#BRAINTUMOR hashtag - Bluesky

1 day ago

Prof. Richard Gilbertson chaired the "Pediatric Brain Tumors: Biology to Therapy" symposium at @theaacr.bsky.social San Diego and presented Elizabeth Cooper’s work on reversing brain tumour immunotolerance in childhood brain tumours
#AACR26 #BrainTumor #PediatricCancer #CancerResearch #Immunotherapy

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Metastasis Research Society

@metastasisresearch

3 days ago

Membership Levels | Metastasis Research

🗓 Mark your calendars: June 28 – July 1, 2026
📍 Location: Salt Lake City, Utah
👉 Join the MRS community: metastasis-research.org/membership-a...
#MRS2026 #GelarehZadeh #NeuroOncology #BrainTumor #LiquidBiopsy #PrecisionMedicine #Metastasis #WomenInMedicine

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Premier Science

@premierscience

4 days ago

Multi-Class Brain Tumor Type Classification Using Convolutional Autoencoder (CAE) With CNN-VGG19: An Experimental Study - Premier Science Autoencoder, brain tumor, convolutional autoencoder, convolutional neural network, CNN with VGG19, deep learning, image processing.

doi.org/10.70389/PJS...

#braintumor #braincancer #CNNVGG29

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JMIR Publications

@jmirpub

6 days ago

Preliminary #usability Assessment of a Rule-Based Digital Self-Monitoring Platform for Patients With Brain Tumors Toward Digital Early Warning Systems: Pilot #feasibility Study Background: Postoperative follow-up after brain tumor surgery is typically limited to intermittent clinic visits, leaving subtle neurological or general deterioration between visits underrecognized. Digital self-monitoring platforms may help fill this gap, but evidence in neuro-oncology is scarce, particularly regarding how patient-reported symptom trajectories can feed into future data-driven early warning systems. Objective: This study aimed to evaluate the #feasibility, use patterns, and preliminary #usability of a smartphone or web-based self-monitoring system for patients after brain tumor surgery and to explore simple rule-based digital alerts as a first step toward an advanced digital early warning framework. Methods: We conducted a single-center prospective pilot study including adults discharged after brain tumor surgery who had access to a smartphone and could use a web app. Participants completed brief symptom surveys consisting of 51 binary items across 7 symptom domains, with an automatically calculated daily total score and score history visualization. #feasibility was assessed by enrollment, retention, submission counts, and submission rates. A total of 4 interpretable alert rules based on current score, short-term worsening, new-onset symptom combinations, and persistence across domains were evaluated using each patient’s last 3 submissions as the analytic unit. Clinical deterioration was defined a priori as objective decline in performance status, new neurological deficit, radiologic progression, or clinically significant laboratory changes. Rule performance metrics and bootstrap CIs were computed. #usability and acceptability were evaluated using the System #usability Scale and additional adherence-related items. Results: Of 64 enrolled patients, 30 (47%) with ≥3 submissions formed the analysis cohort (median age 57, IQR 47.2–64.5 years; n=12.9, 43% malignant tumors); 6 (20%) experienced clinical deterioration during follow-up. Patients contributed a median of 8.5 submissions (mean 19.03, SD 30.12) at 1.7 surveys per week on average, indicating sustained but heterogeneous engagement. The best-performing rule, based on net short-term score increase, achieved an area under the receiver operating characteristic of 0.88, with sensitivity 0.83, specificity 0.92, and accuracy 0.90 on the last-window dataset, outperforming rules based solely on current score or multidomain persistence. Among 23 app users who completed the System #usability Scale, the mean score was 84.0, reflecting high perceived #usability; higher-frequency users reported stronger perceived usefulness and habit-driven use. Conclusions: This pilot study demonstrates that a smartphone or web-based self-monitoring platform for patients with brain tumor is feasible and well accepted and that simple, transparent rules applied to longitudinal symptom scores show potential to capture early signals of clinical deterioration. However, given the small sample size, these predictive metrics are preliminary and require rigorous validation in larger, independent cohorts. These findings support further development of integrated digital early warning systems that combine patient-reported trajectories with clinical and physiological data to enhance postoperative neurosurgical care.

JMIR Formative Res: Preliminary #usability Assessment of a Rule-Based Digital Self-Monitoring Platform for Patients With Brain Tumors Toward Digital Early Warning Systems: Pilot #feasibility Study #DigitalHealth #HealthTech #NeuroOncology #PatientMonitoring #BrainTumor

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