Ventral Anterior Cingulate Atrophy as a Predisposing Factor for Transient Global Amnesia.

Background and Purpose: This study aimed to evaluate the brain magnetic resonance imaging (MRI) of patients with acute transient global amnesia (TGA) using volumetric analysis to verify whether the brains of TGA patients have pre-existing structural abnormalities. Methods: We evaluated the brain MRI data from 87 TGA patients and 20 age- and sex-matched control subjects. We included brain MRIs obtained from TGA patients within 72 hours of symptom onset to verify the pre-existence of structural change. For voxel-based morphometric analyses, statistical parametric mapping was employed to analyze the structural differences between patients with TGA and control subjects. Results: TGA patients exhibited significant volume reductions in the bilateral ventral anterior cingulate cortices (corrected p<0.05). Conclusions: TGA patients might have pre-existing structural changes in bilateral ventral anterior cingulate cortices prior to TGA attacks.

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

Primary Intracranial Ewing Sarcoma With EWSR1-FLI1 Gene Translocation Mimicking a Meningioma and a Multidisciplinary Therapeutic Approach: A Case Report and Systematic Review of Literatures.

Ewing sarcoma and peripheral primitive neuroectodermal tumor (ES/pPNET) is an undifferentiated malignant tumor that is most prevalent in children and young adults and often radiologically mimics a meningioma. A 38-year-old female patient visited our hospital with complaints of right-sided tinnitus, right hemiparesis, and imbalance. She underwent preoperative imaging and was subsequently diagnosed as having a meningioma on the petrous ridge. After partial resection, EWSR1-FLI1 gene fusion was confirmed, and she was diagnosed with ES/pPNET. The tumor was successfully treated using a multidisciplinary approach of adjuvant chemo- and radiotherapy. This case is noteworthy because it is an extremely rare case of an intracranial ES/pPNET, and it is worth sharing our clinical experience that the tumor was successfully treated through a multidisciplinary therapeutic approach even though complete resection was not achieved.

Glymphatic function assessment in Parkinson's disease using diffusion tensor image analysis along the perivascular space.

INTRODUCTION: Glymphatic dysfunction can contribute to α-synucleinopathies. We examined glymphatic function in idiopathic Parkinson's disease (PD) utilizing Diffusion Tensor Image Analysis aLong the Perivascular Space (DTI-ALPS). METHODS: This study enrolled consecutive patients diagnosed with de novo PD between June 2017 and March 2019 who underwent brain DTI with concurrent 123I-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (123I-FP-CIT) SPECT, and age- and sex-matched controls. From DTI-ALPS, the ALPS-index was calculated as a ratio of diffusivities along the x-axis in the region of neural fibers passing vertically to the diffusivities perpendicular to them, which reflected perivascular water motion at the lateral ventricular body level. The ALPS-index of the PD and control groups was compared using Student's t-test; its correlations with clinical scores for motor and cognition (UPDRS-III, MMSE, and MoCA) and striatal dopamine transporter uptake measured by 123I-FP-CIT specific binding ratios (SBRs) were examined using a correlation coefficient. RESULTS: In all, 54 patients in the de novo PD group (31 women, 23 men; mean age, 68.9 ± 9.4 years) and 54 in the control group (mean age, 69.0 ± 10.5 years) were included. The ALPS-index was lower in the PD group than in the controls (1.51 ± 0.22 versus 1.66 ± 0.20; P < 0.001). In the PD group, the ALPS-index negatively correlated with the UPDRS-III score (r = -0.526), and positively correlated with the MMSE (r = 0.377) and MoCA scores (r = 0.382) (all, P < 0.05). No correlation was observed between the ALPS-index and striatal 123I-FP-CIT SBRs (P > 0.05). CONCLUSIONS: DTI-ALPS can reveal glymphatic dysfunction in patients with PD, whose severity correlated with motor and cognitive dysfunction, but not striatal dopamine transporter uptake.

A Nationwide Web-Based Survey of Neuroradiologists' Perceptions of Artificial Intelligence Software for Neuro-Applications in Korea.

OBJECTIVE: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. MATERIALS AND METHODS: In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. RESULTS: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. CONCLUSION: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.

Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake.

PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using 123I-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (123I-FP-CIT) single-photon emission computerized tomography (SPECT) can evaluate Parkinsonism. Nigral hyperintensity from nigrosome-1 and striatal dopamine transporter uptake are reduced in Parkinsonism; however, quantification is only possible with SPECT. Here, we aimed to develop a deep-learning-based regressor model that can predict striatal 123I-FP-CIT uptake on nigrosome magnetic resonance imaging (MRI) as a biomarker for Parkinsonism. METHODS: Between February 2017 and December 2018, participants who underwent 3 T brain MRI including SWI and 123I-FP-CIT SPECT based on suspected Parkinsonism were included. Two neuroradiologists evaluated the nigral hyperintensity and annotated the centroids of nigrosome-1 structures. We used a convolutional neural network-based regression model to predict striatal specific binding ratios (SBRs) measured via SPECT using the cropped nigrosome images. The correlation between measured and predicted SBRs was evaluated. RESULTS: We included 367 participants (203 women (55.3%); age, 69.0 ± 9.2 [range, 39-88] years). Random data from 293 participants (80%) were used for training. In the test set (74 participants [20%]), the measured and predicted 123I-FP-CIT SBRs were significantly lower with the loss of nigral hyperintensity (2.31 ± 0.85 vs. 2.44 ± 0.90) than with intact nigral hyperintensity (4.16 ± 1.24 vs. 4.21 ± 1.35, P < 0.01). The sorted measured 123I-FP-CIT SBRs and the corresponding predicted values were significantly and positively correlated (ρc = 0.7443; 95% confidence interval, 0.6216-0.8314; P < 0.01). CONCLUSION: A deep learning-based regressor model effectively predicted striatal 123I-FP-CIT SBRs based on nigrosome MRI with high correlation using manually-measured values, enabling nigrosome MRI as a biomarker for nigrostriatal dopaminergic degeneration in Parkinsonism.

Altered Brain Glymphatic Flow at Diffusion-Tensor MRI in Rapid Eye Movement Sleep Behavior Disorder.

Background Brain glymphatic dysfunction may contribute to the development of α-synucleinopathies. Yet, noninvasive imaging and quantification remain lacking. Purpose To examine glymphatic function of the brain in isolated rapid eye movement sleep behavior disorder (RBD) and its relevance to phenoconversion with use of diffusion-tensor imaging (DTI) analysis along the perivascular space (ALPS). Materials and Methods This prospective study included consecutive participants diagnosed with RBD, age- and sex-matched control participants, and participants with Parkinson disease (PD) who were enrolled and examined between May 2017 and April 2020. All study participants underwent 3.0-T brain MRI including DTI, susceptibility-weighted and susceptibility map-weighted imaging, and/or dopamine transporter imaging using iodine 123-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane SPECT at the time of participation. Phenoconversion status to α-synucleinopathies was unknown at the time of MRI. Participants were regularly followed up and monitored for any signs of α-synucleinopathies. The ALPS index reflecting glymphatic activity was calculated by a ratio of the diffusivities along the x-axis in the projection and association neural fibers to the diffusivities perpendicular to them and compared according to the groups with use of the Kruskal-Wallis and Mann-Whitney U tests. The phenoconversion risk in participants with RBD was evaluated according to the ALPS index with use of a Cox proportional hazards model. Results Twenty participants diagnosed with RBD (12 men; median age, 73 years [IQR, 66-76 years]), 20 control participants, and 20 participants with PD were included. The median ALPS index was lower in the group with RBD versus controls (1.53 vs 1.72; P = .001) but showed no evidence of a difference compared with the group with PD (1.49; P = .68). The conversion risk decreased with an increasing ALPS index (hazard ratio, 0.57 per 0.1 increase in the ALPS index [95% CI: 0.35, 0.93]; P = .03). Conclusion DTI-ALPS in RBD demonstrated a more severe reduction of glymphatic activity in individuals with phenoconversion to α-synucleinopathies. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Filippi and Balestrino in this issue.

Fully Automated Segmentation Models of Supratentorial Meningiomas Assisted by Inclusion of Normal Brain Images.

To train an automatic brain tumor segmentation model, a large amount of data is required. In this paper, we proposed a strategy to overcome the limited amount of clinically collected magnetic resonance image (MRI) data regarding meningiomas by pre-training a model using a larger public dataset of MRIs of gliomas and augmenting our meningioma training set with normal brain MRIs. Pre-operative MRIs of 91 meningioma patients (171 MRIs) and 10 non-meningioma patients (normal brains) were collected between 2016 and 2019. Three-dimensional (3D) U-Net was used as the base architecture. The model was pre-trained with BraTS 2019 data, then fine-tuned with our datasets consisting of 154 meningioma MRIs and 10 normal brain MRIs. To increase the utility of the normal brain MRIs, a novel balanced Dice loss (BDL) function was used instead of the conventional soft Dice loss function. The model performance was evaluated using the Dice scores across the remaining 17 meningioma MRIs. The segmentation performance of the model was sequentially improved via the pre-training and inclusion of normal brain images. The Dice scores improved from 0.72 to 0.76 when the model was pre-trained. The inclusion of normal brain MRIs to fine-tune the model improved the Dice score; it increased to 0.79. When employing BDL as the loss function, the Dice score reached 0.84. The proposed learning strategy for U-net showed potential for use in segmenting meningioma lesions.

[MRI Findings in Parkinson's Disease: Radiologic Assessment of Nigrostriatal Degeneration]. / 파킨슨병의 ìžê¸°ê³µëª ì˜ìƒ 소견: í‘ì§ˆì„ ì¡°ì²´ 변성의 ì˜ìƒí•™ì  평가.

Parkinson's disease (PD) is a movement disorder that develops due to degenerative loss of dopaminergic cells in the substantia nigra of the midbrain. Recent advances in MRI techniques have demonstrated various imaging findings that can reflect the underlying pathophysiological processes occurring in Parkinson's disease. Many imaging studies have shown that such findings can assist in the diagnosis of Parkinson's disease and its differentiation from atypical parkinsonism. In this review, we present MRI techniques that can be used in clinical assessment, such as nigrosome imaging and neuromelanin imaging, and we provide the detailed imaging features of Parkinson's disease reflecting nigrostriatal degeneration.

Volumetric changes in gray matter after radiotherapy detected with longitudinal magnetic resonance imaging in glioma patients.

BACKGROUND AND PURPOSE: We evaluated volumetric changes in the gray matter (GM) after radiotherapy (RT) and identified factors that were strongly associated with GM volume reduction. MATERIALS AND METHODS: A total of 461 magnetic resonance imagings (MRI) from 105 glioma patients treated with postoperative RT was retrospectively analyzed. Study patients' MRIs were collected at five time points: before RT and 1 month, 6 months, 1 year, and 2 years after RT. Using the 'FastSurfer' platform, a deep learning-based neuroimaging pipeline, 73 regions were automatically segmented from longitudinal MRIs and their volumetric changes were calculated. Regions were grouped into 10 functional fields. A multivariable linear mixed-effects model was established to identify the potential predictors of significant volume reduction. RESULTS: The median age was 50 years (range, 16-86 years). Forty-seven (44.8 %) patients were female and 68 (64.8 %) had glioblastoma. Postoperative RT was delivered at 54-60 Gy with or without concurrent chemotherapy. At 2 years after RT, the median volumetric changes in the overall, ipsilateral, and contralateral GM were -3.5%, -4.5%, and -2.4%, respectively. The functional fields of cognition and execution of movement showed the greatest volume reductions. In the multivariable linear mixed model, female sex (normalized coefficient = -0.14, P < 0.001) and the interaction between age at RT and days after RT (normalized coefficient = -6.48e-6, P < 0.001) were significantly associated with GM reduction. The older patients received RT, the greater volume reduction was seen over time. However, in patients with relatively younger age (e.g., 45, 50, and 60 years for hippocampus, Broca area, and Wernicke area, respectively), the volume was not significantly reduced. CONCLUSIONS: GM volume reduction was identified after RT that could lead to long-term treatment sequelae. Particularly for susceptible patients, individualized treatment and prevention strategies are needed.

Predictive accuracy of T2-FLAIR mismatch sign for the IDH-mutant, 1p/19q noncodeleted low-grade glioma: An updated systematic review and meta-analysis.

BACKGROUND: The T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, has been considered a highly specific imaging biomarker of IDH-mutant, 1p/19q noncodeleted low-grade glioma. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of T2-FLAIR mismatch sign for prediction of a patient with IDH-mutant, 1p/19q noncodeleted low-grade glioma, and identify the causes responsible for the heterogeneity across the included studies. METHODS: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before November 17, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Meta-regression analyses were also performed to determine factors influencing heterogeneity. RESULTS: For all the 10 included cohorts from 8 studies, the pooled sensitivity was 40% (95% confidence interval [CI] 28-53%), and the pooled specificity was 100% (95% CI 95-100%). In the hierarchic summary receiver operating characteristic curve, the difference between the 95% confidence and prediction regions was relatively large, indicating heterogeneity among the studies. Higgins I2 statistics demonstrated considerable heterogeneity in sensitivity (I2 = 83.5%) and considerable heterogeneity in specificity (I2 = 95.83%). Among the potential covariates, it seemed that none of factors was significantly associated with study heterogeneity in the joint model. However, the specificity was increased in studies with all the factors based on the differences in the composition of the detailed tumors. CONCLUSIONS: The T2-FLAIR mismatch sign is near-perfect specific marker of IDH mutation and 1p/19q noncodeletion.

Levetiracetam as a sensitizer of concurrent chemoradiotherapy in newly diagnosed glioblastoma: An open-label phase 2 study.

BACKGROUND: An open-label single-arm phase 2 study was conducted to evaluate the role of levetiracetam as a sensitizer of concurrent chemoradiotherapy (CCRT) for patients with newly diagnosed glioblastoma. This study aimed to determine the survival benefit of levetiracetam in conjunction with the standard treatment for glioblastoma. METHODS: Major eligibility requirements included histologically proven glioblastoma in the supratentorial region, patients 18 years or older, and Eastern Cooperative Oncology Group (ECOG) performance status of 0-2. Levetiracetam was given at 1,000-2,000 mg daily in two divided doses during CCRT and adjuvant chemotherapy thereafter. The primary and the secondary endpoints were 6-month progression-free survival (6mo-PFS) and 24-month overall survival (24mo-OS), respectively. Outcomes of the study group were compared to those of an external control group. RESULTS: Between July 2016 and January 2019, 76 patients were enrolled, and 73 patients were included in the final analysis. The primary and secondary outcomes were improved in the study population compared to the external control (6mo-PFS, 84.9% vs. 72.3%, p = 0.038; 24mo-OS, 58.0% vs. 39.9%, p = 0.018), but the differences were less prominent in a propensity score-matched analysis (6mo-PFS, 88.0% vs. 76.9%, p = 0.071; 24mo-OS, 57.1% vs. 38.8%, p = 0.054). In exploratory subgroup analyses, some results suggested that patients with ages under 65 years or unmethylated MGMT promoter might have a greater survival benefit from the use of levetiracetam. CONCLUSIONS: The use of levetiracetam during CCRT in patients with newly diagnosed glioblastoma may result in improved outcomes, but further investigations are warranted.

Diagnostic assessment of magnetic resonance imaging for patients with intralabyrinthine schwannoma: A systematic review.

OBJECTIVES: Recent advancements in high-resolution imaging have improved the diagnostic assessment of magnetic resonance imaging (MRI) for intralabyrinthine schwannoma (ILS). This systematic review aimed to evaluate the diagnostic performance of MRI for patients with ILS. METHODS: Ovid-MEDLINE and EMBASE databases were searched for related studies on the diagnostic performance of MRI for patients with ILS published up to February 10, 2020. The primary endpoint was the diagnostic performance of MRI for ILS. The quality of the enrolled studies was assessed using tailored questionnaires and the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. RESULTS: Overall, 6 retrospective studies that included 122 patients with ILS from a parent population of 364 were included. The sample size, parent population and its composition, reference standard, detailed parameters of MRI, and even the diagnostic methods varied between the studies. The studies had moderate quality. The sensitivity of combination of T2WI and CE-T1WI was over 90%. Relative sensitivity of T2WI comparative to CE-T1WI ranged from 62% to 100%, and the specificity were 100%. CONCLUSIONS: MRI has acceptable diagnostic performance for ILS. There is a need for well-organized research to reduce the factors causing heterogeneity.

Influence of Concurrent and Adjuvant Temozolomide on Health-Related Quality of Life of Patients with Grade III Gliomas: A Secondary Analysis of a Randomized Clinical Trial (KNOG-1101 Study).

PURPOSE: The KNOG-1101 study showed improved 2-year PFS with temozolomide during and after radiotherapy compared to radiotherapy alone for patients with anaplastic gliomas. This trial investigates the effect of concurrent and adjuvant temozolomide on health-related quality of life (HRQoL). MATERIALS AND METHODS: In this randomized, open-label, phase II trial, 90 patients with World Health Organization grade III glioma were enrolled across multiple centers in South Korea between March 2012 to February 2015 and followed up through 2017. The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire 30 (EORTC QLQ-C30) and 20-item EORTC QLQ-Brain Neoplasm (QLQ-BN20) were used to compare HRQoL between patients assigned to concurrent chemoradiotherapy with temozolomide followed by 6 cycles of adjuvant temozolomide (arm A) and radiotherapy (RT) alone (arm B). RESULTS: Of the 90 patients in the study, 84 patients (93.3%) completed the baseline HRQoL questionnaire. Emotional functioning, fatigue, nausea and vomiting, dyspnea, constipation, appetite loss, diarrhea, seizures, itchy skin, drowsiness, hair loss, and bladder control were not affected by the addition of temozolomide. All other items did not differ significantly between arm A and arm B throughout treatment. Global health status particularly stayed consistent at the end of adjuvant temozolomide (p=0.47) and at the end of RT (p=0.33). CONCLUSION: The addition of concurrent and adjuvant temozolomide did not show negative influence on HRQoL with improvement of progression-free survival for patients with anaplastic gliomas. The absence of systematic and clinically relevant changes in HRQoL suggests that an overall long-term net clinical benefit exists for concurrent and adjuvant temozolomide.

Primary pharyngeal synovial sarcoma in a pediatric patient: A case report.

RATIONALE: Synovial sarcoma is a rare malignant tumor that typically originates from the soft tissue of the extremities. The occurrence of primary pharyngeal synovial sarcoma is even rarer, and few studies have reported its radiological features. Here, we report a case of pediatric primary pharyngeal synovial sarcoma and describe the conventional and advanced magnetic resonance imaging (MRI) findings with pathologic correlation. PATIENT CONCERNS: An 11-year-old girl presented to the otolaryngologic clinic with dysphagia. DIAGNOSIS: Laryngoscopy revealed a large mass in the oropharynx. MRI revealed a well-defined soft tissue mass with a maximal diameter of approximately 5 cm originating from the submucosal space of the oropharynx. The mass was primarily solid and showed homogeneous contrast-enhancement. The mass was hypointense on T1-weighted images and hyperintense on T2-weighted images. The mass showed a homogeneously low apparent diffusion coefficient value on diffusion-weighted imaging, which indicated high tumor cellularity. Dynamic contrast-enhanced MRI revealed a hypovascular tumor with low values of the volume transfer constant between the extracellular extravascular space and blood plasma and blood plasma volume per unit tissue volume. Amide proton transfer-weighted MRI revealed a relatively high amide proton transfer signal in the tumor, indicating a high protein/peptide component. The patient underwent partial surgical resection of the tumor, and the diagnosis of biphasic synovial sarcoma was confirmed on postoperative pathological examination. INTERVENTION: The patient was started on chemotherapy with vincristine, ifosfamide, doxorubicin, and etoposide. OUTCOMES: The tumor did not respond to the 3 cycles of the chemotherapy. Thus, the patient underwent second surgery and subsequent radiation therapy. The patient is now under ifosfamide/carboplatin/etoposide chemotherapy. LESSON: Synovial sarcoma should be considered in the differential diagnosis of pediatric oropharyngeal submucosal tumors. Multimodal MRI may aid diagnosis, although the final diagnosis should be based on the postoperative pathological examination findings.

Deep Learning-Based Computer-Aided Detection System for Automated Treatment Response Assessment of Brain Metastases on 3D MRI.

BACKGROUND: Although accurate treatment response assessment for brain metastases (BMs) is crucial, it is highly labor intensive. This retrospective study aimed to develop a computer-aided detection (CAD) system for automated BM detection and treatment response evaluation using deep learning. METHODS: We included 214 consecutive MRI examinations of 147 patients with BM obtained between January 2015 and August 2016. These were divided into the training (174 MR images from 127 patients) and test datasets according to temporal separation (temporal test set #1; 40 MR images from 20 patients). For external validation, 24 patients with BM and 11 patients without BM from other institutions were included (geographic test set). In addition, we included 12 MRIs from BM patients obtained between August 2017 and March 2020 (temporal test set #2). Detection sensitivity, dice similarity coefficient (DSC) for segmentation, and agreements in one-dimensional and volumetric Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria between CAD and radiologists were assessed. RESULTS: In the temporal test set #1, the sensitivity was 75.1% (95% confidence interval [CI]: 69.6%, 79.9%), mean DSC was 0.69 ± 0.22, and false-positive (FP) rate per scan was 0.8 for BM ≥ 5 mm. Agreements in the RANO-BM criteria were moderate (κ, 0.52) and substantial (κ, 0.68) for one-dimensional and volumetric, respectively. In the geographic test set, sensitivity was 87.7% (95% CI: 77.2%, 94.5%), mean DSC was 0.68 ± 0.20, and FP rate per scan was 1.9 for BM ≥ 5 mm. In the temporal test set #2, sensitivity was 94.7% (95% CI: 74.0%, 99.9%), mean DSC was 0.82 ± 0.20, and FP per scan was 0.5 (6/12) for BM ≥ 5 mm. CONCLUSIONS: Our CAD showed potential for automated treatment response assessment of BM ≥ 5 mm.

Radiologic Differentiation between Granulomatosis with Polyangiitis and Its Mimics Involving the Skull Base in Humans Using High-Resolution Magnetic Resonance Imaging.

Granulomatosis with polyangiitis (GPA) can involve the skull base or the Eustachian tubes. GPA is diagnosed on the basis of clinical manifestations and serological tests, although it is challenging to discriminate GPA from infectious processes driving skull base osteomyelitis (SBO) and malignant processes such as nasopharyngeal carcinoma (NPC). Moreover, current serological tests have a low sensitivity and cannot distinguish GPA from these other conditions. We hypothesized that certain MRI characteristics would differ significantly among conditions and aimed to evaluate whether the features could differentiate between GPA, SBO, and NPC involving the skull base. We retrospectively evaluated the MRI findings of patients with GPA, SBO, and NPC. We performed univariable logistic regression analyses to identify the predictive variables for differentiating between conditions and evaluated their diagnostic values. We showed, for the first time, that certain MRI findings significantly differed between patients with GPA and those with SBO or NPC, including the lesion morphology and extent, the apparent diffusion coefficient (ADC) values, the contrast enhancement patterns, the presence or absence of necrosis, and retropharyngeal lymphadenopathy. In conclusion, utilizing certain MRI features can improve the diagnostic performance of MRI by differentiating GPA with skull base involvement from other conditions with similar radiologic findings, including SBO and NPC, facilitating treatment plans and, thus, improving patient outcomes.

Temporalis muscle thickness as an indicator of sarcopenia predicts progression-free survival in head and neck squamous cell carcinoma.

Temporalis muscle thickness (TMT) on brain magnetic resonance imaging (MRI) is correlated with sarcopenia and can be a predictive marker for survival in patients with brain tumors, but the association of TMT on head and neck computed tomography (CT) with survival in head and neck squamous cell carcinoma (HNSCC) remains unclear. We investigated whether TMT on CT could predict progression-free survival (PFS) in patients with HNSCC. A total of 106 patients with newly diagnosed HNSCC were included in this retrospective study. The patients underwent baseline head and neck CT and/or MRI between July, 2008 and August, 2018. The correlation between TMT on CT and MRI was tested using intraclass correlation coefficient (ICC). The cut-off value of TMT on CT for determining tumor progression was identified using receiver-operating characteristic curve analysis. Uni- and consecutive multi-variable Cox regression models were used to verify the association between TMT and PFS. TMT on CT and MRI showed excellent correlation (ICC, 0.894). After a mean follow-up of 37 months, 49 out of 106 patients showed locoregional recurrence and/or distant metastasis. The cut-off TMT of 6.47 mm showed good performance in predicting tumor progression (area under the curve, 0.779). The Cox regression model showed that TMT ≤ 6.24 mm (median value in study population) was a significant contributing factor for predicting shorter PFS (hazard ratio 0.399; 95% confidence interval 0.209-0.763; P = .005). TMT may be used as a surrogate parameter for pre-treatment sarcopenia and could help predict PFS in patients with HNSCC.

Construction of an MRI-based decision tree to differentiate autoimmune and autoinflammatory inner ear disease from chronic otitis media with sensorineural hearing loss.

Autoimmune and autoinflammatory inner ear diseases (AIED/AID) are characterized by the symptom of sensorineural hearing loss (SNHL). To date, standardized diagnostic tools for AIED/AID are lacking, and clinically differentiating AIED/AID from chronic otitis media (COM) with SNHL is challenging. This retrospective study aimed to construct a magnetic resonance imaging (MRI)-based decision tree using classification and regression tree (CART) analysis to distinguish AIED/AID from COM. In total, 67 patients were enrolled between January 2004 and October 2019, comprising AIED/AID (n = 18), COM (n = 24), and control groups (n = 25). All patients underwent 3 T temporal bone MRI, including post-contrast T1-weighted images (postT1WI) and post-contrast FLAIR images (postFLAIR). Two radiologists evaluated the presence of otomastoid effusion and inner ear contrast-enhancement on MRI. A CART decision tree model was constructed using MRI features to differentiate AIED/AID from COM and control groups, and diagnostic performance was analyzed. High-intensity bilateral effusion (61.1%) and inner ear enhancement (postFLAIR, 93.8%; postT1WI, 61.1%) were the most common findings in the AIED/AID group. We constructed two CART decision tree models; the first used effusion amount as the first partitioning node and postT1WI-inner ear enhancement as the second node, whereas the second comprised two partitioning nodes with the degree of postFLAIR-enhancement of the inner ear. The first and second models enabled distinction of AIED/AID from COM with high specificity (100% and 94.3%, respectively). The amount of effusion and the degree of inner ear enhancement on MRI may facilitate the distinction between AIED/AID and COM with SNHL using decision tree models, thereby contributing to early diagnosis and intervention.

Classification of true progression after radiotherapy of brain metastasis on MRI using artificial intelligence: a systematic review and meta-analysis.

BACKGROUND: Classification of true progression from nonprogression (eg, radiation-necrosis) after stereotactic radiotherapy/radiosurgery of brain metastasis is known to be a challenging diagnostic task on conventional magnetic resonance imaging (MRI). The scope and status of research using artificial intelligence (AI) on classifying true progression are yet unknown. METHODS: We performed a systematic literature search of MEDLINE and EMBASE databases to identify studies that investigated the performance of AI-assisted MRI in classifying true progression after stereotactic radiotherapy/radiosurgery of brain metastasis, published before November 11, 2020. Pooled sensitivity and specificity were calculated using bivariate random-effects modeling. Meta-regression was performed for the identification of factors contributing to the heterogeneity among the studies. We assessed the quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria and a modified version of the radiomics quality score (RQS). RESULTS: Seven studies were included, with a total of 485 patients and 907 tumors. The pooled sensitivity and specificity were 77% (95% CI, 70-83%) and 74% (64-82%), respectively. All 7 studies used radiomics, and none used deep learning. Several covariates including the proportion of lung cancer as the primary site, MR field strength, and radiomics segmentation slice showed a statistically significant association with the heterogeneity. Study quality was overall favorable in terms of the QUADAS-2 criteria, but not in terms of the RQS. CONCLUSION: The diagnostic performance of AI-assisted MRI seems yet inadequate to be used reliably in clinical practice. Future studies with improved methodologies and a larger training set are needed.