Preoperative staging of ovarian cancer: comparison between ultrasound, CT and whole-body diffusion-weighted MRI (ISAAC study).

OBJECTIVES: To compare the performance of transvaginal and transabdominal ultrasound with that of the first-line staging method (contrast-enhanced computed tomography (CT)) and a novel technique, whole-body magnetic resonance imaging with diffusion-weighted sequence (WB-DWI/MRI), in the assessment of peritoneal involvement (carcinomatosis), lymph-node staging and prediction of non-resectability in patients with suspected ovarian cancer. METHODS: Between March 2016 and October 2017, all consecutive patients with suspicion of ovarian cancer and surgery planned at a gynecological oncology center underwent preoperative staging and prediction of non-resectability with ultrasound, CT and WB-DWI/MRI. The evaluation followed a single, predefined protocol, assessing peritoneal spread at 19 sites and lymph-node metastasis at eight sites. The prediction of non-resectability was based on abdominal markers. Findings were compared to the reference standard (surgical findings and outcome and histopathological evaluation). RESULTS: Sixty-seven patients with confirmed ovarian cancer were analyzed. Among them, 51 (76%) had advanced-stage and 16 (24%) had early-stage ovarian cancer. Diagnostic laparoscopy only was performed in 16% (11/67) of the cases and laparotomy in 84% (56/67), with no residual disease at the end of surgery in 68% (38/56), residual disease ≤ 1 cm in 16% (9/56) and residual disease > 1 cm in 16% (9/56). Ultrasound and WB-DWI/MRI performed better than did CT in the assessment of overall peritoneal carcinomatosis (area under the receiver-operating-characteristics curve (AUC), 0.87, 0.86 and 0.77, respectively). Ultrasound was not inferior to CT (P = 0.002). For assessment of retroperitoneal lymph-node staging (AUC, 0.72-0.76) and prediction of non-resectability in the abdomen (AUC, 0.74-0.80), all three methods performed similarly. In general, ultrasound had higher or identical specificity to WB-DWI/MRI and CT at each of the 19 peritoneal sites evaluated, but lower or equal sensitivity in the abdomen. Compared with WB-DWI/MRI and CT, transvaginal ultrasound had higher accuracy (94% vs 91% and 85%, respectively) and sensitivity (94% vs 91% and 89%, respectively) in the detection of carcinomatosis in the pelvis. Better accuracy and sensitivity of ultrasound (93% and 100%) than WB-DWI/MRI (83% and 75%) and CT (84% and 88%) in the evaluation of deep rectosigmoid wall infiltration, in particular, supports the potential role of ultrasound in planning rectosigmoid resection. In contrast, for the bowel serosal and mesenterial assessment, abdominal ultrasound had the lowest accuracy (70%, 78% and 79%, respectively) and sensitivity (42%, 65% and 65%, respectively). CONCLUSIONS: This is the first prospective study to document that, in experienced hands, ultrasound may be an alternative to WB-DWI/MRI and CT in ovarian cancer staging, including peritoneal and lymph-node evaluation and prediction of non-resectability based on abdominal markers of non-resectability. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Denoising Diffusion MRI via Graph Total Variance in Spatioangular Domain.

Diffusion MRI (DMRI) plays an essential role in diagnosing brain disorders related to white matter abnormalities. However, it suffers from heavy noise, which restricts its quantitative analysis. The total variance (TV) regularization is an effective noise reduction technique that penalizes noise-induced variances. However, existing TV-based denoising methods only focus on the spatial domain, overlooking that DMRI data lives in a combined spatioangular domain. It eventually results in an unsatisfactory noise reduction effect. To resolve this issue, we propose to remove the noise in DMRI using graph total variance (GTV) in the spatioangular domain. Expressly, we first represent the DMRI data using a graph, which encodes the geometric information of sampling points in the spatioangular domain. We then perform effective noise reduction using the powerful GTV regularization, which penalizes the noise-induced variances on the graph. GTV effectively resolves the limitation in existing methods, which only rely on spatial information for removing the noise. Extensive experiments on synthetic and real DMRI data demonstrate that GTV can remove the noise effectively and outperforms state-of-the-art methods.

MRI-based radiomics and ADC values are related to recurrence of endometrial carcinoma: a preliminary analysis.

BACKGROUND: To identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC). METHODS: One hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed, and the patients were divided into training and test group. Baseline clinicopathological features and mean ADC (ADCmean), minimum ADC (ADCmin), and maximum ADC (ADCmax) were analyzed. Radiomic parameters were extracted on T2 weighted images and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). In training group, Kaplan-Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radscore with recurrence, and verified in the test group. RESULTS: ADCmean showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, ADCmean, ADCmin and radscore were associated with recurrence. In the training group, multivariate Cox analysis showed that pathological types, ADCmean and radscore were independent risk factors for recurrence, which were verified in the test group. CONCLUSIONS: ADCmean value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan.

Texture Analysis in the Assessment of Rectal Cancer: Comparison of T2WI and Diffusion-Weighted Imaging.

Texture analysis (TA) techniques derived from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps of rectal cancer can both achieve good diagnosis performance. This study was to compare TA from T2WI and ADC maps between different pathological T and N stages to confirm which TA analysis is better in diagnosis performance. 146 patients were enrolled in this study. Tumor TA was performed on every patient's T2WI and ADC maps, respectively; then, skewness, kurtosis, uniformity, entropy, energy, inertia, and correlation were calculated. Our results demonstrated that those significant different parameters derived from T2WI had better diagnostic performance than those from ADC maps in differentiating pT3b-4 and pN1-2 stage tumors. In particular, the energy derived from T2WI was an optimal parameter for diagnostic efficiency. High-resolution T2WI plays a key point in the local stage of rectal cancer; thus, TA derived from T2WI may be a more useful tool to aid radiologists and surgeons in selecting treatment.

Comparison of Diagnostic Performance between Perfusion-Related Intravoxel Incoherent Motion DWI and Dynamic Contrast-Enhanced MRI in Rectal Cancer.

This study was aimed to determine the diagnostic performance of perfusion-related parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) by comparing them with quantitative parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on differentiation grades of rectal cancer. We retrospectively analyzed 98 patients with rectal cancer. Perfusion-related IVIM parameters (D ∗, f, and f·D ∗) and quantitative DCE parameters (K trans, K ep, V e , and V p ) were obtained by plotting the volume-of-interest on in-house software. Furthermore, we compared the difference and diagnostic performance of all well-moderately and poorly differentiated rectal cancer parameters. Finally, we analyzed the correlation between those DCE and IVIM parameters and pathological differentiation grade. The values of f, K trans, and K ep significantly differentiated poor and well-moderate rectal cancers. K trans achieved the highest area under the curve (AUC) value compared to perfusion-related IVIM and DCE parameters. Furthermore, K trans showed a better correlation with pathological differentiation grade than f. The diagnostic efficiency of DCE-MRI was greater than perfusion-related IVIM parameters. The f value derived from perfusion-related IVIM offered a diagnostic performance similar to DCE-MRI for patients with renal insufficiency.

The Risk of Prostate Cancer Progression in Active Surveillance Patients with Bilateral Disease Detected by Combined Magnetic Resonance Imaging-Fusion and Systematic Biopsy.

PURPOSE: We sought to evaluate whether bilateral prostate cancer detected at active surveillance (AS) enrollment is associated with progression to Grade Group (GG) ≥2 and to compare the efficacy of combined targeted biopsy plus systematic biopsy (Cbx) vs systematic biopsy (Sbx) or targeted biopsy alone to detect bilateral disease. MATERIALS AND METHODS: A prospectively maintained database of patients referred to our institution from 2007-2020 was queried. The study cohort included all AS patients with GG1 on confirmatory Cbx and followup of at least 1 year. Cox proportional hazard analysis identified baseline characteristics associated with progression to ≥GG2 at any point throughout followup. RESULTS: Of 579 patients referred, 103 patients had GG1 on Cbx and were included in the study; 49/103 (47.6%) patients progressed to ≥GG2, with 30/72 (41.7%) patients with unilateral disease progressing and 19/31 (61.3%) patients with bilateral disease progressing. Median time to progression was 68 months vs 52 months for unilateral and bilateral disease, respectively (p=0.006). Both prostate specific antigen density (HR 1.72, p=0.005) and presence of bilateral disease (HR 2.21, p=0.012) on confirmatory biopsy were associated with AS progression. At time of progression, GG and risk group were significantly higher in patients with bilateral versus unilateral disease. Cbx detected 16% more patients with bilateral disease than Sbx alone. CONCLUSIONS: Bilateral disease and prostate specific antigen density at confirmatory Cbx conferred greater risk of earlier AS progression. Cbx was superior to Sbx for identifying bilateral disease. AS risk-stratification protocols may benefit from including presence of bilateral disease and should use Cbx to detect bilateral disease.

Correlations between DW-MRI and 18 F-FDG PET/CT parameters in head and neck squamous cell carcinoma following definitive chemo-radiotherapy.

BACKGROUND: Posttreatment diffusion-weighted magnetic resonance imaging (DW-MRI) and 18F-fluorodeoxygluocose (18 F-FDG) positron emission tomography (PET) with computed tomography (PET/CT) have potential prognostic value following chemo-radiotherapy (CRT) for head and neck squamous cell carcinoma (HNSCC). Correlations between these PET/CT (standardized uptake value or SUV) and DW-MRI (apparent diffusion coefficient or ADC) parameters have only been previously explored in the pretreatment setting. AIM: To evaluate stage III and IV HNSCC at 12-weeks post-CRT for the correlation between SUVmax and ADC values and their interval changes from pretreatment imaging. METHODS: Fifty-six patients (45 male, 11 female, mean age 59.9 + - 7.38) with stage 3 and 4 HNSCC patients underwent 12-week posttreatment DW-MRI and 18 F-FDG PET/CT studies in this prospective study. There were 41/56 patients in the cohort with human papilloma virus-related oropharyngeal cancer (HPV OPC). DW-MRI (ADCmax and ADCmin) and 18 F-FDG PET/CT (SUVmax and SUVmax ratio to liver) parameters were measured at the site of primary tumors (n = 48) and the largest lymph nodes (n = 52). Kendall's tau evaluated the correlation between DW-MRI and 18 F-FDG PET/CT parameters. Mann-Whitney test compared the post-CRT PET/CT and DW-MRI parameters between those participants with and without 2-year disease-free survival (DFS). RESULTS: There was no correlation between DW-MRI and 18 F-FDG PET/CT parameters on 12-week posttreatment imaging (P = .455-.794; tau = -0.075-0.25) or their interval changes from pretreatment to 12-week posttreatment imaging (P = .1-.946; tau = -0.194-0.044). The primary tumor ADCmean (P = .03) and the interval change in nodal ADCmin (P = .05) predicted 2-year DFS but none of the 18 F-FDG PET/CT parameters were associated with 2-year DFS. CONCLUSIONS: There is no correlation between the quantitative DWI-MRI and 18 F-FDG PET/CT parameters derived from 12-week post-CRT studies. These parameters may be independent biomarkers however in this HPV OPC dominant cohort, only selected ADC parameters demonstrated prognostic significance. Study was prospectively registered at http://www.controlled-trials.com/ISRCTN58327080.

Understanding diffusion-weighted MRI analysis: Repeatability and performance of diffusion models in a benign breast lesion cohort.

Diffusion-weighted MRI (DWI) is an important tool for oncology research, with great clinical potential for the classification and monitoring of breast lesions. The utility of parameters derived from DWI, however, is influenced by specific analysis choices. The purpose of this study was to critically evaluate repeatability and curve-fitting performance of common DWI signal representations, for a prospective cohort of patients with benign breast lesions. Twenty informed, consented patients with confirmed benign breast lesions underwent repeated DWI (3 T) using: sagittal single-shot spin-echo echo planar imaging, bipolar encoding, TR/TE: 11,600/86 ms, FOV: 180 x 180 mm, matrix: 90 x 90, slices: 60 x 2.5 mm, iPAT: GRAPPA 2, fat suppression, and 13 b-values: 0-700 s/mm2 . A phase-reversed scan (b = 0 s/mm2 ) was acquired for distortion correction. Voxel-wise repeat-measures coefficients of variation (CoVs) were derived for monoexponential (apparent diffusion coefficient [ADC]), biexponential (intravoxel incoherent motion: f, D, D*) and stretched exponential (α, DDC) across the parameter histograms for lesion regions of interest (ROIs). Goodness-of-fit for each representation was assessed by Bayesian information criterion. The volume of interest (VOI) definition was repeatable (CoV 13.9%). Within lesions, and across both visits and the cohort, there was no dominant best-fit model, with all representations giving the best fit for a fraction of the voxels. Diffusivity measures from the signal representations (ADC, D, DDC) all showed good repeatability (CoV < 10%), whereas parameters associated with pseudodiffusion (f, D*) performed poorly (CoV > 50%). The stretching exponent α was repeatable (CoV < 12%). This pattern of repeatability was consistent over the central part of the parameter percentiles. Assumptions often made in diffusion studies about analysis choices will influence the detectability of changes, potentially obscuring useful information. No single signal representation prevails within or across lesions, or across repeated visits; parameter robustness is therefore a critical consideration. Our results suggest that stretched exponential representation is more repeatable than biexponential, with pseudodiffusion parameters unlikely to provide clinically useful biomarkers.

Classification of paediatric brain tumours by diffusion weighted imaging and machine learning.

To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10-3 mm2 s-1 with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.

Evolution of the stroke paradigm: A review of delayed recanalization.

While the time window for reperfusion after ischemic stroke continues to increase, many patients are not candidates for reperfusion under current guidelines that allow for reperfusion within 24 h after last known well time; however, many case studies report favorable outcomes beyond 24 h after symptom onset for both spontaneous and medically induced recanalization. Furthermore, modern imaging allows for identification of penumbra at extended time points, and reperfusion risk factors and complications are becoming better understood. Taken together, continued urgency exists to better understand the pathophysiologic mechanisms and ideal setting of delayed recanalization beyond 24 h after onset of ischemia.

Brain Injury After Transcatheter Replacement of Bicuspid Versus Tricuspid Aortic Valves.

BACKGROUND: An increasing number of bicuspid aortic valve (BAV) patients are undergoing transcatheter aortic valve replacement (TAVR), but the risk of brain injury in diffusion-weighted magnetic resonance imaging (DW-MRI) is currently unknown. OBJECTIVES: This study sought to evaluate the risk of brain injury in BAV patients following TAVR. METHODS: A total of 204 consecutive severe aortic stenosis patients who underwent TAVR were enrolled. A total of 83 (40.7%) patients were BAV patients, and the other 121 patients were tricuspid aortic valve (TAV) patients. All patients received DW-MRI at baseline, and after TAVR. RESULTS: Median ages (76 years [interquartile range (IQR): 71 to 81 years] vs. 79 years [IQR: 74 to 83 years]; p = 0.004) and Society of Thoracic Surgeons scores (4.87 [IQR: 3.72 to 8.54] vs. 6.38 [IQR: 3.96 to 9.50]; p = 0.044) of the BAV and TAV patients were significantly different, while the overt stroke rates (2.4% vs. 1.7%; p = 0.704) were comparable between the 2 groups. BAV patients were associated with higher number of new lesions (4.0 [IQR: 1.0 to 8.0] vs. 2.0 [IQR: 1.0 to 5.0]; p = 0.008), total lesion volume (290 mm3 [IQR: 70 to 930 mm3] vs. 140 mm3 [IQR: 35 to 480 mm3]; p = 0.008), and the volume per lesion (70.0 mm3 [IQR: 45.0 to 115.0 mm3] vs. 57.5 mm3 [IQR: 24.5 to 93.0 mm3]; p = 0.037) in DW-MRI. Moreover, the proportion of patients with lesions larger than 1 cm3 (28.6% vs. 10.9%; p = 0.005) was higher in BAV patients than in TAV patients. CONCLUSIONS: BAV patients may encounter more severe brain injuries not only due to greater number of lesions, but also due to larger lesion size in the early phase after TAVR. (Transcatheter Aortic Valve Replacement Single Center Registry in Chinese Population [TORCH]; NCT02803294).

The prognosis of brain magnetic resonance imaging injury pattern for outcomes of hypothermia-treated infants.

Magnetic resonance imaging (MRI) can be a tool that allows the observation of structural injury patterns after cooling. The aim of this study was to determine the early pattern of brain injury in the MRIs of infants with hypoxic ischemic encephalopathy (HIE) after cooling and to search for any clinical factors related to abnormal MRI findings.The study retrospectively recruited 118 infants who were treated with therapeutic hypothermia (TH) between 2013 and 2016.Forty-three patients had normal brain MRI, and 75 had abnormal brain MRI findings. The TH-treated infants with abnormal brain MRI readings showed significantly more clinical seizures and the use of additional antiepileptic drugs (AEDs) than the normal MRI group. As a long-term outcome, more lesions in the basal ganglia and thalamus, posterior limb of internal capsule, or severe white matter lesions were associated with abnormal neurodevelopmental outcomes at 18 to 24 months of age.A higher frequency of clinical seizures and AED use were related to abnormal brain injury on MRI. A significant risk for poor long-term outcomes was found in the abnormal brain MRI group.

Magnetic Resonance of Rectal Cancer Response to Therapy: An Image Quality Comparison between 3.0 and 1.5 Tesla.

PURPOSE: To evaluate signal intensity (SI) differences between 3.0 T and 1.5 T on T2-weighted (T2w), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) in rectal cancer pre-, during, and postneoadjuvant chemoradiotherapy (CRT). MATERIALS AND METHODS: 22 patients with locally advanced rectal cancer were prospectively enrolled. All patients underwent T2w, DWI, and ADC pre-, during, and post-CRT on both 3.0 T MRI and 1.5 T MRI. A radiologist drew regions of interest (ROIs) of the tumor and obturator internus muscle on the selected slice to evaluate SI and relative SI (rSI). Additionally, a subanalysis evaluating the SI before and after-CRT (∆SI pre-post) in complete responder patients (CR) and nonresponder patients (NR) on T2w, DWI, and ADC was performed. RESULTS: Significant differences were observed for T2w and DWI on 3.0 T MRI compared to 1.5 T MRI pre-, during, and post-CRT (all P < 0.001), whereas no significant differences were reported for ADC among all controls (all P > 0.05). rSI showed no significant differences in all the examinations for all sequences (all P > 0.05). ∆SI showed significant differences between 3.0 T and 1.5 T MRI for DWI-∆SI in CR and NR (188.39 ± 166.90 vs. 30.45 ± 21.73 and 169.70 ± 121.87 vs. 22.00 ± 31.29, respectively, all P 0.02) and ADC-∆SI for CR (-0.58 ± 0.27 vs. -0.21 ± 0.24P value 0.02), while no significant differences were observed for ADC-∆SI in NR and both CR and NR for T2w-∆SI. CONCLUSION: T2w-SI and DWI-SI showed significant differences for 3.0 T compared to 1.5 T in all three controls, while ADCSI showed no significant differences in all three controls on both field strengths. rSI was comparable for 3.0 T and 1.5 T MRI in rectal cancer patients; therefore, rectal cancer patients can be assessed both at 3.0 T MRI and 1.5 T MRI. However, a significant DWI-∆SI and ADC-∆SI on 3.0 T in CR might be interpreted as a better visual assessment in discriminating response to therapy compared to 1.5 T. Further investigations should be performed to confirm future possible clinical application.

Multiple mathematical models of diffusion-weighted imaging for endometrial cancer characterization: Correlation with prognosis-related risk factors.

PURPOSE: To investigate mono-exponential, bi-exponential, and stretched-exponential models of diffusion-weighted imaging (DWI) for evaluation of prognosis-related risk factors of endometrial cancer (EC). METHOD: Sixty-one consecutive patients with EC who preoperatively underwent pelvic MRI with multiple b value DWI between September 2016 and May 2018 were enrolled. The apparent-diffusion-coefficient (ADC), bi-exponential model parameters (D, D* and f) and stretched-exponential model parameters (DDC and α) were measured and compared to analyze the following prognosis-related risk factors confirmed by pathology: histological grade, depth of myometrial invasion, cervical stromal infiltration (CSI) and lymphovascular invasion (LVSI). A stepwise multilvariate logistic regression and the receiver operating characteristic (ROC) curves were performed for further statistical analysis. RESULTS: Lower ADC, D, f, and DDC were observed in tumor with high grade compared with a low-grade group, and the largest area under curve (AUC) was obtained when combining f and DDC values. ADC, D, f, DDC, and α were significantly different in patients with deep myometrial invasion (DMI) compared to those without DMI; the combination of f, DDC and α showed the highest AUC. Significantly different ADC and f were found between patients' presence and absence CSI; the f values showed the highest diagnostic performance with an AUC of 0.825. Regarding the LVSI, ADC, D*, f, and DDC were significantly lower in tumors with LVSI compared to those without LVSI; the combination of f and DDC showed the largest AUC. CONCLUSION: Multiple mathematical DWI models are a useful approach for the prediction of prognosis-related risk factors in EC.

Outcomes assessment in intrahepatic cholangiocarcinoma using qualitative and quantitative imaging features.

BACKGROUND: To assess the performance of imaging features, including radiomics texture features, in predicting histopathologic tumor grade, AJCC stage, and outcomes [time to recurrence (TTR) and overall survival (OS)] in patients with intrahepatic cholangiocarcinoma (ICC). METHODS: Seventy-three patients (26 M/47F, mean age 63y) with pre-operative imaging (CT, n = 37; MRI, n = 21; CT and MRI, n = 15] within 6 months of resection were included in this retrospective study. Qualitative imaging traits were assessed by 2 observers. A 3rd observer measured tumor apparent diffusion coefficient (ADC), enhancement ratios (ERs), and Haralick texture features. Blood biomarkers and imaging features were compared with histopathology (tumor grade and AJCC stage) and outcomes (TTR and OS) using log-rank, generalized Wilcoxon, Cox proportional hazards regression, and Fisher exact tests. RESULTS: Median TTR and OS were 53.9 and 79.7 months. ICC recurred in 64.4% (47/73) of patients and 46.6% (34/73) of patients died. There was fair accuracy for some qualitative imaging features in the prediction of worse tumor grade (maximal AUC of 0.68 for biliary obstruction on MRI, p = 0.032, observer 1) and higher AJCC stage (maximal AUC of 0.73 for biliary obstruction on CT, p = 0.002, observer 2; and AUC of 0.73 for vascular involvement on MRI, p = 0.01, observer 2). Cox proportional hazards regression analysis showed that CA 19-9 [hazard ratio (HR) 2.44/95% confidence interval (CI) 1.31-4.57/p = 0.005)] and tumor size on imaging (HR 1.13/95% CI 1.04-1.22/p = 0.003) were significant predictors of TTR, while CA 19-9 (HR 4.08/95% CI 1.75-9.56, p = 0.001) and presence of metastatic lymph nodes at histopathology (HR 2.86/95% CI 1.35-6.07/p = 0.006) were significant predictors of OS. On multivariable analysis, satellite lesions on CT (HR 2.79/95%CI 1.01-7.15/p = 0.032, observer 2), vascular involvement on MRI (HR 0.10/95% CI 0.01-0.85/p = 0.032, observer 1), and texture feature MRI variance (HR 0.55/95% CI 0.31-0.97, p = 0.040) predicted TTR once adjusted for the independent predictors CA 19-9 and tumor size on imaging. Several qualitative and quantitative features demonstrated associations with TTR, OS, and AJCC stage at univariable analysis (range: HR 0.35-19; p < 0.001-0.045), however none were predictive of OS at multivariable analysis when adjusted for CA 19-9 and metastatic lymph nodes (p > 0.088). CONCLUSIONS: There was reasonable accuracy in predicting tumor grade and higher AJCC stage in ICC utilizing certain qualitative and quantitative imaging traits. Serum CA 19-9, tumor size, presence of metastatic lymph nodes, and qualitative imaging traits of satellite lesions and vascular involvement are predictors of patient outcomes, along with a promising predictive ability of certain quantitative texture features.

Motion correction and noise removing in lung diffusion-weighted MRI using low-rank decomposition.

Lung diffusion-weighted magnetic resonance imaging (DWI) has shown a promising value in lung lesion detection, diagnosis, differentiation, and staging. However, the respiratory and cardiac motion, blood flow, and lung hysteresis may contribute to the blurring, resulting in unclear lung images. The image blurring could adversely affect diagnosis performance. The purpose of this study is to reduce the DWI blurring and assess its positive effect on diagnosis. The retrospective study includes 71 patients. In this paper, a motion correction and noise removal method using low-rank decomposition is proposed, which can reduce the DWI blurring by exploit the spatiotemporal continuity sequences. The deblurring performances are evaluated by qualitative and quantitative assessment, and the performance of diagnosis of lung cancer is measured by area under curve (AUC). In the view of the qualitative assessment, the deformation of the lung mass is reduced, and the blurring of the lung tumor edge is alleviated. Noise in the apparent diffusion coefficient (ADC) map is greatly reduced. For quantitative assessment, mutual information (MI) and Pearson correlation coefficient (Pearson-Coff) are 1.30 and 0.82 before the decomposition and 1.40 and 0.85 after the decomposition. Both the difference in MI and Pearson-Coff are statistically significant (p < 0.05). For the positive effect of deblurring on diagnosis of lung cancer, the AUC was improved from 0.731 to 0.841 using three-fold cross validation. We conclude that the low-rank matrix decomposition method is promising in reducing the errors in DWI lung images caused by noise and artifacts and improving diagnostics. Further investigations are warranted to understand the full utilities of the low-rank decomposition on lung DWI images. Graphical abstract.

Pretreatment apparent diffusion coefficient does not predict therapy response to neoadjuvant chemotherapy in breast cancer.

BACKGROUND: Some reports indicated that apparent diffusion coefficient can predict pathologic response to treatment in breast cancer (BC). The purpose of the present meta-analysis was to provide evident data regarding use of ADC values for prediction of treatment response in BC. METHODS: MEDLINE library, EMBASE and SCOPUS databases were screened for associations between ADC and treatment response for neoadjuvant chemotherapy in breast cancer (BC) up to March 2020. Overall, 22 studies met the inclusion criteria. For the present analysis, the following data were extracted from the collected studies: authors, year of publication, study design, number of patients/lesions, mean and standard deviation of the pretreatment ADC values. The methodological quality of the included studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for responders and non responders. RESULTS: The acquired 22 studies comprised 1827 patients with different BC. Of the 1827 patients, 650 (35.6%) were reported as responders and 1177 (64.4%) as non-responders to the neoadjuvant chemotherapy. The pooled calculated pretreatment mean ADC value of BC in responders was 0.98 (95% CI = [0.94; 1.03]). In non-responders, it was 1.05 (95% CI = [1.00; 1.10]). The ADC values of the groups overlapped significantly. CONCLUSION: Pretreatment ADC alone cannot predict response to neoadjuvant chemotherapy in BC.

An Assessment of the Relationship between Structural and Functional Imaging of Cerebrovascular Disease and Cognition-Related Fibers.

In order to assess the relationship between structural and functional imaging of cerebrovascular disease and cognition-related fibers, this paper chooses a total of 120 patients who underwent cerebral small vessel disease (CSVD) treatment at a designated hospital by this study from June 2013 to June 2018 and divides them into 3 groups according to the random number table method: vascular dementia (VaD) group, vascular cognitive impairment no dementia (VCIND) group, and noncognition impairment (NCI) group with 40 cases of patients in each group. Cognitive function measurement and imaging examination were performed for these 3 groups of patients, and the observation indicators of cognitive state examination (CSE), mental assessment scale (MAS), clock drawing test (CDT), adult intelligence scale (AIS), frontal assessment battery (FAB), verbal fluency test (VFT), trail making test (TMT), cognitive index (CI), white matter lesions (WML), third ventricle width (TVW), and frontal horn index (FHI) were tested, respectively. The results shows that the average scores of CSE, MAS, AIS, and VFT in the VaD and VCIND group are lower than those of the NCI group and the differences are statistically significant (P < 0.05); the average scores of FAB, TMT, and CI in the VaD group are higher than those of the VCIND group and the differences are also statistically significant (P < 0.05); the average scores of FHI and TVW in the VaD group are lower than those of the VCIND and NCI group with statistically significant differences (P < 0.05); the average scores of WML, CDT, and AIS in the VaD group are higher than those of the VCIND and NCI group with statistically significant differences (P < 0.05). Therefore, it is believed that the structural and functional imaging features of cerebrovascular disease are closely related to cognition-related fibers, and the incidence of white matter lesions is closely related to the degree of lesions and cognitive dysfunction of cerebral small vessel disease, in which a major risk factor for cognitive dysfunction in patients with small blood vessels is the severity of white matter lesions; brain imaging and neuropsychiatric function assessment can better understand the relationship between cerebrovascular disease and cognitive impairment. The results of this study provide a reference for the further research studies on the relationship between structural and functional imaging of cerebrovascular disease and cognition-related fibers.

Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data.

Spherical deconvolution is a widely used approach to quantify the fiber orientation distribution (FOD) from diffusion MRI data of the brain. The damped Richardson-Lucy (dRL) is an algorithm developed to perform robust spherical deconvolution on single-shell diffusion MRI data while suppressing spurious FOD peaks due to noise or partial volume effects. Due to recent progress in acquisition hardware and scanning protocols, it is becoming increasingly common to acquire multi-shell diffusion MRI data, which allows for the modelling of multiple tissue types beyond white matter. While the dRL algorithm could, in theory, be directly applied to multi-shell data, it is not optimised to exploit its information content to model the signal from multiple tissue types. In this work, we introduce a new framework based on dRL - dubbed generalized Richardson-Lucy (GRL) - that uses multi-shell data in combination with user-chosen tissue models to disentangle partial volume effects and increase the accuracy in FOD estimation. Further, GRL estimates signal fraction maps associated to each user-selected model, which can be used during fiber tractography to dissect and terminate the tracking without need for additional structural data. The optimal weighting of multi-shell data in the fit and the robustness to noise and to partial volume effects of GRL was studied with synthetic data. Subsequently, we investigated the performance of GRL in comparison to dRL and to multi-shell constrained spherical deconvolution (MSCSD) on a high-resolution diffusion MRI dataset from the Human Connectome Project and on an MRI dataset acquired at 3T on a clinical scanner. In line with previous studies, we described the signal of the cerebrospinal-fluid and of the grey matter with isotropic diffusion models, whereas four diffusion models were considered to describe the white matter. With a third dataset including small diffusion weightings, we studied the feasibility of including intra-voxel incoherent motion effects due to blood pseudo-diffusion in the modelling. Further, the reliability of GRL was demonstrated with a test-retest scan of a subject acquired at 3T. Results of simulations show that GRL can robustly disentangle different tissue types at SNR above 20 with respect to the non-weighted image, and that it improves the angular accuracy of the FOD estimation as compared to dRL. On real data, GRL provides signal fraction maps that are physiologically plausible and consistent with those obtained with MSCSD, with correlation coefficients between the two methods up to 0.96. When considering IVIM effects, a high blood pseudo-diffusion fraction is observed in the medial temporal lobe and in the sagittal sinus. In comparison to dRL and MSCSD, GRL provided sharper FODs and less spurious peaks in presence of partial volume effects, but the FOD reconstructions are also highly dependent on the chosen modelling of white matter. When performing fiber tractography, GRL allows to terminate fiber tractography using the signal fraction maps, which results in a better tract termination at the grey-white matter interface or at the outer cortical surface. In terms of inter-scan reliability, GRL performed similarly to or better than compared methods. In conclusion, GRL offers a new modular and flexible framework to perform spherical deconvolution of multi-shell data.

Deep flow-net for EPI distortion estimation.

INTRODUCTION: Geometric distortions along the phase encoding direction caused by off-resonant spins are a major issue in EPI based functional and diffusion imaging. The widely used blip up/down approach estimates the underlying distortion field from a pair of images with inverted phase encoding direction. Typically, iterative methods are used to find a solution to the ill-posed problem of finding the displacement field that maps up/down acquisitions onto each other. Here, we explore the use of a deep convolutional network to estimate the displacement map from a pair of input images. METHODS: We trained a deep convolutional U-net architecture that was previously used to estimate optic flow between moving images to learn to predict the distortion map from an input pair of distorted EPI acquisitions. During the training step, the network minimizes a loss function (similarity metric) that is calculated from corrected input image pairs. This approach does not require the explicit knowledge of the ground truth distortion map, which is difficult to get for real life data. RESULTS: We used data from a total of Ntrain â€‹= â€‹22 healthy subjects to train our network. A separate dataset of Ntest â€‹= â€‹12 patients including some with abnormal findings and unseen acquisition modes, e.g. LR-encoding, coronal orientation) was reserved for testing and evaluation purposes. We compared our results to FSL's topup function with default parameters that served as the gold standard. We found that our approach results in a correction accuracy that is virtually identical to the optimum found by an iterative search, but with reduced computational time. CONCLUSION: By using a deep convolutional network, we can reduce the processing time to a few seconds per volume, which is significantly faster than iterative approaches like FSL's topup which takes around 10min on the same machine (but using only 1 CPU). This facilitates the use of a blip up/down scheme for all diffusion-weighted acquisitions and potential real-time EPI distortion correction without sacrificing accuracy.