Multiparametric Magnetic Resonance Imaging Research Articles

Advanced prostate cancer diagnosed by bone metastasis biopsy immediately after initial negative prostate biopsy: a case report and literature review

Prostate cancer (PCa) is a prevalent male malignancy that originates in the epithelial cells of the prostate. In terms of incidence and mortality of malignant tumors in men, PCa ranks second and fifth globally and first and third among men in Europe and the United States, respectively. These figures have gradually increased in recent years. The primary modalities used to diagnose PCa include prostate-specific antigen (PSA), multiparametric magnetic resonance imaging (mpMRI), and prostate puncture biopsy. Among these techniques, prostate puncture biopsy is considered the gold standard for the diagnosis of PCa; however, this method carries the potential for missed diagnoses. The preoperative evaluation of the patient in this study suggested advanced PCa. However, the initial prostate puncture biopsy was inconsistent with the preoperative diagnosis, and instead of waiting for a repeat puncture of the prostate primary, we performed a biopsy of the rib metastasis, which was later diagnosed as advanced PCa.

A pilot study on interobserver variability in organ-at-risk contours in magnetic resonance imaging-guided online adaptive radiotherapy for pancreatic cancer

PurposeDifferences in the contours created during magnetic resonance imaging-guided online adaptive radiotherapy (MRgOART) affect dose distribution. This study evaluated the interobserver error in delineating the organs at risk (OARs) in patients with pancreatic cancer treated with MRgOART. Moreover, we explored the effectiveness of drugs that could suppress peristalsis in restraining intra-fractional motion by evaluating OAR visualization in multiple patients.MethodsThis study enrolled three patients who underwent MRgOART for pancreatic cancer. The study cohort was classified into three conditions based on the MRI sequence and butylscopolamine administration (Buscopan): 1, T2 imaging without butylscopolamine administration; 2, T2 imaging with butylscopolamine administration; and 3, multi-contrast imaging with butylscopolamine administration. Four blinded observers visualized the OARs (stomach, duodenum, small intestine, and large intestine) on MR images acquired during the initial and final MRgOART sessions. The contour was delineated on a slice area of ±2 cm surrounding the planning target volume. The dice similarity coefficient (DSC) was used to evaluate the contour. Moreover, the OARs were visualized on both MR images acquired before and after the contour delineation process during MRgOART to evaluate whether peristalsis could be suppressed. The DSC was calculated for each OAR.ResultsInterobserver errors in the OARs (stomach, duodenum, small intestine, large intestine) for the three conditions were 0.636, 0.418, 0.676, and 0.806; 0.725, 0.635, 0.762, and 0.821; and 0.841, 0.677, 0.762, and 0.807, respectively. The DSC was higher in all conditions with butylscopolamine administration compared with those without it, except for the stomach in condition 2, as observed in the last session of MR image. The DSCs for OARs (stomach, duodenum, small intestine, large intestine) extracted before and after contouring were 0.86, 0.78, 0.88, and 0.87; 0.97, 0.94, 0.90, and 0.94; and 0.94, 0.86, 0.89, and 0.91 for conditions 1, 2, and 3, respectively.ConclusionButylscopolamine effectively reduced interobserver error and intra-fractional motion during the MRgOART treatment.

Autonomous Tumor Signature Extraction Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study

Background: Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI. Methods: Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing “green” in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) and p-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer. Results: The highest R (R > 0.45), AUC (>0.90), and lowest p-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the “greenest” parts from the selected blob. Conclusions: The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness.

MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence.

This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion. MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine. In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine. MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images. Multiparametric preoperative MR imaging scans of 339 pediatric patients (n = 293 internal and n = 46 external cohorts) with a variety of tumor subtypes were preprocessed and manually segmented into 4 tumor subregions, ie, enhancing tumor, nonenhancing tumor, cystic components, and peritumoral edema. After training, performances of the 2 models on internal and external test sets were evaluated with reference to ground truth manual segmentations. Additionally, concordance was assessed by comparing the volume of the subregions as a percentage of the whole tumor between model predictions and ground truth segmentations using the Pearson or Spearman correlation coefficients and the Bland-Altman method. The mean Dice score for nnU-Net internal test set was 0.9 (SD, 0.07) (median, 0.94) for whole tumor; 0.77 (SD, 0.29) for enhancing tumor; 0.66 (SD, 0.32) for nonenhancing tumor; 0.71 (SD, 0.33) for cystic components, and 0.71 (SD, 0.40) for peritumoral edema, respectively. For DeepMedic, the mean Dice scores were 0.82 (SD, 0.16) for whole tumor; 0.66 (SD, 0.32) for enhancing tumor; 0.48 (SD, 0.27) for nonenhancing tumor; 0.48 (SD, 0.36) for cystic components, and 0.19 (SD, 0.33) for peritumoral edema, respectively. Dice scores were significantly higher for nnU-Net (P ≤ .01). Correlation coefficients for tumor subregion percentage volumes were higher (0.98 versus 0.91 for enhancing tumor, 0.97 versus 0.75 for nonenhancing tumor, 0.98 versus 0.80 for cystic components, 0.95 versus 0.33 for peritumoral edema in the internal test set). Bland-Altman plots were better for nnU-Net compared with DeepMedic. External validation of the trained nnU-Net model on the multi-institutional Brain Tumor Segmentation Challenge in Pediatrics (BraTS-PEDs) 2023 data set revealed high generalization capability in the segmentation of whole tumor, tumor core (a combination of enhancing tumor, nonenhancing tumor, and cystic components), and enhancing tumor with mean Dice scores of 0.87 (SD, 0.13) (median, 0.91), 0.83 (SD, 0.18) (median, 0.89), and 0.48 (SD, 0.38) (median, 0.58), respectively. The pediatric-specific data-trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.

3D inversion recovery ultrashort echo time MRI can detect demyelination in cuprizone-treated mice

PurposeTo test the ability of inversion-recovery ultrashort echo time (IR-UTE) MRI to directly detect demyelination in mice using a standard cuprizone mouse model.MethodsNon-aqueous myelin protons have ultrashort T2s and are “invisible” with conventional MRI sequences but can be detected with UTE sequences. The IR-UTE sequence uses an adiabatic inversion-recovery preparation to suppress the long T2 water signal so that the remaining signal is from the ultrashort T2 myelin component. In this study, eight 8-week-old C57BL/6 mice were fed cuprizone (n = 4) or control chow (n = 4) for 5 weeks and then imaged by 3D IR-UTE MRI. The differences in IR-UTE signal were compared in the major white matter tracts in the brain and correlated with the Luxol Fast Blue histochemical marker of myelin.ResultsIR-UTE signal decreased in cuprizone-treated mice in white matter known to be sensitive to demyelination in this model, such as the corpus callosum, but not in white matter known to be resistant to demyelination, such as the internal capsule. These findings correlated with histochemical staining of myelin content.Conclusions3D IR-UTE MRI was sensitive to cuprizone-induced demyelination in the mouse brain, and is a promising noninvasive method for measuring brain myelin content.

The Value of Adding Exosome-Based Prostate Intelliscore to Multiparametric Magnetic Resonance Imaging in Prostate Biopsy: A Retrospective Analysis

Introduction: Currently, there is limited evidence for the relationship of Exosome-based Prostate Intelliscore (EPI) and multiparametric magnetic resonance imaging (mpMRI) in stratifying risk for clinically significant prostate cancer. Using a retrospective cohort study design, we sought to characterize the relationship between these two noninvasive metrics and prostate biopsy outcome. Methods: Data were collected via electronic medical record for all patients who underwent EPI testing from 1 January 2019 to 3 February 2022 and had available medical records at a single mid-western university medical center. Positive test result was defined as >15.6 for EPI, ≥3 PI-RADS score and ≥3 + 4 Gleason Score for biopsy findings. Utility of EPI, mpMRI and combined use was characterized through calculation of sensitivity, specificity, positive predictive value, negative predictive value, and ROC analysis. Results: A total of 226 patients were identified as receiving EPI testing for risk stratification of clinically significant prostate cancer. Sensitivity for EPI was 91%, mpMRI was 90%, and the highest was combined use at 96%. With ROC analysis, AUC for EPI alone was 0.57 (95% CI, 0.47–0.67) and 0.78 (95% CI, 0.70–0.87) for mpMRI alone. With prior positive EPI result, AUC for combined use with mpMRI was 0.80 (95% CI, 0.71–0.89). Further subgroup analysis resulted in increased AUC values of EPI 0.67 (95% CI, 0.48–0.87), mpMRI 0.90 (95% CI, 0.76–1.0), and combined 0.90 (95% CI, 0.75–1.0) in the African American population. Discussion: We observed that the combined use of EPI and mpMRI led to an avoided biopsy in 43% of patients. Using both parameters increased the overall sensitivity and diagnostic accuracy in detecting clinically significant prostate cancer. The best test performance was observed in the African American cohort. Identifying optimal noninvasive tools to assess risk for prostate cancer is crucial to providing accurate and cost-effective care. Future study should utilize a prospective study design to further support the combined use of these metrics.

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed.

Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN.

This study developed an unsupervised motion artifact reduction method for MRI images of patients with brain tumors. The proposed novel design uses multi-parametric multicenter contrast-enhanced T1W (ceT1W) and T2-FLAIR MRI images.&#xD;Approach: The proposed framework included two generators, two discriminators, and two feature extractor networks. A 3-fold cross-validation was used to train and fine-tune the hyperparameters of the proposed model using 230 brain MRI images with tumors, which were then tested on 148 patients' in-vivo datasets. An ablation was performed to evaluate the model's compartments. Our model was compared with Pix2pix and CycleGAN. Six evaluation metrics were reported, including normalized mean squared error (NMSE), structural similarity index (SSIM), multi-scale-SSIM (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multi-scale gradient magnitude similarity deviation (MS-GMSD). Artifact reduction and consistency of tumor regions, image contrast, and sharpness were evaluated by three evaluators using Likert scales and compared with ANOVA and Tukey's HSD tests. &#xD;Main results: On average, our method outperforms comparative models to remove heavy motion artifacts with the lowest NMSE (18.34±5.07%) and MS-GMSD (0.07±0.03) for heavy motion artifact level. Additionally, our method creates motion-free images with the highest SSIM (0.93±0.04), PSNR (30.63±4.96), and VIF (0.45±0.05) values, along with comparable MS-SSIM (0.96±0.31). Similarly, our method outperformed comparative models in removing in-vivo motion artifacts for different distortion levels except for MS- SSIM and VIF, which have comparable performance with CycleGAN. Moreover, our method had a consistent performance for different artifact levels. For the heavy level of motion artifacts, our method got the highest Likert scores of 2.82±0.52, 1.88±0.71, and 1.02±0.14 (p-values<<0.0001) for our method, CycleGAN, and Pix2pix respectively. Similar trends were also found for other motion artifact levels.&#xD;Significance: Our proposed unsupervised method was demonstrated to reduce motion artifacts from the ceT1W brain images under a multi-parametric framework.

Automatic geometry-based estimation of the locus coeruleus region on T1-weighted magnetic resonance images

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. For out-of-sample segmentation, we compare the results with atlas-based segmentation, as well as test the hypothesis that using the phase image as input can improve the robustness. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report provides an evaluation of computational methods estimating neural structure.

A pictorial essay of PI-RADS pearls and pitfalls: toward less ambiguity and better practice.

Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases.

"Seeing Is Believing": Additive Utility of 68Ga-PSMA-11 PET/CT in Prostate Cancer Diagnosis.

Widespread adoption of mpMRI has led to a decrease in the number of patients requiring prostate biopsies. 68Ga-PSMA-11 PET/CT has demonstrated added benefits in identifying csPCa. Integrating the use of these imaging techniques may hold promise for predicting the presence of csPCa without invasive biopsy. A retrospective analysis of 42 consecutive patients who underwent mpMRI, 68Ga-PSMA-11 PET/CT, prostatic biopsy, and radical prostatectomy (RP) was carried out. A lesion-based model (n = 122) using prostatectomy histopathology as reference standard was used to analyze the accuracy of 68Ga-PSMA-11 PET/CT, mpMRI alone, and both in combination to identify ISUP-grade group ≥ 2 lesions. 68Ga-PSMA-11 PET/CT demonstrated greater specificity and positive predictive value (PPV), with values of 73.3% (vs. 40.0%) and 90.1% (vs. 82.2%), while the mpMRI Prostate Imaging Reporting and Data System (PI-RADS) 4-5 had better sensitivity and negative predictive value (NPV): 90.2% (vs. 78.5%) and 57.1% (vs. 52.4%), respectively. When used in combination, the sensitivity, specificity, PPV, and NPV were 74.2%, 83.3%, 93.2%, and 51.0%, respectively. Subgroup analysis of PI-RADS 3, 4, and 5 lesions was carried out. For PI-RADS 3 lesions, 68Ga-PSMA-11 PET/CT demonstrated a NPV of 77.8%. For PI-RADS 4-5 lesions, 68Ga-PSMA-11 PET/CT achieved PPV values of 82.1% and 100%, respectively, with an NPV of 100% in PI-RADS 5 lesions. A combination of 68Ga-PSMA-11 PET/CT and mpMRI improved the radiological diagnosis of csPCa. This suggests that avoidance of prostate biopsy prior to RP may represent a valid option in a selected subgroup of high-risk patients with a high suspicion of csPCa on mpMRI and 68Ga-PSMA-11 PET/CT.

A novel quantitative method to evaluate lumbar disc degeneration: MRI histogram analysis.

This study aimed to use MRI histogram analysis to routine MRI sequences to evaluate lumbar disc degeneration (LDD), illustrate the correlation between this novel method and the traditional Pfirrmann classification method, and more importantly, perform comprehensive agreement analysis of MRI histogram analysis in various situations to evaluate its objectivity and stability. Lumbar MRI images from 133 subjects were included in this study. LDD was classified into grades by Pfirrmann classification and was measured as peak separation value by MRI histogram analysis. Correlation analysis between the two methods was performed and cutoff values were determined. In addition, the agreement analysis of peak separation value was performed by intraclass correlation coefficient (ICC) in four scenarios, including inter-resolution, inter-observer, inter-regions of interest (ROI) and inter-slice. Peak separation values were strongly correlated with Pfirrmann grades (r = - 0.847). The inter-resolution agreements of peak separation value between original image resolution of 2304 × 2304 and compressed image resolutions (1152 × 1152, 576 × 576, 288 × 288) were good to excellent (ICCs were 0.916, 0.876 and 0.822), except 144 × 144 was moderate (ICC = 533). The agreements of inter-observer (ICC = 0.982) and inter-ROI (ICC = 0.915) were excellent. Compared with the mid-sagittal slice, the inter-slice agreements were good for the first adjacent slices (ICCs were 0.826 and 0.844), and moderate to good for the second adjacent slices (ICC = 0.733 and 0.753). MRI histogram analysis, used in routine MRI sequences, demonstrated a strong correlation with Pfirrmann classification and good agreements in various scenarios, expanding the range of application and providing an effective, objective and quantitative tool to evaluate LDD.

Early therapy evaluation of intra-arterial trastuzumab injection in a human breast cancer xenograft model using multiparametric MR imaging.

To investigate the treatment efficacy of intra-arterial (IA) trastuzumab treatment using multiparametric magnetic resonance imaging (MRI) in a human breast cancer xenograft model. Human breast cancer cells (BT474) were stereotaxically injected into the brains of nude mice to obtain a xenograft model. The mice were divided into four groups and subjected to different treatments (IA treatment [IA-T], intravenous treatment [IV-T], IA saline injection [IA-S], and the sham control group). MRI was performed before and at 7 and 14 d after treatment to assess the efficacy of the treatment. The tumor volume, apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) MRI parameters (Ktrans, Kep, Ve, and Vp) were measured. Tumor volumes in the IA-T group at 14 d after treatment were significantly lower than those in the IV-T group (13.1 mm3 [interquartile range 8.48-16.05] vs. 25.69 mm3 [IQR 20.39-30.29], p = 0.005), control group (IA-S, 33.83 mm3 [IQR 32.00-36.30], p<0.01), and sham control (39.71 mm3 [IQR 26.60-48.26], p <0.001). The ADC value in the IA-T group was higher than that in the control groups (IA-T, 7.62 [IQR 7.23-8.20] vs. IA-S, 6.77 [IQR 6.48-6.87], p = 0.044 and vs. sham control, 6.89 [IQR 4.93-7.48], p = 0.004). Ktrans was significantly decreased following the treatment compared to that in the control groups (p = 0.002 and p<0.001 for vs. IA-S and sham control, respectively). Tumor growth was decreased in the IV-T group compared to that in the sham control group (25.69 mm3 [IQR 20.39-30.29] vs. 39.71 mm3 [IQR 26.60-48.26], p = 0.27); there was no significant change in the MRI parameters. IA treatment with trastuzumab potentially affects the early response to treatment, including decreased tumor growth and decrease of Ktrans, in a preclinical brain tumor model.

Abstract PO2-23-04: Magnetic Seeds, used to Locate the Metastatic Axillary Lymph Node placed before Neoadjuvant Chemotherapy for Breast Cancer Treatment, do not interfere the pre-surgical MRI Breast response assessment

Abstract Background: Axillary management in clinically node-positive patients who converted to ycN0 after neoadjuvant chemotherapy (NAC) for breast cancer (BC) remains under research with the aim of de-escalation of axillary lymph node dissection (ALND). A magnetic seed is one of the several positive lymph nodes marking techniques used in targeted axillary dissection (TAD) for patients with cN1 breast cancer undergoing neoadjuvant systemic therapy. It has been criticized for its potential masking effect due to the artefact it produces in the MRI sequences used to assess a proper breast response. This study evaluates if the magnetic seeds placed before NAC for guided TAD have influenced in the breast MRI response assessments. Methods: From October 2021 to June 2023, patients with cT0-4 cN1 breast cancer who were candidates for primary systemic treatment were prospectively recruited. Once a breast lesion and axillary node positivity were confirmed, a based hydrogel marker and a magnetic seed, respectively, were placed prior NAC. Once systemic treatment was completed, imaging techniques were used to assess the response, with MRI being the imaging technique of choice to assess breast response and axillary ultrasound the imaging technique of choice to assess axillary response. Surgical pathological confirmation of response in the axilla was by TAD, including sentinel lymph node and magnetic clipped node resection. Results: A total of 43 patients were included and their characteristics are shown in Table 1. In 16 patients (37%) the tumour was located in the upper outer quadrant (UOQ). In this group, the susceptibility artefact produced by the magnetic seed in the MRI created a black hole obscuring the axilla with a mean diameter of 69 mm (range: 57-76 mm). In no case did the halo interfere with the correct MRI visualisation of the breast marker. By MRI, the mean distance of the artifact halo from the breast marker or residual breast lesion was 40 mm (range: 13-79 mm). Overall, complete radiological response (ycCR) was reported in 20 patients (46%), breast only in 21 (49%) and axilla only in 38 (88%) patients. In 5 patients, preoperative axillary ultrasound revealed residual axillary disease, confirmed by FNA, lead to direct ALND. In the remaining 38 patients the axillary TAD assessment was completed. In all cases, the magnetic clipped node was successfully surgically removed. Pathological complete response (pCR) was confirmed in 9 (21%), breast only in 11 (26%) and axilla only in 13 (30%) patients. MRI diagnostic performance in detecting residual breast disease after NAC was not altered by the magnetic clipped node artifact when comparing the tumour location in the UOQ to the other quadrants (AUC 0.764 vs. 0.702; p=0.698). Conclusions: Surgical axillary staging remains the most reliable method for assessing the axillary response after NAC. Target axillary dissection guided by magnetic seed placed before NAC is an effective and accurate technique. The artifact generated in the MRI does not interfere with the pre-surgical breast response assessment after neoadjuvant systemic treatment. Table 1: Patients and tumor characteristics Citation Format: Martin Espinosa-Bravo, Joaquin Rivero Deniz, Clara Morales Comas, Javier de la Torre Fernandez de Vega, Irene Vives Rosello, Enrica Esposito, Vicente Peg Camara, María De Les Neus Rus Calafell, Ignacio Miranda Gomez, Christian Siso Raber. Magnetic Seeds, used to Locate the Metastatic Axillary Lymph Node placed before Neoadjuvant Chemotherapy for Breast Cancer Treatment, do not interfere the pre-surgical MRI Breast response assessment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-23-04.

Abstract PO2-22-06: MRI response Assessment of Single Fraction Pre-operative Stereotactic Radiotherapy: Preliminary results

Abstract Background: Pre-operative stereotactic breast irradiation (SPBI) improves targeting precision and has the potential to decrease treatment-related toxicity while downstaging or eliminating cancer. Accurate pCR prediction may allow omission of surgery. We are reporting initial interim results of the performance of multiparametric breast MRI in identifying pathologic response rate after single-fraction pre-operative SPBI in early stage estrogen receptor-positive (ER+), HER-2 negative breast cancer. Methods: In an ongoing single center prospective single-arm trial 22 patients with ER+/HER2-negative, cN0, unifocal, invasive breast cancer ≤3 cm underwent single-dose ablative SPBI, followed by definitive surgery per standard of care. Residual disease vs complete imaging response to SPBI was assessed on baseline and post-SPBI/pre-operative breast MRI. Pathologic response was categorized using MDACC Residual Cancer Burden (RCB) Categories. Wilcoxon rank sum test was used to assess the correlation of pathologic complete response (pCR) or RCB status on final path with MRI lesion size, volume, diffusion weighted imaging apparent diffusion coefficient (ADC) value, and when available, presence of residual enhancement on pre-operative MRI. MRI volume and ADC values were obtained using a dedicated CAD software. Area under the receiver operating Curve(AUC) was constructed to assess the diagnostic performance of pre-operative MRI on predicting pathologic response. Results Mean patient age was 64.9 (SD±7.3) years, tumor diameter 1.2(± 0.6) cm, pre-SPBI volume 6.4 ± 9.1 cm3, post SPBI volume 1.8 ± 5.6 cm3, ADC 0.91 ×10-3 mm2/s. At surgical pathology, 6(27.2%) achieved pCR/RCB-0, 9(41%) RCB-I, 7(31.8%) RCB-II. Mean baseline MRI size (1.2 ± 0.7 vs 1.3 ± 0.6, p=0.88), enhancement volumes (6.0±6.2 vs 7.9±9.9 cm3, p=0.59), DWI ADC values (1.1 ± 0.3 vs 0.9 ± 0.2 mm2/s, p-0.48) were not significantly different between tumors that showed pCR vs RCB I-II at surgical pathology. Time elapsed from SBRT to surgery were significantly longer in patients with pCR (264.3 ± 74.2 vs 183.7 ± 68.3 days, p=0.04) On pre-op MRI, mean enhancement volume was smaller in cancers that had pCR, however it did not reach significance (0.1 ± 0.1 vs 2.1 ± 6.0 cm3, p=0.2). Of 18 patients with available pre-operative MRI, residual enhancement was present in 7, of these 6/7(87%) had RCB I-II, and 1/7(13%) RCB-0 at surgical pathology. Conversely, in 11 patients with no residual enhancement on MRI, pathology showed pCR/RCB-0 in 5(45.5%), while 6(54.5%) had RCB I or II. Presence of residual enhancement on pre-operative MRI had an AUC of 0.68 (95%CI 0.43; 0.93, p=0.2) in predicting residual cancer after SPBI. Conclusion: Detecting residual enhancement on pre-operative MRI is a strong indicator of residual disease after SPBI, while lack of enhancement is not a reliable predictor of pCR. Our preliminary results do not indicate an association between baseline MRI enhancement volume, DWI ADC and pathologic response. Citation Format: Baṣak Dogan, Sunati Sahoo, Marilyn Leitch, Prasanna Alluri, Robert Timmerman, Deborah Farr, Asal Rahimi. MRI response Assessment of Single Fraction Pre-operative Stereotactic Radiotherapy: Preliminary results [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-22-06.

The efficacy of using a multiparametric magnetic resonance imaging-based radiomics model to distinguish glioma recurrence from pseudoprogression

ObjectiveThe early differential diagnosis of the postoperative recurrence or pseudoprogression (psPD) of a glioma is of great guiding significance for individualized clinical treatment. This study aimed to evaluate the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics model to distinguish between the postoperative recurrence and psPD of a glioma early on and in a noninvasive manner. MethodsA total of 52 patients with gliomas who attended the Hainan Provincial People's Hospital between 2000 and 2021 and met the inclusion criteria were selected for this study. 1137 and 1137 radiomic features were extracted from T1 enhanced and T2WI/FLAIR sequence images, respectively.After clearing some invalid information and LASSO screening, a total of 9 and 10 characteristic radiological features were extracted and randomly divided into the training set and the test set according to 7:3 ratio. Select-Kbest and minimum Absolute contraction and selection operator (LASSO) were used for feature selection. Support vector machine and logistic regression were used to form a multi-parameter model for training and prediction. The optimal sequence and classifier were selected according to the area under the curve (AUC) and accuracy. ResultsRadiomic models 1, 2 and 3 based on T1WI, T2FLAIR and T1WI + T2T2FLAIR sequences have better performance in the identification of postoperative recurrence and false progression of T1 glioma. The performance of model 2 is more stable, and the performance of support vector machine classifier is more stable. The multiparameter model based on CE-T1 + T2WI/FLAIR sequence showed the best performance (AUC:0.96, sensitivity: 0.87, specificity: 0.94, accuracy: 0.89,95% CI:0.93–1). ConclusionThe use of multiparametric MRI-based radiomics provides a noninvasive, stable, and accurate method for differentiating between the postoperative recurrence and psPD of a glioma, which allows for timely individualized clinical treatment.

The utility of multiparametric MRI in reducing diagnostic uncertainty for primary central nervous system lymphoma

AimA key limitation in the early treatment initiation in primary central nervous system lymphoma (PCNSL) is the diagnostic delay caused by lack of recognition of a lesion as a possible lymphoma, steroid initiation, and lesion involution, often resulting in inconclusive biopsy. We highlight the importance of multiparametric MRI (MPMRI), which incorporates diffusion weighted imaging (DWI), dynamic susceptibility contrast-enhanced perfusion weighted imaging (DSC-PWI) and proton magnetic resonance spectroscopy (1H-MRS) in addition to standard MRI sequences in resolving diagnostic uncertainty for PCNSL. Materials and methodsWe present a consecutive series of 10 patients at our centre with histology-proven PCNSL (specifically, diffuse large B-cell lymphoma of the CNS) who underwent multiparametric MRI. We retrospectively analyse the qualitative and semi-quantitative parameters and assess their radiological concordance for this diagnosis. ResultsWe note overall low apparent diffusion coefficient on DWI (mean ADCmin of 0.74), high percentage signal recovery on perfusion weighted imaging (mean 170%), a high choline-creatine ratio and a high-grade lipid peak on MRS giving a “twin-tower” appearance. Nine of ten patients had MRMRI findings concordant for PCNSL, defined as at least 3 of 4 parameters being consistent for PCNSL. ConclusionWe propose that concordance between these imaging multiparametric modalities could be used as a radiological predictor of PCNSL, reducing diagnostic delays, providing a more accurate biopsy target, and resulting in quicker treatment initiation.

Multiparametric magnetic resonance imaging-based assessment of the effect of adenomyosis on determining the depth of myometrial invasion in endometrial cancer.

Accurate preoperative diagnosis of endometrial cancer (EC) with deep myometrial invasion (DMI) is critical to deciding whether to perform lymphadenectomy. However, the presence of adenomyosis makes distinguishing DMI from superficial myometrial invasion (SMI) on magnetic resonance imaging (MRI) challenging. We aimed to evaluate the accuracy of multiparametric MRI (mpMRI) in diagnosing DMI in EC coexisting with adenomyosis (EC-A) compared with EC without coexisting adenomyosis and to evaluate the effect of different adenomyosis subtypes on myometrial invasion (MI) depth in EC. Patients with histologically confirmed International Federation of Gynecology and Obstetrics (FIGO) stage I EC who underwent preoperative MRI were consecutively included in this 2-center retrospective study. Institution 1 was searched from January 2017 to November 2022 and institution 2 was searched from June 2017 to March 2021. Patients were divided into 2 groups: group A, patients with EC-A; group B, EC patients without coexisting adenomyosis, matched 1:2 according to age ±5 years and tumor grade. A senior radiologist assessed the MRI adenomyosis classification in group A. Then, 2 radiologists (R1/R2) independently interpreted T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), T1-weighted contrast-enhanced (T1CE), and a combination of all images (mpMRI) respectively, and then assessed MI depth. Accuracy, sensitivity, specificity, and the areas under the receiver operating curve (AUC) were calculated. The chi-square test was used to compare the accuracy of diagnosing DMI. Interobserver agreement was evaluated using the Kappa test. A total of 70 cases in group A and 140 cases in group B were included. The accuracy, sensitivity, and specificity of consensus were 94.3% [95% confidence interval (CI): 88.9-99.7%] vs. 92.1% (95% CI: 87.7-96.6%), 60.0% (95% CI: 17-92.7%) vs. 86.7% (95% CI: 68.4-95.6%), and 96.9% (95% CI: 88.4-95.5%) vs. 93.6% (95% CI: 86.8-97.2%) (group A vs. group B, respectively). There was no significant difference in the diagnostic accuracy of DMI on each sequence between the groups (Reviewer 1/Reviewer 2): PT2WI=0.14/0.17, PDWI=0.50/0.33, PT1CE=0.90/0.18, PmpMRI=0.50/0.37. The AUC for T2WI, DWI, T1CE, and mpMRI (Reviewer 1/Reviewer 2), respectively, were 0.54 (95% CI: 0.42-0.66)/0.78 (95% CI: 0.67-0.87), 0.63 (95% CI: 0.50-0.74)/0.77 (95% CI: 0.65-0.86), 0.69 (95% CI: 0.57-0.80)/0.79 (95% CI: 0.68-0.88), and 0.91 (95% CI: 0.82-0.97)/0.89 (95% CI: 0.79-0.95) (group A) and 0.83 (95% CI: 0.76-0.89)/0.85 (95% CI: 0.78-0.90), 0.83 (95% CI: 0.76-0.89)/0.86 (95% CI: 0.79-0.91), 0.88 (95% CI: 0.82-0.93)/0.86 (95% CI: 0.80-0.92), and 0.91 (95% CI: 0.85-0.95)/0.87 (95% CI: 0.80-0.92) (group B). Interobserver agreement was highest with mpMRI [κ=0.387/0.695 (case/control)]. The consensus results of MRI categorization of adenomyosis revealed no significant difference in the accuracy of diagnosing DMI by adenomyosis subtype (Pspatial relationship>0.99, Paffected area=0.52, Paffected pattern=0.58, Paffected size>0.99). The presence of adenomyosis or adenomyosis subtype had no significant effect on the interpretation of the depth of MI. T1CE can increase the contrast between adenomyosis and cancer foci; therefore, the information provided by T1CE should be valued.