Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.

Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from a national brain tumor consortium (n = 184; median age, 7 years [range, 1-23 years]; 94 male patients) and a pediatric cancer center (n = 100; median age, 8 years [range, 1-19 years]; 47 male patients) to develop and evaluate deep learning neural networks for pediatric low-grade glioma segmentation using a stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally tested on an independent test set and subjected to randomized blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests. Results The best AI model used in-domain stepwise transfer learning (median Dice score coefficient, 0.88 [IQR, 0.72-0.91] vs 0.812 [IQR, 0.56-0.89] for baseline model; P = .049). With external testing, the AI model yielded excellent accuracy using reference standards from three clinical experts (median Dice similarity coefficients: expert 1, 0.83 [IQR, 0.75-0.90]; expert 2, 0.81 [IQR, 0.70-0.89]; expert 3, 0.81 [IQR, 0.68-0.88]; mean accuracy, 0.82). For clinical benchmarking (n = 100 scans), experts rated AI-based segmentations higher on average compared with other experts (median Likert score, 9 [IQR, 7-9] vs 7 [IQR 7-9]) and rated more AI segmentations as clinically acceptable (80.2% vs 65.4%). Experts correctly predicted the origin of AI segmentations in an average of 26.0% of cases. Conclusion Stepwise transfer learning enabled expert-level automated pediatric brain tumor autosegmentation and volumetric measurement with a high level of clinical acceptability. Keywords: Stepwise Transfer Learning, Pediatric Brain Tumors, MRI Segmentation, Deep Learning Supplemental material is available for this article. © RSNA, 2024

Bone Marrow Changes in Adolescent Girls With Anorexia Nervosa

Early osteoporosis is common among adolescent girls with anorexia nervosa (AN) and may result from premature conversion of red (RM) to yellow bone marrow. We performed right knee magnetic resonance imaging (MRI) on a 1.0 T extremity scanner in 20 patients and 20 healthy controls, aged 16.2 ± 1.6 years (mean ± SD). Coronal T1-weighted (T1W) images and T1 maps were generated from T1 relaxometry images. Blinded radiologists visually assessed RM in the distal femoral and proximal tibial metaphyses in T1W images using a scale of signal intensity from 0 (homogeneous hyperintensity, no RM) to 4 (all dark, complete RM). Subjects with AN exhibited nearly twofold lower metaphyseal RM scores in both the femur (0.64 versus 1.22, p = .03) and tibia (0.54 versus 0.96, p = .08). In relaxometric measurements of four selected regions (femur and tibia amd epiphysis and metaphysis), subjects with AN showed higher mean epiphyseal but lower metaphyseal T1. The net AN-control difference between epiphysis and metaphysis was 70 ms in the femur (+31 versus −35 ms, p = .02) and of smaller magnitude in the tibia. In relaxometry data from the full width of the femur adjacent to the growth plate, AN subjects showed mean T1 consistently lower than in controls by 30 to 50 ms in virtually every part of the sampling region. These findings suggest that adolescents with AN exhibit premature conversion of hematopoietic to fat cells in the marrow of the peripheral skeleton potentially owing to adipocyte over osteoblast differentiation in the mesenchymal stem cell pool. © 2010 American Society for Bone and Mineral Researc

Magnetic resonance imaging and spectroscopy evidence of efficacy for adrenal and gonadal hormone replacement therapy in anorexia nervosa.

PURPOSE: Dehydroepiandrosterone (DHEA)+estrogen/progestin therapy for adolescent girls with anorexia nervosa (AN) has the potential to arrest bone loss. The primary aim of this study was to test the effects of DHEA+estrogen/progestin therapy in adolescent girls with AN on bone marrow in the distal femur using magnetic resonance imaging (MRI) and spectroscopy. METHODS: Seventy adolescent girls with AN were enrolled in a double blind, randomized, placebo-controlled trial at two urban hospital-based programs. INTERVENTION: Seventy-six girls were randomly assigned to receive 12months of either oral micronized DHEA or placebo. DHEA was administered with conjugated equine estrogens (0.3mg daily) for 3months, then an oral contraceptive (20μg ethinyl estradiol/ 0.1mg levonorgestrel) for 9months. The primary outcome measure was bone marrow fat by MRI and magnetic resonance spectroscopy (MRS). RESULTS: T2 of the water resonance dropped significantly less in the active vs. placebo group over 12months at both the medial and lateral distal femur (p=0.02). Body mass index (BMI) was a significant effect modifier for T1 and for T2 of unsaturated (T2 CONCLUSIONS: These findings suggest treatment with oral DHEA+estrogen/progestin arrests the age- and disease-related changes in marrow fat composition in the lateral distal femur reported previously in this population

Early experience alters brain function and structure

To investigate the effects of early experience on brain function and structure

Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning

PURPOSE: 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. METHODS: 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. RESULTS: 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]; <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], =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], <0.05 for each). Additionally, the AI segmentations had significantly higher ( <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. CONCLUSIONS: 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