR+ HER2- breast cancer. MRI analysis was performed on DCE T1-weighted images. MRI voxels were assigned eight habitats based on high (H) or low (L) maximum enhancement and the sequentially numbered dynamic sequence of maximum enhancement (H1-4, L1-4). MRI response was analyzed by percent tumor volume remaining (%VR = volume post-SABR/volume pre-SABR), and percent habitat makeup (%HM of habitat X = habitat X voxels/total voxels in the segmented volume). These were correlated with percent tumor bed cellularity (%TC) for pathologic response. Sixteen patients completed the trial. The %TC ranged 20%-80%. MRI %VR demonstrated strong correlations with %TC (Pearson R = 0.7-0.89). Pre-SABR tumor %HMs differed significantly from whole breasts (P = 0.005 to <0.00001). Post-SABR %HM of tumor habitat H4 demonstrated the largest change, increasing 13% (P = 0.039). Conversely, combined %HM for H1-3 decreased 17% (P = 0.006). This change correlated with %TC (P < 0.00001) and distinguished pathologic partial responders (≤70 %TC) from nonresponders with 94% accuracy, 93% sensitivity, 100% specificity, 100% positive predictive value, and 67% negative predictive value. In patients undergoing preoperative SABR treatment for ER/PR+ HER2- breast cancer, quantitative MRI habitat analysis of %VR and %HM change correlates with pathologic response.

Ultra-High-Field 7 T Magnetic Resonance Imaging Including Dynamic and Static Contrast-Enhanced T1-Weighted Imaging Improves Detection of Secreting Pituitary Microadenomas.

A prospective preoperative evaluation of 7 T ultra-high-field magnetic resonance imaging (MRI) in patients with suspected pituitary microadenomas for both adenoma detection and intrasellar localization compared with 3 T MRI was carried out. Patients underwent prospective preoperative standardized 3 and 7 T MRI. A distinct qualitative (lesion detection, intrasellar lesion location) and quantitative (lesion diameters, T1/T2 signal intensity ratio of the lesion to normal pituitary gland tissue) analysis was performed, along with an evaluation of image quality (IQ) regarding overall IQ, anatomical parameters, and artifacts; the findings of the qualitative analysis were compared with intraoperative findings and endocrinological outcomes. Sixteen patients (mean age, 43 ± 16 years; 13 women) with pituitary microadenomas were included. Using 7 T MRI allowed the detection of 15 microadenomas-3 more than 3 T MRI. In addition, 7 T MRI allowed more precise lesion localization with 93.75% (15/16) agreement with intraoperative findings, compared with 75% (12/16) agreement using 3 T MRI. Lesion diameters showed no significant difference between 3 and 7 T MRI. T1 and T2 signal intensity ratio between microadenomas and normal pituitary gland tissue were higher in 7 T MRI than in 3 T MRI. The overall IQ and the IQ of each anatomical parameter of 7 T MRI were rated higher than those of 3 T MRI. No significant differences in susceptibility or head motion artifacts were observed between 3 and 7 T MRI; however, 7 T MRI was more susceptible to pulsation artifacts. Ultra-high-field MRI surpasses 3 T MRI in pituitary microadenoma detection and enables more precise delineation with higher correlation with intraoperative findings. Thus, 7 T sellar imaging is a promising option-especially in previously magnetic resonance-negative patients with endocrinologically confirmed hormone oversecretion-and helps reduce the need for invasive diagnostics.

Multiparametric ultrasound versus multiparametric MRI to diagnose prostate cancer (CADMUS): a prospective, multicentre, paired-cohort, confirmatory study.

Multiparametric MRI of the prostate followed by targeted biopsy is recommended for patients at risk of prostate cancer. However, multiparametric ultrasound is more readily available than multiparametric MRI. Data from paired-cohort validation studies and randomised, controlled trials support the use of multiparametric MRI, whereas the evidence for individual ultrasound methods and multiparametric ultrasound is only derived from case series. We aimed to establish the overall agreement between multiparametric ultrasound and multiparametric MRI to diagnose clinically significant prostate cancer. We conducted a prospective, multicentre, paired-cohort, confirmatory study in seven hospitals in the UK. Patients at risk of prostate cancer, aged 18 years or older, with an elevated prostate-specific antigen concentration or abnormal findings on digital rectal examination underwent both multiparametric ultrasound and multiparametric MRI. Multiparametric ultrasound consisted of B-mode, colour Doppler, real-time elastography, and contrast-enhanced ultrasound. Multiparametric MRI included high-resolution T2-weighted images, diffusion-weighted imaging (dedicated high B 1400 s/mm2 or 2000 s/mm2 and apparent diffusion coefficient map), and dynamic contrast-enhanced axial T1-weighted images. Patients with positive findings on multiparametric ultrasound or multiparametric MRI underwent targeted biopsies but were masked to their test results. If both tests yielded positive findings, the order of targeting at biopsy was randomly assigned (1:1) using stratified (according to centre only) block randomisation with randomly varying block sizes. The co-primary endpoints were the proportion of positive lesions on, and agreement between, multiparametric MRI and multiparametric ultrasound in identifying suspicious lesions (Likert score of ≥3), and detection of clinically significant cancer (defined as a Gleason score of ≥4 + 3 in any area or a maximum cancer core length of ≥6 mm of any grade [PROMIS definition 1]) in those patients who underwent a biopsy. Adverse events were defined according to Good Clinical Practice and trial regulatory guidelines. The trial is registered on ISRCTN, 38541912, and ClinicalTrials.gov, NCT02712684, with recruitment and follow-up completed. Between March 15, 2016, and Nov 7, 2019, 370 eligible patients were enrolled; 306 patients completed both multiparametric ultrasound and multiparametric MRI and 257 underwent a prostate biopsy. Multiparametric ultrasound was positive in 272 (89% [95% CI 85-92]) of 306 patients and multiparametric MRI was positive in 238 patients (78% [73-82]; difference 11·1% [95% CI 5·1-17·1]). Positive test agreement was 73·2% (95% CI 67·9-78·1; κ=0·06 [95% CI -0·56 to 0·17]). Any cancer was detected in 133 (52% [95% CI 45·5-58]) of 257 patients, with 83 (32% [26-38]) of 257 being clinically significant by PROMIS definition 1. Each test alone would result in multiparametric ultrasound detecting PROMIS definition 1 cancer in 66 (26% [95% CI 21-32]) of 257 patients who had biopsies and multiparametric MRI detecting it in 77 (30% [24-36]; difference -4·3% [95% CI -8·3% to -0·3]). Combining both tests detected 83 (32% [95% CI 27-38]) of 257 clinically significant cancers as per PROMIS definition 1; of these 83 cancers, six (7% [95% CI 3-15]) were detected exclusively with multiparametric ultrasound, and 17 (20% [12-31]) were exclusively detected by multiparametric MRI (agreement 91·1% [95% CI 86·9-94·2]; κ=0·78 [95% CI 0·69-0·86]). No serious adverse events were related to trial activity. Multiparametric ultrasound detected 4·3% fewer clinically significant prostate cancers than multiparametric MRI, but it would lead to 11·1% more patients being referred for a biopsy. Multiparametric ultrasound could be an alternative to multiparametric MRI as a first test for patients at risk of prostate cancer, particularly if multiparametric MRI cannot be carried out. Both imaging tests missed clinically significant cancers detected by the other, so the use of both would increase the detection of clinically significant prostate cancers compared with using each test alone. The Jon Moulton Charity Trust, Prostate Cancer UK, and UCLH Charity and Barts Charity.

Dynamic Liver Magnetic Resonance Imaging During Free Breathing: A Feasibility Study With a Motion Compensated Variable Density Radial Acquisition and a Viewsharing High-Pass Filtering Reconstruction.

Robust dynamic contrast-enhanced T1-weighted images are crucial for accurate detection and categorization of focal liver lesions in liver/abdominal magnetic resonance imaging (MRI). As optimal dynamic imaging usually requires multiple breath-holds, its inherent susceptibility to motion artifacts frequently results in degraded image quality in incompliant patients. Because free-breathing imaging may overcome this drawback, the intention of this study was to evaluate a dynamic MRI sequence acquired during free breathing using the variable density, elliptical centric golden angle radial stack-of-stars radial sampling scheme, which so far has not been implemented in 4-dimensional applications. In a prospective pilot study, 27 patients received a routine abdominal MRI protocol including the prototype free-breathing sequence (4DFreeBreathing) for dynamic imaging. This enables more convenient and faster reconstruction through variable density, elliptical centric golden angle radial stack-of-stars without the use of additional reconstruction hardware, and even higher motion robustness through soft-gating. A standard breath-hold sequence performed subsequently served as reference standard. Of the continuous dynamic data sets, each dynamic phase was analyzed regarding image quality, motion artifacts and vessel conspicuity using 5-point Likert scales. Furthermore, correct timing of the late arterial phase was compared with the preexaminations. 4DFreeBreathing delivered motion-free dynamic images with high temporal resolution in each subject. Overall image quality scores were rated good or excellent for 4DFreeBreathing and the gold standard without significant differences (P = 0.34). There were significantly less motion artifacts in the 4DFreeBreathing sequence (P < 0.0001), whereas vessel conspicuity in each dynamic phase was comparable for both groups (P = 0.45, P > 0.99, P = 0.22, respectively). Correct timing of the late arterial phase could be achieved in 27 of 27 (100%) examinations using 4DFreeBreathing versus 35 of 53 (66%) preexaminations using gold standard (P < 0.001). The benefit of convenient and fast image reconstruction combined with the superiority in motion robustness and timing compared with standard breath hold sequences renders 4DFreeBreathing an attractive alternative to existing free-breathing techniques in dynamic liver MRI.

Qualitative Heterogeneous Signal Drop on Chemical Shift (CS) MR Imaging: Correlative Quantitative Analysis between CS Signal Intensity Index and Contrast Washout Parameters Using T1-Weighted Sequences.

The aim of this study was to calculate MRI quantitative parameters extracted from chemical-shift (CS) and dynamic contrast-enhanced (DCE) T1-weighted (T1-WS) images of adrenal lesions (AL) with qualitative heterogeneous signal drop on CS T1-WS and compare them to those of AL with homogeneous or no signal drop on CS T1-WS. On 3 T MRI, 65 patients with a total of 72 AL were studied. CS images were qualitatively assessed for grouping AL as showing homogeneous (Group 1, n = 19), heterogeneous (Group 2, n = 23), and no (Group 3, n = 30) signal drop. Histopathology or follow-up data served as reference standard to classify AL. ROIs were drawn both on CS and DCE images to obtain adrenal CS signal intensity index (ASII), absolute (AWO), and relative washout (RWO) values. Quantitative parameters (QP) were compared with ANOVA analysis and post hoc Dunn’s test. The performance of QP to classify AL was assessed with receiver operating characteristic analysis. CS ASII values were significantly different among the three groups (p < 0.001) with median values of 71%, 53%, and 3%, respectively. AWO/RWO values were similar in Groups 1 (adenomas) and 2 (benign AL) but significantly (p < 0.001) lower in Group 3 (20 benign AL and 10 malignant AL). With cut-offs, respectively, of 60% (Group 1 vs. 2), 20% (Group 2 vs. 3), and 37% (Group 1 vs. 3), CS ASII showed areas under the curve of 0.85, 0.96, and 0.93 for the classification of AL, overall higher than AWO/RWO. In conclusion, AL with qualitative heterogeneous signal drop at CS represent benign AL with QP by DCE sequence similar to those of AL with homogeneous signal drop at CS, but different to those of AL with no signal drop at CS; ASII seems to be the only quantitative parameter able to differentiate AL among the three different groups.

Differential diagnosis of parotid gland tumours: Application of SWI combined with DWI and DCE-MRI

BackgroundParotid tumours (PTs) have a variety of pathological types, and the surgical procedures differ depending on the tumour type. However, accurate diagnosis of PTs from the current preoperative examinations is unsatisfactory. MethodsThis retrospective study was approved by the Ethics Committee of our hospital, and the requirement for informed consent was waived. A total of 73 patients with PTs, including 55 benign and 18 malignant tumours confirmed by surgical pathology, were enrolled. All patients underwent diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), susceptibility-weighted imaging (SWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI). The signal uniformity and capsule on T2WI, apparent diffusion coefficient (ADC) derived from DWI, semi-quantitative parameter time-intensity curve (TIC) pattern, and quantitative parameters including transfer constant (Ktrans), extravascular extracellular volume fraction (Ve), wash-out constant (Kep) calculated from DCE-MRI, and intratumoural susceptibility signal (ITSS) obtained from SWI were assessed and compared between benign and malignant PTs. Logistic regression analysis was used to select the predictive parameters for the classification of benign and malignant parotid gland tumours, and receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic performance. ResultsMalignant PTs tended to exhibit a type C TIC pattern, whereas benign tumours tended to be type A and B (p < 0.001). Benign PTs had less ITSS than malignant tumours (p < 0.001). Multivariate analyses showed that ADC, Ve, and ITSS were predictors of tumour classification. ROC analysis showed that the area under the curve (AUC) of ADC, Ve, ITSS, and ADC combined with Ve were 0.623, 0.615, 0.826, and 0.782, respectively, in differentiating between malignant and benign PTs. When ITSS was added, the AUCs of ADC, Ve, and ADC combined with Ve increased to 0.882, 0.848, and 0.930, respectively. ConclusionSWI offers incremental diagnostic value to DWI and DCE-MRI in the characterisation of parotid gland tumours.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer.

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

Toward Computer-Assisted Triaging of Magnetic Resonance Imaging-Guided Biopsy in Preoperative Breast Cancer Patients.

Incidental MR-detected breast lesions (ie, additional lesions to the index cancer) pose challenges in the preoperative workup of patients with early breast cancer. We pursue computer-assisted triaging of magnetic resonance imaging (MRI)-guided breast biopsy of additional lesions at high specificity. We investigated 316 consecutive female patients (aged 26 to 76 years; mean, 54 years) with early breast cancer who received preoperative multiparametric breast MRI between 2013 and 2016. In total, 82 (26%) of 316 patients had additional breast lesions on MRI. These 82 patients had 101 additional lesions in total, 51 were benign and 50 were malignant. We collected 4 clinical features and 46 MRI radiomic features from T1-weighted dynamic contrast-enhanced imaging, high-temporal-resolution dynamic contrast-enhanced imaging, T2-weighted imaging, and diffusion-weighted imaging. A multiparametric computer-aided diagnosis (CAD) model using 10-fold cross-validated ridge regression was constructed. The sensitivities were calculated at operating points corresponding to 98%, 95%, and 90% specificity. The model calibration performance was evaluated by calibration plot analysis and goodness-of-fit tests. The model was tested in an independent testing cohort of 187 consecutive patients from 2017 and 2018 (aged 35 to 76 years; mean, 59 years). In this testing cohort, 45 (24%) of 187 patients had 55 additional breast lesions in total, 23 were benign and 32 were malignant. The multiparametric CAD model correctly identified 48% of the malignant additional lesions with a specificity of 98%. At specificity 95% and 90%, the sensitivity was 62% and 72%, respectively. Calibration plot analysis and goodness-of-fit tests indicated that the model was well fitted.In the independent testing cohort, the specificity was 96% and the sensitivity 44% at the 98% specificity operating point of the training set. At operating points 95% and 90%, the specificity was 83% at 69% sensitivity and the specificity was 78% at 81% sensitivity, respectively. The multiparametric CAD model showed potential to identify malignant disease extension with near-perfect specificity in approximately half the population of preoperative patients originally indicated for a breast biopsy. In the other half, patients would still proceed to MRI-guided biopsy to confirm absence of malignant disease. These findings demonstrate the potential to triage MRI-guided breast biopsy.

Diagnostic accuracy of multiparametric magnetic resonance imaging combined with clinical parameters in the detection of clinically significant prostate cancer: A novel diagnostic model.

To evaluate the diagnostic accuracy of multiparametric magnetic resonance imaging in the detection of prostate cancer, according to Prostate Imaging Reporting and Data System, and the usefulness of combining clinical parameters to improve patients' risk assessment. Overall, 201 patients underwent multiparametric magnetic resonance imaging investigation with a 3-T magnet and a 32-channel body coil based on triplanar high-resolution T2-weighted, diffusion-weighted and T1-weighted dynamic contrast-enhanced imaging before, during and after intravenous administration of paramagnetic contrast agent. Random transrectal ultrasound-guided biopsy was carried out for all eligible patients. If a Prostate Imaging Reporting and Data System ≥3 lesion was present, a targeted biopsy with magnetic resonance imaging-transrectal ultrasound fusion-guided system was carried out. Sensitivity, specificity, positive predictive value and negative predictive value of Prostate Imaging Reporting and Data System ≥3 lesions for the detection of prostate cancer were 65.1%, 54.9%, 43.1% and 75.0% respectively, with an accuracy of 64.2% (55.1-72.7%). At uni- and multivariate analysis, age ≥70years and prostate-specific antigen density ≥0.15ng/mL/mL were significantly associated with prostate cancer. A new risk model named "modified Prostate Imaging Reporting and Data System" was created considering age and prostate-specific antigen density in addition to the Prostate Imaging Reporting and Data System score showing an improved correlation with prostate cancer compared with the Prostate Imaging Reporting and Data System alone (area under curve 71.4%, 95% confidence interval 62.2-80.5 vs area under curve 62.6%, 95% confidence interval 52.1-73; P≤0.0001). The accuracy of Prostate Imaging Reporting and Data System alone in the diagnosis of prostate cancer might be suboptimal, whereas a novel risk model based on the combination of multiparametric magnetic resonance imaging data with clinical parameters could offer higher discrimination and improve the ability of diagnosing clinically significant disease.

A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer.

BackgroundRecently, radiomics has emerged as a non-invasive, imaging-based tissue characterization method in multiple cancer types. One limitation for robust and reproducible analysis lies in the inter-reader variability of the tumor annotations, which can potentially cause differences in the extracted feature sets and results. In this study, the diagnostic potential of a rapid and clinically feasible VOI (Volume of Interest)-based approach to radiomics is investigated to assess MR-derived parameters for predicting molecular subtype, hormonal receptor status, Ki67- and HER2-Expression, metastasis of lymph nodes and lymph vessel involvement as well as grading in patients with breast cancer.MethodsA total of 98 treatment-naïve patients (mean 59.7 years, range 28.0–89.4) with BI-RADS 5 and 6 lesions who underwent a dedicated breast MRI prior to therapy were retrospectively included in this study. The imaging protocol comprised dynamic contrast-enhanced T1-weighted imaging and T2-weighted imaging. Tumor annotations were obtained by drawing VOIs around the primary tumor lesions followed by thresholding. From each segmentation, 13.118 quantitative imaging features were extracted and analyzed with machine learning methods. Validation was performed by 5-fold cross-validation with 25 repeats.ResultsPredictions for molecular subtypes obtained AUCs of 0.75 (HER2-enriched), 0.73 (triple-negative), 0.65 (luminal A) and 0.69 (luminal B). Differentiating subtypes from one another was highest for HER2-enriched vs triple-negative (AUC 0.97), followed by luminal B vs triple-negative (0.86). Receptor status predictions for Estrogen Receptor (ER), Progesterone Receptor (PR) and Hormone receptor positivity yielded AUCs of 0.67, 0.69 and 0.69, while Ki67 and HER2 Expressions achieved 0.81 and 0.62. Involvement of the lymph vessels could be predicted with an AUC of 0.8, while lymph node metastasis yielded an AUC of 0.71. Models for grading performed similar with an AUC of 0.71 for Elston-Ellis grading and 0.74 for the histological grading.ConclusionOur preliminary results of a rapid approach to VOI-based tumor-annotations for radiomics provides comparable results to current publications with the perks of clinical suitability, enabling a comprehensive non-invasive platform for breast tumor decoding and phenotyping.

Prolonged time between intravenous contrast administration and image acquisition results in increased synovial thickness at magnetic resonance imaging in patients with juvenile idiopathic arthritis.

Post-contrast synovial thickness measurement is necessary for scoring disease activity in juvenile idiopathic arthritis (JIA). However, the timing of post-contrast sequences varies widely among institutions. This variation in timing could influence thickness measurements. To measure thickness of the synovial membrane on early and late post-contrast knee magnetic resonance (MR) images of patients with JIA. Dynamic contrast-enhanced T1-weighted knee MR images of 53 children with JIA with current or past knee arthritis were used to study synovial thickness at time point 1 (about 1min) and time point 2 (about 5min after contrast administration). Two experienced readers, who were blinded for the time point, independently measured synovial thickness at a predefined, marked location in the patellofemoral compartment on randomized images. Synovial thickness at the two time points was compared using the Wilcoxon signed rank test. Repeatibility of the synovial thickness measurements was studied using intraclass correlation coefficients and Bland-Altman plots. Median synovial thickness of the 53 patients (median age: 13.5years, 59% female) increased with prolonged post-contrast interval with a synovial thickness of 1.4mm at time point 1 and a synovial thickness of 1.5mm at time point 2 (P<0.001). Repeated synovial thickness measurements showed an intraclass correlation coefficient (ICC) of 0.75, P<0.05 for time point 1 and an ICC of 0.91, P<0.05 for time point 2. Post-contrast synovial membrane thickness measurements are time-dependent. Therefore, standardization of post-contrast image acquisition timing is important to achieve consistent grading of synovial inflammation.

Multiparametric MRI Characterization of Funaki Types of Uterine Fibroids Considered for MR-Guided High-Intensity Focused Ultrasound (MR-HIFU) Therapy

To compare quantitative multiparametric magnetic resonance imaging (mpMRI) data of symptomatic uterine fibroids being considered for MR-guided high-intensity focused ultrasound ablation with fibroid characterization based on the Funaki Classification scheme. This was a prospective, Institutional Review Board -approved, Health Insurance Portability, and Accountability Act-compliant study. Informed consent was obtained. From December 2013 to April 2015, 48 women with symptomatic fibroids underwent screening with mpMRI protocol including sagittal/axial T2-weighted fast spin-echo, sagittal diffusion-weighted, and sagittal dynamic contrast-enhanced 3D T1-weighted gradient echo imaging on a 3T magnet. All fibroids were assigned Funaki type 1, 2, or 3 based on T2-weighted imaging. Differences in size, perfusion, and diffusion/intravoxel incoherent motion parameters among the three Funaki types were determined using linear mixed model. A logistic regression analysis was performed to select the best model in predicting type 3 fibroids. A total of 100 fibroids were assessed (20 type 1, 66 type 2, and 14 type 3). Apparent diffusion coefficient and D of type 3 fibroids were significantly higher than those of type 1 (P < 0.0001, P < 0.0001) and 2 fibroids (P = 0.004, P < 0.0001) respectively. Transfer constant of type 3 fibroids was significantly higher than type 1 (P = 0.0357), but not than type 2 (P = 0.0752). A cutoff value of D = 1 × 10-3 mm2/s offers an accuracy, sensitivity, and specificity of 76%, 71%, and 77%, respectively, for the diagnosis of Funaki 3 fibroids. mpMRI-derived quantitative parameters may enable a more objective selection of patients prior to MR-guided high-intensity focused ultrasound therapy.

Accuracy of magnetic resonance imaging for predicting pathological complete response of breast cancer after neoadjuvant chemotherapy: association with breast cancer subtype.

A pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is a signature of favorable prognosis in breast cancer. The aim of this study was to assess the accuracy of magnetic resonance imaging (MRI) in predicting the pCR after NAC. 265 women with stage II or III breast cancer who underwent surgery after NAC were retrospectively investigated for MRI findings before and after the NAC. Correlation of pCR with an “imaging complete response” (iCR), defined as no detectable tumor on all serial images with dynamic contrast-enhanced T1-weighted imaging, was evaluated with respect to each tumor subtype. Of 265 cases, 44 (16.6 %) and 24 (9.1 %) were diagnosed as iCR and pCR, respectively. Nineteen of the 44 iCR cases (43.2 %) were assessed as pCR, and 216 (97.7 %) of the 221 non-iCR cases were assessed as non-pCR. The accuracy (ACC), the pCR predictive value (PPV) and the non-pCR predictive value (NPV) were 88.7, 43.2, and 97.7 %, respectively. When assessed according to each tumor subtype, the ACC, PPV and NPV were 93.2, 21.4 and 100 % for luminal subtype, 70.8, 0 and 89.5 % for luminal/HER2 subtype, 75, 57.1 and 88.8 % for HER2-enriched subtype, and 90.9, 72.7 and 97 % for triple-negative subtype, respectively. MRI is a valuable modality for predicting pCR of breast cancer after NAC treatment. However, its accuracy varies greatly in different breast cancer subtypes. Whereas MRI closely predicts pCR in the triple-negative subtype, iCR in the luminal subtype is often an over-estimation. On the other hand, residual lesions identified by MRI are reliable markers of non-pCR for the luminal subtype.