Browsing by Author "Mankad, Kshitij"

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    ItemOpen Access
    An international study presenting a federated learning AI platform for pediatric brain tumors.
    (Nature communications, 2024-09) Lee, Edward H; Han, Michelle; Wright, Jason; Kuwabara, Michael; Mevorach, Jacob; Fu, Gang; Choudhury, Olivia; Ratan, Ujjwal; Zhang, Michael; Wagner, Matthias W; Goetti, Robert; Toescu, Sebastian; Perreault, Sebastien; Dogan, Hakan; Altinmakas, Emre; Mohammadzadeh, Maryam; Szymanski, Kathryn A; Campen, Cynthia J; Lai, Hollie; Eghbal, Azam; Radmanesh, Alireza; Mankad, Kshitij; Aquilina, Kristian; Said, Mourad; Vossough, Arastoo; Oztekin, Ozgur; Ertl-Wagner, Birgit; Poussaint, Tina; Thompson, Eric M; Ho, Chang Y; Jaju, Alok; Curran, John; Ramaswamy, Vijay; Cheshier, Samuel H; Grant, Gerald A; Wong, S Simon; Moseley, Michael E; Lober, Robert M; Wilms, Mattias; Forkert, Nils D; Vitanza, Nicholas A; Miller, Jeffrey H; Prolo, Laura M; Yeom, Kristen W
    While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Here, we present FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, and evaluate its performance on a diverse, realistic, multi-center cohort. Pediatric brain tumors were targeted due to the scarcity of such datasets, even in tertiary care hospitals. Our platform orchestrates federated training for joint tumor classification and segmentation across 19 international sites. FL-PedBrain exhibits less than a 1.5% decrease in classification and a 3% reduction in segmentation performance compared to centralized data training. FL boosts segmentation performance by 20 to 30% on three external, out-of-network sites. Finally, we explore the sources of data heterogeneity and examine FL robustness in real-world scenarios with data imbalances.
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    ItemOpen Access
    Improved prediction of postoperative pediatric cerebellar mutism syndrome using an artificial neural network.
    (Neuro-oncology advances, 2022-01) Sidpra, Jai; Marcus, Adam P; Löbel, Ulrike; Toescu, Sebastian M; Yecies, Derek; Grant, Gerald; Yeom, Kristen; Mirsky, David M; Marcus, Hani J; Aquilina, Kristian; Mankad, Kshitij

    Background

    Postoperative pediatric cerebellar mutism syndrome (pCMS) is a common but severe complication that may arise following the resection of posterior fossa tumors in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalization of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN).

    Methods

    An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset was collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumors. The ANN was trained on this dataset and its performance was evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. The area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed-rank test, with P < .05 considered statistically significant.

    Results

    Two hundred and four children were included, of whom 80 developed pCMS. The performance of the ANN (AUC 0.949; accuracy 90.9%) exceeded that of logistic regression (P < .05) and both external models (P < .001).

    Conclusion

    Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods.
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    Spatiotemporal changes in along-tract profilometry of cerebellar peduncles in cerebellar mutism syndrome.
    (NeuroImage. Clinical, 2022-01) Toescu, Sebastian M; Bruckert, Lisa; Jabarkheel, Rashad; Yecies, Derek; Zhang, Michael; Clark, Christopher A; Mankad, Kshitij; Aquilina, Kristian; Grant, Gerald A; Feldman, Heidi M; Travis, Katherine E; Yeom, Kristen W
    Cerebellar mutism syndrome, characterised by mutism, emotional lability and cerebellar motor signs, occurs in up to 39% of children following resection of medulloblastoma, the most common malignant posterior fossa tumour of childhood. Its pathophysiology remains unclear, but prior studies have implicated damage to the superior cerebellar peduncles. In this study, the objective was to conduct high-resolution spatial profilometry of the cerebellar peduncles and identify anatomic biomarkers of cerebellar mutism syndrome. In this retrospective study, twenty-eight children with medulloblastoma (mean age 8.8 ± 3.8 years) underwent diffusion MRI at four timepoints over one year. Forty-nine healthy children (9.0 ± 4.2 years), scanned at a single timepoint, served as age- and sex-matched controls. Automated Fibre Quantification was used to segment cerebellar peduncles and compute fractional anisotropy (FA) at 30 nodes along each tract. Thirteen patients developed cerebellar mutism syndrome. FA was significantly lower in the distal third of the left superior cerebellar peduncle pre-operatively in all patients compared to controls (FA in proximal third 0.228, middle and distal thirds 0.270, p = 0.01, Cohen's d = 0.927). Pre-operative differences in FA did not predict cerebellar mutism syndrome. However, post-operative reductions in FA were highly specific to the distal left superior cerebellar peduncle, and were most pronounced in children with cerebellar mutism syndrome compared to those without at the 1-4 month follow up (0.325 vs 0.512, p = 0.042, d = 1.36) and at the 1-year follow up (0.342, vs 0.484, p = 0.038, d = 1.12). High spatial resolution cerebellar profilometry indicated a site-specific alteration of the distal segment of the superior cerebellar peduncle seen in cerebellar mutism syndrome which may have important surgical implications in the treatment of these devastating tumours of childhood.