Magnetic Resonance Imaging Imaging Characteristics Research Articles

Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in Breast Cancer.

To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer. The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model. The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value. The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans

Magnetic resonance imaging-based prediction models for differentiating intraspinal schwannomas from meningiomas: classification and regression tree and random forest analysis.

Due to the variations in surgical approaches and prognosis between intraspinal schwannomas and meningiomas, it is crucial to accurately differentiate between the two prior to surgery. Currently, there is limited research exploring the implementation of machine learning (ML) methods for distinguishing between these two types of tumors. This study aimed to establish a classification and regression tree (CART) model and a random forest (RF) model for distinguishing schwannomas from meningiomas. We retrospectively collected 88 schwannomas (52 males and 36 females) and 51 meningiomas (10 males and 41 females) who underwent magnetic resonance imaging (MRI) examinations prior to the surgery. Simple clinical data and MRI imaging features, including age, sex, tumor location and size, T1-weighted images (T1WI) and T2-weighted images (T2WI) signal characteristics, degree and pattern of enhancement, dural tail sign, ginkgo leaf sign, and intervertebral foramen widening (IFW), were reviewed. Finally, a CART model and RF model were established based on the aforementioned features to evaluate their effectiveness in differentiating between the two types of tumors. Meanwhile, we also compared the performance of the ML models to the radiologists. The receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the models and clinicians' discrimination performance. Our investigation reveals significant variations in ten out of 11 variables in the training group and five out of 11 variables in the test group when comparing schwannomas and meningiomas (P<0.05). Ultimately, the CART model incorporated five variables: enhancement pattern, the presence of IFW, tumor location, maximum diameter, and T2WI signal intensity (SI). The RF model combined all 11 variables. The CART model, RF model, radiologist 1, and radiologist 2 achieved an area under the curve (AUC) of 0.890, 0.956, 0.681, and 0.723 in the training group, and 0.838, 0.922, 0.580, and 0.659 in the test group, respectively. The RF prediction model exhibits more exceptional performance than an experienced radiologist in discriminating intraspinal schwannomas from meningiomas. The RF model seems to be better in discriminating the two tumors than the CART model.

Pre-operative enhanced magnetic resonance imaging combined with clinical features predict early recurrence of hepatocellular carcinoma after radical resection.

Indentifying predictive factors for postoperative recurrence of hepatocellular carcinoma (HCC) has great significance for patient prognosis. To explore the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) combined with clinical features in predicting early recurrence of HCC after resection. A total of 161 patients with pathologically confirmed HCC were enrolled. The patients were divided into early recurrence and non-early recurrence group based on the follow-up results. The clinical, laboratory, pathological results and Gd-EOB-DTPA enhanced MRI imaging features were analyzed. Of 161 patients, 73 had early recurrence and 88 were had non-early recurrence. Univariate analysis showed that patient age, gender, serum alpha-fetoprotein level, the Barcelona Clinic Liver Cancer stage, China liver cancer (CNLC) stage, microvascular invasion (MVI), pathological satellite focus, tumor size, tumor number, tumor boundary, tumor capsule, intratumoral necrosis, portal vein tumor thrombus, large vessel invasion, nonperipheral washout, peritumoral enhancement, hepatobiliary phase (HBP)/tumor signal intensity (SI)/peritumoral SI, HBP peritumoral low signal and peritumoral delay enhancement were significantly associated with early recurrence of HCC after operation. Multivariate logistic regression analysis showed that patient age, MVI, CNLC stage, tumor boundary and large vessel invasion were independent predictive factors. External data validation indicated that the area under the curve of the combined predictors was 0.861, suggesting that multivariate logistic regression was a reasonable predictive model for early recurrence of HCC. Gd-EOB-DTPA enhanced MRI combined with clinical features would help predicting the early recurrence of HCC after operation.

Ultrasound-guided percutaneous high-frequency irreversible electroporation in porcine livers using four electrode needles: A feasibility and safety study.

Malignant liver tumors seriously endanger human health. Among different therapeutic approaches, high-frequency irreversible electroporation (H-FIRE) is a recently emerging tumor ablation technique. The objective of this study was to evaluate the feasibility and safety of ultrasound-guided percutaneous H-FIRE using four electrode needles in porcine livers. Twelve experimental pigs underwent percutaneous H-FIRE ablation using a compound steep-pulse therapeutic device. Liver tissues adjacent to the gallbladder, blood vessels, and bile ducts were selected as the ablation targets. Pigs were randomly divided into three groups: (1) immediately after ablation (N = 4), (2) 2 days after ablation (N = 4), and (3) 7 days after ablation (N = 4). Blood routine, liver and kidney function, and myocardial enzyme levels were measured before and after ablation. Ultrasound, contrast-enhanced ultrasound (CEUS), contrast-enhanced magnetic resonance imaging (MRI), and hematoxylin-eosin staining were performed to evaluate the ablation performance. Ultrasound-guided percutaneous H-FIRE ablations using four electrode needles were successfully performed in all 12 experimental pigs. The general conditions of the pigs, including postoperative activities and feeding behaviors, were normal, with no significant changes compared with the preoperative conditions. The imaging features of ultrasound, CEUS, and MRI demonstrated no significant changes in the gallbladder walls, bile ducts, or blood vessels close to the ablation areas. Laboratory tests showed that liver function indices and myocardial enzymes increased temporarily after H-FIRE ablation, but decreased to normal levels at 7 days after ablation. Histopathological examinations of porcine liver specimens showed that this technique could effectively ablate the target areas without damaging the surrounding or internal vascular systems and gallbladder. This study demonstrated the feasibility and safety of ultrasound-guided percutaneous H-FIRE ablation in porcine livers invivo, and proposed a four-needle method to optimize its clinical application.

Intradural lumbar disc herniation at the L3–L4 level: A case report and literature review

Abstract Only 0.26% of all disc herniations are caused by intradural disc herniation (IDH), making it a rare pathology. We present the case of a 44-year-old man who suffered from right radicular low back pain and motor impairments. At the level of L3–L4, magnetic resonance imaging (MRI) revealed an intradural, extra-axial spinal mass. This individual had several specific MRI imaging characteristics. Laminectomy, durotomy, and transforaminal lumbar interbody fusion surgery was performed, and the disc was completely removed. At the 6-month follow-up, the patient showed full recovery. The diagnosis of IDH is very challenging and every spine surgeon must be aware. The best treatment for this pathology is surgical intervention.

Hirayama Disease with Isolated Spinal Cord Atrophy

This report presents a young-aged man with Hirayama disease who had muscle atrophy and weakness strictly confined to the C8/T1 myotome that consistently accorded with electromyographic results. In the magnetic resonance imaging (MRI) evaluation, there was only a prominent isolated spinal cord atrophy without any typical flexion MRI features of Hirayama disease. We speculate that it may be possible to have only isolated spinal cord atrophy as a limited form in Hirayama disease even in the absence of anterior displacement of the dura in the flexed position. These findings emphasize the importance of understanding both typical and atypical limited findings of the MRI imaging features of Hirayama disease, because it’s critical to distinguish Hirayama disease, mostly showing benign course, from fatal conditions like amyotrophic lateral sclerosis.

A Radiological Review of the Varied Duodenal Lesions with Histopathological Correlation

Majority of the duodenal lesions have received little attention in imaging literature in comparison with jejunum and ileum as most of them are primarily investigated with conventional endoscopy. Cross sectional imaging techniques–Multi Detector Computed Tomography (MDCT) and Magnetic Resonance Imaging (MRI) have dramatic improvement in diagnostic capabilities as they can analyse the wall of the duodenum, intraluminal content, submucosal extent of the disease, and infiltration into the surrounding viscera. Few of the duodenal lesions have overlapping imaging characteristics and it is crucial to correlate these conditions with histopathological analysis for definitive diagnosis. The main aim of this imaging review is to elucidate the diverse spectrum of duodenal lesions with emphasis on CT and MRI imaging features.

Relationship between carotid atherosclerotic plaque characteristics in magnetic resonance imaging and perioperative hemodynamic instability

Objective: To analyze the relationship between carotid atherosclerotic plaque characteristics in magnetic resonance imaging (MRI) and perioperative hemodynamic instability in patients with severe carotid artery stenosis undergoing carotid artery stenting (CAS). Methods: A total of 89 patients with carotid artery stenosis who underwent CAS treatment at Beijing Tsinghua Changgung Hospital affiliated to Tsinghua University from January 1, 2017, to December 31, 2021, were prospectively included. Among them, 74 were male and 15 were female, with an age range of 43 to 87 years (mean age: 67.8±8.2 years). Preoperative examinations included carotid artery MRI vessel wall imaging to analyze the existence of large lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), and fibrous cap rupture in carotid artery plaques. Plaques without the above-mentioned risk factors were defined as stable plaque group (34 cases), while those with such risk factors were defined as vulnerable plaque group (55 cases). The number of risk factors present in each plaque was also calculated. Intraoperative changes in blood pressure and heart rate were recorded, and the use of dopamine postoperatively was noted. Using the risk factors that the plaque has as independent variables and the clinical outcomes as dependent variables, the RR values were calculated, and the differences in clinical outcomes of patients with different risk factors were compared. Results: The incidence rates of hypotension and bradycardia were higher in patients with vulnerable plaques than those with stable plaques (60.0% (33/55) vs 14.7%(5/34) and 38.2%(21/55) vs 14.7%(5/34), respectively; both P<0.05). Based on MRI imaging features, the large LRNC was present in 45 cases, with RR values for hypotension and bradycardia of 3.15 (1.69-5.87) and 2.20 (1.07-4.53), respectively; IPH was present in 37 cases, with RR values for hypotension and bradycardia of 2.70 (1.61-4.55) and 2.25 (1.15-4.39), respectively; and fibrous cap rupture was present in 29 cases, with RR values for hypotension and bradycardia of 1.50 (0.94-2.40) and 1.29 (0.67-2.49), respectively. The higher the number of risk factors in vulnerable plaques, the higher the incidence of intraoperative blood pressure and heart rate decrease: when the number of risk factors ranged from 0 to 3, the incidence of blood pressure decrease was 14.7% (5/34), 9/18, 11/18, and 13/19, respectively (P<0.001), and the incidence of heart rate decrease was 14.7% (5/34), 6/18, 7/18, and 8/19, respectively (P=0.022). There was no significant difference in the number of cases of dopamine use between the two groups (P>0.05). Conclusion: Patients with a higher number of risk factors for vulnerable carotid plaques, as indicated by carotid artery MRI vessel wall imaging, are at a higher risk of experiencing blood pressure and heart rate decrease during CAS surgery.

Analysis of the relationship between MRI imaging characteristics and clinical symptoms and therapeutic efficacy in adenomyosis patients

Objective: To investigate the relationship between magnetic resonance imaging (MRI) imaging characteristics and clinical symptoms and therapeutic efficacy in adenomyosis patients. Methods: The clinical characteristics of the adenomyosis questionnaire was self-designed. This was a retrospective study. From September 2015 to September 2020, totally 459 patients were diagnosed with adenomyosis and underwent pelvic MRI examination at Peking University Third Hospital. Clinical characteristics and treatment were collected, MRI was used to determine the lesion location, and to measure the maximum lesion thickness, the maximum myometrium thickness, uterine cavity length, uterine volume, the minimum distance between the lesion and serosa or endometrium, and whether combined with ovarian endometrioma. The difference of MRI imaging characteristics in patients with adenomyosis and its relationship with clinical symptoms and therapeutic efficacy were analyzed. Results: (1) Among the 459 patients, the age was (39.1±6.4) years. There were 376 patients (81.9%, 376/459) with dysmenorrhea. Whether patients had dysmenorrhea were related to uterine cavity length, uterine volume, ratio of the maximum lesion thickness to the maximum myometrium thickness, and whether patients had ovarian endometrioma (all P<0.001). Multivariate analysis suggested that ovarian endometrioma was the risk factor for dysmenorrhea (OR=0.438, 95%CI: 0.226-0.850, P=0.015). There were 195 patients (42.5%, 195/459) with menorrhagia. Whether patients had menorrhagia were related to age, whether patients had ovarian endometrioma, uterine cavity length, the minimum distance between lesion and endometrium or serosa, uterine volume, ratio of the maximum lesion thickness to the maximum myometrium thickness (all P<0.001). Multivariate analysis suggested that ratio of the maximum lesion thickness to the maximum myometrium thickness was the risk factor for menorrhagia (OR=774.791, 95%CI: 3.500-1.715×105, P=0.016). There were 145 patients (31.6%, 145/459) with infertility. Whether the patients had infertility were related to age, the minimum distance between lesion and endometrium or serosa, and whether patients had ovarian endometrioma (all P<0.01). Multivariate analysis suggested that young and large uterine volume were risk factors for infertility (OR=0.845, 95%CI: 0.809-0.882, P<0.001; OR=1.001, 95%CI: 1.000-1.002, P=0.009). (2) The success rate of in vitro fertilization-embryo transfer (IVF-ET) was 39.2% (20/51). Dysmenorrhea, high maximum visual analogue scale score and large uterine volume affected the success rate of IVF-ET (all P<0.05). The smaller the maximum lesion thickness, the smaller the distance between the lesion and serosa, the larger the distance between the lesion and endometrium, the smaller the uterine volume, and the smaller the ratio of the maximum lesion thickness to the maximum myometrium thickness, the better the therapeutic efficacy of progesterones (all P<0.05). Conclusions: Concomitant ovarian endometrioma increases the risk of dysmenorrhea in patients with adenomyosis. The ratio of the maximum lesion thickness to the maximum myometrium thickness is an independent risk factor for menorrhagia. Young and large uterine volume may increase the risk of infertility. Severe dysmenorrhea and large uterine volume affect the success rate of IVF-ET. The therapeutic efficacy of progesterones is relatively better when the lesion is small and far away from the endometrium.

Development and validation of a MRI-based combined radiomics nomogram for differentiation in chondrosarcoma.

This study aims to develop and validate the performance of an unenhanced magnetic resonance imaging (MRI)-based combined radiomics nomogram for discrimination between low-grade and high-grade in chondrosarcoma. A total of 102 patients with 44 in low-grade and 58 in high-grade chondrosarcoma were enrolled and divided into training set (n=72) and validation set (n=30) with a 7:3 ratio in this retrospective study. The demographics and unenhanced MRI imaging characteristics of the patients were evaluated to develop a clinic-radiological factors model. Radiomics features were extracted from T1-weighted (T1WI) images to construct radiomics signature and calculate radiomics score (Rad-score). According to multivariate logistic regression analysis, a combined radiomics nomogram based on MRI was constructed by integrating radiomics signature and independent clinic-radiological features. The performance of the combined radiomics nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness. Using multivariate logistic regression analysis, only one clinic-radiological feature (marrow edema OR=0.29, 95% CI=0.11-0.76, P=0.012) was found to be independent predictors of differentiation in chondrosarcoma. Combined with the above clinic-radiological predictor and the radiomics signature constructed by LASSO [least absolute shrinkage and selection operator], a combined radiomics nomogram based on MRI was constructed, and its predictive performance was better than that of clinic-radiological factors model and radiomics signature, with the AUC [area under the curve] of the training set and the validation set were 0.78 (95%CI =0.67-0.89) and 0.77 (95%CI =0.59-0.94), respectively. DCA [decision curve analysis] showed that combined radiomics nomogram has potential clinical application value. The MRI-based combined radiomics nomogram is a noninvasive preoperative prediction tool that combines clinic-radiological feature and radiomics signature and shows good predictive effect in distinguishing low-grade and high-grade bone chondrosarcoma, which may help clinicians to make accurate treatment plans.

Diagnostic Accuracy of Magnetic Resonance Imaging Features and Tumor-to-Nipple Distance for the Nipple-Areolar Complex Involvement of Breast Cancer: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis evaluated the accuracy of preoperative breast magnetic resonance imaging (MRI) features and tumor-to-nipple distance (TND) for diagnosing occult nipple-areolar complex (NAC) involvement in breast cancer. The MEDLINE, Embase, and Cochrane databases were searched for articles published until March 20, 2022, excluding studies of patients with clinically evident NAC involvement or those treated with neoadjuvant chemotherapy. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Two reviewers independently evaluated studies that reported the diagnostic performance of MRI imaging features such as continuity to the NAC, unilateral NAC enhancement, non-mass enhancement (NME) type, mass size (> 20 mm), and TND. Summary estimates of the sensitivity and specificity curves and the summary receiver operating characteristic (SROC) curve of the MRI features for NAC involvement were calculated using random-effects models. We also calculated the TND cutoffs required to achieve predetermined specificity values. Fifteen studies (n = 4002 breast lesions) were analyzed. The pooled sensitivity and specificity (with 95% confidence intervals) for NAC involvement diagnosis were 71% (58-81) and 94% (91-96), respectively, for continuity to the NAC; 58% (45-70) and 97% (95-99), respectively, for unilateral NAC enhancement; 55% (46-64) and 83% (75-88), respectively, for NME type; and 88% (68-96) and 58% (40-75), respectively, for mass size (> 20 mm). TND had an area under the SROC curve of 0.799 for NAC involvement. A TND of 11.5 mm achieved a predetermined specificity of 85% with a sensitivity of 64%, and a TND of 12.3 mm yielded a predetermined specificity of 83% with a sensitivity of 65%. Continuity to the NAC and unilateral NAC enhancement may help predict occult NAC involvement in breast cancer. To achieve the desired diagnostic performance with TND, a suitable cutoff value should be considered.

Imaging features of hepatobiliary MRI and the risk of hepatocellular carcinoma development

Objective This study aimed to determine whether hepatocellular carcinoma (HCC) risk and time to HCC development differ according to hepatobiliary magnetic resonance imaging (MRI) findings among people at risk for developing HCC. Materials and methods A total of 199 patients aged 40 years or older with liver cirrhosis or chronic liver disease who underwent gadoxetic acid-enhanced hepatobiliary MRI between 2011 and 2015 were analyzed. An independent radiologist retrospectively reviewed MRI findings, blinded to clinical information, and categorized them into low-risk features, high-risk features and high-risk nodules. High-risk features were defined as liver cirrhosis diagnosed by imaging. High-risk nodules were defined as LR-3 or LR-4 nodules based on LI-RADS version 2018. The primary outcome was development of HCC within 5-year of MRI evaluation. Results HCC was diagnosed in 28 patients (14.1%). HCC development was null for those with low-risk features (n = 84). The cumulative incidence rates of HCC were 0%, 2.3%, 13.4% and 22.1% at 1-, 2-, 3- and 5-year for those with high-risk features (n= 64), and were 19.1%, 31.8%, 37.3% and 46.7% at 1-, 2-, 3- and 5-year for those with high-risk nodules (n= 51). Among 28 patients developed HCC, the median time from baseline MRI to HCC diagnosis was 33.1 months (interquartile range: 25.9–46.7 months) for high-risk feature group, and 17.3 months (interquartile range: 6.2–26.5 months) for high-risk nodule group. Conclusions HCC risk and time to HCC development differ according to baseline hepatobiliary MRI findings, indicating that hepatobiliary MRI findings can be used as biomarkers to differentiate HCC risk.

Discriminating Aging Cognitive Decline Spectrum Using PET and Magnetic Resonance Image Features.

The population aging increased the prevalence of brain diseases, like Alzheimer's disease (AD). Early identification of individuals with higher odds of cognitive decline is essential to maintain quality of life. Imaging evaluation of individuals at risk of cognitive decline includes biomarkers extracted from brain positron emission tomography (PET) and structural magnetic resonance imaging (MRI). We propose investigating ensemble models to classify groups in the aging cognitive decline spectrum by combining features extracted from single imaging modalities and combinations of imaging modalities (FDG+AMY+MRI, and a PET ensemble). We group imaging data of 131 individuals into four classes related to the individuals' cognitive assessment in baseline and follow-up: stable cognitive non-impaired; individuals converting to mild cognitive impairment (MCI) syndrome; stable MCI; and Alzheimer's clinical syndrome. We assess the performance of four algorithms using leave-one-out cross-validation: decision tree classifier, random forest (RF), light gradient boosting machine (LGBM), and categorical boosting (CAT). The performance analysis of models is evaluated using balanced accuracy before and after using Shapley Additive exPlanations with recursive feature elimination (SHAP-RFECV) method. Our results show that feature selection with CAT or RF algorithms have the best overall performance in discriminating early cognitive decline spectrum mainly using MRI imaging features. Use of CAT or RF algorithms with SHAP-RFECV shows good discrimination of early stages of aging cognitive decline, mainly using MRI image features. Further work is required to analyze the impact of selected brain regions and their correlation with cognitive decline spectrum.

A preoperative model based on gadobenate-enhanced MRI for predicting microvascular invasion in hepatocellular carcinomas (≤ 5 cm).

PurposeThe present study aimed to develop and validate a preoperative model based on gadobenate-enhanced magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) size of ≤5 cm. In order to provide preoperative guidance for clinicians to optimize treatment options.Methods164 patients with pathologically confirmed HCC and preoperative gadobenate-enhanced MRI from July 2016 to December 2020 were retrospectively included. Univariate and multivariate logistic regression (forward LR) analyses were used to determine the predictors of MVI and the model was established. Four-fold cross validation was used to verify the model, which was visualized by nomograms. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical utility.ResultsElevated alpha-fetoprotein (HR 1.849, 95% CI: 1.193, 2.867, P=0.006), atypical enhancement pattern (HR 3.441, 95% CI: 1.523, 7.772, P=0.003), peritumoral hypointensity on HBP (HR 7.822, 95% CI: 3.317, 18.445, P<0.001), and HBP hypointensity (HR 3.258, 95% CI: 1.381, 7.687, P=0.007) were independent risk factors to MVI and constituted the HBP model. The mean area under the curve (AUC), sensitivity, specificity, and accuracy values for the HBP model were as follows: 0.830 (95% CI: 0.784, 0.876), 0.71, 0.78, 0.81 in training set; 0.826 (95% CI:0.765, 0.887), 0.8, 0.7, 0.79 in test set. The decision curve analysis (DCA) curve showed that the HBP model achieved great clinical benefits.ConclusionIn conclusion, the HBP imaging features of Gd-BOPTA-enhanced MRI play an important role in predicting MVI for HCC. A preoperative model, mainly based on HBP imaging features of gadobenate-enhanced MRI, was able to excellently predict the MVI for HCC size of ≤5cm. The model may help clinicians preoperatively assess the risk of MVI in HCC patients so as to guide clinicians to optimize treatment options.

Breast MRI for Evaluating Residual Tumor Size Following Neoadjuvant Chemotherapy: Clinicopathologic Factors and MRI Imaging Features Affecting its Accuracy.

The aim of the study was to investigate the accuracy of breast Magnetic Resonance Imaging (MRI) for evaluating residual tumor size following Neoadjuvant Chemotherapy (NAC) and to identify clinicopathologic and MRI features affecting its accuracy. We retrospectively assessed 109 women who underwent preoperative Dynamic Contrast-Enhanced (DCE) MRI following NAC and subsequent surgery between April 2016 and August 2020. Preoperative MRI features, including Breast Imaging Reporting and Data System lexicon characteristics, size of residual enhancing lesion, tumor shrinkage pattern, and clinicopathologic features, were investigated, and MRI and pathology findings were compared. Residual tumor size on MRI showed high agreement with residual invasive tumor size on pathologic examination (ICC, 0.808, p<0.001). The residual tumor size measured by MRI and final pathologic size were concordant in 63/109 cases (57.8%), while MRI overestimated the size in 35/109 cases (32.1%). For estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative tumors, MRI tended to underestimate the residual tumor size compared with HER2-positive cancers (p=0.002) and triple-negative cancers (p=0.12). On MRI, tumors with concentric shrinkage patterns after NAC showed less size discrepancy with final pathologic tumor size than those with non-concentric patterns (p=0.026). In ER-positive/HER2-negative cancers, MRI tends to underestimate the residual tumor size, compared to in other subtypes. Tumors with concentric shrinkage patterns after NAC showed less MRI/pathology size discrepancy.

Torcular Dural Sinus Malformation: Fetal and Postnatal Imaging Findings and Their Associations With Clinical Outcomes

BackgroundTorcular dural sinus malformations (tDSMs) are rare vascular malformations that present in fetuses and infants. Existing data on prognostic imaging features, as well as the associated morbidity and mortality, are limited and variable. We therefore reviewed cases of tDSMs diagnosed on fetal magnetic resonance imaging (MRI) at our referral center to identify pre- and postnatal MRI imaging features associated with long-term outcomes. MethodsWe searched our imaging database for fetal and postnatal MRI reports of tDSM cases. The electronic medical record was then reviewed for pre- and postnatal clinical data, including follow-up imaging. Neurological outcomes were characterized using the previously reported scale based on the Bicêtre Score. Imaging features association with outcome scores were compared using the Fisher exact test. ResultsSixteen cases of tDMS diagnosed by fetal MRI with postnatal clinical follow-up were identified, 11 of whom underwent postnatal MRI. The majority of cases of tDSM (73%) decreased in size or resolved on postnatal follow-up study without treatment. Restricted diffusion and parenchymal hemorrhage on fetal MRI were the only imaging features identified significantly associated with unfavorable neurological outcome or death, present in two patients with poor outcomes (two of two) and only one with a normal outcome (one of 14) (P = 0.025). ConclusionsFindings of tDSM on fetal MRI most often regress and/or resolve with normal or mild neurological outcomes, with the most significant predictor of poor outcome being the presence of parenchymal injury on fetal MRI. In addition, a subset will present with venolymphatic malformations.

Prevalence of Brain Incidental Lesions Detected by 68Ga-DOTA Peptides PET/CT.

Background and Objectives: 68Ga-DOTA peptides positron emission tomography/computed tomography (PET/CT) is usually applied for the study of neuroendocrine tumours, but other tumours such as meningioma may also have an increased radiopharmaceutical uptake. The aim of this retrospective study was to establish the prevalence and the meaning of brain incidental uptake among patients who performed 68Ga-DOTA peptides PET/CT for other reasons. Materials and Methods: Overall, 510 68Ga-DOTA peptides PET/CT scans performed between January 2018 and February 2022 from 430 patients were reviewed for the analysis of incidental brain radiopharmaceutical uptake. All brain incidentalomas were compared with brain magnetic resonance imaging (MRI) and/or contrast-enhanced CT performed within an average time interval of ±60 days from PET/CT scan. Results: A total of 48 patients (14%) presented incidental focal intracranial radiotracer uptake. Thirty-eight (11%) of them had a suspected meningioma confirmed by MRI or contrast-enhanced CT imaging features. The remaining 10 had a final diagnosis different from meningioma (5 as brain metastases and 2 as venous anomalies) or were lost during the follow-up without performing MRI (n = 3). The average maximal standardized uptake value (SUVmax) of the suspected meningioma was 16.5 (range 5–33), and the average lesion to brain SUVmax ratio was 351 (range 80–550). Conclusions: Brain incidental uptake from the 68Ga-DOTA peptides PET/CT is not so rare, and meningioma is the most frequent cause.

DIPG-48. MRI volumetric and machine learning based analyses predict survival outcome in pediatric diffuse midline glioma

INTRODUCTION: Diffuse midline glioma (DMG) is a fatal childhood CNS tumor. Magnetic resonance imaging (MRI) is the gold standard for DMG diagnosis and monitoring of response to therapy. Leveraging novel MRI analytical approaches, including volumetric and machine learning based analyses, may aid in the prediction of patient overall survival (OS) and help to identify high-risk cases. METHODS: T1- and T2-weighted MR images were retrospectively collected from children and young adults diagnosed with DMG (n=43). MRI features, including manually determined 3D tumor volume (T2), T1 contrast-enhancing tumor volume, T1 relative to T2 volume (T1/T2), tumor relative to whole brain volume, tumor average intensity, and tumor heterogeneity (i.e., intensity skewness and kurtosis), were evaluated at upfront diagnosis. MRI features were analyzed to identify significant predictors of OS outcome, which was defined as OS shorter, or longer, than one year from diagnosis. A support vector machine was used to predict OS outcomes using combinations of these features. RESULTS: The presence of T1 contrast-enhancing tumor at diagnosis (p=0.01), and a high T1/T2 ratio (>25%, p=0.009), predicted significantly shorter median OS. Moreover, feature selection identified T2 mean intensity (p<0.001), T2 image intensity skew (p=0.006), T1/T2 ratio (p=0.02), and T1 volume relative to whole brain (p=0.03) as significant predictors of OS outcome (short versus long). Combining T2 mean intensity, T2 image skew, T1 segment kurtosis and patient gender resulted in OS outcome prediction accuracy of 83.3% (sensitivity=85%, specificity=81.8%, n=42 cases).CONCLUSION: We have identified MRI volume and imaging features that significantly predict OS outcome in children diagnosed with DMG. Our findings provide a framework for incorporating MRI volumetric and machine learning analyses into the clinical setting, allowing for the customization of treatment based on tumor risk characteristics.

Differentiation of Small Hepatic Abscess From Hepatic Metastasis With a Combination of Imaging Parameters.

We aimed to evaluate the diagnostic performance of the combination imaging features to differentiate small (the lesion size of 3 cm or less) hepatic abscess from metastasis. This retrospective study included patients with preexisting malignancy and small hepatic lesions who underwent contrast-enhanced computed tomography (CT) and gadoxetic acid-enhanced magnetic resonance imaging (MRI) within 4 days between March 2017 and July 2020. Two radiologists independently evaluated the imaging features of each focal hepatic lesion. Laboratory parameters were also recorded. Significant parameters differentiating hepatic abscess from hepatic metastasis were identified by univariate generalized estimating equation regression. We compared the diagnostic performances of laboratory parameters, imaging features, and their combinations. We included 16 patients (10 males and 6 females) with 35 hepatic abscesses and 21 patients (13 males and 8 females) with 62 metastases with a mean age of 70.3 years in this study. Abnormal segmental neutrophil, pathy parenchymal enhancement on CT, and absence of dark rim on MRI were associated with hepatic abscess (all P < 0.01). The combination of CT and MRI parameters showed significantly higher specificity and positive predictive value than CT alone ( P ≤ 0.031), without significant difference in sensitivity and negative predictive value. We have demonstrated that the combination of CT and MRI imaging features is helpful for the differentiation of small hepatic abscess from metastasis.

Classification of Benign and Malignant Features of Glioma and Prediction of Early Metastasis and Recurrence Based on Enhanced MRI Imaging

This work was aimed to establish a feature model for glioma grading and early metastasis and recurrence risk prediction based on contrast-enhanced magnetic resonance imaging (MRI). A total of 145 patients diagnosed with glioma by pathological examination were selected as the research subjects (training cohort: nasty 80; validation cohort: nasty 65). The imaging parameters T1-weighted (CET1WI), axial T2-weighted (T2WI), and apparent diffusion coefficient (ADC) were selected for the extraction of size and shape, intensity, histogram, and texture features. Image dimensionality reduction, feature selection, and model building were performed using the LASSO regression method. Using imaging features as potential predictors and evaluation indicators, the accuracy, sensitivity, and specificity of all prediction models and the area under the curve (AUC) of the receiver operating characteristic curve were calculated. Moreover, a predictive model for glioma grading and early metastasis risk was constructed. The results showed that under a single imaging parameter (T1-CE, DDC, T2WI-FLAIR, ADCslow, Alpha, ADC, CBF, and ADCfast), the diagnostic accuracy, sensitivity, specificity, AUC, and 95% confidence interval (CI) of low-grade gliomas (LGG), high-grade gliomas (HGG), and recurrent and nonrecurrent gliomas were significantly different (P &lt; 0.05). The texture features, histogram features, and mean AUC of distinguishing low-grade and high-grade gliomas were 0.958, 0.945, and 0.954, respectively. The texture features, histogram features, and mean AUC for distinguishing recurrent and nonrecurrent gliomas were 0.949, 0.876, and 0.900, respectively. In short, the use of enhanced MRI imaging features can realize the prediction of early grading and recurrence of glioma and is of great significance for the early classification of benign and malignant characteristics of tumors.