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The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on F-PSMA-1007 PET. | LitMetric

AI Article Synopsis

  • Convolutional Neural Networks (CNNs) are changing contouring practices in radiation oncology, but their effectiveness varies across different medical scanners and institutions, prompting this study on data from specific institutions.
  • The study trained nnU-Net using PET images from four institutions and tested the model's adaptability by excluding data from each center in training, applying 5-Fold Cross-Validation for comparison.
  • Results showed a mixed training approach had a median Dice Similarity Score (DSC) of 0.76, comparable to the DSC of models trained with data exclusion, but significant improvements were noted for specific cohorts, indicating multi-center training benefits.

Article Abstract

Purpose: Convolutional Neural Networks (CNNs) have emerged as transformative tools in the field of radiation oncology, significantly advancing the precision of contouring practices. However, the adaptability of these algorithms across diverse scanners, institutions, and imaging protocols remains a considerable obstacle. This study aims to investigate the effects of incorporating institution-specific datasets into the training regimen of CNNs to assess their generalization ability in real-world clinical environments. Focusing on a data-centric analysis, the influence of varying multi- and single center training approaches on algorithm performance is conducted.

Methods: nnU-Net is trained using a dataset comprising 161 F-PSMA-1007 PET images collected from four distinct institutions (Freiburg: n = 96, Munich: n = 19, Cyprus: n = 32, Dresden: n = 14). The dataset is partitioned such that data from each center are systematically excluded from training and used solely for testing to assess the model's generalizability and adaptability to data from unfamiliar sources. Performance is compared through a 5-Fold Cross-Validation, providing a detailed comparison between models trained on datasets from single centers to those trained on aggregated multi-center datasets. Dice Similarity Score, Hausdorff distance and volumetric analysis are used as primary evaluation metrics.

Results: The mixed training approach yielded a median DSC of 0.76 (IQR: 0.64-0.84) in a five-fold cross-validation, showing no significant differences (p = 0.18) compared to models trained with data exclusion from each center, which performed with a median DSC of 0.74 (IQR: 0.56-0.86). Significant performance improvements regarding multi-center training were observed for the Dresden cohort (multi-center median DSC 0.71, IQR: 0.58-0.80 vs. single-center 0.68, IQR: 0.50-0.80, p < 0.001) and Cyprus cohort (multi-center 0.74, IQR: 0.62-0.83 vs. single-center 0.72, IQR: 0.54-0.82, p < 0.01). While Munich and Freiburg also showed performance improvements with multi-center training, results showed no statistical significance (Munich: multi-center DSC 0.74, IQR: 0.60-0.80 vs. single-center 0.72, IQR: 0.59-0.82, p > 0.05; Freiburg: multi-center 0.78, IQR: 0.53-0.87 vs. single-center 0.71, IQR: 0.53-0.83, p = 0.23).

Conclusion: CNNs trained for auto contouring intraprostatic GTV in F-PSMA-1007 PET on a diverse dataset from multiple centers mostly generalize well to unseen data from other centers. Training on a multicentric dataset can improve performance compared to training exclusively with a single-center dataset regarding intraprostatic F-PSMA-1007 PET GTV segmentation. The segmentation performance of the same CNN can vary depending on the dataset employed for training and testing.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304577PMC
http://dx.doi.org/10.1186/s13014-024-02491-wDOI Listing

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