1naresh2naresh<pre>Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => Controlled
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

    [35300b4e-1847-47b8-978b-308f02803568] => Array
        (
            [runtime-id] => 35300b4e-1847-47b8-978b-308f02803568
            [type] => toll-free-key
            [service-id] => ajnr-ac.highwire.org
            [access-type] => Controlled
            [privilege] => Array
                (
                    [a7239afe-d63a-4e28-b23f-25be83026bc5] => Array
                        (
                            [runtime-id] => a7239afe-d63a-4e28-b23f-25be83026bc5
                            [type] => toll-free-key
                        )

                )

            [credentials] => Array
                (
                    [method] => toll-free-key
                    [value] => tf_ipsecsha;b56c73ea62d2985d64116c1061fdeb4202a1281f
                )

        )

)
1naresh2naresh<pre>Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => FreeToRead
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

    [20e6f1dd-3d0f-4060-825a-baf417694cd4] => Array
        (
            [runtime-id] => 20e6f1dd-3d0f-4060-825a-baf417694cd4
            [type] => toll-free-key
            [service-id] => ajnr-ac.highwire.org
            [access-type] => FreeToRead
            [privilege] => Array
                (
                    [f7d177f1-2a27-42e5-bc8d-302cadfbce09] => Array
                        (
                            [runtime-id] => f7d177f1-2a27-42e5-bc8d-302cadfbce09
                            [type] => toll-free-key
                        )

                )

            [credentials] => Array
                (
                    [method] => toll-free-key
                    [value] => tf_ipsecsha;b56c73ea62d2985d64116c1061fdeb4202a1281f
                )

        )

)
PT  - JOURNAL ARTICLE
AU  - Zhang, Jikai
AU  - LaBella, Dominic
AU  - Zhang, Dylan
AU  - Houk, Jessica L.
AU  - Rudie, Jeffrey D.
AU  - Zou, Haotian
AU  - Warman, Pranav
AU  - Mazurowski, Maciej A.
AU  - Calabrese, Evan
TI  - Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma
AID  - 10.3174/ajnr.A8580
DP  - 2024 Nov 15
TA  - American Journal of Neuroradiology
PG  - ajnr.A8580
4099  - http://www.ajnr.org/content/early/2024/11/14/ajnr.A8580.short
4100  - http://www.ajnr.org/content/early/2024/11/14/ajnr.A8580.full
AB  - BACKGROUND AND PURPOSE: To develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center.MATERIALS AND METHODS: We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 +/-13 years. 372/634 (59%) patients were male, and 262/634 (41%) patients were female. Study data consisted of 3,403 brain MRI exams and corresponding standardized, radiologist-based brain tumor response assessments (BT-RADS). An artificial intelligence (AI)-based brain tumor response assessment algorithm was developed using automated, volumetric tumor segmentation. AI-based response assessments were evaluated for agreement with radiologist-based response assessments and ability to stratify patients by overall survival. Metrics were computed to assess the agreement using BTRADS as the ground-truth, fixed-time point survival analysis was conducted to evaluate the survival stratification, and associated P-values were calculated.RESULTS: For all BT-RADS categories, AI-based response assessments showed moderate agreement with radiologists’ response assessments (F1 = 0.587-0.755). Kaplan-Meier survival analysis revealed statistically worse overall fixed time point survival for patients assessed as image worsening equivalent to RANO progression by human alone compared to by AI alone (log-rank P=0.007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction (P=0.012).CONCLUSIONS: AI-based volumetric glioblastoma MRI response assessment following BT-RADS criteria yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival.ABBREVIATIONS: GBM＝ Glioblastoma; RANO＝ Response Assessment in Neuro-Oncology; BTRADS＝ Brain Tumor Reporting and Data System; NLP = Natural Language Processing.