Matthew A. Howard

Pilot Study: Zwitterionic Conditioning of the Decompressive Craniectomy Site for Early Cranioplasty Feasibility

Daniel C. Bartelt, Terry C. Yin, Steffen G. Osborn, Kameron R. Hansen et autres

Background: Early cranioplasty after decompressive craniectomy (DC) for moderate-to-severe traumatic brain injury (TBI) improves functional recovery. However, patient candidacy is limited by persistent brain swelling, soft-tissue inflammation, and fibrotic wound remodeling. Zwitterion (ZI) hydrogels exhibit antifouling and anti-inflammatory properties that may improve …

us (code pays fourni par la source)

Figure 5 from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

Violin plots showing the model scores for the 256 GBM and 73 CNS-DLBCL cases in stage 3, obtained from the models developed using the (A) ensemble approach and (B) loss approach.

Figure 3 from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

Description of the three-stage study design.

Supplementary Figure S1 from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

Cartoon comparing AUC and cross entropy loss on five GBM patients and five CNS-DLBCL cases. Grey denotes GBM and black denotes CNS-DLBCL. All models have the same AUC (AUC=100%); however, Model 4 has the smallest cross entropy loss.

Figure 4 from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

Violin plots showing the model scores from the 240 retrospective GBM cases in stage 2 from the models developed using the (A) ensemble approach and (B) loss approach.

Supplementary Table S1 from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

Sensitivity and specificity of the loss model applied to the stage 3 cohort. All possible model score thresholds from the loss model are shown. The yellow highlighted row denotes the score threshold required to obtain 90% sensitivity. The orange highlighted row denotes …

Figure 7 from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

Evaluating the effect on model performance (AUC) when performing ensemble across an increasingly larger number of models. Five independent sets of 5-fold cross-validation were evaluated. One 5-fold cross-validation set denotes performing ensemble across the resulting k = 5 models. Two 5-fold cross-validation …

Figure 6 from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

Model performance on the 256 GBM and 73 CNS-DLBCL independent test cases from stage 3 from the model developed using the ensemble approach (A) overall, (B) stratified by sex, and (C) stratified by age. Results for the model developed using the loss …

Table 1 from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

Demographic and MRI characteristics of patients involved in stage 1, stage 2, and stage 3.

Data from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast …

Supplementary Figure S2 from MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma

Mana Moassefi, Paul A. Decker, Gian Marco Conte, Matthew Kosel et autres

The MRI model was run on 34 patients with tumefactive demyelination. (A) Distribution of predicted MRI score for the 34 patients. (B) Distribution of predicted MRI score by age at diagnosis. The blue line denotes a loess fit and the grey shaded …

One-Day Intracranial Accelerated iTBS: Antidepressant Effects and Mechanistic Insights from a Patient with Major Depression and Epilepsy

Ariane Rhone, Umair Hassan, Madaline M. Mocchi, Aaron Boes et autres

Major depression is debilitating and often resistant to pharmacological intervention. Accelerated, transcranial magnetic stimulation protocols involving intermittent theta-burst stimulation (iTBS) to dorsolateral prefrontal cortex (DLPFC) can produce rapid antidepressant effects, but durability is limited and repeated treatments are often necessary. In the …