{Reference Type}: Preprint
{Title}: Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning.
{Author}: Boyd A;Ye Z;Prabhu S;Tjong MC;Zha Y;Zapaishchykova A;Vajapeyam S;Hayat H;Chopra R;Liu KX;Nabavidazeh A;Resnick A;Mueller S;Haas-Kogan D;Aerts HJWL;Poussaint T;Kann BH;
{Journal}: medRxiv
{Volume}: 0
{Issue}: 0
{Year}: 2023 Sep 18
暂无{DOI}: 10.1101/2023.06.29.23292048
{Abstract}: <B>UNASSIGNED: </B>Artificial intelligence (AI)-automated tumor delineation for pediatric gliomas would enable real-time volumetric evaluation to support diagnosis, treatment response assessment, and clinical decision-making. Auto-segmentation algorithms for pediatric tumors are rare, due to limited data availability, and algorithms have yet to demonstrate clinical translation.<BR><B>UNASSIGNED: </B>We leveraged two datasets from a national brain tumor consortium (n=184) and a pediatric cancer center (n=100) to develop, externally validate, and clinically benchmark deep learning neural networks for pediatric low-grade glioma (pLGG) segmentation using a novel in-domain, stepwise transfer learning approach. The best model [via Dice similarity coefficient (DSC)] was externally validated and subject to randomized, blinded evaluation by three expert clinicians wherein clinicians assessed clinical acceptability of expert- and AI-generated segmentations via 10-point Likert scales and Turing tests.<BR><B>UNASSIGNED: </B>The best AI model utilized in-domain, stepwise transfer learning (median DSC: 0.877 [IQR 0.715-0.914]) versus baseline model (median DSC 0.812 [IQR 0.559-0.888]; p<0.05). On external testing (n=60), the AI model yielded accuracy comparable to inter-expert agreement (median DSC: 0.834 [IQR 0.726-0.901] vs. 0.861 [IQR 0.795-0.905], p=0.13). On clinical benchmarking (n=100 scans, 300 segmentations from 3 experts), the experts rated the AI model higher on average compared to other experts (median Likert rating: 9 [IQR 7-9]) vs. 7 [IQR 7-9], p<0.05 for each). Additionally, the AI segmentations had significantly higher (p<0.05) overall acceptability compared to experts on average (80.2% vs. 65.4%). Experts correctly predicted the origins of AI segmentations in an average of 26.0% of cases.<BR><B>UNASSIGNED: </B>Stepwise transfer learning enabled expert-level, automated pediatric brain tumor auto-segmentation and volumetric measurement with a high level of clinical acceptability. This approach may enable development and translation of AI imaging segmentation algorithms in limited data scenarios.