Postcontrast T1-weighted Images Research Articles

Deep learning MRI models for the differential diagnosis of tumefactive demyelination versus IDH-wildtype glioblastoma.

Diagnosis of tumefactive demyelination can be challenging. The diagnosis of indeterminate brain lesions on MRI often requires tissue confirmation via brain biopsy. Noninvasive methods for accurate diagnosis of tumor and non-tumor etiologies allows for tailored therapy, optimal tumor control, and a reduced risk of iatrogenic morbidity and mortality. Tumefactive demyelination has imaging features that mimic isocitrate dehydrogenase-wildtype glioblastoma (IDHwt GBM). We hypothesized that deep learning applied to postcontrast T1-weighted (T1C) and T2-weighted (T2) MRI images can discriminate tumefactive demyelination from IDHwt GBM. Patients with tumefactive demyelination (n=144) and IDHwt GBM (n=455) were identified by clinical registries. A 3D DenseNet121 architecture was used to develop models to differentiate tumefactive demyelination and IDHwt GBM using both T1C and T2 MRI images, as well as only T1C and only T2 images. A three-stage design was used: (i) model development and internal validation via five-fold cross validation using a sex-, age-, and MRI technology-matched set of tumefactive demyelination and IDHwt GBM, (ii) validation of model specificity on independent IDHwt GBM, and (iii) prospective validation on tumefactive demyelination and IDHwt GBM. Stratified AUCs were used to evaluate model performance stratified by sex, age at diagnosis, MRI scanner strength, and MRI acquisition. The deep learning model developed using both T1C and T2 images had a prospective validation area under the receiver operator characteristic curve (AUC) of 88% (95% CI: 0.82 - 0.95). In the prospective validation stage, a model score threshold of 0.28 resulted in 91% sensitivity of correctly classifying tumefactive demyelination and 80% specificity (correctly classifying IDHwt GBM). Stratified AUCs demonstrated that model performance may be improved if thresholds were chosen stratified by age and MRI acquisition. MRI images can provide the basis for applying deep learning models to aid in the differential diagnosis of brain lesions. Further validation is needed to evaluate how well the model generalizes across institutions, patient populations, and technology, and to evaluate optimal thresholds for classification. Next steps also should incorporate additional tumor etiologies such as CNS lymphoma and brain metastases. AUC = area under the receiver operator characteristic curve; CNS = central nervous system; CNSIDD = central nervous system inflammatory demyelinating disease; FeTS = federated tumor segmentation; GBM = glioblastoma; IDHwt = isocitrate dehydrogenase wildtype; IHC = immunohistochemistry; MOGAD = myelin oligodendrocyte glycoprotein antibody associated disorder; MS = multiple sclerosis; NMOSD = neuromyelitis optica spectrum disorder; wt = wildtype.

The "Outline Sign": Thin Hyperenhancing Perimeter as an MR Imaging Feature of Meningioma. A Useful Tool in the Temporal Bone Region for Differentiating Meningiomas from Schwannomas and Paragangliomas.

This study investigates the practicality and utility of the "outline sign," which refers to the thin curvilinear hyperenhancing line that may be seen along the margin of a meningioma on a spin-echo postcontrast T1-weighted image. For cases in which the differential diagnosis may include other tumors, visualization of the outline sign may help to increase the diagnostic confidence for a meningioma. Therefore, in the temporal bone region such as the cerebellopontine angle or jugular foramen, where differential considerations may include a schwannoma or paraganglioma, we additionally investigated whether the outline sign may be observed in these nonmeningioma lesions. A total of 39 clinical MRIs of meningiomas, schwannomas, and paragangliomas with confirmed histopathologic data were studied retrospectively. Two experienced head and neck radiologists independently assessed for the presence or absence of an outline sign and subsequently formed a consensus opinion while blinded to patient information and histopathologic data. Interreader reliability was assessed by Cohen κ statistics. Simple bivariate comparisons were performed on the consensus opinions to assess for statistical differences in presence of the sign in meningiomas versus schwannomas and paragangliomas. Sensitivity, specificity, and accuracy of the sign with respect to identifying an underlying meningioma were calculated. Both readers displayed identical opinions in assessment of the outline sign in 34 of the 39 cases (87%), including 13 of the 14 meningiomas (93%), with substantial agreement (Cohen κ of 0.74). The outline sign was present in 12 of 14 meningiomas (86%), which was significantly greater in frequency compared with schwannomas (3 of 22, 14%) and paragangliomas (1 of 3, 33%). The outline sign demonstrated high sensitivity (86%), specificity (84%), and accuracy (85%) in identifying an underlying meningioma. The outline sign can serve as a useful tool for diagnosing meningiomas. It may help distinguish meningiomas from other enhancing tumors, for example schwannomas and paragangliomas in the temporal bone region.

The Added Role of Diffusion-Weighted Magnetic Resonance Imaging in Staging Uterine Cervical Cancer.

Cervical cancer is considered one of the most common gynecological malignancies with an increased incidence in developing countries. Magnetic resonance imaging (MRI) plays a valuable role in staging cervical cancer and providing valuable information necessary for selecting the appropriate treatment plan, while closely correlating with the prognosis of the patient. The aim of this study is to assess the diagnostic value of diffusion-weighted imaging (DWI) in the preoperative loco-regional staging of cervical carcinoma. Our purpose is to establish apparent diffusion coefficient (ADC) values of cervical carcinoma compared with normal cervical tissue and their variability based on different pathological characteristics of the lesions. It is a retrospective analysis of 57 patients diagnosed with cervical cancer, who underwent MRI examinations. The study evaluated the aspect of the lesions on T2-weighted imaging, DWI, ADC maps, and pre- and post-contrast T1-weighted imaging with fat saturation. The ADC mean values ranged between 0.63 × 10-3 mm2/second and 0.99 × 10-3 mm2/second (mean 0.79) for tumoral tissue and 1.33 × 10-3 mm2/second and 1.74 × 10-3 mm2/second (mean 1.59) for surrounding non-affected cervical tissue. The ADC mapping showed a decreasing trend with the increased sizes of the tumors (p<0.001). The ADC mean showed lower values with increased International Federation of Gynecology and Obstetrics (FIGO) stage of the tumors. The ADC mean value for cases that had spread to other organs (IVA+IVB) was significantly lower than that of the early stages (IB1 + IB2 + IIA2), stage IIB, and stages IIIA+IIIC1+IIIC2 (p<0.001). The ADC mean value of stage III disease was significantly lower than that of stage IIB, respectively early stages (p<0.001). The ADC mean value of the stage IIB tumor was significantly lower than that of the early stages (p<0.001). The differences in ADC mean values based on the histopathological type and differentiation grade were not statistically significant. The ADC mean value of the cases with positive pelvic lymph nodes was significantly lower than in those with negative lymph nodes (p<0.001). ADC mean values of cervical carcinoma are significantly lower than those from unaffected uterine tissue and they also correlate with the severity of the disease. The advancements and additional capabilities DWI can bring are the elements of interest in this article. Using DWI means a more accurate capability in diagnosing cervical cancer, providing a compelling argument for its integration into standard clinical practice. This study discusses the quantitative imaging parameters of DWI such as ADC values, which can provide objective measurements for tumor evaluation. These parameters can be standardized and used across different institutions, enhancing the reproducibility and reliability of imaging findings.

Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma.

Aim: A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. In this multicenter study, we implemented this model to test its ability to predict survival in patients with recurrent glioblastoma (rGBM) treated with chemotherapy.Methods: Pre- and post-contrast T1-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans. Univariate and multivariate Cox regression analyses were used to test the influence of contrast-enhancing tumor volume, total cellularity, mean Cyt and mean ECF at baseline, immediately post-treatment and the pre- and post-treatment rate of change in volume and cellularity on overall survival (OS).Results: Smaller Cyt and larger ECF after treatment were significant predictors of OS, independent of tumor volume and other clinical prognostic factors (HR=3.23×10-6, p<0.001 and HR=2.39×105, p<0.001, respectively). Both post-treatment volumetric growth rate and the rate of change in cellularity were significantly correlated with OS (HR=1.17, p=0.003 and HR=1.14, p=0.01, respectively).Conclusion: Changes in histological characteristics estimated from a radio-pathomic ML model are a promising tool for evaluating treatment response and predicting outcome in rGBM.

NIMG-88. EVALUATION OF DIFFUSION AND PERFUSION MRI FEATURES FOR DIFFERENTIATING RECURRENT BRAIN METASTASES AND RADIATION NECROSIS

Abstract BACKGROUND Brain metastases (BM) are a frequent and serious complication of cancer. Differentiating between BM and radiation-induced necrosis (RN) after radiotherapy remains challenging. Diffusion-weighted imaging (DWI) derived apparent diffusion coefficient (ADC) maps and perfusion-weighted imaging (PWI) derived relative cerebral blood volume (rCBV) have the potential to distinguish between BM and RN. Despite the potential of these imaging modalities, previous studies have been limited by the absence of histopathological confirmation. In this study, we utilized a patient cohort that underwent stereotactic biopsy as part of their Laser Interstitial Thermal Therapy (LITT) following suspicious radiographic progression. This allowed us to evaluate the efficacy of ADC and rCBV imaging features with histopathological validation. METHODS In this study, 29 patients underwent MRI scans (3 with 1.5T, 26 with 3T) prior to LITT. Pathology confirmed 11 patients with BM and 18 with RN. Regions of interest (ROIs) in the contrast-enhanced lesion and T2 hyperintensity lesion were identified using post-contrast T1-weighted images and fluid-attenuated inversion recovery (FLAIR) images. Mean values and histogram-based features (standard deviation, 10%, 90%) of lesion volume, ADC, and rCBV within tumor ROIs were compared between the BM and RN using the Wilcoxon rank-sum test. RESULTS Our findings indicated that the mean values of ADC in the enhancing and non-enhancing lesion ROIs did not significantly differ between BM and RN patients. However, in the contrast-enhanced lesion regions, we observed a significant difference in the standard deviation of rCBV (p&amp;lt;0.05), and the 90th percentile value of rCBV also suggested potential differences between BM and RN (p=0.069). In the T2 hyperintensity lesion regions, lesion volume (p&amp;lt;0.05) and the mean of rCBV (p&amp;lt;0.05) show significant differences between BM and RN. CONCLUSION This study demonstrates the potential of using rCBV to aid in the clinical diagnosis of BM. Additionally, our findings indicate that T2 hyperintensity regions, beyond just contrast-enhancing lesions, may provide valuable information for differential diagnosis.

Prediction of tumor response to neoadjuvant chemotherapy in high-grade osteosarcoma using clustering-based analysis of magnetic resonance imaging: an exploratory study.

To evaluate the ability of magnetic resonance imaging (MRI)-based clustering analysis to predict the pathological response to neoadjuvant chemotherapy (NACT) in patients with primary high-grade osteosarcoma. Twenty-two patients were included in this retrospective study. All patients underwent MRIs before and after NACT. The entire tumor volume was manually delineated on post-contrast T1-weighted images and subsegmented into three clusters using the K-means algorithm. Histogram-based parameters were calculated for each lesion. The response to NACT was obtained from the histopathological assessment of the tumor necrosis rate following resection. The Mann-Whitney test was used to compare poor and fair-to-good responders. The receiver operating characteristic curve was used to evaluate the diagnostic performance of the optimal parameters. At baseline, poor responders showed a significantly larger volume of cluster1 (Vol1) than fair-to-good responders (p = 0.038). After NACT, they exhibited a lower 10th percentile (P10) and kurtosis (p = 0.038 and 0.002, respectively). Vol1 at baseline and P10 after NACT had an AUC of 77% (95% CI 56-98%). The kurtosis after NACT had the best discriminative power, with an AUC of 89.7% (95% CI 75-100%). The MRI-based histogram and clustering analysis provided a good ability to differentiate between poor and fair-to-good responders before and after NACT. Further investigations using larger datasets are required to corroborate our findings.

NIMG-36. PERFUSION, DIFFUSION, AND ANATOMICAL MRI BIOMARKERS FOR ASSESSING HISTOLOGICALLY-CONFIRMED MALIGNANT TRANSFORMATION IN MOLECULAR SUBTYPES OF IDH-MUTANT GLIOMAS

Abstract BACKGROUND Isocitrate dehydrogenase-mutant (IDHm) gliomas often initially present as low-grade (grade 2) gliomas but are expected to later progress into more aggressive higher-grade (grade 3-4) gliomas through malignant transformation (MT). MT is often determined non-invasively by emergence of contrast-enhancement on post-contrast T1-weighted magnetic resonance imaging (MRI), but contrast-enhancement is an imperfect surrogate for histologically-confirmed MT. This study explored perfusion, diffusion, and anatomical MRI biomarkers besides contrast-enhancement to study histologically-confirmed MT in IDHm 1p/19q-intact astrocytomas (IDHm-A) and IDHm 1p/19q-co-deleted oligodendrogliomas (IDHm-O). METHODS N=64 patients with initial histopathological grade 2 IDH-mutant glioma diagnosis who underwent repeated tissue sampling and were classified as histologically-confirmed MT (n=35) or non-MT (n=29) were retrospectively studied. Pre-surgical anatomical MRI (n=64) and, if available, diffusion-weighted imaging (n=61) and dynamic susceptibility contrast perfusion MRI (n=53) were analyzed. Tumor volumes of interest were segmented on the whole T2/FLAIR-hyperintense tumor. Measurable contrast enhancement (&amp;gt;1000mm3), tumor volume, sphericity, median apparent diffusion coefficient (ADC), and normalized relative cerebral blood volume (nrCBV) were compared between MT vs. non-MT IDHm-A and IDHm-O. RESULTS 81% of contrast-enhancing IDHm-A and 100% of contrast-enhancing IDHm-O underwent MT, while 41% of MT IDHm-A and 62% of MT IDHm-O were non-enhancing. Tumor volumes were significantly larger in the MT vs. non-MT groups for both IDHm-A (P=0.02) and IDHm-O (P=0.04). Whole tumor nrCBV was significantly higher (P=0.002), whole tumor ADC trended lower (P=0.06), and non-enhancing tumor sphericity was significantly lower (P=0.03) in MT vs. non-MT IDHm-A. There were no significant differences in ADC, nrCBV, nor sphericity when comparing MT vs. non-MT IDHm-O (P&amp;gt;0.05). CONCLUSIONS Many MT IDHm-gliomas remain non-enhancing. Tumor volumes can determine MT for both IDHm-A and IDHm-O. Diffusion and perfusion MRI show differences for MT in IDHm-A but not IDHm-O, which may reflect the different tumor biology of these IDHm molecular subtypes and their need for separate imaging biomarkers.

NIMG-05. ARTIFICIAL INTELLIGENCE-BASED RESPONSE ASSESSMENT FOR PATIENTS RECEIVING LASER INTERSTITIAL THERMAL THERAPY

Abstract BACKGROUND Laser interstitial thermal therapy (LITT) is a minimally invasive option for tissue diagnosis, cytoreduction, and rapid post-operative return to systemic therapies for patients with brain tumors or radiation necrosis. As ablated tissue remains in situ, post-LITT edema may be associated with apparent increased lesion size and transient clinical worsening, complicating assessment of progression. METHODS All patients receiving LITT at a single center for tumors or radiation necrosis from 2015 – 2023 with ≥ 9 months of MRI follow-up were retrospectively reviewed. A 3D U-net segmentation model implemented in nnU-Net was developed for automated segmentation of Contrast-enhancing Lesion Volume (CeLV) of LITT-treated lesions on T1-weighted post-contrast MR images. CeLVs were analyzed to establish volumetric post-LITT response assessments. RESULTS Across 384 unique MRI exams, 61LITT-treated lesions and 6 control cases of medically-managed radiation necrosis were analyzed. Automated segmentation was qualitatively accurate in 367/384 (95.6%) images. CeLV increased to a median of 68.3% (IQR 35.1 – 109.2%) from baseline at 1 – 3 months from LITT (P = 0.0012) and returned to baseline thereafter. Based on established criteria, volumetric disease progression was defined as lesion expansion 40% from volumetric nadir or baseline. Twenty-one of 56 (37.5%) patients experienced volumetric progression with a progression-free survival of 21.4 (6.0 – 93.4) months. Patients experiencing volumetric progression had lower mOS (17.3 vs 62.1 months, P = 0.0015). CONCLUSIONS We observed a stereotyped increase in CeLV at 1 – 3 months post-LITT, which resolved within 6 months of the procedure. Post-LITT disease response can be reliably and feasibly assessed with criteria that account for this transient CeLV expansion. Automated lesion segmentation may speed adoption of volumetric response assessment criteria to clinical practice.

Saturation transfer (CEST and MT) MRI for characterization of U-87 MG glioma in the rat.

The focus of this work was to identify the optimal magnetic resonance imaging (MRI) contrast between orthotopic U-87 MG tumours and normal appearing brain with the eventual goal of treatment response monitoring. U-87 MG human glioblastoma cells were injected into the brain of RNU nude rats (n= 9). The rats were imaged at 7T at three timepoints for all animals: 3-5, 7-9, and 11-13 days after implantation. Whole-brain T1-weighted (before and after gadolinium contrast agent injection), diffusion, and fluid-attenuated inversion recovery scans were performed. In addition, single-slice saturation-transfer-weighted chemical exchange saturation transfer (CEST), magnetization transfer (MT), and water saturation shift referencing (WASSR) contrast Z-spectra and T1 and T2 maps were also acquired. The MT and WASSR Z-spectra and T1 map were fitted to a two-pool quantitative MT model to estimate the T2 of the free and macromolecular-bound water molecules, the relative macromolecular pool size (M0, MT), and the magnetization exchange rate from the macromolecular pool to the free pool (RMT). The T1-corrected apparent exchange-dependent relaxation (AREX) metric to isolate the CEST contributions was also calculated. The lesion on M0, MT and AREX maps with a B1 of 2μT best matched the hyperintensity on the post-contrast T1-weighted image. There was also good separation in Z-spectra between the lesion and contralateral cortex in the 2-μT CEST and 3- and 5-μT MT Z-spectra at all time points. A pairwise Wilcoxon signed-rank tests with Holm-Bonferroni adjustment on MRI parameters was performed and the differences between enhancing lesion and contralateral cortex for the MT ratio with 2μT saturation at 3.6ppm frequency offset (corresponding to the amide chemical group) and M0, MT were both strongly significant (p< 0.001) at all time points. This work has identified that differences between enhancing lesion and contralateral cortex are strongest in MTR with B1=2μT at 3.6ppm and relative macromolecular pool size (M0, MT) images over entire period of 3-13 days after cancer cell implantation.

Ovarian-adnexal reporting and data system MRI scoring: diagnostic accuracy, interobserver agreement, and applicability to machine learning

Abstract Objectives To evaluate the interobserver agreement and diagnostic accuracy of ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) and applicability to machine learning. Methods Dynamic contrast-enhanced pelvic MRI examinations of 471 lesions were retrospectively analysed and assessed by 3 radiologists according to O-RADS MRI criteria. Radiomic data were extracted from T2 and post-contrast fat-suppressed T1-weighted images. Using these data, an artificial neural network (ANN), support vector machine, random forest, and naive Bayes models were constructed. Results Among all readers, the lowest agreement was found for the O-RADS 4 group (kappa: 0.669; 95% confidence interval [CI] 0.634-0.733), followed by the O-RADS 5 group (kappa: 0.709; 95% CI 0.678-0.754). O-RADS 4 predicted a malignancy with an area under the curve (AUC) value of 74.3% (95% CI 0.701-0.782), and O-RADS 5 with an AUC of 95.5% (95% CI 0.932-0.972) (P &amp;lt; .001). Among the machine learning models, ANN achieved the highest success, distinguishing O-RADS groups with an AUC of 0.948, a precision of 0.861, and a recall of 0.824. Conclusion The interobserver agreement and diagnostic sensitivity of the O-RADS MRI in assigning O-RADS 4-5 were not perfect, indicating a need for structural improvement. Integrating artificial intelligence into MRI protocols may enhance their performance. Advances in knowledge Machine learning can achieve high accuracy in the correct classification of O-RADS MRI. Malignancy prediction rates were 74% for O-RADS 4 and 95% for O-RADS 5.

Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy. This retrospective study included 154 patients (38 female; mean age 66.8years) who underwent contrast-enhanced MRI prior to radiation segmentectomy. Radiomics features were manually extracted on volumes of interest on post-contrast T1-weighted images at the portal venous phase (PVP). Tumor-based response assessment was evaluated 6months post-treatment using mRECIST. A logistic regression model was used to predict binary response outcome [complete response at 6months with no-re-treatment (response group) against the rest (non-response group, including partial response, progressive disease, stable disease and complete response after re-treatment within 6months after radiation segmentectomy) using baseline clinical parameters and radiomics features. We accessed the value of different sets of predictors using cross-validation technique. AUCs were compared using DeLong tests. A total 168 HCCs (mean size 2.9 ± 1.7cm) were analyzed in 154 patients. The response group consisted of 113 HCCs and the non-response group of 55 HCCs. Baseline clinical parameters (AUC 0.531; sensitivity, 0.781; specificity, 0.279; positive predictive value (PPV), 0.345; negative predictive value (NPV), 0.724) and AFP (AUC 0.632; sensitivity, 0.833; specificity, 0.466; PPV, 0.432; NPV, 0.851) showed poor performance for response prediction. The model using a combination of radiomics features and clinical parameters/AFP showed the best performance (AUC 0.736; sensitivity, 0.706; specificity, 0.662; PPV 0.504; NPV, 0.822), significantly better than the clinical model (p < 0.001) or AFP alone (p < 0.001). The combination of radiomics features from pre-treatment MRI with clinical parameters and AFP showed fair performance for predicting HCC response to radiation segmentectomy, better than that of AFP. These results need further validation.

Differentiation of MS lesions through analysis of microvascular distribution

Abstract Conventional MRI is crucial for diagnosing multiple sclerosis (MS) but lacks precision, leading to the clinico-radiological paradox and misdiagnosis risk, especially when confronted with unspecific lesions not related to MS. Advancements in perfusion-weighted imaging (PWI) with an algorithm designed for diseases with anticipated contrast agent extravasation offer insight into microvascular impairment and flow heterogeneity. Our study aimed to assess these factors in MS patients and their association with clinically relevant white matter injury and disease course. We evaluated 60 adults with white matter lesions (WML), including 50 diagnosed with MS or MS syndromes and 10 non-diseased symptomatic controls (SC) with unspecific WML. MRI included conventional three-dimensional (3D) T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), 3D magnetization-prepared 2 rapid acquisition gradient-echo (MP2RAGE), post-contrast 3D T1-weighted (T1) images and Dynamic Susceptibility Contrast (DSC) PWI at 3T. WML masks of “unspecific T2-FLAIR lesions”, “MS T2-FLAIR lesions”, and “MS T1-lesions” were manually outlined and validated by a neuroradiologist. DSC-derived parameters were analyzed in WML masks and healthy-appearing tissue. MS T2-FLAIR lesions showed increased flow heterogeneity and vasodilation compared to unspecific T2-FLAIR lesions in SC, as well as compared to unspecific T2-FLAIR lesions within the MS group. MS T1-lesions exhibited more homogenized flow. Our findings suggest that DSC-PWI combined with lesion delineation, can provide clinically relevant differentiation of MS lesions from unspecific WML, highlighting potential microvascular pathology previously overlooked in MS.

Multi-Site Benchmark Study for Standardized Relative Cerebral Blood Volume in Untreated Brain Metastases Using the DSC-MRI Consensus Acquisition Protocol.

A national consensus recommendation for the collection of DSC (dynamic susceptibility contrast) MRI perfusion data, used to create maps of relative cerebral blood volume (rCBV), has been recently established for primary and metastatic brain tumors. The goal was to reduce inter-site variability and improve ease of comparison across time and sites, fostering widespread use of this informative measure. To translate this goal into practice the prospective collection of consensus DSC-MRI data and characterization of derived rCBV maps in brain metastases is needed. The purpose of this multi-site study was to determine rCBV in untreated brain metastases in comparison to glioblastoma and normal appearing brain using the national consensus protocol. Subjects from three sites with untreated enhancing brain metastases underwent DSC-MRI according to a recommended option that uses a mid-range flip angle, GRE-EPI acquisition and the administration of both a pre-load and 2nd DSC-MRI dose of 0.1 mmol/kg GBCA. Quantitative maps of standardized rCBV (sRCBV) were generated and enhancing lesion ROIs determined from post-contrast T1-weighted images alone or calibrated difference maps, termed delta T1 (dT1) maps. Mean sRCBV for metastases were compared to normal appearing white matter (NAWM) and glioblastoma (GBM) from a previous study. Comparisons were performed using either the Wilcoxon signed-rank test for paired comparisons or the Mann-Whitney nonparametric test for unpaired comparisons. 49 patients with a primary histology of lung (n=25), breast (n=6), squamous cell carcinoma (SCC) (n=1), melanoma (n=5), gastrointestinal (GI) (n=3) and genitourinary (GU) (n=9) were included in comparison to GBM (n=31). The mean sRCBV of all metastases (1.83+/-1.05) were significantly lower (p=0.0009) than mean sRCBV for GBM (2.67±1.34) with both statistically greater (p<0.0001) than NAWM (0.68 +/- 0.18). Histologically distinct metastases are each statistically greater than NAWM (p<0.0001) with lung (p=0.0002) and GU (p=.02) sRCBV being significantly different than GBM sRCBV. 49 patients with a primary histology of lung (n=25), breast (n=6), squamous cell carcinoma (SCC) (n=1), melanoma (n=5), gastrointestinal (GI) (n=3) and genitourinary (GU) (n=9) were included in comparison to GBM (n=31). The mean sRCBV of all metastases (1.83+/-1.05) were significantly lower (p=0.0009) than mean sRCBV for GBM (2.67+1.34) with both statistically greater (p<0.0001) than NAWM (0.68 +/- 0.18). Histologically distinct metastases are each statistically greater than NAWM (p<0.0001) with lung (p=0.0002) and GU (p=.02) sRCBV being significantly different than GBM sRCBV. dT1=delta T1; GBCA=gadolinium-based contrast agent; NAWM=normal appearing white matter; normalized relative cerebral blood volume=nRCBV; relative cerebral blood volume=rCBV; standardized relative cerebral blood volume=sRCBV.

Comparative effectiveness of two abbreviated rectal MRI protocols in assessing tumor response to neoadjuvant chemoradiotherapy in patients with rectal cancer.

The present study aimed to compare the effectiveness of two abbreviated magnetic resonance imaging (MRI) protocols in assessing the response to neoadjuvant chemoradiotherapy (CRT) in patients with rectal cancer. Data from the examinations of 62 patients with rectal cancer who underwent neoadjuvant CRT and standard contrast-enhanced rectal MRI were retrospectively evaluated. Standard contrast-enhanced T2-weighted imaging (T2-WI), post-contrast T1-weighted imaging (T1-WI) and diffusion-weighted imaging (DWI) MRI, as well as two abbreviated protocols derived from these images, namely protocol AB1 (T2-WI and DWI) and protocol AB2 (post-contrast fat-suppressed (FS) T1-WI and DWI), were assessed. Measurements of lesion length and width, lymph node short-axis length, tumor staging, circumferential resection margin (CRM), presence of extramural venous invasion (EMVI), luminal mucin accumulation (MAIN), mucinous response, mesorectal fascia (MRF) involvement, and MRI-based tumor regression grade (mrTRG) were obtained. The reliability and compatibility of the AB1 and AB2 protocols in the evaluation of tumor response were analyzed. The imaging performed according to the AB1 and AB2 protocols revealed significant decreases in lesion length, width and lymph node size after CRT. These protocols also showed reductions in lymph node positivity, CRM, MRF, EMVI.Furthermore, both protocols were found to be reliable in determining lesion length and width. Additionally, compliance was observed between the protocols in determining lymph node size and positivity, CRM involvement, and EMVI after CRT. In conclusion, the use of abbreviated MRI protocols, specifically T2-WI with DWI sequences or post-contrast FS T1-WI with DWI sequences, is effective for evaluating tumor response in patients with rectal cancer following neoadjuvant CRT. The AB protocols examined in this study yielded similar results in terms of lesion length and width, lymph node positivity, CRM involvement, EMVI, MAIN, and MRF involvement.

Demystifying the challenging diagnosis of post-radiation nasopharyngeal necrosis on multimodality imaging.

Post-radiation nasopharyngeal necrosis (PRNN) is a rare but life-threatening condition that often poses a diagnostic challenge in imaging studies owing to its overlapping features with recurrent nasopharyngeal tumours. We herein describe the characteristic imaging appearance of PRNN on post-contrast T1-weighted magnetic resonance imaging, diffusion-weighted imaging (DWI) and fluorodeoxyglucose (FDG)-PET/CT which may provide insights into its pathological findings.

Ventral Slot Surgery for Management of Ganglioneuroma and IVDD in a Cat

Abstract A Shorthair castrated 8-year-old tomcat was brought to a veterinary hospital for slowly progressive onset of tetraplegia developing within last 14 days. The clinical and blood examinations at admission showed no abnormalities, the neurological examination showed severe cervical pain and tetraplegia with lower motor neuron deficits on thoracic limbs and normal withdrawal with increased patellar reflexes on pelvic limbs. Deep pain perception was present on all four limbs. The neuroanatomic localisation was C6-T2 lesion. Magnetic resonance imaging of the cervical spine demonstrated severe spondylosis deformans, degeneration of multiple IVD, and ventral extradural right sided spinal cord compression at the level C7-T1. Mildly hyperintense on T2-weighted and STIR, mildly hypointense on T1-weighted pre-contrast, and homogeneously contrast-enhancing mass on T1-weighted post-contrast images was observed at this area. This article describes an interesting case of a tumor in a cat localised in the cervicothoracic spinal segment extradurally compressing the spinal cord and causing severe neurological dysfunction. These clinical symptoms were successfully treated through a ventral slot surgery, the tumor was well circumscribed and comfortably removable through this approach. This rare tumor is considered benign and prognosis is favourable after surgical removal due to its non-invasive growth pattern. After surgery, the cat was observed continuously for a few months and the recovery was sufficient with no recurrence of neurological symptoms by now. This case is worth attention mainly because, despite the serious clinical presentation, it has a good long term prognosis and does not require a complex surgical approach.

An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma

To evaluate the performance of magnetic resonance imaging (MRI) multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma (HGG) from low-grade glioma (LGG). We retrospectively collected multi-sequence MR images from 305 glioma patients, including 189 HGG patients and 116 LGG patients. The region of interest (ROI) of T1-weighted images (T1WI), T2-weighted images (T2WI), T2 fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI) were delineated to extract the radiomics features. A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data. The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy, balanced accuracy, area under the ROC curve (AUC), specificity, and sensitivity. The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG. Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in twodimensional plane. Convergence experiments were used to verify the feasibility of the model. For differentiation of HGG from LGG with a missing rate of 10%, the proposed model achieved accuracy, balanced accuracy, AUC, specificity, and sensitivity of 0.777, 0.768, 0.826, 0.754 and 0.780, respectively. The fused latent features showed excellent performance in the class separability experiment, and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30% and 50%. The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models, demonstrating its potential for efficient processing of non-holonomic multimodal data.

Predictors of Hydrocephalus Risk After Stereotactic Radiosurgery for Vestibular Schwannomas: Utility of the Evans Index.

Hydrocephalus after Gamma Knife® stereotactic radiosurgery (SRS) for vestibular schwannomas is a rare but manageable occurrence. Most series report post-SRS communicating hydrocephalus in about 1% of patients, thought to be related to a release of proteinaceous substances into the cerebrospinal fluid. While larger tumor size and older patient age have been associated with post-SRS hydrocephalus, the influence of baseline ventricular anatomy on hydrocephalus risk remains poorly defined. A single-institution retrospective cohort study examining patients who developed symptomatic communicating hydrocephalus after undergoing Gamma Knife® SRS for unilateral vestibular schwannomas from 2011 to 2021 was performed. Patients with prior hydrocephalus and cerebrospinal fluid diversion or prior surgical resection were excluded. Baseline tumor volume, third ventricle width, and Evans Index (EI)-maximum width of the frontal horns of the lateral ventricles/maximum internal diameter of the skull-were measured on axial postcontrast T1-weighted magnetic resonance imaging. A total of 378 patients met the inclusion criteria; 14 patients (3.7%) developed symptomatic communicating hydrocephalus and 10 patients (2.6%) underwent shunt placement and 4 patients (1.1%) were observed with milder symptoms. The median age of patients who developed hydrocephalus was 69 years (IQR, 67-72) and for patients younger than age 65 years, the risk was 1%. For tumor volumes <1 cm3, the risk of requiring shunting was 1.2%. The odds of developing symptomatic hydrocephalus were 5.0 and 7.7 times higher in association with a baseline EI > 0.28 (P = .024) and tumor volume >3 cm3 (P = .007), respectively, in multivariate analysis. Fourth ventricle distortion on pre-SRS imaging was significantly associated with hydrocephalus incidence (P < .001). Patients with vestibular schwannoma with higher baseline EI, larger tumor volumes, and fourth ventricle deformation are at increased odds of developing post-SRS hydrocephalus. These patients should be counseled regarding risk of hydrocephalus and carefully monitored after SRS.

Meningioma Detection and Characterization Using Brain MR Imaging: A Case Study

Meningiomas commonly manifest as granulations, dense clusters exhibiting hyperintense signals on the meningeal layers and close to cranial nerves. Detailed and isotropic imaging are key in the diagnostic evaluation. Nevertheless, standard MRI sequences have been inadequate in capturing these granulations, incorporating the T2 3D CISS axial sequence for improved visualization. Our case study aims to meticulously evaluate the diagnostic imaging strategies used for meningioma cases, explicitly focusing on cranial nerve imaging. The MRI protocol encompassed axial, coronal, and sagittal T2_TSE and DWI sequences and post-contrast T1-weighted imaging. A detailed calibration of the 3D CISS sequence parameters was performed to enhance the imaging of cranial nerves affected by the suspected meningioma. Our findings highlight the necessity of a meticulous MRI protocol, which includes precise imaging sequences, to evaluate meningiomas effectively, particularly for assessing the potential involvement of the cranial nerves.

Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer.

Machine learning classifiers are increasingly used to create predictive models for pathological complete response (pCR) in breast cancer after neoadjuvant therapy (NAT). Few studies have compared the effectiveness of different ML classifiers. This study evaluated radiomics models based on pre- and post-contrast first-phase T1 weighted images (T1WI) in predicting breast cancer pCR after NAT and compared the performance of ML classifiers. This retrospective study enrolled 281 patients undergoing NAT from the Duke-Breast-Cancer-MRI dataset. Radiomic features were extracted from pre- and post-contrast first-phase T1WI images. The Synthetic Minority Oversampling Technique (SMOTE) was applied, then the dataset was randomly divided into training and validation groups (7:3). The radiomics model was built using selected optimal features. Support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were classifiers. Receiver operating characteristic curves were used to assess predictive performance. LightGBM performed best in predicting pCR [area under the curve (AUC): 0.823, 95% confidence interval (CI) [0.743-0.902], accuracy 74.0%, sensitivity 85.0%, specificity 67.2%]. During subgroup analysis, RF was most effective in pCR prediction in luminal breast cancers (AUC: 0.914, 95% CI [0.847-0.981], accuracy 87.0%, sensitivity 85.2%, specificity 88.1%). In triple-negative breast cancers, LightGBM performed best (AUC: 0.836, 95% CI [0.708-0.965], accuracy 78.6%, sensitivity 68.2%, specificity 90.0%). The LightGBM-based radiomics model performed best in predicting pCR in patients with breast cancer. RF and LightGBM showed promising results for luminal and triple-negative breast cancers, respectively.