Contrast-enhanced MRI Scans Research Articles

NIMG-13. CHANGES IN CORTICAL ARCHITECTURE ASSOCIATED WITH GLIOBLASTOMA ASSOCIATED EPILEPSY

Abstract BACKGROUND Emerging evidence suggests both local hyper-excitability promoting epilepsy, and synaptic and paracrine mediated integration of Glioblastoma into brain-wide neural networks. In this study we sought clinical evidence of disease progression, and concurrent radiological evidence of differences in remote cortical architecture, in Glioblastoma patients with and without epilepsy. METHODS 55 patients with biopsy-confirmed Glioblastoma, mean-age 59.4yrs±10.9yrs, were studied. 23 patients had a diagnosis of Glioblastoma-related epilepsy on presentation. We compared clinical markers of disease progression in individuals with and without epilepsy. Further, we explored differences in cortical architecture between both groups. Utilising pre-operative T1-weighted, contrast-enhanced MRI scans (n=45), we performed vertex-wise measurements of cortical thickness (CT) and cortical surface area (CSA) using the Virtual Brain Grafting Toolbox and FreeSurfer v.7.0. Radiologically-identifiable tumour and oedema was masked and excluded from analysis. Scans with right-hemispheric tumours were flipped to the left. Linear mixed-effects models were applied to pre-processed imaging-data. RESULTS Individuals with epilepsy had shorter progression-free survival (median 4.2 months) than those without seizures (9.3 months, p<0.05) with gold-standard treatment. No significant difference between lesion volumes (tumour and oedema) existed between the two groups (unpaired t-test, p>0.05). Controlling for age and tumour location, significant increases (p<0.01) in CT and CSA were observed in patients with Glioblastoma-related epilepsy in lesion-ipsilateral supramarginal gyrus, superior parietal lobule, precuneus/cuneus and temporal lobe compared to those without epilepsy. Interestingly, significant (p<0.05) increases in CT in lesion-contralateral supramarginal gyrus were additionally observed in those with epilepsy compared to those without. CONCLUSIONS Glioblastoma-related epilepsy was associated with poorer survival outcomes and changes in remote cortical architecture. Implicated regions form part of the default mode network, which has previously been identified as a crucial hub for epilepsy. Biological underpinnings of cortical network alterations, and their possible role in promoting not only hyper-excitability but also Glioblastoma progression, require correlation with histopathological characteristics and validation in larger study cohorts.

Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network.

To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of virtual contrast-enhanced (vCE) breast MRI. The IRB-approved, retrospective study included 1064 multiparametric breast MRI scans (age: 52 ± 12 years) obtained from 2017 to 2020 (single site, two 3-T MRI). Eleven independent neural networks were trained to derive vCE images from varying input combinations of T1w, T2w, and multi-b-value DWI sequences (b-value = 50-1500 s/mm2). Three readers evaluated the vCE images with regard to qualitative scores of diagnostic image quality, image sharpness, satisfaction with contrast/signal-to-noise ratio, and lesion/non-mass enhancement conspicuity. Quantitative metrics (SSIM, PSNR, NRMSE, and median symmetrical accuracy) were analyzed and statistically compared between the input combinations for the full breast volume and both enhancing and non-enhancing target findings. The independent test set consisted of 187 cases. The quantitative metrics significantly improved in target findings when multi-b-value DWI sequences were included during vCE training (p < 0.05). Non-significant effects (p > 0.05) were observed for the quantitative metrics on the full breast volume when comparing input combinations including T1w. Using T1w and DWI acquisitions during vCE training is necessary to achieve high satisfaction with contrast/SNR and good conspicuity of the enhancing findings. The input combination of T1w, T2w, and DWI sequences with three b-values showed the best qualitative performance. vCE breast MRI performance is significantly influenced by input sequences. Quantitative metrics and visual quality of vCE images significantly benefit when multi b-value DWI is added to morphologic T1w-/T2w sequences as input for model training. Question How do different MRI sequences impact the performance of virtual contrast-enhanced (vCE) breast MRI? Findings The input combination of T1-weighted, T2-weighted, and diffusion-weighted imaging sequences with three b-values showed the best qualitative performance. Clinical relevance While in the future neural networks providing virtual contrast-enhanced images might further improve accessibility to breast MRI, the significant influence of input data needs to be considered during translational research.

Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study

PurposeTo investigate the value of multi-parametric MRI-based radiomics for preoperative prediction of lung metastases from soft tissue sarcoma (STS).MethodsIn total, 122 patients with clinicopathologically confirmed STS who underwent pretreatment T1-weighted contrast-enhanced (T1-CE) and T2-weighted fat-suppressed (T2FS) MRI scans were enrolled between Jul. 2017 and Mar. 2021. Radiomics signatures were established by calculating and selecting radiomics features from the two sequences. Clinical independent predictors were evaluated by statistical analysis. The radiomics nomogram was constructed from margin and radiomics features by multivariable logistic regression. Finally, the study used receiver operating characteristic (ROC) and calibration curves to evaluate performance of radiomics models. Decision curve analyses (DCA) were performed to evaluate clinical usefulness of the models.ResultsThe margin was considered as an independent predictor (p < 0.05). A total of 4 MRI features were selected and used to develop the radiomics signature. By incorporating the margin and radiomics signature, the developed nomogram showed the best prediction performance in the training (AUCs, margin vs. radiomics signature vs. nomogram, 0.609 vs. 0.909 vs. 0.910) and validation (AUCs, margin vs. radiomics signature vs. nomogram, 0.666 vs. 0.841 vs. 0.894) cohorts. DCA indicated potential usefulness of the nomogram model.ConclusionsThis feasibility study evaluated predictive values of multi-parametric MRI for the prediction of lung metastasis, and proposed a nomogram model to potentially facilitate the individualized treatment decision-making for STSs.

Generating Contrast-Enhanced Liver MRI Images from Native Sequences

Abstract Generating synthetic contrast-enhanced liver MRI scans from native MRI images can serve to mitigate the issue of sparse contrast-enhanced image datasets while concurrently circumventing the time-consuming and costly process of administering contrast agents during image acquisition. In this study, we conducted three experiments using paired image-to-image translation techniques. Native T1 sequences showing the abdominal liver region served as the input, while contrast-enhanced T1 sequences were the target. The data preprocessing methods and image boundaries were varied for the individual experiments in addition to the implementation of a 5-fold cross-validation for the top-performing approach. Focusing on liver regions achieved the best results with a mean absolute error (MAE) of 0.0837 ± 0.0068, a mean squared error (MSE) of 0.0128 ± 0.0023 and a peak signal-to-noise ratio (PSNR) of 18.99 ± 0.81 dB. Our findings serve as a proof-of-concept, demonstrating the feasibility of generating contrast-enhanced MRI images. However, the current state necessitates further enhancements for effectively addressing the challenge posed by limited dataset sizes with difficult anatomical circumstances as well as MR imaging-related heterogenous tissue contrasts.

Use of Pretreatment Perfusion MRI-based Intratumoral Heterogeneity to Predict Pathologic Response of Triple-Negative Breast Cancer to Neoadjuvant Chemoimmunotherapy.

Background Neoadjuvant chemoimmunotherapy (NACI) has significantly increased the rate of pathologic complete response (pCR) in patients with early-stage triple-negative breast cancer (TNBC), although predictors of response to this regimen have not been identified. Purpose To investigate pretreatment perfusion MRI-based radiomics as a predictive marker for pCR in patients with TNBC undergoing NACI. Materials and Methods This prospective study enrolled women with early-stage TNBC who underwent NACI at two different centers from August 2021 to July 2023. Pretreatment dynamic contrast-enhanced MRI scans obtained using scanners from multiple vendors were analyzed using the Tofts model to segment tumors and analyze pharmacokinetic parameters. Radiomics features were extracted from the rate constant for contrast agent plasma-to-interstitial transfer (or Ktrans), volume fraction of extravascular and extracellular space (Ve), and maximum contrast agent uptake rate (Slopemax) maps and analyzed using unsupervised correlation and least absolute shrinkage and selector operator, or LASSO, to develop a radiomics score. Score effectiveness was assessed using the area under the receiver operating characteristic curve (AUC), and multivariable logistic regression was used to develop a multimodal nomogram for enhanced prediction. The discrimination, calibration, and clinical utility of the nomogram were evaluated in an external test set. Results The training set included 112 female participants from center 1 (mean age, 52 years ± 11 [SD]), and the external test set included 83 female participants from center 2 (mean age, 47 years ± 11). The radiomics score demonstrated an AUC of 0.80 (95% CI: 0.70, 0.89) for predicting pCR. A nomogram incorporating the radiomics score, grade, and Ki-67 yielded an AUC of 0.86 (95% CI: 0.78, 0.94) in the test set. Associations were found between higher radiomics score (>0.25) and tumor size (P < .001), washout enhancement (P = .01), androgen receptor expression (P = .009), and programmed death ligand 1 expression (P = .01), demonstrating a correlation with tumor immune environment in participants with TNBC. Conclusion A radiomics score derived from pharmacokinetic parameters at pretreatment dynamic contrast-enhanced MRI exhibited good performance for predicting pCR in participants with TNBC undergoing NACI, and could potentially be used to enhance clinical decision making. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Rauch in this issue.

Augmented reality and optical navigation assisted orbital surgery: a novel integrated workflow

Abstract Objectives Due to the close topographical relationship of functional relevant anatomic structures, limited space and cosmetic aspects orbital surgery will remain a challenging discipline. Therefore, novel technical capabilities are necessary for further surgical progress. We here tested the integration of augmented reality and optical navigation in one workflow for interdisciplinary decision-making, feasibility and intraoperative guidance. Methods High-resolution contrast-enhanced MRI and CT scans were automated and manual-assisted segmented to achieve a detailed three-dimensional (3D) model of the individual patho-anatomical relationships. Augmented reality was used for interdisciplinary preoperative planning and intraoperative intuitive navigation. Mayfield clamp head holder in combination with optical surface matching registration assured navigation assisted microsurgery. Results Combinations of different MRI-sequences and CT-scans were necessary for detailed 3D-modeling. Modeling was time consuming and only viable in the hands of medical, surgical and anatomical trained staff. Augmented reality assured a quick, intuitive interdisciplinary orientation. Intraoperative surface matching registration enabled precise navigation in the orbital space. Conclusions Optical Navigation and microscope integration achieved a straightforward microsurgical workflow and should be implemented regularly. Augmented reality represented a useful tool for preoperative interdisciplinary planning and intraoperative intuitive orientation. It further stated an excellent educational tool.

A non-inferiority study of MRI versus CT for staging and image-defined risk factor assessment in the preoperative work-up of abdominopelvic neuroblastoma

BackgroundNeuroblastoma accounts for 15 % of cancer deaths in children. Complete surgical resection is associated with a higher overall survival rate but also a higher morbidity rate. An international group of experts has defined a nomenclature of image-defined risk factors (IDRFs) for the determination of operability and the anticipation of reasonably foreseeable complications of surgery. However, there is no consensus on the optimal imaging modality (CT or MRI) for the assessment of IDRFs. The objective of the present study was to determine the non-inferiority of MRI vs. CT in the preoperative assessment of abdominopelvic neuroblastoma. The secondary objective was to assess the contribution of gadolinium contrast enhancement. MethodsAll children diagnosed with abdominopelvic neuroblastoma and whose preoperative work-up included a contrast-enhanced CT or MRI scan of the abdomen and pelvis between January 2014 and January 2023 were included. To evaluate the IDRFs, all the images were reviewed in three steps: (i) non-contrast MRI scans, (ii) both non-contrast and contrast-enhanced MRI scans, and (iii) contrast-enhanced CT scans. ResultsTwenty-five patients were found to be eligible, and fifteen were included. The mean time interval between MRI and preoperative CT was 23 days. In all patients, the identified IDRFs were similar for all three imaging modalities. Fourteen patients underwent full resection of the tumour. The surgical reports were fully consistent with the IDRFs described on CT and/or MRI. ConclusionA high-resolution three-dimensional T2 MRI sequence agreed fully with contrast-enhanced CT for the detection of IDRFs. Contrast-enhanced MRI did not add value. However, surgeons will need time to adapt to this MRI-based approach and learn how to interpret the results with confidence.

Optimizing ultrafast dynamic contrast-enhanced MRI scan duration in the differentiation of benign and malignant breast lesions.

To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions. The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance. A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05). Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions. By evaluating scan durations (40.5-135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy. Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy.

MRI of shoulder girdle in polymyalgia rheumatica: inflammatory findings and their diagnostic value

BackgroundNon-synovial inflammation as detected by MRI is characteristic in polymyalgia rheumatica (PMR) with potentially high diagnostic value.ObjectiveThe objective is to describe inflammatory MRI findings in the shoulder girdle of patients...

Hepatic Involvement in Acquired Immunodeficiency Syndrome-Associated Kaposi's Sarcoma: A Descriptive Analysis on CT, MRI, and Ultrasound.

To retrospectively analyse the different imaging manifestations of acquired immunodeficiency syndrome-associated hepatic Kaposi's sarcoma (AIDS-HKS) on CT, MRI, and Ultrasound. Eight patients were enrolled in the study. Laboratory tests of liver function were performed. The CT, MRI, and Ultrasound manifestations were reviewed by two radiologists and two sonographers, respectively. The distribution and imaging signs of AIDS-HKS were evaluated. AIDS-HKS patients commonly presented multiple lesions, mainly distributed around the portal vein on CT, MRI, and Ultrasound. AIDS-HKS presented as ring enhancement in the arterial phase on contrast-enhanced CT and MRI scanning, and nodules gradually strengthen in the portal venous phase and the delayed phase. AIDS-HKS presented as intrahepatic bile duct dilatation and bile duct wall thickening around the lesion. Five patients (62.5%, 5/8) were followed up. After chemotherapy, the lesions were completely relieved (60.0%), or decreased (40.0%). AIDS-HKS presented as multiple nodular lesions with different imaging features. The combination of different imaging methods was helpful for the imaging diagnosis of AIDS-HKS.

NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans

PurposeThis study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA).MethodsWe used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared.ResultsThe nnU-Net models significantly outperformed the MGA (p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945–0.979], skin: 0.997 [95CI: 0.984–0.999], tumor: 0.926 [95CI: 0.508–0.968], and ventricles: 0.910 [95CI: 0.812–0.968]. Compared to the MGA’s median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000–0.926], and ventricles: 0.856 [95CI: 0.216–0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0–1616]) than the MGA (mean: 2851 s [95CI: 1482–6246]).ConclusionsThe nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.

Utility of contrast-enhanced MRI radiomics features combined with clinical indicators for predicting induction chemotherapy response in primary central nervous system lymphoma.

To assess the utility of combining contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics features with clinical variables in predicting the response to induction chemotherapy (IC) for primary central nervous system lymphoma (PCNSL). A total of 131 patients with PCNSL (101 in the training set and 30 in the testing set) who had undergone contrast-enhanced MRI scans were retrospectively analyzed. Pyradiomics was utilized to extract radiomics features, and the clinical variables of the patients were gathered. Radiomics prediction models were developed using different combinations of feature selection methods and machine learning models, and the best combination was ultimately chosen. We screened clinical variables associated with treatment outcomes and developed clinical prediction models. The predictive performance of radiomics model, clinical model, and combined model, which integrates the best radiomics model and clinical characteristics, was independently assessed and compared using Receiver Operating Characteristic (ROC) curves. In total, we extracted 1598 features. The best radiomics model we selected as the best utilized T-test and Recursive Feature Elimination (RFE) for feature selection and logistic regression for model building. Serum Interleukin 2 Receptor (IL-2R) and Eastern Cooperative Oncology Group (ECOG) Score were utilized to develop a clinical predictive model for assessing the response to induction chemotherapy. The results of the testing set revealed that the combined prediction model (radiomics and IL-2R) achieved the highest area under the ROC curve at 0.868 (0.683, 0.967), followed by the radiomics model at 0.857 (0.681, 0.957), and the clinical prediction model (IL-2R and ECOG) at 0.618 (0.413, 0.797). The combined model was significantly more accurate than the clinical model, with an AUC of 0.868 compared to 0.618 (P < 0.05). While the radiomics model had slightly better predictive power than the clinical model, this difference was not statistically significant (AUC, 0.857 vs. 0.618, P > 0.05). Our prediction model, which combines radiomics signatures from CE-MRI with serum IL-2R, can effectively stratify patients with PCNSL before high-dose methotrexate (HD-MTX) -based chemotherapy.

PerSurge (NOA-30) phase II trial of perampanel treatment around surgery in patients with progressive glioblastoma

BackgroundGlioblastoma is the most frequent and a particularly malignant primary brain tumor with no efficacy-proven standard therapy for recurrence. It has recently been discovered that excitatory synapses of the AMPA-receptor subtype form between non-malignant brain neurons and tumor cells. This neuron-tumor network connectivity contributed to glioma progression and could be efficiently targeted with the EMA/FDA approved antiepileptic AMPA receptor inhibitor perampanel in preclinical studies. The PerSurge trial was designed to test the clinical potential of perampanel to reduce tumor cell network connectivity and tumor growth with an extended window-of-opportunity concept.MethodsPerSurge is a phase IIa clinical and translational treatment study around surgical resection of progressive or recurrent glioblastoma. In this multicenter, 2-arm parallel-group, double-blind superiority trial, patients are 1:1 randomized to either receive placebo or perampanel (n = 66 in total). It consists of a treatment and observation period of 60 days per patient, starting 30 days before a planned surgical resection, which itself is not part of the study interventions. Only patients with an expected safe waiting interval are included, and a safety MRI is performed. Tumor cell network connectivity from resected tumor tissue on single cell transcriptome level as well as AI-based assessment of tumor growth dynamics in T2/FLAIR MRI scans before resection will be analyzed as the co-primary endpoints. Secondary endpoints will include further imaging parameters such as pre- and postsurgical contrast enhanced MRI scans, postsurgical T2/FLAIR MRI scans, quality of life, cognitive testing, overall and progression-free survival as well as frequency of epileptic seizures. Further translational research will focus on additional biological aspects of neuron-tumor connectivity.DiscussionThis trial is set up to assess first indications of clinical efficacy and tolerability of perampanel in recurrent glioblastoma, a repurposed drug which inhibits neuron-glioma synapses and thereby glioblastoma growth in preclinical models. If perampanel proved to be successful in the clinical setting, it would provide the first evidence that interference with neuron-cancer interactions may indeed lead to a benefit for patients, which would lay the foundation for a larger confirmatory trial in the future.Trial registrationEU-CT number: 2023-503938-52-00 30.11.2023.

Generative AI in glioma: Ensuring diversity in training image phenotypes to improve diagnostic performance for IDH mutation prediction.

This study evaluated whether generative artificial intelligence (AI)-based augmentation (GAA) can provide diverse and realistic imaging phenotypes and improve deep learning-based classification of isocitrate dehydrogenase (IDH) type in glioma compared with neuroradiologists. For model development, 565 patients (346 IDH-wildtype, 219 IDH-mutant) with paired contrast-enhanced T1 and FLAIR MRI scans were collected from tertiary hospitals and The Cancer Imaging Archive. Performance was tested on internal (119, 78 IDH-wildtype, 41 IDH-mutant [IDH1 and 2]) and external test sets (108, 72 IDH-wildtype, 36 IDH-mutant). GAA was developed using a score-based diffusion model and ResNet50 classifier. The optimal GAA was selected in comparison with the null model. Two neuroradiologists (R1, R2) assessed realism, diversity of imaging phenotypes, and predicted IDH mutation. The performance of a classifier trained with optimal GAA was compared with that of neuroradiologists using the area under the receiver operating characteristics curve (AUC). The effect of tumor size and contrast enhancement on GAA performance was tested. Generated images demonstrated realism (Turing's test: 47.5-50.5%) and diversity indicating IDH type. Optimal GAA was achieved with augmentation with 110 000 generated slices (AUC: 0.938). The classifier trained with optimal GAA demonstrated significantly higher AUC values than neuroradiologists in both the internal (R1, P = .003; R2, P < .001) and external test sets (R1, P < .01; R2, P < .001). GAA with large-sized tumors or predominant enhancement showed comparable performance to optimal GAA (internal test: AUC 0.956 and 0.922; external test: 0.810 and 0.749). The application of generative AI with realistic and diverse images provided better diagnostic performance than neuroradiologists for predicting IDH type in glioma.

Radiomics signatures for predicting the Ki-67 level and HER-2 status based on bone metastasis from primary breast cancer.

This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher's exact test, Student's t-test, and Mann-Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710-0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652-0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686-0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506-0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.

Increasing discrepancy of MR imaging and CSF study in patients with leptomeningeal seeding from lung adenocarcinoma after targeted therapy using a tyrosine kinase inhibitor.

To evaluate the correlation between contrast-enhanced (CE) MRI and cerebrospinal fluid (CSF) cytology for the evaluation of leptomeningeal metastasis (LM) on MRI after targeted therapy with tyrosine kinase inhibitors. We retrospectively reviewed the data of nonsmall cell lung cancer patients registered with NCT03257124 from May 2017 to December 2018, with progressive disease despite targeted therapy. Twenty-nine patients whose MRI scans exhibited LM at the time of registration were enrolled. During the targeted therapy with osimertinib, MRI scans, and subsequent CSF examinations were performed in every 2 months. In total, 113 MRI scans and CSF cytology data after treatment were collected. For each CE MRI scan, LM positivity was evaluated on 3D T1-weighted image (T1WI) and 2D FLAIR. The correlation between MRI and CSF cytology results and the diagnostic performance of MRI with CSF cytology as a reference standard were evaluated. After treatment, MRI revealed positivity for LM in 81 and negativity in 32. CSF results were positive in 69 examinations and negative in 44. The diagnostic accuracy of CE 3D T1WI and 2D FLAIR was 0.52 and 0.46, respectively. After targeted therapy, discrepancy in the CSF and MRI results tended to increase over time. The proportions of concordant MRI and CSF cytology results after targeted therapy were 66%, 58%, 62%, and 47% at the first, second, third, and fourth follow-up, respectively. The discrepancy of MRI in evaluation of LM and CSF cytology increases over time after targeted therapy with osimertinib. LM positivity on MRI could be a surrogate imaging marker in the pre- and immediate posttargeted-treatment with Osimertinib but not after sessions of osimertinib.

Miliary cerebral metastases: prevalence and radiological findings

Aims: Miliary cerebral metastases, also known as carcinomatosis encephalitis, represent an unusual form of metastatic disease in the brain. Due to their rarity, limited literature is available, restricting our understanding of this condition. This study aimed to evaluate the prevalence and imaging characteristics of miliary cerebral metastases in a cohort of metastatic patients.&#x0D; Methods: A retrospective review was conducted on approximately 618 contrast-enhanced MRI scans from patients with metastatic disease who had not undergone surgical intervention or received radiotherapy. Cases of miliary cerebral metastases were identified and analyzed. Demographic data, primary cancer types, non-contrast CT and MRI findings of miliary metastasis cases were evaluated.&#x0D; Results: Miliary cerebral metastases were identified in 6 out of the 618 metastatic patients included in the study. The radiological features included small, disseminated hyperintense lesions visible on post-contrast T1-weighted images. These lesions were diffusely scattered throughout the brain, predominantly at the grey-white matter junction.&#x0D; Conclusion: Our findings highlight the rarity of miliary cerebral metastases, supporting the limited cases reported in the literature. These findings underscore the need for increased clinical awareness and further research into this condition. High-resolution, contrast-enhanced MRI plays a vital role in detecting and characterizing miliary cerebral metastases, aiding in their management.

Assessing Quantitative Parenchymal Features at Baseline Dynamic Contrast-enhanced MRI and Cancer Occurrence in Women with Extremely Dense Breasts.

Background Automated identification of quantitative breast parenchymal enhancement features on dynamic contrast-enhanced (DCE) MRI scans could provide added value in assessment of breast cancer risk in women with extremely dense breasts. Purpose To automatically identify quantitative properties of the breast parenchyma on baseline DCE MRI scans and assess their association with breast cancer occurrence in women with extremely dense breasts. Materials and Methods This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. MRI was performed in eight hospitals between December 2011 and January 2016. After segmentation of fibroglandular tissue, quantitative features (including volumetric density, volumetric morphology, and enhancement characteristics) of the parenchyma were extracted from baseline MRI scans. Principal component analysis was used to identify parenchymal measures with the greatest variance. Multivariable Cox proportional hazards regression was applied to assess the association between breast cancer occurrence and quantitative parenchymal features, followed by stratification of significant features into tertiles. Results A total of 4553 women (mean age, 55.7 years ± 6 [SD]) with extremely dense breasts were included; of these women, 122 (3%) were diagnosed with breast cancer. Five principal components representing 96% of the variance were identified, and the component explaining the greatest independent variance (42%) consisted of MRI features relating to volume of enhancing parenchyma. Multivariable analysis showed that volume of enhancing parenchyma was associated with breast cancer occurrence (hazard ratio [HR], 1.09; 95% CI: 1.01, 1.18; P = .02). Additionally, women in the high tertile of volume of enhancing parenchyma showed a breast cancer occurrence twice that of women in the low tertile (HR, 2.09; 95% CI: 1.25, 3.61; P = .005). Conclusion In women with extremely dense breasts, a high volume of enhancing parenchyma on baseline DCE MRI scans was associated with increased occurrence of breast cancer as compared with a low volume of enhancing parenchyma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Grimm in this issue.

Evaluating drug delivery enhancement following ultrasound treatment.

Focused ultrasound in conjunction with circulating microbubbles is gaining pace as a new method for non-invasive and targeted drug delivery across biological barriers, such as the blood–brain barrier or the blood–tumour barrier. 1 Meng Y Hynynen K Lipsman N Applications of focused ultrasound in the brain: from thermoablation to drug delivery. Nat Rev Neurol. 2021; 17: 7-22 Crossref PubMed Scopus (110) Google Scholar The gold-standard method of confirming the success of ultrasound treatment has thus far been acquiring a contrast-enhanced MRI scan with a gadolinium-based contrast agent. Although contrast-enhanced MRI provides invaluable information about the location and extent of blood–brain barrier opening, it does not provide a quantifiable estimate of drug delivery enhancement. Repeated blood–brain barrier opening with an implantable ultrasound device for delivery of albumin-bound paclitaxel in patients with recurrent glioblastoma: a phase 1 trialLIPU-MB using a skull-implantable ultrasound device transiently opens the blood–brain barrier allowing for safe, repeated penetration of cytotoxic drugs into the brain. This study has prompted a subsequent phase 2 study combining LIPU-MB with albumin-bound paclitaxel plus carboplatin ( NCT04528680 ), which is ongoing. Full-Text PDF

Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images.

Background Reducing the amount of contrast agent needed for contrast-enhanced breast MRI is desirable. Purpose To investigate if generative adversarial networks (GANs) can recover contrast-enhanced breast MRI scans from unenhanced images and virtual low-contrast-enhanced images. Materials and Methods In this retrospective study of breast MRI performed from January 2010 to December 2019, simulated low-contrast images were produced by adding virtual noise to the existing contrast-enhanced images. GANs were then trained to recover the contrast-enhanced images from the simulated low-contrast images (approach A) or from the unenhanced T1- and T2-weighted images (approach B). Two experienced radiologists were tasked with distinguishing between real and synthesized contrast-enhanced images using both approaches. Image appearance and conspicuity of enhancing lesions on the real versus synthesized contrast-enhanced images were independently compared and rated on a five-point Likert scale. P values were calculated by using bootstrapping. Results A total of 9751 breast MRI examinations from 5086 patients (mean age, 56 years ± 10 [SD]) were included. Readers who were blinded to the nature of the images could not distinguish real from synthetic contrast-enhanced images (average accuracy of differentiation: approach A, 52 of 100; approach B, 61 of 100). The test set included images with and without enhancing lesions (29 enhancing masses and 21 nonmass enhancement; 50 total). When readers who were not blinded compared the appearance of the real versus synthetic contrast-enhanced images side by side, approach A image ratings were significantly higher than those of approach B (mean rating, 4.6 ± 0.1 vs 3.0 ± 0.2; P < .001), with the noninferiority margin met by synthetic images from approach A (P < .001) but not B (P > .99). Conclusion Generative adversarial networks may be useful to enable breast MRI with reduced contrast agent dose. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bahl in this issue.