Contrast-enhanced Magnetic Resonance Imaging Research Articles

Contrast agent-free functional magnetic resonance imaging with matrix pencil decomposition to quantify abnormalities in lung perfusion and ventilation in patients with cystic fibrosis.

Previous studies showed that contrast-enhanced (CE) morpho-functional magnetic resonance imaging (MRI) detects abnormalities in lung morphology and perfusion in patients with cystic fibrosis (CF). Novel matrix pencil decomposition MRI (MP-MRI) enables quantification of lung perfusion and ventilation without intravenous contrast agent administration. To compare MP-MRI with established morpho-functional MRI and spirometry in patients with CF. Thirty-nine clinically stable patients with CF (mean age 21.6 ± 10.7 years, range 8-45 years) prospectively underwent morpho-functional MRI including CE perfusion MRI, MP-MRI and spirometry. Two blinded chest radiologists assessed morpho-functional MRI and MP-MRI employing the validated chest MRI score. In addition, MP-MRI data were processed by automated software calculating perfusion defect percentage (QDP) and ventilation defect percentage (VDP). MP perfusion score and QDP correlated strongly with the CE perfusion score (both r = 0.81; p < 0.01). MP ventilation score and VDP showed strong inverse correlations with percent predicted FEV1 (r = -0.75 and r = -0.83; p < 0.01). The comparison of visual and automated parameters showed that both MP perfusion score and QDP, and MP ventilation score and VDP were strongly correlated (r = 0.74 and r = 0.78; both p < 0.01). Further, the MP perfusion score and MP ventilation score, as well as QDP and VDP were strongly correlated (r = 0.88 and r = 0.86; both p < 0.01). MP-MRI detects abnormalities in lung perfusion and ventilation in patients with CF without intravenous or inhaled contrast agent application, and correlates strongly with the well-established CE perfusion MRI score and spirometry. Automated analysis of MP-MRI may serve as quantitative noninvasive outcome measure for diagnostic monitoring and clinical trials.

Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study

ObjectiveTo develop a nomogram based on tumor and peritumoral edema (PE) radiomics features extracted from preoperative multiparameter MRI for predicting brain invasion (BI) in atypical meningioma (AM).MethodsIn this retrospective study, according to the 2021 WHO classification criteria, a total of 469 patients with pathologically confirmed AM from three medical centres were enrolled and divided into training (n = 273), internal validation (n = 117) and external validation (n = 79) cohorts. BI was diagnosed based on the histopathological examination. Preoperative contrast-enhanced T1-weighted MR images (T1C) and T2-weighted MR images (T2) for extracting meningioma features and T2-fluid attenuated inversion recovery (FLAIR) sequences for extracting meningioma and PE features were obtained. The multiple logistic regression was applied to develop separate multiparameter radiomics models for comparison. A nomogram was developed by combining radiomics features and clinical risk factors, and the clinical usefulness of the nomogram was verified using decision curve analysis.ResultsAmong the clinical factors, PE volume and PE/tumor volume ratio are the risk of BI in AM. The combined nomogram based on multiparameter MRI radiomics features of meningioma and PE and clinical indicators achieved the best performance in predicting BI in AM, with area under the curve values of 0.862 (95% CI, 0.819–0.905) in the training cohort, 0.834 (95% CI, 0.780–0.908) in the internal validation cohort and 0.867 (95% CI, 0.785–0.950) in the external validation cohort, respectively.ConclusionsThe nomogram based on tumor and PE radiomics features extracted from preoperative multiparameter MRI and clinical factors can predict the risk of BI in patients with AM.

Prediction of peritoneal free cancer cells in gastric cancer patients by golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging.

Peritoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging (GRASP DCE-MRI) to predict the presence of peritoneal free cancer cells in gastric cancer patients. All enrolled patients were consecutively divided into analysis and validation groups. Preoperative magnetic resonance imaging (MRI) scans and perfusion were performed in patients with gastric cancer undergoing surgery, and peritoneal lavage specimens were collected for examination. Based on the peritoneal lavage cytology (PLC) results, patients were divided into negative and positive lavage fluid groups. The data collected included clinical and MR information. A nomogram prediction model was constructed to predict the positive rate of peritoneal lavage fluid, and the validity of the model was verified based on data from the verification group. There was no statistical difference between the proportion of PLC-positive cases predicted by GRASP DCE-MR and the actual PLC test. MR tumor stage, tumor thickness, and perfusion parameter Tofts-Ketty model volume transfer constant (Ktrans) were independent predictors of positive peritoneal lavage fluid. The nomogram model featured a concordance index (C-index) of 0.785 and 0.742 for the modeling and validation groups, respectively. GRASP DCE-MR could effectively predict peritoneal free cancer cells in gastric cancer patients. The nomogram model constructed using these predictors may help clinicians to better predict the risk of peritoneal free cancer cells being present in gastric cancer patients.

Associations between regional blood-brain barrier permeability, aging, and Alzheimer's disease biomarkers in cognitively normal older adults.

Increased blood-brain barrier permeability (BBBp) has been hypothesized as a feature of aging that may lead to the development of Alzheimer's disease (AD). We sought to identify the brain regions most vulnerable to greater BBBp during aging and examine their regional relationship with neuroimaging biomarkers of AD. We studied 31 cognitively normal older adults (OA) and 10 young adults (YA) from the Berkeley Aging Cohort Study (BACS). Both OA and YA received dynamic contrast-enhanced MRI (DCE-MRI) to quantify Ktrans values, as a measure of BBBp, in 37 brain regions across the cortex. The OA also received Pittsburgh compound B (PiB)-PET to create distribution volume ratios (DVR) images and flortaucipir (FTP)- PET to create partial volume corrected standardized uptake volume ratios (SUVR) images. Repeated measures ANOVA assessed the brain regions where OA showed greater BBBp than YA. In OA, Ktrans values were compared based on sex, Aβ positivity status, and APOE4 carrier status within a composite region across the areas susceptible to aging. We used linear models and sparse canonical correlation analysis (SCCA) to examine the relationship between Ktrans and AD biomarkers. OA showed greater BBBp than YA predominately in the temporal lobe, with some involvement of parietal, occipital and frontal lobes. Within an averaged ROI of affected regions, there was no difference in Ktrans values based on sex or Aβ positivity, but OA who were APOE4 carriers had significantly higher Ktrans values. There was no direct relationship between averaged Ktrans and global Aβ pathology, but there was a trend for an Ab status by tau interaction on Ktrans in this region. SCCA showed increased Ktrans was associated with increased PiB DVR, mainly in temporal and parietal brain regions. There was not a significant relationship between Ktrans and FTP SUVR. Our findings indicate that the BBB shows regional vulnerability during normal aging that overlaps considerably with the pattern of AD pathology. Greater BBBp in brain regions affected in aging is related to APOE genotype and may also be related to the pathological accumulation of Aβ.

Intrauterine chilled saline instillation reduces endometrial impairment on MRI after ultrasound-guided percutaneous microwave ablation of uterine adenomyosis

ObjectiveTo investigate whether intrauterine chilled saline can reduce endometrial impairment during US-guided percutaneous microwave ablation (PMWA) of adenomyosis.MethodsAn open-label, randomized trial was conducted with sixty symptomatic adenomyosis patients who were randomly assigned (1:1) to receive PMWA treatment assisted by intrauterine saline instillation (study group) or traditional PMWA treatment alone (control group). The primary endpoint was endometrial perfusion impairment grade on post-ablation contrast-enhanced MRI. The secondary endpoints were endometrial dehydration grade, ablation rate, and intra-ablation discomfort.ResultsThe baseline characteristics of the two groups were similar. The incidence rates of endometrial perfusion impairment on MRI in the study and control groups were 6.7% (2/30) and 46.7% (14/30), respectively (p < 0.001). There were 28 (93.3%), 2 (6.7%), 0, and 0 patients in the study group and 16 (53.3%), 7 (23.3%), 5 (16.7%), and 2 (6.7%) in the control group (p < 0.001) who had grade 0, 1, 2, and 3 perfusion impairment, respectively. Additionally, there were 27 (90%), 3 (10%), and 0 patients in the study group and 19 (63.3%), 10 (33.3%), and 1 (3.3%) in the control group who had grade 0, 1, and 2 endometrial dehydration (p = 0.01). The ablation rates achieved in the study and control groups were 93.3 ± 17% (range: 69.2–139.6%) and 99.7 ± 15.7% (range: 71.5–129.8%), and they were not significantly different (p = 0.14). No significant difference was found in the intra-ablation discomfort.ConclusionIntrauterine chilled saline can effectively reduce endometrial impairment after PMWA treatment for adenomyosis.Critical relevance statementThis trial demonstrated that the instillation of intrauterine chilled saline reduced endometrial impairment on MRI during PMWA of adenomyosis. This approach allows more precise and safe ablation in clinical practice.Key PointsEndometrial impairment occurs in the PMWA treatment of adenomyosis.Intrauterine chilled saline can reduce endometrial impairment during PMWA for adenomyosis.An intrauterine catheter is a practical endometrial protecting method during thermal ablation.Trial registration:Chinese Clinical Trial Registry, ChiCTR2100053582. Registered 24 November 2021, www.chictr.org.cn/showproj.html?proj=141090.Graphical

Evaluation of the extracranial “multifocal arcuate sign,” a novel MRI finding for the diagnosis of giant cell arteritis, on STIR and contrast-enhanced T1-weighted images

BackgroundWhile early diagnosis of giant cell arteritis (GCA) based on clinical criteria and contrast-enhanced MRI findings can lead to early treatment and prevention of blindness and cerebrovascular accidents, previously reported diagnostic methods which utilize contrast-enhanced whole head images are cumbersome. Diagnostic delay is common as patients may not be aware of initial symptoms and their significance. To improve current diagnostic capabilities, new MRI-based diagnostic criteria need to be established. This study aimed to evaluate the “multifocal arcuate sign” on short tau inversion recovery (STIR) and contrast-enhanced T1-weighted (CE-T1W) images as a novel extracranial finding for the diagnosis of GCA.MethodsA total of 17 consecutive patients (including five with GCA) who underwent CE-T1W and whole-brain axial STIR imaging simultaneously between June 2010 and April 2020 were enrolled. We retrospectively reviewed their MR images. The “multifocal arcuate sign” was defined as “multiple distant arcuate areas with high signal intensity in extracranial soft tissues such as subcutaneous fat, muscles, and tendons.” Extracranial abnormal high-signal-intensity areas were classified as “None,” when no lesions were detected; “Monofocal,” when lesions were detected only in one place; and “Multifocal,” when lesions were detected in multiple places. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of “Multifocal” areas were calculated using cross tabulation. Fisher’s exact test was used to compare “Multifocal” areas in five patients with GCA and those with other diseases. In addition, mean Cohen’s kappa and Fleiss’ kappa statistics were used to compare inter-reader agreement.ResultsThe sensitivity, specificity, PPV, and NPV of the “multifocal arcuate sign” in patients with GCA were 60%, 92–100%, 75–100%, and 85–86%, respectively. Significantly more patients with GCA had “Multifocal” areas compared to those with other diseases (Fisher’s exact test, p = 0.008–0.027). Mean Cohen’s kappa and Fleiss’ kappa for inter-reader agreement with respect to the five GCA patients were 0.52 and 0.49, respectively, for both STIR and CE-T1W sequences.ConclusionsThe new radiologic finding of “multifocal arcuate sign” on STIR and CE-T1W images may be used as a radiologic criterion for the diagnosis of GCA, which can make plain MRI a promising diagnostic modality.

Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. Retrospective. 395 patients with 1149 FLLs. The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. Stage 2.

Effectiveness of Genicular Artery Embolization for Reducing Synovitis as Assessed by Contrast-Enhanced MR Imaging in Knee Osteoarthritis: A Pilot Study

PurposeTo investigate the impact of genicular artery embolization (GAE) on synovitis in knee osteoarthritis (OA) using contrast-enhanced magnetic resonance (MR) imaging and to assess its predictive role in pain response. Materials and MethodsA single-center retrospective analysis was conducted using contrast-enhanced MR imaging on 33 patients treated with GAE for knee OA between December 2022 and March 2023. MR assessments before the procedure and at 3 months after embolization were utilized in a semiquantitative scoring system for synovitis severity and distribution analysis. Pain and function through Western Ontario and McMaster Universities Osteoarthritis Index and visual analog scale scores were also assessed. ResultsSignificant synovitis reduction was noted after GAE, particularly in parapatellar and periligamentous areas. Synovial contrast enhancement scores significantly decreased from 5.1 (SD ± 2.0) to 2.9 (SD ± 2.0) at 3 months (P < .001), with a moderate negative correlation between synovial enhancement scores and pain levels (P = .005). ConclusionsGAE significantly reduced synovitis in knee OA, evidenced by contrast-enhanced MR imaging. The correlation between preprocedural synovial contrast enhancement scores and pain relief after the procedure, although promising, requires careful interpretation because of the complex factors affecting pain in knee OA.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BackgroundThe detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.MethodsWe recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan–Meier curve and log-rank tests were used for survival analysis.ResultsOverall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.ConclusionsThe proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Contrast-enhanced pelvic magnetic resonance imaging (MRI) for the prediction of treatment response in mucinous rectal cancer.

In mucinous rectal cancer, it can be difficult to differentiate between cellular and acellular mucin. The purpose of this study was to evaluate, in patients with mucinous rectal cancer, the value of static enhancement (enh) and pharmacokinetic parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting pathologic complete response. This retrospective cross-sectional study performed at Memorial Sloan Kettering Cancer Center included 43 patients (24 males and 19 females; mean age, 57 years) with mucinous rectal cancer who underwent MRI at baseline as well as after neoadjuvant chemoradiotherapy but before surgical resection between 2008 and 2019. Two radiologists independently segmented tumors on contrast-enhanced axial 3D T1-weighted images and sagittal DCE magnetic resonance images. On contrast-enhanced axial T1-weighted images, the static parameters enh and relative enhancement (renh) were estimated. On DCE images, the pharmacokinetic parameters Ktrans, kep, relative Ktrans (rKtrans), and relative kep (rkep) were estimated. Associations between all parameters with pathologic complete response were tested using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis was performed to assess the area under the curve (AUC) for each parameter. Of the 43 patients who were included in the study, 42/43 (98%) had evaluable contrast-enhanced axial T1-weighted images and 35/43 (81%) had evaluable DCE images. Of the patients with evaluable contrast-enhanced axial T1-weighted images, 9/42 (21%) had pathologic complete response and 33/42 (79%) did not have pathologic complete response. For reader 1, enh(pre-neoadjuvant chemotherapy), enh(post-neoadjuvant chemotherapy), and renh were significant predictors of pathologic complete response [P=0.045 (AUC =0.73), 0.039 (AUC =0.74), and 0.0042, respectively]. For reader 2, enh(pre-neoadjuvant chemotherapy) and renh were significant predictors [P=0.021 (AUC =0.77) and 0.002, respectively]. For renh, the AUC was 0.83 for reader 1, and 0.82 for reader 2. Meanwhile, of those patients with evaluable DCE images, 9/35 (26%) had pathologic complete response and 26/35 (74%) did not have pathologic complete response. Ktrans(pre-neoadjuvant chemotherapy), kep(pre-neoadjuvant chemotherapy), and rkep were significant predictors [P=0.016 (AUC =0.73), 0.00057 (AUC =0.81), and 0.0096 (AUC =0.74), respectively]. Static and pharmacokinetic parameters of contrast-enhanced MRI show promise to predict neoadjuvant treatment response. Static enh parameters, which are simpler to assess, showed the strongest prediction.

Racial differences in background parenchymal enhancement, a novel marker of breast cancer.

10528 Background: Breast density is a risk factor for breast cancer that can guide eligibility for supplemental screening including MRI, but Black women are less likely to qualify for supplemental screening due to having less dense breasts on average despite higher rates of breast cancer mortality. Background parenchymal enhancement (BPE), the enhancement of fibroglandular breast tissue on contrast-enhanced MRI, is a novel biomarker that has been shown to be a superior breast cancer risk marker than breast density. Women with high BPE levels have up to a four-fold increased risk of breast cancer, even after adjusting for density, but no studies have examined BPE exclusively among Black women. Methods: We identified Black and white women ages 40-74 with no prior history of breast cancer who had a mammogram and subsequent MRI as part of a screening or diagnostic work-up from 2016-2022 at our institution. BPE was qualitatively defined according to BI-RADS (Breast Imaging Reporting and Data System), with four ordinal levels of increasing enhancement: minimal, mild, moderate, and marked. We conducted a cross-sectional study to perform a logistic regression with race as the exposure and moderate or marked BPE as the outcome, using minimal and mild BPE as the reference group, adjusted for age and BI-RADS density. Results: We included 1027 Black women and 4834 white women in our analysis. Fewer Black women had extremely dense breasts compared to white women (6% vs 13%, p= 0.04), however, there was a larger proportion of high BPE (moderate or marked) in Black women compared to white women (32% vs 28%, p = 0.03) (Table). There was an inverse association between age and BPE levels (p = 0.03), and a positive association between density and BPE levels regardless of race (p&lt; 0.01) (not shown). Black women were 39% more likely to have high BPE levels (95% CI: 1.39 [1.19, 1.63]), adjusted for age and density. Conclusions: Black women have lower density but higher BPE than white women, suggesting that BPE may improve identification of breast cancer risk for Black women. Participants [Table: see text]

Microglial activation-sensitive gadolinium complex as a potential MRI contrast agent for Alzheimer’s disease diagnosis

Numerous studies have developed gadolinium (Gd)-based contrast agents (GBCAs) for in-vivo magnetic resonance imaging (MRI) with the aim of diagnosing Alzheimer’s disease (AD). Nevertheless, GBCAs that are capable of identifying microglial activation, a critical early indicator of AD pathology, for in-vivo diagnosis remain scarce. In response to this, we synthesized a novel GBCA, Gd-DO3A-Va, designed specifically to detect microglial activation using MRI. This innovative GBCA, which was conjugated with vanillic acid, demonstrated a high selectivity for microglial activation by targeting Toll-like receptor 4 (TLR4). The use of Gd-DO3A-Va in in-vivo imaging successfully highlighted microglial activation in a transgenic AD mouse model. In particular, the use of Gd-DO3A-Va for contrast-enhanced T1-weighted MRI enabled the detection of microglial activation within the hippocampus and cortex, regions notably affected by AD. The observed enhancements in the MR contrast correlated well with immunohistological evidence of microglial activation. Consequently, Gd-DO3A-Va represents a highly promising GBCA for the identification of microglial activation, providing a novel pathway for the molecular diagnosis of AD.

Isolated cardiac sarcoidosis: a case report

Introduction. Ventricular arrhythmias are a risk factor for sudden cardiac death. Abnormalities of cardiac rhythm and conduction may be the only subjective manifestation of isolated cardiac sarcoidosis, which occurs in 25% of the total number of patients with this disease.Brief description. We present a case of isolated cardiac sarcoidosis in a young female patient, the main clinical manifestation of which was attacks of palpitations caused by ventricular tachycardia and episodes of cardiogenic shock without a cardiovascular history. Echocardiography and contrast-enhanced cardiac magnetic resonance imaging (MRI) revealed asymmetric left ventricular hypertrophy and focal myocardial masses. Cardiac tumor and sarcoidosis were suspected. The final diagnosis was based on the results of histological and immunohistochemical analysis of endomyocardial biopsies documenting sarcoidosis. No systemic manifestations of sarcoidosis were found.Discussion. The case demonstrates the potential of a comprehensive paraclinical study in the diagnosis of cardiac sarcoidosis and its differential diagnosis with other cardiac pathologies.Conclusion. Cardiac sarcoidosis is difficult to diagnose and requires histological verification in patients with LV hypertrophy and arrhythmias, and physicians should be aware about this rare disease with unfavorable prognosis without specific treatment.

Radiogenomics of homologous recombination deficiency (HRD) in breast cancer: Radiomics-based image biomarker for HRD.

554 Background: Homologous recombination deficiency (HRD) in breast cancer is an actionable target, with treatment efficacy potentially linked to timely detection. Notably, BRCA1 and BRCA2 are key genes implicated in HRD, and their pathogenic germline mutations are crucial criteria for the use of poly (ADP-ribose) polymerase inhibitors (PARPi). Although germline BRCA1/2 mutations are detectable in only a small fraction (1-5%) of breast cancers, recent whole-genome sequencing (WGS) studies have revealed that up to 22% of breast cancers also exhibit HRD-like genomic characteristics, suggesting a broader potential for HRD-targeted therapy. This has spurred the development of genomic-feature-based HRD identification methods. However, the invasive and costly nature of these methods poses a challenge. This has prompted our investigation into non-invasive image biomarkers for HRD identification. Methods: Invasive breast cancer patients were recruited from March 2021 to August 2022. Eligibility criteria included availability of fresh-frozen tumor tissue for WGS and suitability for dynamic contrast-enhanced (DCE) MRI to facilitate radiomics analysis. The association between HRD and radiomic features as well as clinicopathologic factors was investigated through rigorous genomic and statistical analysis. Results: This study encompassed 145 patients aged 20 to 69 years, of whom 16.6% (24 out of 145) were genomically identified as HRD. All patients harboring pathogenic germline mutations in BRCA1 (n=3) or BRCA2 (n=2), along with concomitant somatic LOH, exhibited HRD. A comprehensive analysis of 214 radiomic features revealed that tumor sphericity, informational measure of correlation 1 (IMC1), size-zone non-uniformity normalized (SZNN), and small area emphasis (SAE) derived from subtracted early dynamic T1-weighted imaging were significantly correlated with HRD. Moreover, these radiomic features demonstrated substantial diagnostic value, as evidenced by their ROC-AUC and PR-AUC performance against baseline models: Sphericity (ROC: 0.62, p=0.055; PR: 0.22, p=0.053), SAE (ROC: 0.63, p=0.052; PR: 0.32, p=0.007), SZNN (ROC: 0.63, p=0.04; PR: 0.30, p=0.007), and IMC1 (ROC: 0.62, p=0.067; PR: 0.29, p=0.02). Additionally, our findings suggest that these radiomic features are more directly related to HRD than to the triple-negative phenotype, which is commonly linked to HRD. Conclusions: We found that four radiomic features from MRI have significant predictive value for identifying HRD in breast cancer. Given their fair predictability and immediate availability, these features are ideally suited for a screening approach, potentially identifying more patients eligible for targeted treatment. This finding represents a significant advancement in the fields of radiology and precision oncology, opening new avenues for patient-specific treatment strategies.

The Role of MRI in Breast Cancer and Breast Conservation Therapy.

Contrast-enhanced breast MRI has an established role in aiding in the detection, evaluation, and management of breast cancer. This article discusses MRI sequences, the clinical utility of MRI, and how MRI has been evaluated for use in breast radiotherapy treatment planning. We highlight the contribution of MRI in the decision-making regarding selecting appropriate candidates for breast conservation therapy and review the emerging role of MRI-guided breast radiotherapy.

Delta-radiomics Analysis Based on Multi-phase Contrast-enhanced MRI to Predict Early Recurrence in Hepatocellular Carcinoma After Percutaneous Thermal Ablation

Rationale and ObjectivesIt is critical to predict early recurrence (ER) after percutaneous thermal ablation (PTA) for hepatocellular carcinoma (HCC). We aimed to develop and validate a delta-radiomics nomogram based on multi-phase contrast-enhanced magnetic resonance imaging (MRI) to preoperatively predict ER of HCC after PTA. Materials and MethodsWe retrospectively enrolled 164 patients with HCC and divided them into training, temporal validation, and other-scanner validation cohorts (n=110, 29, and 25, respectively). The volumes of interest of the intratumoral and/or peritumoral regions were delineated on preoperative multi-phase MR images. Original radiomics features were extracted from each phase, and delta-radiomics features were calculated. Logistic regression was used to train the corresponding radiomics models. The clinical and radiological characteristics were evaluated and combined to establish a clinical-radiological model. A fusion model comprising the best radiomics scores and clinical-radiological risk factors was constructed and presented as a nomogram. The performance of each model was evaluated and recurrence-free survival (RFS) was assessed. ResultsChild-Pugh grade B, high-risk tumor location, and an incomplete/absent tumor capsule were independent predictors of ER. The optimal radiomics model comprised 12 delta-radiomics features with areas under the curve (AUCs) of 0.834, 0.795, and 0.769 in the training, temporal validation, and other-scanner validation cohorts, respectively. The nomogram showed the best predictive performance with AUCs as 0.893, 0.854, and 0.827 in the three datasets. There was a statistically significant difference in RFS between the risk groups calculated using the delta-radiomics model and nomogram. ConclusionsThe nomogram combined with the delta-radiomic score and clinical-radiological risk factors could non-invasively predict ER of HCC after PTA.

Use of an AI-powered visualization platform to support precision clinical decision-making in patients with breast cancer undergoing neoadjuvant chemotherapy.

e12618 Background: Chemotherapy to reduce cancer burden and tumor size ("downstaging") is widely used in neoadjuvant treatment of early-stage breast cancer (ESBC) and can have a dramatic impact on selection of further treatment courses, including surgery. The resulting decision-making process between physicians and cancer patients requires detailed and effective communication of the patient's disease status, and clear presentation of this information in Tumor Board settings. Artificial intelligence (AI) -powered visualization platforms offer the potential to aid in evaluating breast tumor location and morphology, and accordingly optimize treatment. Additionally, these tools offer enhanced communication with patients regarding future treatment options, particularly serving as clinical decision support (CDS) tools. Methods: We assessed the performance of an AI platform that employs dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to render three-dimensional (3D) visualizations of breast tumors and 5 additional chest tissues on an independent validation dataset comprise of 100 ESBC cases with ground-truth tumor labels vetted by 2 breast-specialized radiologists. The platform provides 3D depictions of the tumor and its multi-tissue context, as well as functionalities for quantifying tumor morphology, tumor-to-landmark structure distances, excision volumes, and approximate surgical margins. We subsequently evaluated the tool's utility in a real-world setting to understand how the tool impacts clinical decision-making and patient communication, specifically around surgical planning in the context of neoadjuvant chemotherapy (NACT). Results: The platform generated accurate 3D tumor maps, as evidenced by a median Dice score of 75.6% and a median Hausdorff distances of 15.1 mm relative to the radiologist validation set. The automatic longest tumor dimension calculations were linearly correlated with radiologist direct measurements (Pearson correlation coefficient = 0.78) and had a median absolute difference of 6.3 mm. We found strong overlap between the platform’s ground-truth convex hulls, as shown by a median Dice score of 81.5%. When used by a skilled breast-specialized surgeon, the tool enabled rapid and intuitive patient-physician communication of the location and extent of disease, and potential degrees of downstaging to breast-conserving surgery. Conclusions: Detailed 3D tumor and surrounding multi-tissue depictions offer both qualitative and quantitative comprehension of cancer topology and may aid in formulating an optimal treatment approach for breast cancer treatment. We show that detailed depictions enhance communication between physicians and patients and may aid in streamlining decision-making.

Predicting peripheral neuropathy following neoadjuvant therapy in patients with breast cancer.

e12639 Background: Administration of neoadjuvant chemotherapy (NACT) or radiation is widely used to improve outcomes in patients with breast cancer. This strategy has demonstrated remarkable efficacy in reducing tumor burden and increasing the likelihood of breast-conserving surgery. However, neoadjuvant therapy can induce peripheral neuropathy, a condition characterized by nerve damage or dysfunction in the extremities that often manifests as pain, tingling, or weakness in the hands and feet. The exact mechanism remains unclear, although likely involves the cumulative toxic effects of therapy on peripheral nerves. Treatment-induced neuropathy poses a substantial unaddressed challenge that can significantly impact the patient’s quality of life. Therefore, predicting and ameliorating neuropathy are crucial aspects of comprehensive cancer care. Methods: We used an AI spatial biology platform to create and assess 3D depictions of a cohort of breast cancer patients based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. Clinical, pathological, and radiological data were collected on 74 patients from Yale New Haven Hospital. Due to missing regimen data, the final dataset included 71 patients, with mean age 51.1 (14.5) years. An AI-based segmentation algorithm was successfully employed on 63 patients, of whom 13 had neuropathy following NACT. Vascular and microvascular (MV/V) kinetic parameters were computed for each, and radiomic features were extracted. Machine learning models were developed for clinical features, MV/V features, and the combined set of features. All models were tested with leave-one-out cross-validation. Results: The clinical model did not forecast neuropathy in any patient, indicating poor predictive ability. The MV/V model performed the best, with accuracy, precision, recall, and AUROC of 0.84, 1.0, 0.18, and 0.69, respectively. The combined model showed intermediate performance, with accuracy, precision, recall, and AUROC of 0.81, 0.5, 0.18, and 0.58, respectively. The patient population limits broad conclusions, but suggests features that are aligned with increased occurrence of neuropathy. In particular, vascular features that describe transfer of nutrients between tissues, as well as the density of blood vessels within and around the tumor, are prominent within both the MV/V and combined models. Conclusions: These results indicate that features associated with the degree of proximal and intratumoral vascularity may be predictive of peripheral neuropathy in breast cancer patients. Moreover, these features can be extracted from standard-of-care DCE-MRI data. Expansion of this vascular analysis could enable physicians to better plan and manage care for patients by tailoring treatment dose. [Table: see text]

Differentiation between primary central nervous system lymphomas and gliomas according to pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging

PurposeIt is difficult to differentiate between primary central nervous system lymphoma and primary glioblastoma due to their similar MRI findings. This study aimed to assess whether pharmacokinetic parameters derived from dynamic contrast-enhanced MRI could provide valuable insights for differentiation. MethodsSeventeen cases of primary central nervous system lymphoma and twenty-one cases of glioblastoma as confirmed by pathology, were retrospectively analyzed. Pharmacokinetic parameters, including Ktrans, Kep, Ve, and the initial area under the Gd concentration curve, were measured from the enhancing tumor parenchyma, peritumoral parenchyma, and contralateral normal parenchyma. Statistical comparisons were made using Mann–Whitney U tests for Ve and Matrix Metallopeptidase-2, while independent samples t-tests were used to compare pharmacokinetic parameters in the mentioned regions and pathological indicators of enhancing tumor parenchyma, such as vascular endothelial growth factor and microvessel density. The pharmacokinetic parameters with statistical differences were evaluated using receiver-operating characteristics analysis. Except for the Wilcoxon rank sum test for Ve, the pharmacokinetic parameters were compared within the enhancing tumor parenchyma, peritumoral parenchyma, and contralateral normal parenchyma of the primary central nervous system lymphomas and glioblastomas using variance analysis and the least-significant difference method. ResultsStatistical differences were observed in Ktrans and Kep within the enhancing tumor parenchyma and in Kep within the peritumoral parenchyma between these two tumor types. Differences were also found in Matrix Metallopeptidase-2, vascular endothelial growth factor, and microvessel density within the enhancing tumor parenchyma of these tumors. When compared with the contralateral normal parenchyma, pharmacokinetic parameters within the peritumoral parenchyma and enhancing tumor parenchyma exhibited variations in glioblastoma and primary central nervous system lymphoma, respectively. Moreover, the receiver-operating characteristics analysis showed that the diagnostic efficiency of Kep in the peritumoral parenchyma was notably higher. ConclusionPharmacokinetic parameters derived from dynamic contrast-enhanced MRI can differentiate primary central nervous system lymphoma and glioblastoma, especially Kep in the peritumoral parenchyma.

Impact of gadopiclenol on decision making in patients with brain metastases: A post-hoc study.

e14003 Background: Gadopiclenol (Elucirem, Guerbet, France) is a new high relaxivity macrocyclic gadolinium-based contrast agent (GBCA), recently approved by the FDA and EMA at the dose of 0.05 mmol/kg. The aim of this post-hoc analysis was to evaluate the impact of contrast-enhanced MRI with gadopiclenol at 0.1 mmol/kg on decision making and radiotherapy (RT) treatment planning of brain metastases (BM). Methods: This is a post-hoc analysis of data from a phase IIb study where patients underwent two separate MRI examinations, one with gadopiclenol at 0.025, 0.05, 0.1 or 0.2 mmol/kg and one with gadobenate dimeglumine at 0.1 mmol/kg. MR images of patients who received both GBCAs at 0.1 mmol/kg, with ≥1 BM detected in either of the scans, were subjected to a blinded reader analysis and contouring by two radiation oncology experts. For each patient, treatment plans (stereotactic radiosurgery [SRS] or whole-brain radiotherapy [WBRT]) were determined for both MRIs, with the gross target volume (GTV) indicating the contrast-enhancing aspects of the tumor. Mean GTVs and normal tissue volumes receiving 12 Gy (V12), as well as the Dice similarity coefficient (DSC) were obtained for the paired contours. The Spearman´s rank (ρ) correlation was additionally calculated. Furthermore, three experts blindly evaluated the contrast enhancement of each lesion for contouring purposes and subjectively qualified them as “better”, in detriment of the counterpart, or “equal”. Results: In total, images from 13 adult patients were analyzed. MRI with gadopiclenol depicted additional BM as compared with gadobenate dimeglumine in 7 patients (54%). The treatment plan was changed in 2 patients (15%), from no treatment to SRS and from SRS to WBRT. Gadopiclenol depicted additional BM in these 2 patients (from 0 and 10 BM with gadobenate dimeglumine to 1 and 15 BM with gadopiclenol). The mean GTVs and V12 were comparable between gadopiclenol and gadobenate dimeglumine (p=0.694, p=1.974). The mean DSC was 0.70 (SD: 0.14, ρ0.82). From a total of 36 answers, contrast enhancement was qualified as better with gadopiclenol in 21 (58.3%) evaluations, better with gadobenate dimeglumine in 8 (22.2%) evaluations, while no difference was observed in 7 (19.4%) evaluations. Conclusions: Gadopiclenol at 0.1 mmol/kg improved BM detection and contrast enhancement with potential impact on RT treatment decisions.