Abstract
The Gleason groups serve as the primary histological grading system for prostate cancer, providing crucial insights into the cancer’s potential for growth and metastasis. In clinical practice, pathologists determine the Gleason groups based on specimens obtained from ultrasound-guided biopsies. In this study, we investigate the feasibility of directly estimating the Gleason groups from MRI scans to reduce otherwise required biopsies. We identify two characteristics of this task, ordinality and the resulting dependent yet unknown variances between Gleason groups. In addition to the inter-/intra-observer variability in a multi-step Gleason scoring process based on the interpretation of Gleason patterns, our MR-based prediction is also subject to specimen sampling variance and, to a lesser degree, varying MR imaging protocols. To address this challenge, we propose a novel Poisson ordinal network (PON). PONs model the prediction using a Poisson distribution and leverages Poisson encoding and Poisson focal loss to capture a learnable dependency between ordinal classes (here, Gleason groups), rather than relying solely on the numerical ground-truth (e.g. Gleason Groups 1–5 or Gleason Scores 6–10). To improve this modelling efficacy, PONs also employ contrastive learning with a memory bank to regularise intra-class variance, decoupling the memory requirement of contrast learning from the batch size. Experimental results based on the images labelled by saturation biopsies from 265 prior-biopsy-blind patients, across two tasks demonstrate the superiority and effectiveness of our proposed method. The source code is available at https://github.com/Yinsongxu/PON.git.
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References
Ahmed, H.U., Bosaily, A.E.S., Brown, L.C., Gabe, R., Kaplan, R., Parmar, M.K., Collaco-Moraes, Y., Ward, K., Hindley, R.G., Freeman, A., et al.: Diagnostic accuracy of multi-parametric mri and trus biopsy in prostate cancer (promis): a paired validating confirmatory study. The Lancet 389(10071), 815–822 (2017)
Beckham, C., Pal, C.: Unimodal probability distributions for deep ordinal classification. In: International Conference on Machine Learning. pp. 411–419. PMLR (2017)
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 68(6), 394–424 (2018)
Cao, R., Bajgiran, A.M., Mirak, S.A., Shakeri, S., Zhong, X., Enzmann, D., Raman, S., Sung, K.: Joint prostate cancer detection and gleason score prediction in mp-mri via focalnet. IEEE transactions on medical imaging 38(11), 2496–2506 (2019)
Gayo, I.J., Saeed, S.U., Barratt, D.C., Clarkson, M.J., Hu, Y.: Strategising template-guided needle placement for mr-targeted prostate biopsy. In: MICCAI Workshop on Cancer Prevention through Early Detection. pp. 149–158. Springer (2022)
Hou, L., Yu, C.P., Samaras, D.: Squared earth mover’s distance-based loss for training deep neural networks. arXiv preprint arXiv:1611.05916 (2016)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
Potdar, K., Pardawala, T.S., Pai, C.D.: A comparative study of categorical variable encoding techniques for neural network classifiers. International journal of computer applications 175(4), 7–9 (2017)
Qin, Z., Zhang, P., Li, X.: Ultra fast deep lane detection with hybrid anchor driven ordinal classification. IEEE transactions on pattern analysis and machine intelligence (2022)
Saha, A., Bosma, J., Twilt, J., van Ginneken, B., Yakar, D., Elschot, M., Veltman, J., Fütterer, J., de Rooij, M., et al.: Artificial intelligence and radiologists at prostate cancer detection in mri-the pi-cai challenge. In: Medical Imaging with Deep Learning, short paper track (2023)
Shen, Z., Yang, Q., Shen, Y., Giganti, F., Stavrinides, V., Fan, R., Moore, C., Rusu, M., Sonn, G., Torr, P., et al.: Collaborative quantization embeddings for intra-subject prostate mr image registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 237–247. Springer (2022)
Stark, J.R., Perner, S., Stampfer, M.J., Sinnott, J.A., Finn, S., Eisenstein, A.S., Ma, J., Fiorentino, M., Kurth, T., Loda, M., et al.: Gleason score and lethal prostate cancer: does 3+ 4= 4+ 3? Journal of Clinical Oncology 27(21), 3459 (2009)
Valerio, M., Anele, C., Charman, S.C., van der Meulen, J., Freeman, A., Jameson, C., Singh, P.B., Emberton, M., Ahmed, H.U.: Transperineal template prostate-mapping biopsies: an evaluation of different protocols in the detection of clinically significant prostate cancer. BJU international 118(3), 384–390 (2016)
Wang, P., Han, K., Wei, X.S., Zhang, L., Wang, L.: Contrastive learning based hybrid networks for long-tailed image classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 943–952 (2021)
Yan, W., Chiu, B., Shen, Z., Yang, Q., Syer, T., Min, Z., Punwani, S., Emberton, M., Atkinson, D., Barratt, D.C., Hu, Y.: Combiner and hypercombiner networks: Rules to combine multimodality mr images for prostate cancer localisation. Medical Image Analysis 91, 103030 (2024)
Zhang, Y., Chen, J., Wang, K., Xie, F.: Ecl: Class-enhancement contrastive learning for long-tailed skin lesion classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 244–254. Springer (2023)
Acknowledgments
This work was supported by the International Alliance for Cancer Early Detection, a partnership between Cancer Research UK [C28070/A30912; C73666/A31378], Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London and the University of Manchester. This work was also supported by the China Scholarship Council.
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Xu, Y. et al. (2024). Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15005. Springer, Cham. https://doi.org/10.1007/978-3-031-72086-4_53
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