MRI Apparent Diffusion Coefficient Research Articles

Combining morphological and functional imaging parameters to diagnose primary bone neoplasms in the skull base, spine and sacrum.

Morphological magnetic resonance (MR) and computed tomography (CT) features are used in combination with histology for diagnosis and treatment selection of primary bone neoplasms. Isolated functional MRI parameters have shown potential in diagnosis. Our goal is to facilitate diagnosis of primary bone neoplasms of the skull base, mobile spine and sacrum, by a comprehensive approach, combining morphological and functional imaging parameters. Pre-treatment MR of 80 patients with histologically proven diagnosis of a primary bone neoplasm of the skull base, mobile spine and sacrum were retrospectively analyzed for morphological and functional MRI parameters. Functional parameters were measured in 4 circular regions of interest per tumor placed on non-adjacent scan slices. Differences in values of functional parameters between different histologies were analyzed with Dunn's test. Chordomas were the predominant histology (60.0%). Most neoplasms (80.0%) originated in the midline and had geographical (78.2%) bone destruction. Amorphous-type calcification (pre-existing bone) was seen only in chordomas. Homogeneous contrast enhancement pattern was seen only in chondrosarcoma and plasmacytoma. Ktrans and Kep were significantly lower in both chordoma, and chondrosarcoma compared to giant cell tumor of the bone (p = 0.006 - 0.011), and plasmacytoma (p = 0.004 - 0.014). Highest diffusion-weighted MRI apparent diffusion coefficient (ADC) values corresponded to chondrosarcoma and were significantly higher to those of chordoma (p = 0.008). We identified the most discriminating morphological parameters and added functional MR parameters based on histopathological features that are useful in making a confident diagnosis of primary bone neoplasms in the skull base, mobile spine and sacrum.

Evaluation of Thyroid Nodule by US Including Elastography versus MRI T2W and Diffusion Sequences

Abstract Background The thyroid gland frequently develops thyroid nodules. Effective treatment of malignant thyroid nodules depends on an early and accurate diagnosis. Benign and malignant thyroid nodules are typically distinguished using conventional thyroid ultrasonography (US). In order to explore changes in tissue stiffness, strain elastography (SE) uses an external force and a deformation response detector. A promising method for distinguishing between benign and malignant thyroid nodules is ultrasound elastography. Malignant versus benign thyroid nodules might be distinguished using MRI apparent diffusion coefficient (ADC) values. This study compares MRI T2W and diffusion sequences with ultrasound, which includes elastography, to assess thyroid nodules. Methods This cross-sectional research was carried out at Ain-Shams University's radiodiagnosis department. It included 23 female or male patients with suspicious thyroid nodule by clinical examination, or ultrasonography including some cases elastography if present for further work up with MR diffusion subjected to DWI and ADC value and results were compared with histopathology if present or follow up of the patients. The duration of the study was 24 months. Results Diffusion had an accuracy of 91.3%, while the ADC value and elastography had a greater diagnostic accuracy with an area under the curve of 95.7% in discriminating between benign and malignant cases. Conclusions When describing thyroid nodules, ADC value provided better diagnostic performance than US TIRADS rating. Over and above ACR-TIRADS classification, useful information is added by ultrasound elastography and strain ratio analysis of thyroid nodules. It is advised as an auxiliary tool. However, the distinction of cancerous and benign thyroid nodules might potentially be done with greater precision using diffusion weighted MRI.

Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma.

Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.

The Combination of Gd-EOB-DTPA Enhanced T1 Mapping with Apparent Diffusion Coefficient could Improve the Diagnostic Efficacy of Hepatocellular Carcinoma Grading.

Accurately predicting the hepatocellular carcinoma (HCC) grade may facilitate the rational selection of treatment strategies. The diagnostic efficacy of the combination of Gadolinium ethoxybenzy diethylenetriamine pentaacetic (Gd-EOB-DTPA) enhancement T1 mapping and apparent diffusion coefficient (ADC) values in predicting HCC grade needs further validation. This study aimed to assess the capacity of Gd-EOB-DTPA-enhanced T1 mapping and ADC values, both individually and in combination, to discriminate between different grades of HCC. From July 2017 to February 2020, 96 patients (male, 83; mean age, 53.67 years; age range, 29-71 years) clinically diagnosed with HCC were included in the present study. All patients underwent Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI, including T1 mapping sequence) before surgery or biopsy. All the patients were categorized into 3 groups according to the pathological results (including 24 cases of well-differentiated HCCs, 59 cases of moderately differentiated HCCs, 13 cases of and poorly differentiated HCCs). The mean Gd-EOB-DTPA enhanced T1 values (&#916;T1=[(T1pre-T1post)/T1pre]×100%) and ADC values between different grading groups of HCC were calculated and compared. The area under the characteristics curve (AUC), the diagnostic threshold, sensitivity, and specificity of &#916;T1 and ADC for differential diagnosis were analyzed. Mean &#916;T1 was 58% for well-differentiated HCCs, 50% for moderately-differentiated HCCs, and 43% for poorly-differentiated HCCs. &#916;T1 showed statistical differences between the groups (P<0.001). The mean ADC values of the 3 groups were 1.11×10-3 mm2/s, 0.91×10-3 mm2/s, and 0.80×10-3mm2/s, respectively. ADC showed statistical differences between the groups (P<0.001). In discriminating well- differentiated group from the moderately differentiated group, the AUC of &#916;T1 was 0.751 (95% CI: 0.642, 0.859), the AUC of ADC was 0.782 (95% CI: 0.671, 0.894), the AUC of combined model was 0.811 (95% CI: 0.709, 0.914). In discriminating the poorly differentiated group from the moderately differentiated group, the AUC of &#916;T1 was 0.768 (95% CI: 0.634, 0.902), the AUC of ADC was 0.754 (95% CI: 0.603, 0.904), and the AUC of the combined model was 0.841 (95% CI: 0.729, 0.953). Gd-EOB-DTPA enhanced T1 mapping, and ADC values have complementary effects on the sensitivity and specificity for identifying different HCC grades. A combined model of Gd-EOB-DTPA-enhanced MRI T1 mapping and ADC values could improve diagnostic performance for predicting HCC grades.</p>.

Apparent Diffusion Coefficient on Diffusion-Weighted Magnetic Resonance Imaging to Predict the Prognosis of Patients with Endometrial Cancer: A Meta-Analysis.

Apparent diffusion coefficient (ADC) derived from diffusion-weighted magnetic resonance imaging (DWI) may help diagnose endometrial cancer (EC). However, the association between ADC and the recurrence and survival of EC remains unknown. We performed a systematic review and meta-analysis to investigate whether pretreatment ADC on DWI could predict the prognosis of women with EC. PubMed, Embase, and Cochrane's Library were searched for relevant cohort studies comparing the clinical outcomes between women with EC having low versus high ADC on pretreatment DWI. Two authors independently conducted data collection, literature searching, and statistical analysis. Using a heterogeneity-incorporating random-effects model, we analyzed the results. In the meta-analysis, 1358 women with EC were included from eight cohort studies and followed for a median duration of 40 months. Pooled results showed that a low pretreatment ADC on DWI was associated with poor disease-free survival (DFS, hazard ratio [HR]: 3.29, 95% CI: 2.04 to 5.31, p < 0.001; I2 = 41%). Subgroup analysis according to study design, tumor stage, MRI Tesla strength, ADC cutoff, follow-up duration, and study quality score showed consistent results (p for subgroup analysis all > 0.05). The predictive value of low ADC for poor DFS in women with EC decreased in multivariate studies compared to univariate studies (HR: 2.59 versus 32.57, p = 0.002). Further studies showed that a low ADC was also associated with poor overall survival (HR: 3.36, 95% CI: 1.33 to 8.50, p = 0.01, I2 = 0). In conclusion, a low ADC on pretreatment DWI examination may predict disease recurrence and survival in women with EC.

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.

Predicting the Consistency of Pituitary Macroadenomas: The Utility of Diffusion-Weighted Imaging and Apparent Diffusion Coefficient Measurements for Surgical Planning.

Understanding the consistency of pituitary macroadenomas is crucial for neurosurgeons planning surgery. This retrospective study aimed to evaluate the utility of diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) as non-invasive imaging modalities for predicting the consistency of pituitary macroadenomas. This could contribute to appropriate surgical planning and therefore reduce the likelihood of incomplete resections. The study included 45 patients with pathologically confirmed pituitary macroadenomas. Conventional MRI sequences, DWIs, ADC maps, and pre- and post-contrast MRIs were performed. Two neuroradiologists assessed all of the images. Neurosurgeons assessed the consistency of the tumor macroscopically, and histopathologists examined it microscopically. The MRI findings were compared with postoperative data. According to the operative data, macroadenomas were divided into the two following categories based on their consistency: aspirable (n = 27) and non-aspirable tumors (n = 18). A statistically significant difference in DWI findings was found when comparing macroadenomas of different consistencies (p < 0.001). Most aspirable macroadenomas (66.7%) were hyperintense according to DWI and hypointense on ADC maps, whereas most non-aspirable macroadenomas (83.3%) were hypointense for DWI and hyperintense on ADC maps. At a cut-off value of 0.63 × 10-3 mm2/s, the ADC showed a sensitivity of 85.7% and a specificity of 75% for the detection of non-aspirable macroadenomas (AUC, 0.946). The study concluded that DWI should be routinely performed in conjunction with ADC measurements in the preoperative evaluation of pituitary macroadenomas. This approach may aid in surgical planning, ensure that appropriate techniques are utilized, and reduce the risk of incomplete resection.

Role of intravoxel incoherent motion MRI in diagnosis and assessment of invasiveness of urothelial bladder carcinoma

BackgroundUrinary bladder cancer is a common malignancy. Preoperative accurate judgment of the degree of tissue invasion is crucial to determine the plan of therapy. We aimed in the current study to evaluate the role of intravoxel incoherent motion (IVIM) MRI and apparent diffusion coefficient (ADC) map in differentiating muscle invasive from non-muscle invasive carcinomas.ResultsValues of IVIM-derived true diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (F) and ADC were lower in muscle invasive carcinomas than non-invasive masses with p values < 0.001 and 0.001, respectively. In MIBC, ADC was (0.9 × 10−3 mm2/s), D was (0.8 × 10−3 mm2/s), D* was (6.9 × 10−3 mm2/s) and F was (16.9%). In NMIBC, ADC was (1 × 10−3 mm2/s), D was (1.1 × 10−3 mm2/s), D* was (10.2 × 10−3 mm2/s) and F was (17.7%). Mass D cut off values, sensitivity and specificity and AUC were < 0.9, 83.93, 96.30 and 0.945, respectively. Mass ADC cut off values, sensitivity and specificity and AUC were < 1.09, 87.50, 51.85 and 0.721, respectively. The degree of agreement between IVIM parameters and the pathological staging was assessed by Kappa test. The best agreement was detected for mass D (K = 0.796), while the mass ADC and wall beside mass D showed moderate and fair agreement (k = 0.418 and 0.325, respectively).ConclusionsIVIM MRI parameters and ADC map can be utilized to detect the degree of tissue invasion caused by bladder cancer.

Comparing Texture Analysis of Apparent Diffusion Coefficient MRI in Hepatocellular Adenoma and Hepatocellular Carcinoma.

This study aimed to assess the effectiveness of using MRI-apparent diffusion coefficient (ADC) map-driven radiomics to differentiate between hepatocellular adenoma (HCA) and hepatocellular carcinoma (HCC) features. The study involved 55 patients with liver tumors (20 with HCA and 35 with HCC), featuring 106 lesions equally distributed between hepatic carcinoma and hepatic adenoma who underwent texture analysis on ADC map MR images. The analysis identified several imaging features that significantly differed between the HCA and HCC groups. Four classification models were compared for distinguishing HCA from HCC including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), random forest (RF), and k-nearest neighbor (KNN). The k-nearest neighbor (KNN) classifierdisplayed the top accuracy (0.89) and specificity (0.90). Linear-SVM and KNN classifiers showcased the leading sensitivity (0.88) for both, with the KNN classifier achieving the highest precision (0.9). In comparison, the conventional interpretation had lower sensitivity (70.1%) and specificity (77.9%). The study found that utilizing ADC maps for texture analysis in MR images is a viable method to differentiate HCA from HCC, yielding promising results in identified texture features.

Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity

Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO≥75%pred and DLCO<75%pred). In this retrospective study, 71 ex-smokers (50-85yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure. Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p<.001), residual-volume to total-lung-capacity ratio (p=.003), St. George's Respiratory Questionnaire (p=.029), and six-minute-walk distance (6MWD) (p<.001), but similar relative area of the lung <-950 Hounsfield-units (RA950) (p=.9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC=0.81), which correlated with DLCO (ρ=-0.29, p=.02), MRI ADC (ρ=0.23, p=.048), and 6MWD (ρ=-0.25, p=.02). In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements.

The value of an apparent diffusion coefficient histogram model in predicting meningioma recurrence.

To investigate the predictive value of a model combining conventional MRI features and apparent diffusion coefficient (ADC) histogram parameters for meningioma recurrence. Seventy-two meningioma patients confirmed by surgical and pathological findings in our hospital (January 2017-June 2020) were retrospectively and divided into the recurrence and non-recurrence group. MaZda software was used to delineate the region of interest at the largest tumor level and generate histogram parameters. Univariate and multivariate logistic regression analysis were used to construct the nomogram for predicting recurrence. The predictive efficacy and diagnostic of this model were assessed by calibration and decision curveanalysis, and receiver operating characteristic curve, respectively. Maximum diameter, necrosis, enhancement uniformity, age, Simpson, tumor shape, and ADC first percentile (ADCp1) were significantly different between the two groups (p < 0.05), with the latter four being independent risk factors for recurrence. The model constructed combining the four factors had the best predictive efficacy, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.965(0.892-0.994), 90.3%, 92.6%, 88.9%, 83.3%, and 95.2%, respectively. The calibration curve showed good agreement between the model-predicted and actual probabilities of recurrence. The decision curveanalysis indicated good clinical availability of the model. This model based on conventional MRI features and ADC histogram parameters can directly and reliably predict meningioma recurrence, providing a guiding basis for selecting treatment options and individualized treatment.

Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response

BackgroundTo evaluate the correlation between synthetic MRI (syMRI) relaxometry and apparent diffusion coefficient (ADC) maps in different breast cancer subtypes and treatment response subgroups.MethodsTwo hundred sixty-three neoadjuvant therapy (NAT)-treated breast cancer patients with baseline MRI were enrolled. Tumor annotations were obtained by drawing regions of interest (ROIs) along the lesion on T1/T2/PD and ADC maps respectively. Histogram features from T1/T2/PD and ADC maps were respectively calculated, and the correlation between each pair of identical features was analyzed. Meanwhile, features between different NAT treatment response groups were compared, and their discriminatory power was evaluated.ResultsAmong all patients, 20 out of 27 pairs of features weakly correlated (r = – 0.13–0.30). For triple-negative breast cancer (TNBC), features from PD map in the pathological complete response (pCR) group (r = 0.60–0.86) showed higher correlation with ADC than that of the non-pCR group (r = 0.30–0.43), and the mean from the ADC and PD maps in the pCR group strongly correlated (r = 0.86). For HER2-positive, few correlations were found both in the pCR and non-pCR groups. For luminal HER2-negative, T2 map correlated more with ADC than T1 and PD maps. Significant differences were seen in T2 low percentiles and median in the luminal-HER2 negative subtype, yielding moderate AUCs (0.68/0.72/0.71).ConclusionsThe relationship between ADC and PD maps in TNBC may indicate different NAT responses. The no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.Critical relevance statementThe relationship between ADC and PD maps in TNBC may indicate different NAT responses, and the no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.Key points• The relationship between ADC and PD in TNBC indicates different NAT responses.• The no-to-weak correlations between ADC and syMRI complementarily evaluate tumor microenvironment.• T2 low percentiles and median predict NAT response in luminal-HER2-negative subtype.Graphical

P13.06.A RADIOMIC FEATURES FOR RISK-STRATIFICATION IN PATIENTS WITH BRAIN METASTASES

Abstract BACKGROUND Recently, non-invasive characterization of brain tumors on MRI has emerged as a promising field of research. The identification of quantitative imaging biomarkers, also known as radiomics, may complement molecular characterization and thereby improve clinical management of neuro-oncologic patients. In this study we aimed to identify imaging predictors with improved performance over clinical parameters in order to stratify patients with brain metastases into high and low risk groups for overall survival (OS). MATERIAL AND METHODS 422 patients (allocated in a 3:1 ratio to a discovery [n=317] and test [n= 105] set) with first diagnosis of brain metastases from different primary tumors from two neuro-oncologic centers were included. In each patient, eight clinical features (age, gender, KPS, systemic disease status, presence of extracranial metastases, number of cerebral metastases, primary tumor and available individual prognostic molecular status eg HER2, BRAF,⋯) were gathered and a total of 321 radiomic MRI features (including shape, first-order and higher-order features) from cerebral MRI (contrast T1-weighted and apparent diffusion coefficient maps) were extracted. Radiomic and clinical features of patients in the discovery set were subjected to different machine learning models in order to classify patients into low- and high-risk groups for OS. By performing a comprehensive grid search the best machine learning model according to the macro-evaluated score and accuracy was identified and evaluated on the testing set. In addition, a subgroup analysis, based on the primary tumor entity was done and confusion matrices were calculated in order to evaluate final predictions. RESULTS With an extra trees classifier including all clinical and 30 radiomic features, we were able to stratify patients into a high- and low-risk group for OS (test set: macro-evaluated = 0.60, accuracy = 0.67). The best performing model was a gradient boosting model only including clinical features (test set: macro-evaluated = 0.62, accuracy = 0.72), while the radiomic features alone led to the poorest results (test set: macro-evaluated = 0.52, accuracy = 0.63). Interestingly, in the subgroup of melanoma patients, the predictive power of radiomic features outperformed the clinical and the combined (radiomics and clinical) features. With a prediction solely based on radiomic features, 80% and 67% of patients were correctly classified into the low- and the high-risk group for OS, respectively. CONCLUSION In conclusion, we found that in the entire study population of patients with brain metastases radiomic features did not allow for a better prediction of the clinical outcome compared to the clinical parameters alone. However, in the subgroup of melanoma patients the predictive power of radiomic features alone was superior compared to clinical features or all features combined.

Differentiating sinonasal malignant melanoma from squamous cell carcinoma using DWI combined with conventional MRI.

This study aimed to investigate the feasibility of diffusion-weighted imaging (DWI) in combination with conventional MRI features to differentiate sinonasal malignant melanoma (SNMM) from sinonasal squamous cell carcinoma (SNSCC). A total of 37 patients with SNMM and 44 patients with SNSCC were retrospectively reviewed. Conventional MRI features and apparent diffusion coefficients (ADCs) were evaluated independently by two experienced head and neck radiologists. ADCs were obtained from two different regions of interest (ROIs) including maximum slice (MS) and small solid sample (SSS). Multivariate logistic regression analysis was performed to identify significant MR imaging features in discriminating between SNMM and SNSCC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance. SNMMs were more frequently located in the nasal cavity, with well-defined border, T1 Septate Pattern (T1-SP) and heterogeneous T1 hyperintensity, whereas SNSCCs were more frequently located in the paranasal sinus, with homogenous T1 isointensity, ill-defined border, reticular or linear T2 hyperintensity, and pterygopalatine fossa or orbital involvement (all p < 0.05). The mean ADCs of SNMM (MS ADC, 0.85 × 10-3mm2/s; SSS ADC, 0.69 × 10-3mm2/s) were significantly lower than those of SNSCC (MS ADC, 1.05 × 10-3mm2/s; SSS ADC, 0.82 × 10-3mm2/s) (p < 0.05). With a combination of location, T1 signal intensity, reticular or linear T2 hyperintensity, and a cut-off MS ADC of 0.87 × 10-3mm2/s, the sensitivity, specificity, and AUC were 97.3%, 68.2%, and 0.89, respectively. DWI combined with conventional MRI can effectively improve the diagnostic performance in differentiating SNMM from SNSCC.

Combined MRI Ischemic Burden and Early EEG Background Improve Outcome Prediction After Pediatric Cardiac Arrest (S4.001)

<h3>Objective:</h3> To assess the association of EEG background with MRI ischemic burden after pediatric cardiac arrest (CA) and determine whether addition of MRI ischemic burden to EEG and clinical variables improves outcome prediction. <h3>Background:</h3> The association of early EEG background with MRI ischemic burden and their combined association with pediatric CA outcome is unclear. <h3>Design/Methods:</h3> Retrospective single-center cohort study of pediatric CA from 2005–2019. Initial EEG background within 24 hours of CA was classified as normal, slow/disorganized, discontinuous/burst-suppression, or attenuated/featureless. MRI within 7 days of CA was assessed for ischemic burden, defined as brain tissue percentage with apparent diffusion coefficient (ADC)&lt;650×10⁻⁶mm²/s. Outcomes were unfavorable neurologic status (Pediatric Cerebral Performance Category change ≥1 from baseline resulting in hospital discharge score ≥3) and death. Kruskal-Wallis test evaluated association of EEG with MRI. Logistic regression and likelihood ratio test assessed outcome prediction. Area under the receiver operating curve (AUROC) evaluated predictive accuracy. <h3>Results:</h3> We evaluated 90 children with median age 1.6(IQR:0.6–5.8) years. EEG background was normal in 16(18%), slow/disorganized in 42(47%), discontinuous/burst-suppression in 12(13%), and attenuated/featureless in 20(22%). Median percent of ischemic brain tissue was 5%(IQR:1–18%). Unfavorable neurologic status occurred in 58(64%), of whom 28(31%) died. Worse EEG background was associated with greater brain ischemia (χ²=27.9;p&lt;0.001). A model of EEG background, number of epinephrine doses, post-arrest lactate and witnessed status yielded an AUROC of 0.90 for unfavorable neurologic status and 0.92 for death. Addition of MRI to this model significantly increased the AUROC from 0.90 to 0.92 for unfavorable neurologic status (χ²=4.8;p=0.03) and from 0.92 to 0.97 for death (χ²=19.1;p&lt;0.001). <h3>Conclusions:</h3> Early EEG background was associated with increased MRI ischemic burden within 7 days of cardiac arrest. Addition of MRI ADC to clinical factors and EEG background improved outcome prediction with high predictive accuracy. MRI and EEG, though associated, independently contributed to a multimodal model of outcome. <b>Disclosure:</b> Dr. Bach has nothing to disclose. The institution of Dr. Kirschen has received research support from NIH. Dr. Fung has nothing to disclose. Dr. Abend has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Epilepsy Foundation. The institution of Dr. Abend has received research support from NIH. The institution of Dr. Abend has received research support from PCORI. Dr. Abend has received publishing royalties from a publication relating to health care. Miss Mondal has nothing to disclose. Dr. Ampah has nothing to disclose. Dr. HUH has nothing to disclose. Dr. Chen has nothing to disclose. The institution of Dr. Yuan has received research support from Masimo Corp. Ms. Graham has nothing to disclose. Dr. Berman has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for McGowan Associates. Dr. Berman has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for McGowan Associates. Dr. Vossough has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Syneos Health. Dr. Vossough has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for DeepSight. Dr. Vossough has received publishing royalties from a publication relating to health care. Alexis Topjian has received personal compensation in the range of $5,000-$9,999 for serving as an Expert Witness for plantiff and defense.

Evaluating drug distribution in children and young adults with diffuse midline glioma of the pons (DIPG) treated with convection-enhanced drug delivery.

To determine an imaging protocol that can be used to assess the distribution of infusate in children with DIPG treated with CED. 13 children diagnosed with DIPG received between 3.8 and 5.7 ml of infusate, through two pairs of catheters to encompass tumor volume on day 1 of cycle one of treatment. Volumetric T2-weighted (T2W) and diffusion-weighted MRI imaging (DWI) were performed before and after day 1 of CED. Apparent diffusion coefficient (ADC) maps were calculated. The tumor volume pre and post CED was automatically segmented on T2W and ADC on the basis of signal intensity. The ADC maps pre and post infusion were aligned and subtracted to visualize the infusate distribution. There was a significant increase (p < 0.001) in mean ADC and T2W signal intensity (SI) ratio and a significant (p < 0.001) increase in mean tumor volume defined by ADC and T2W SI post infusion (mean ADC volume pre: 19.8 ml, post: 24.4 ml; mean T2W volume pre: 19.4 ml, post: 23.4 ml). A significant correlation (p < 0.001) between infusate volume and difference in ADC/T2W SI defined tumor volume was observed (ADC, r = 0.76; T2W, r = 0.70). Finally, pixel-by-pixel subtraction of the ADC maps pre and post infusion demonstrated a volume of high signal intensity, presumed infusate distribution. ADC and T2W MRI are proposed as a combined parameter method for evaluation of CED infusate distribution in brainstem tumors in future clinical trials.

Development and validation of apparent diffusion coefficient histogram-based nomogram for predicting malignant transformation of sinonasal inverted papilloma.

To develop and validate a nomogram based on whole-tumour histograms of apparent diffusion coefficient (ADC) maps for predicting malignant transformation (MT) in sinonasal inverted papilloma (IP). This retrospective study included 209 sinonasal IPs with and without MT, which were assigned into a primary cohort (n = 140) and a validation cohort (n = 69). Eight ADC histogram features were extracted from the whole-tumour region of interest. Morphological MRI features and ADC histogram parameters were compared between the two groups (with and without MT). Stepwise logistic regression was used to identify independent predictors and to construct models. The predictive performances of variables and models were assessed using the area under the curve (AUC). The optimal model was presented as a nomogram, and its calibration was assessed. Four morphological features and seven ADC histogram parameters showed significant differences between the two groups in both cohorts (all p < 0.05). Maximum diameter, loss of convoluted cerebriform pattern, ADC10th and ADCSkewness were identified as independent predictors to construct the nomogram. The nomogram showed significantly better performance than the morphological model in both the primary (AUC, 0.96 vs 0.88; p = 0.006) and validation (AUC, 0.96 vs 0.88; p = 0.015) cohorts. The nomogram showed good calibration in both cohorts. Decision curve analysis demonstrated that the nomogram is clinically useful. The developed nomogram, which incorporates morphological MRI features and ADC histogram parameters, can be conveniently used to facilitate the pre-operative prediction of MT in IPs.

Optic nerve head diffusion-weighted magnetic resonance imaging and apparent diffusion coefficient values as measuring tools for papilledema grade in idiopathic intracranial hypertension

Aim The aim of this study was to explore if there is a relationship between the grade of papilledema and MRI diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values of the optic nerve head (ONH) in patients having idiopathic intracranial hypertension (IIH). Patients and methods This is a prospective study that was conducted between March 2020 and June 2022 on 285 patients diagnosed as having IIH, who were subjected to meticulous clinical examination, visual field testing, color vision testing, and orbital MRI done within one week of diagnosis. ONH signal intensity was graded and then compared with papilledema grade, and the results were analyzed. Results The study included 285 patients (156 females and 129 males), with a mean age of 30.8±9.6 years, definitely diagnosed as having IIH with lumbar puncture opening pressure ranging from 18 to 58 cmH2O (mean=35.6±9.8 cmH2O). The papilledema grades were grade I (121 patients), grade II (136 patients), grade III (21 patients), and grade IV (seven patients). ADC values of the ONH were found to be gradually decreased with higher papilledema grades among cases. In addition, according to papilledema grading, in grade I, 67% of the patients showed no DWI changes, whereas in grade II, 97% of the patients showed DWI changes in the ONH. In grades III and IV, all patients showed DWI changes. Conclusion MRI can be considered as a diagnostic tool for papilledema. In addition, low ADC values and diffusion restriction can be used as indicators for the severity of ONH insult in cases of increased intracranial tension.

Dynamic contrast-enhanced MRI parameters and apparent diffusion coefficient as treatment response markers of skull base osteomyelitis: a preliminary study.

Currently, there is no definitive consensus on the optimal imaging modality for determining the treatment response in patients with skull base osteomyelitis (SBO). This study aimed to investigate the utility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and apparent diffusion coefficient (ADC) as treatment response markers of SBO. This study included 6 patients with SBO, who underwent both pre- and post-treatment DCE-MRI and diffusion-weighted imaging (DWI). Quantitative DCE-MRI parameters and ADC of the region-of-interest were analysed. These normalized parameters were calculated by dividing the region-of-interest by the reference region. The Wilcoxon signed rank test was used to compare these parameters between pre- and post-treatment periods. The normalized fraction of the extravascular extracellular space (Ve) and ADC of the post-treatment status of SBO was significantly lower than those of pre-treatment measures (p = 0.03). The normalized fraction of blood plasma (Vp), normalized rate of transfer from the blood plasma into the extravascular extracellular space (Ktrans), and normalized backflow leakage of material from the extravascular extracellular space into the blood plasma (Kep) demonstrated no significant differences between pre- and post-treatment. DCE-MRI parameters Ve and ADC demonstrated a significant reduction when comparing measures across the pre- and post-treatment periods. These parameters may potentially serve as a valuable surrogate treatment response marker for SBO activity.

MRI comparative study of diffuse midline glioma, H3 K27-altered and glioma in the midline without H3 K27-altered

PurposeThe MRI features of Diffuse midline glioma, H3 K27-altered and glioma in the midline without H3 K27-altered were compared and analyzed, and the changes in the apparent diffusion coefficient (ADC) of the two groups were quantitatively analyzed.MethodsThe MRI images of 35 patients with Diffuse midline gliomas, H3 K27-altered and gliomas in the midline without H3 K27-altered were analyzed retrospectively. The location, edge, signal, peritumoral edema and enhancement characteristics of the lesions were observed, and the changes in ADC values were analyzed.ResultsIn the H3 K27-altered group, 85.7% (12/14) of the tumors were located in the thalamus and brainstem compared with 28.6% (6/21) in the no H3 K27-altered group. In the H3 K27-altered group, for tumors only located in the midline area, only 14.3% (1/7) had irregular shapes and unclear boundaries, while for tumors also invaded the extramidline tissues 85.7% (6/7) had irregular shapes and unclear boundaries.The"basilar artery wrapped sign” was found in 6 patients with tumors located in the pons in the H3 K27-altered group, but none in the no H3 K27-altered group had this sign. In the H3 K27-altered group, only 14.3% (1/7) of the tumors confined to the midline area had small cystic degeneration and necrosis, while for tumors also invaded the extramidline tissues, 100% (7/7) of the tumors had cystic degeneration and necrosis, and the cystic degeneration and necrosis only located in the extramidline region of the tumor in 6 cases.A total of 78.6% (11/14) of tumors in the H3 K27-altered group showed mild to moderate enhancement, while 47.6% (10/21) of tumors in the no H3 K27-altered group showed mild to moderate enhancement. The average peritumoral edema index was 1.13 in the H3 K27-altered group and 1.75 in the no H3 K27-altered group. The average ADC value of tumor in the H3 K27-altered group was 7.83 × 10− 4 mm2/s, and the ratio to normal brain tissue was 0.844, while the values in the no H3 K27-altered group were 13.5 × 10− 4 mm2/s and 1.75, respectively.ConclusionCompared with gliomas in the midline without H3 K27-altered, The MRI findings and ADC value of Diffuse midline gliomas, H3K27-altered have some characteristics, which can help improve the diagnosis and differential diagnosis.