Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning
<|reference_start|>Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning: Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cross-sections sliced from 3D biopsy tissue specimens. This process impedes the analysis of complex tissue structures such as glands, which can vary depending on the tissue slice examined. We propose a novel digital pathology data source called a "volumetric core," obtained via the extraction and co-alignment of serially sectioned tissue sections using a novel morphology-preserving alignment framework. We trained an attention-based multiple-instance learning (ABMIL) framework on deep features extracted from volumetric patches to automatically classify the Gleason Grade Group (GGG). To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning. We ran our morphology-preserving alignment framework to construct 10,210 volumetric cores, leaving out 30% for pretraining. The rest of the dataset was used to train ABMIL, which resulted in a 0.958 macro-average AUC, 0.671 F1 score, 0.661 precision, and 0.695 recall averaged across all five GGG significantly outperforming the 2D baselines.<|reference_end|>
@article{redekop2024digital,
    title={Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate
  Cancer Using Deep Learning},
    author={Ekaterina Redekop, Mara Pleasure, Zichen Wang, Anthony Sisk, Yang
  Zong, Kimberly Flores, William Speier, Corey W. Arnold},
    journal={arXiv preprint arXiv:2409.08331},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08331},
    primaryClass={eess.IV cs.CV q-bio.QM}
}