Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: a clinical cohort study.

OBJECTIVES: We aimed to evaluate whether deep learning-based detection and quantification of brain metastasis (BM) may suggest treatment options for patients with BMs. METHODS: The deep learning system (DLS) for detection and quantification of BM was developed in 193 patients and applied to 112 patients that were newly detected on black-blood contrast-enhanced T1-weighted imaging. Patients were assigned to one of 3 treatment suggestion groups according to the European Association of Neuro-Oncology (EANO)-European Society for Medical Oncology (ESMO) recommendations using number and volume of the BMs detected by the DLS: short-term imaging follow-up without treatment (group A), surgery or stereotactic radiosurgery (limited BM, group B), or whole-brain radiotherapy or systemic chemotherapy (extensive BM, group C). The concordance between the DLS-based groups and clinical decisions was analyzed with or without consideration of targeted agents. The performance of distinguishing high-risk (B + C) was calculated. RESULTS: Among 112 patients (mean age 64.3 years, 63 men), group C had the largest number and volume of BM, followed by group B (4.4 and 851.6 mm3) and A (1.5 and 15.5 mm3). The DLS-based groups were concordant with the actual clinical decisions, with an accuracy of 76.8% (86 of 112). Modified accuracy considering targeted agents was 81.3% (91 of 112). The DLS showed 95% (82/86) sensitivity and 81% (21/26) specificity for distinguishing the high risk. CONCLUSION: DLS-based detection and quantification of BM have the potential to be helpful in the determination of treatment options for both low- and high-risk groups of limited and extensive BMs. CLINICAL RELEVANCE STATEMENT: For patients with newly diagnosed brain metastasis, deep learning-based detection and quantification may be used in clinical settings where prompt and accurate treatment decisions are required, which can lead to better patient outcomes. KEY POINTS: • Deep learning-based brain metastasis detection and quantification showed excellent agreement with ground-truth classifications. • By setting an algorithm to suggest treatment based on the number and volume of brain metastases detected by the deep learning system, the concordance was 81.3%. • When dividing patients into low- and high-risk groups, the sensitivity for detecting the latter was 95%.

Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: validation in a multi-center diagnostic cohort.

OBJECTIVES: To develop a deep learning (DL) for detection of brain metastasis (BM) that incorporates both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced DL) and evaluate it in a clinical cohort in comparison with human readers and DL using gradient-echo-based imaging only (GRE DL). MATERIALS AND METHODS: DL detection was developed using data from 200 patients with BM (training set) and tested in 62 (internal) and 48 (external) consecutive patients who underwent stereotactic radiosurgery and diagnostic dual-enhanced imaging (dual-enhanced DL) and later guide GRE imaging (GRE DL). The detection sensitivity and positive predictive value (PPV) were compared between two DLs. Two neuroradiologists independently analyzed BM and reference standards for BM were separately drawn by another neuroradiologist. The relative differences (RDs) from the reference standard BM numbers were compared between the DLs and neuroradiologists. RESULTS: Sensitivity was similar between GRE DL (93%, 95% confidence interval [CI]: 90-96%) and dual-enhanced DL (92% [89-94%]). The PPV of the dual-enhanced DL was higher (89% [86-92%], p < .001) than that of GRE DL (76%, [72-80%]). GRE DL significantly overestimated the number of metastases (false positives; RD: 0.05, 95% CI: 0.00-0.58) compared with neuroradiologists (RD: 0.00, 95% CI: - 0.28, 0.15, p < .001), whereas dual-enhanced DL (RD: 0.00, 95% CI: 0.00-0.15) did not show a statistically significant difference from neuroradiologists (RD: 0.00, 95% CI: - 0.20-0.10, p = .913). CONCLUSION: The dual-enhanced DL showed improved detection of BM and reduced overestimation compared with GRE DL, achieving similar performance to neuroradiologists. CLINICAL RELEVANCE STATEMENT: The use of deep learning-based brain metastasis detection with turbo spin-echo imaging reduces false positive detections, aiding in the guidance of stereotactic radiosurgery when gradient-echo imaging alone is employed. KEY POINTS: •Deep learning for brain metastasis detection improved by using both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced deep learning). •Dual-enhanced deep learning increased true positive detections and reduced overestimation. •Dual-enhanced deep learning achieved similar performance to neuroradiologists for brain metastasis counts.

Prediction of pseudoprogression in post-treatment glioblastoma using dynamic susceptibility contrast-derived oxygenation and microvascular transit time heterogeneity measures.

OBJECTIVES: To evaluate the added value of MR dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI)-derived tumour microvascular and oxygenation information with cerebral blood volume (CBV) to distinguish pseudoprogression from true progression (TP) in post-treatment glioblastoma. METHODS: This retrospective single-institution study included patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma and a newly developed or enlarging measurable contrast-enhancing mass within 12 weeks after concurrent chemoradiotherapy. CBV, capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), and cerebral metabolic rate of oxygen (CMRO2) were obtained from DSC-PWI. Predictors were selected using univariable logistic regression, and performance was measured with adjusted diagnostic odds with tumour volume and area under the curve (AUC) of receiver operating characteristics analysis. RESULTS: A total of 103 patients were included (mean age, 59.6 years; 59 women), with 67 cases of TP and 36 cases of pseudoprogression. Pseudoprogression exhibited higher CTH (4.0 vs. 3.4, p = .019) and higher OEF (12.7 vs. 10.7, p = .014) than TP, but a similar CBV (1.48 vs. 1.53, p = .13) and CMRO2 (7.7 vs. 7.3s, p = .598). Independent of tumour volume, both high CTH (adjusted odds ratio [OR] 1.52; 95% confidence interval [CI]: 1.11-2.09, p = .009) and high OEF (adjusted OR 1.17; 95% CI:1.03-1.33, p = .016) were predictors of pseudoprogression. The combination of CTH, OEF, and CBV yielded higher diagnostic performance (AUC 0.71) than CBV alone (AUC 0.65). CONCLUSION: High intratumoural capillary transit heterogeneity and high oxygen extraction fraction derived from DSC-PWI have enhanced the diagnostic value of CBV in pseudoprogression of post-treatment IDH-wild type glioblastoma. CLINICAL RELEVANCE STATEMENT: In the early post-treatment stage of glioblastoma, pseudoprogression exhibited both high oxygen extraction fraction and high capillary transit heterogeneity and these dynamic susceptibility contrast-perfusion weighted imaging derived parameters have added value in cerebral blood volume-based noninvasive differentiation of pseudoprogression from true progression. KEY POINTS: • Capillary transit time heterogeneity and oxygen extraction fraction can be measured noninvasively through processing of dynamic susceptibility contrast imaging. • Pseudoprogression exhibited higher capillary transit time heterogeneity and higher oxygen extraction fraction than true progression. • A combination of cerebral blood volume, capillary transit time heterogeneity, and oxygen extraction fraction yielded the highest diagnostic performance (area under the curve 0.71).

Vessel size and perfusion-derived vascular habitat refines prediction of treatment failure to bevacizumab in recurrent glioblastomas: validation in a prospective cohort.

OBJECTIVES: Anti-angiogenic therapy may not benefit all patients with recurrent glioblastomas, and imaging biomarker predicting treatment response to anti-angiogenic therapy is currently limited. We aimed to develop and validate vascular habitats based on perfusion and vessel size to predict time to progression (TTP) in patients with recurrent glioblastomas treated with bevacizumab. METHODS: Sixty-nine patients with recurrent glioblastomas treated with bevacizumab who underwent pretreatment MRI with dynamic susceptibility contrast imaging and vessel architectural imaging were enrolled. Vascular habitats were constructed using vessel size index (VSI) and relative cerebral blood volume (rCBV). Associations with vascular habitats and TTP were analyzed using Cox proportional hazard regression analysis. In a prospectively enrolled validation cohort consisting of 15 patients ( ClinicalTrials.gov identifier; NCT04143425), stratification of TTP was demonstrated by the Kaplan-Meier method (log-rank test) using vascular habitats. RESULTS: Three vascular habitats consisting of high, intermediate, and low angiogenic habitats were identified with rCBV and VSI. Both high angiogenic and intermediate angiogenic habitats were significantly associated with a shorter TTP (hazard ratio [HR], 2.78 and 1.82, respectively; largest p = .003) and so was rCBV (HR, 2.15; p = .02). Concordance probability index of vascular habitat combining high and intermediate angiogenic habitats was 0.74. Vascular habitats stratified patients as good or poor responder in a prospective cohort (p = .059). CONCLUSIONS: Perfusion- and vessel size-derived vascular habitats predicted TTP in recurrent glioblastomas treated with anti-angiogenic therapy and aided patient stratification in a prospective validation cohort. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04143425 KEY POINTS: • High and intermediate angiogenic habitats predicted TTP in recurrent glioblastomas treated with anti-angiogenic therapy. • Vascular habitats combining high and intermediate angiogenic habitats aided patient stratification for anti-angiogenic therapy in recurrent glioblastomas.

Tumor habitat analysis by magnetic resonance imaging distinguishes tumor progression from radiation necrosis in brain metastases after stereotactic radiosurgery.

OBJECTIVES: The identification of viable tumor after stereotactic radiosurgery (SRS) is important for future targeted therapy. This study aimed to determine whether tumor habitat on structural and physiologic MRI can distinguish viable tumor from radiation necrosis of brain metastases after SRS. METHOD: Multiparametric contrast-enhanced T1- and T2-weighted imaging, apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) were obtained from 52 patients with 69 metastases, showing enlarging enhancing masses after SRS. Voxel-wise clustering identified three structural MRI habitats (enhancing, solid low-enhancing, and nonviable) and three physiologic MRI habitats (hypervascular cellular, hypovascular cellular, and nonviable). Habitat-based predictors for viable tumor or radiation necrosis were identified by logistic regression. Performance was validated using the area under the curve (AUC) of the receiver operating characteristics curve in an independent dataset with 24 patients. RESULTS: None of the physiologic MRI habitats was indicative of viable tumor. Viable tumor was predicted by a high-volume fraction of solid low-enhancing habitat (low T2-weighted and low CE-T1-weighted values; odds ratio [OR] 1.74, p <.001) and a low-volume fraction of nonviable tissue habitat (high T2-weighted and low CE-T1-weighted values; OR 0.55, p <.001). Combined structural MRI habitats yielded good discriminatory ability in both development (AUC 0.85, 95% confidence interval [CI]: 0.77-0.94) and validation sets (AUC 0.86, 95% CI:0.70-0.99), outperforming single ADC (AUC 0.64) and CBV (AUC 0.58) values. The site of progression matched with the solid low-enhancing habitat (72%, 8/11). CONCLUSION: Solid low-enhancing and nonviable tissue habitats on structural MRI can help to localize viable tumor in patients with brain metastases after SRS. KEY POINTS: • Structural MRI habitats helped to differentiate viable tumor from radiation necrosis. • Solid low-enhancing habitat was most helpful to find viable tumor. • Providing spatial information, the site of progression matched with solid low-enhancing habitat.

Impact of transdermal trigeminal electrical neuromodulation on subjective and objective sleep parameters in patients with insomnia: a pilot study.

PURPOSE: Transcutaneous trigeminal electrical neuromodulation (TTEN) is a new treatment modality that has a potential to improve sleep through the suppression of noradrenergic activity. This study aimed to explore the changes of subjective and objective sleep parameters after 4-weeks of daily session of transcutaneous trigeminal electrical neuromodulation in a group of patients with insomnia. METHODS: In a group of patients with insomnia, TTEN targeting the ophthalmic division of the trigeminal nerve was utilized to test the effects of transcutaneous trigeminal electrical neuromodulation. Patients went through daily 20-min sessions of TTEN for 4 weeks. Polysomnography parameters, Pittsburgh sleep quality index, insomnia severity index, and Epworth sleepiness scale were obtained pre- and post-intervention. Changes in these parameters were compared and analyzed. RESULTS: Among 13 patients with insomnia there was a statistically significant reduction in Pittsburgh sleep quality index, insomnia severity index, and Epworth sleepiness scale scores after 4-week daily sessions of TTEN. There were no differences in polysomnography parameters pre- and post-intervention. CONCLUSION: This is the first study to demonstrate the effects of TTEN in a group of insomnia patients. TTEN may improve subjective parameters in patients with insomnia. Further replication studies are needed to support this finding. TRIAL REGISTRATION: The data presented in the study are from a study exploring the effect of TTEN on insomnia ( www. CLINICALTRIALS: gov , registration number: NCT04838067, date of registration: April 8, 2021, "retrospectively registered").

Low conductivity on electrical properties tomography demonstrates unique tumor habitats indicating progression in glioblastoma.

OBJECTIVES: Tissue conductivity measurements made with electrical properties tomography (EPT) can be used to define temporal changes in tissue habitats on longitudinal multiparametric MRI. We aimed to demonstrate the added insights for identifying tumor habitats obtained by including EPT with diffusion- and perfusion-weighted MRI, and to evaluate the use of these tumor habitats for determining tumor treatment response in post-treatment glioblastoma. METHODS: Tumor habitats were developed from EPT, diffusion-weighted, and perfusion-weighted MRI in 60 patients with glioblastoma who underwent concurrent chemoradiotherapy. Voxels from EPT, apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) maps were clustered into habitats, and each habitat was serially examined to assess its temporal change. The usefulness of temporal changes in tumor habitats for diagnosing tumor progression and treatment-related change was investigated using logistic regression. The performance of significant predictors was measured using the area under the curve (AUC) from receiver-operating-characteristics analysis with 1000-fold bootstrapping. RESULTS: Five tumor habitats were identified, and of these, the hypervascular cellular habitat (odds ratio [OR] 5.45; 95% CI, 1.75-31.42; p = .02), hypovascular low conductivity habitat (OR 2.00; 95% CI, 1.45-3.05; p < .001), and hypovascular intermediate habitat (OR 1.57; 95% CI, 1.18-2.30; p = .006) were predictive of tumor progression. Low EPT and low CBV reflected a unique hypovascular low conductivity habitat that showed the highest diagnostic performance (AUC 0.86; 95% CI, 0.76-0.96). The combined habitats showed high performance (AUC 0.90; 95% CI, 0.82-0.98) in the differentiation of tumor progression from treatment-related change. CONCLUSION: EPT reveals low conductivity habitats that can improve the diagnosis of tumor progression in post-treatment glioblastoma. KEY POINTS: • Electrical properties tomography (EPT) demonstrated lower conductivity in tumor progression than in treatment-related change. • EPT allowed identification of a unique hypovascular low conductivity habitat when combined with cerebral blood volume mapping. • Tumor habitats with a hypovascular low conductivity habitat, hypervascular cellular habitat, and hypovascular intermediate habitat yielded high diagnostic performance for diagnosing tumor progression.

Spatiotemporal habitats from multiparametric physiologic MRI distinguish tumor progression from treatment-related change in post-treatment glioblastoma.

OBJECTIVES: We aimed to develop multiparametric physiologic MRI-based spatial habitats and to evaluate whether temporal changes in these habitats help to distinguish tumor progression from treatment-related change in post-treatment glioblastoma. METHODS: This retrospective, single-institution study included patients with glioblastoma treated by concurrent chemoradiotherapy who had newly developed or enlarging, measurable contrast-enhancing mass. Contrast-enhancing mass was divided into three spatial habitats by K-means clustering of voxel-wise ADC and CBV values. Temporal changes of these habitats between two consecutive examinations prior to the diagnosis of tumor progression or treatment-related change were assessed. Predictors were selected using logistic regression and the performance was measured with an area under the receiver operating characteristics curve (AUC). Spatiotemporal habitats were further analyzed for correlation with the site of tumor progression. RESULTS: There were 75 patients (mean, 58 years; range, 26-81 years; 43 men) with 48 cases of tumor progression and 39 cases of treatment-related change including 12 patient overlaps at different time points. Three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified. Increase in the hypervascular cellular (OR 4.55, p = .002) and hypovascular cellular habitat (OR 1.22, p < .001) was predictive of tumor progression. Combination of spatiotemporal habitats yielded a high diagnostic performance with an AUC of 0.89 (95% CI, 0.87-0.92). An increase in hypovascular cellular habitat predicted the site of tumor progression in 84% [21/25] of cases with tumor progression. CONCLUSIONS: Temporal changes in spatial habitats derived from multiparametric physiologic MRI provided diagnostic value in distinguishing tumor progression from treatment-related change and predicted site of tumor progression in post-treatment glioblastoma. KEY POINTS: • In post-treatment glioblastoma, three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified, and an increase in the hypervascular cellular (OR 4.55, p = .002) and hypovascular cellular habitat (OR 1.22, p < .001) was predictive of tumor progression. • Combination of spatiotemporal habitats yielded a high diagnostic performance with an AUC of 0.89 (95% CI, 0.87-0.92). • An increase in hypovascular cellular habitat predicted the site of tumor progression in 84% (21/25) of cases with tumor progression.

Uncovering gene expression signatures and diagnostic - Biomarkers in hepatocellular carcinoma through multinomial logistic regression analysis.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide, and classifying the developmental stages of HCC can help with early prognosis and treatment. This study aimed to investigate diagnostic and prognostic molecular signatures underlying the progression of HCC, including tumor initiation and growth, and to classify its developmental stages based on gene expression levels. We integrated data from two cancer systems, including 78 patients with Edmondson-Steiner (ES) grade and 417 patients with TNM stage cancer. Functional profiling was performed using identified signatures. Using a multinomial logistic regression model (MLR), we classified controls, early-stage HCC, and advanced-stage HCC. The model was validated in three independent cohorts comprising 45 patients (neoplastic stage), 394 patients (ES grade), and 466 patients (TNM stage). Multivariate Cox regression was employed for HCC prognosis prediction. We identified 35 genes with gradual upregulation or downregulation in both ES grade and TNM stage patients during HCC progression. These genes are involved in cell division, chromosome segregation, and mitotic cytokinesis, promoting tumor cell proliferation through the mitotic cell cycle. The MLR model accurately differentiated controls, early-stage HCC, and advanced-stage HCC across multiple cancer systems, which was further validated in various independent cohorts. Survival analysis revealed a subset of five genes from TNM stage (HR: 3.27, p < 0.0001) and three genes from ES grade (HR: 7.56, p < 0.0001) that showed significant association with HCC prognosis. The identified molecular signature not only initiates tumorigenesis but also promotes HCC development. It has the potential to improve clinical diagnosis, prognosis, and therapeutic interventions for HCC. This study enhances our understanding of HCC progression and provides valuable insights for precision medicine approaches.

Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study.

OBJECTIVES: To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting. MATERIALS AND METHODS: In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having newly developed BM from two tertiary university hospitals, which performed black-blood imaging between January 2020 and April 2021. Four neuroradiologists independently evaluated BM either with segmented masks and BM counts provided (with DLS) or not provided (without DLS) on a clinical trial imaging management system (CTIMS). To assess reading reproducibility, BM count agreement between the readers and the reference standard were calculated using limits of agreement (LoA). Readers' workload was assessed with reading time, which was automatically measured on CTIMS, and were compared between with and without DLS using linear mixed models considering the imaging center. RESULTS: In the validation cohort, the detection sensitivity and positive predictive value of the DLS were 90.2% (95% confidence interval [CI]: 88.1-92.2) and 88.2% (95% CI: 85.7-90.4), respectively. The difference between the readers and the reference counts was larger without DLS (LoA: -0.281, 95% CI: -2.888, 2.325) than with DLS (LoA: -0.163, 95% CI: -2.692, 2.367). The reading time was reduced from mean 66.9 s (interquartile range: 43.2-90.6) to 57.3 s (interquartile range: 33.6-81.0) (P <.001) in the with DLS group, regardless of the imaging center. CONCLUSION: Deep learning-based BM detection and counting with black-blood imaging improved reproducibility and reduced reading time, on multi-center validation.

Normative Issues in Next Generation Sequencing Gene Testing.

Despite the commercialization of Next generation sequencing (NGS) gene testing, only a few studies have addressed the various ethical and legal problems associated with NGS testing in Korea Here, we reviewed the normative issues that emerged at each stage of the wet analysis and bioinformatics analysis of NGS gene testing. In particular, it was in mind to apply various international guidelines and the principles of bioethics to actual clinical practice. Considering the characteristics of NGS testing, wet analysis of additional testing can be justified if presumptive consent is recognized. Furthermore, the medical relationship between diseases needs to be established and it should be clear that the patient would have given consent if the patient had been aware of the correlation between genes. At the stage of bioinformatics analysis, the question of unsolicited findings arises. In case of unsolicited and relevant findings, according to American College of Medical Genetics and Genomics (ACMG), a recognized relationship between genes and diseases needs to be established. In case of unsolicited and not-relevant findings, it is almost impossible to determine whether knowing or not knowing the findings is more beneficial to the patient. However, it seems to be certain that the psychological harm an individual may suffer from such information is likely to be greater if the disease is severe and if there is no cure. The list of genes for which the ACMG guidelines impose reporting obligations is a good reference for judgment.

Characterization of signature trends across the spectrum of non-alcoholic fatty liver disease using deep learning method.

AIMS: The timely diagnosis of different stages in NAFLD is crucial for disease treatment and reversal. We used hepatocellular ballooning to determine different NAFLD stages. MAIN METHODS: We analyzed differentially expressed genes (DEGs) in 78 patients with NAFLD and in healthy controls from previously published RNA-seq data. We identified two expression types in NAFLD progression, calculated the predictive power of candidate genes, and validated them in an independent cohort. We also performed cancer studies with these candidates retrieved from the Cancer Genome Atlas. KEY FINDINGS: We identified 103 DEGs in NAFLD patients compared to healthy controls: 75 genes gradually increased or decreased in the NAFLD stage, whereas 28 genes showed differences only in NASH. The former were enriched in negative regulation and binding-related genes; the latter were involved in positive regulation and cell proliferation. Feature selection showed the gradual up- or down-regulation of 21 genes in NASH compared to controls; 18 were highly expressed only in NASH. Using deep-learning method with subset of features from lasso regression, we obtained reliable determination performance in NAFL and NASH (accuracy: 0.857) and validated these genes using an independent cohort (accuracy: 0.805). From cancer studies, we identified significant differential expression of several candidate genes in LIHC; 5 genes were gradually up-regulated and 6 showing high expression only in NASH were influential to patient survival. SIGNIFICANCE: The identified biomolecular signatures may determine the spectrum of NAFLD and its relationship with HCC, improving clinical diagnosis and prognosis and enabling a therapeutic intervention for NAFLD.

Tumor Habitat Analysis Using Longitudinal Physiological MRI to Predict Tumor Recurrence After Stereotactic Radiosurgery for Brain Metastasis.

OBJECTIVE: It is difficult to predict the treatment response of tissue after stereotactic radiosurgery (SRS) because radiation necrosis (RN) and tumor recurrence can coexist. Our study aimed to predict tumor recurrence, including the recurrence site, after SRS of brain metastasis by performing a longitudinal tumor habitat analysis. MATERIALS AND METHODS: Two consecutive multiparametric MRI examinations were performed for 83 adults (mean age, 59.0 years; range, 27-82 years; 44 male and 39 female) with 103 SRS-treated brain metastases. Tumor habitats based on contrast-enhanced T1- and T2-weighted images (structural habitats) and those based on the apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) images (physiological habitats) were defined using k-means voxel-wise clustering. The reference standard was based on the pathology or Response Assessment in Neuro-Oncologycriteria for brain metastases (RANO-BM). The association between parameters of single-time or longitudinal tumor habitat and the time to recurrence and the site of recurrence were evaluated using the Cox proportional hazards regression analysis and Dice similarity coefficient, respectively. RESULTS: The mean interval between the two MRI examinations was 99 days. The longitudinal analysis showed that an increase in the hypovascular cellular habitat (low ADC and low CBV) was associated with the risk of recurrence (hazard ratio [HR], 2.68; 95% confidence interval [CI], 1.46-4.91; P = 0.001). During the single-time analysis, a solid low-enhancing habitat (low T2 and low contrast-enhanced T1 signal) was associated with the risk of recurrence (HR, 1.54; 95% CI, 1.01-2.35; P = 0.045). A hypovascular cellular habitat was indicative of the future recurrence site (Dice similarity coefficient = 0.423). CONCLUSION: After SRS of brain metastases, an increased hypovascular cellular habitat observed using a longitudinal MRI analysis was associated with the risk of recurrence (i.e., treatment resistance) and was indicative of recurrence site. A tumor habitat analysis may help guide future treatments for patients with brain metastases.

Alterations of Resting-State Locus Coeruleus Functional Connectivity After Transdermal Trigeminal Electrical Neuromodulation in Insomnia.

Background: Transdermal trigeminal electrical neuromodulation (TTEN) is a novel treatment modality that is known for noradrenergic modulation through the trigeminal nerve and locus coeruleus (LC). This study aimed to demonstrate the alterations of LC functional connectivity (FC) in patients with insomnia after a 4-week TTEN. Methods: The Cefaly device targeting the ophthalmic division of the trigeminal nerve was applied to a total of 12 patients with insomnia to monitor for the effects of TTEN. All the patients went through a 4-week daily 20 min TTEN sessions before bedtime. Baseline and post-TTEN demographic data, polysomnography (PSG) parameters, and insomnia severity index (ISI) were attained. Data from pre- and post-intervention resting-state functional magnetic resonance imaging (MRI) were collected. LC FC differences were measured between the pre-and post-TTEN groups through seed-to-voxel analysis. Correlation analyses were conducted between LC FC changes after TTEN, ISI score changes, and PSG parameter changes. Results: There was a significantly decreased LC FC with occipital and temporal cortices after a 4-week TTEN. However, there was no significant correlation between LC FC, ISI score changes, and PSG parameter changes. Conclusion: By targeting hyperarousal symptoms of insomnia, TTEN can be a promising intervention that can modulate LC FC in patients with insomnia patients. The data presented in the study are from a study exploring the effect of TTEN on insomnia (www.clinicaltrials.gov, NCT04838067).

Subcortical and Cerebellar Neural Correlates of Prodromal Alzheimer's Disease with Prolonged Sleep Latency.

BACKGROUND: Despite the important associations among sleep, Alzheimer's disease (AD), subcortical structures, and the cerebellum, structural and functional magnetic resonance imaging (MRI) with regard to these regions and sleep on patients in AD trajectory are scarce. OBJECTIVE: This study aimed to evaluate the influence of prolonged sleep latency on the structural and functional alterations in the subcortical and cerebellar neural correlates in amyloid-ß positive amnestic mild cognitive impairment patients (Aß+aMCI). METHODS: A total of 60 patients with aMCI who were identified as amyloid positive ([18F] flutemetamol+) were recruited in the study, 24 patients with normal sleep latency (aMCI-n) and 36 patients prolonged sleep latency (aMCI-p). Cortical thickness and volumes between the two groups were compared. Volumetric analyses were implemented on the brainstem, thalamus, and hippocampus. Subcortical and cerebellar resting state functional connectivity (FC) differences were measured between the both groups through seed-to-voxel analysis. Additionally, group x Aß interactive effects on FC values were tested with a general linear model. RESULT: There was a significantly decreased brainstem volume in aMCI-p subjects. We observed a significant reduction of the locus coeruleus (LC) FC with frontal, temporal, insular cortices, hippocampus, and left thalamic FC with occipital cortex. Moreover, the LC FC with occipital cortex and left hippocampal FC with frontal cortex were increased in aMCI-p subjects. In addition, there was a statistically significant group by regional standardized uptake value ratio interactions discovered in cerebro-cerebellar networks. CONCLUSION: The aforementioned findings suggest that prolonged sleep latency may be a detrimental factor in compromising structural and functional correlates of subcortical structures and the cerebellum, which may accelerate AD pathophysiology.

Impact of APOE ε4 Carrier Status on Associations Between Subthreshold, Positive Amyloid-ß Deposition, Brain Function, and Cognitive Performance in Cognitively Normal Older Adults: A Prospective Study.

Background: A growing body of evidence suggests a deteriorating effect of subthreshold amyloid-beta (Aß) accumulation on cognition before the onset of clinical symptoms of Alzheimer's disease (AD). Despite the association between the Aß-dependent pathway and the APOE ε4 allele, the impact of this allele on the progression from the subthreshold Aß deposits to cognitive function impairment is unclear. Furthermore, the comparative analysis of positive Aß accumulation in the preclinical phase is lacking. Objective: This study aimed to explore the differential effect of the APOE ε4 carrier status on the association between Aß deposition, resting-state brain function, and cognitive performance in cognitively normal (CN) older adults, depending on the Aß burden status. Methods: One hundred and eighty-two older CN adults underwent resting-state functional magnetic resonance imaging, [18F] flutemetamol (FMM) positron emission tomography, a neuropsychological battery, and APOE genotyping. We evaluated the resting-state brain function by measuring the local and remote functional connectivity (FC) and measured the remote FC in the default-mode network (DMN), central-executive network (CEN), and salience network (SN). In addition, the subjects were dichotomized into those with subthreshold and positive Aß deposits using a neocortical standardized uptake value ratio with the cut-off value of 0.62, which was calculated with respect to the pons. Results: The present result showed that APOE ε4 carrier status moderated the relationship between Aß deposition, local and remote resting-state brain function, and cognitive performance in each CN subthreshold and positive Aß group. We observed the following: (i) the APOE ε4 carrier status-Aß deposition and APOE ε4 carrier status-local FC interaction for the executive and memory function; (ii) the APOE ε4 carrier status-regional Aß accumulation interaction for the local FC; and (iv) the APOE ε4 carrier status-local FC interaction for the remote inter-network FC between the DMN and CEN, contributing higher cognitive performance in the APOE ε4 carrier with higher inter-network FC. Finally, these results were modulated according to Aß positivity. Conclusion: This study is the first attempt to thoroughly examine the influence of the APOE ε4 carrier status from the subthreshold to positive Aß accumulation during the preclinical phase.

A Normative Review on Non-Invasive Prenatal Diagnosis (NIPD): Focusing on the German Discussion on PrenaTest®.

This article aims to introduce German discussion on the approval of the non-invasive prenatal diagnosis (NIPD), which started with the development of PrenaTest® by LifeCodexx AG. The discussion started with the concern that the non-invasive nature of NIPD, such as PrenaTest®, may rapidly expand the use and scope of similar tests, thus leading to a new era of eugenics. Based on this concern, the need for clear clinical guidelines on specific indications for NIPD has been suggested. Along the same line, it was discussed whether PrenaTest® is against the Basic Law prohibiting discrimination on grounds of disability and whether the test is outside the scope of the purpose of gene testing limited by Genetic Diagnosis Act. Through such discussion, the Federal Ministry of Health of Germany established the preconditions for inclusion of NIPD in the German public health insurance system. For this, the German motherhood guideline was amended and the information for the insured persons provided to pregnant women was included in the amended guideline. Such discussion made in Germany provides insight on which points should be considered when various gene testings are accepted in Korea , in which genetic communication has not been systematized yet. In particular, German counseling system for pregnant women will provide valuable insights for Korea where the direction for regulations on abortion has not been established even after the ruling by the Constitutional Court that charges for abortion are against the constitution.

Distinctive Association of the Functional Connectivity of the Posterior Cingulate Cortex on Memory Performances in Early and Late Amnestic Mild Cognitive Impairment Patients.

BACKGROUND: Attempts have been made to explore the biological basis of neurodegeneration in the amnestic mild cognitive impairment (MCI) stage, subdivided by memory performance. However, few studies have evaluated the differential impact of functional connectivity (FC) on memory performances in early- and late-MCI patients. OBJECTIVE: This study aims to explore the difference in FC of the posterior cingulate cortex (PCC) among healthy controls (HC) (n = 37), early-MCI patients (n = 30), and late-MCI patients (n = 35) and to evaluate a group-memory performance interaction against the FC of PCC. METHODS: The subjects underwent resting-state functional MRI scanning and a battery of neuropsychological tests. RESULTS: A significant difference among the three groups was found in FC between the PCC (seed region) and bilateral crus cerebellum, right superior medial frontal gyrus, superior temporal gyrus, and left middle cingulate gyrus (Monte Carlo simulation-corrected p < 0.01; cluster p < 0.05). Additionally, the early-MCI patients displayed higher FC values than the HC and late-MCI patients in the right superior medial frontal gyrus, cerebellum crus 1, and left cerebellum crus 2 (Bonferroni-corrected p < 0.05). Furthermore, there was a significant group-memory performance interaction (HC vs. early MCI vs. late MCI) for the FC between PCC and bilateral crus cerebellum, right superior medial frontal gyrus, superior temporal gyrus, and left middle cingulate gyrus (Bonferroni-corrected p < 0.05). CONCLUSION: These findings contribute to the biological implications of early- and late-MCI stages, categorized by evaluating the impairment of memory performance. Additionally, comprehensively analyzing the structural differences in the subdivided amnestic MCI (aMCI) stages could deepen our understanding of these biological meanings.

Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Is Associated with Patient Outcomes in IDH-Wildtype Glioblastoma.

PURPOSE: Heterogeneity in glioblastomas is associated with poorer outcomes, and physiologic heterogeneity can be quantified with noninvasive imaging. We developed spatial habitats based on multiparametric physiologic MRI and evaluated associations between temporal changes in these habitats and progression-free survival (PFS) after concurrent chemoradiotherapy (CCRT) in patients with glioblastoma. EXPERIMENTAL DESIGN: Ninety-seven patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma were enrolled and two serial MRI examinations after CCRT were analyzed. Cerebral blood volumes and apparent diffusion coefficients were grouped using k-means clustering into three spatial habitats. Associations between temporal changes in spatial habitats and PFS were investigated using Cox proportional hazard modeling. The performance of significant predictors for PFS and overall survival (OS) was measured using a discrete increase of habitat (habitat risk score) in a temporal validation set from a prospective registry (n = 53, ClinicalTrials.gov NCT02619890). The site of progression was matched with the spatiotemporal habitats. RESULTS: Three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified. A short-term increase in the hypervascular cellular habitat (HR, 40.0; P = 0.001) and hypovascular cellular habitat was significantly associated with shorter PFS (HR, 3.78; P < 0.001) after CCRT. Combined with clinical predictors, the habitat risk score showed a C-index of 0.79 for PFS and 0.74 for OS and stratified patients with short, intermediate, and long PFS (P = 0.016). An increase in the hypovascular cellular habitat predicted tumor progression sites. CONCLUSIONS: Hypovascular cellular habitats derived from multiparametric physiologic MRIs may be useful predictors of clinical outcomes in patients with posttreatment glioblastoma.