Logo PTI Logo FedCSIS

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 32

Detecting Cancerous Regions in DCE MRI using Functional Data, XGboost and Neural Networks

, , , , ,

DOI: http://dx.doi.org/10.15439/2022F128

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 2330 ()

Full text

Abstract. Cancerous region detection in the prostate is performed by multiparametric magnetic resonance imaging using different imaging sequences. One of those modalities is dynamic contrast enhancement. The authors of this paper are testing possible modifications of workflow which use this modality for more accurate cancerous region detection in the prostate. The introduced changes are timestamp mapping in the segmentation step, proportionate Simple Linear Iterative Clustering region number to prostate region size in each slice, new definition of labels and new extracted features. Furthermore, experiments are performed for segmentation in a single timestamp only. The experiments test the effect of modification on curve classification by using XGBoost classification and flat neural network approaches. Lastly, the authors perform hyperparameter tuning of both approaches.


  1. R. Alkadi, F. Taher, A. El-Baz, and N. Werghi, “A deep learning-based approach for the detection and localization of prostate cancer in T2 magnetic resonance images,” Journal of digital imaging. 32(5), (2019):793-807.
  2. S. Alqahtani and et al, “Prediction of prostate cancer Gleason score upgrading from biopsy to radical prostatectomy using pre-biopsy multiparametric MRI PIRADS scoring system,” Scientific reports 10.1 (2020): 1-9.
  3. P. Banerjee, “A Guide on XGBoost hyperparameters tuning,” Kaggle. (2020)
  4. T. Barrett and et al, “Ratio of Tumor to Normal Prostate Tissue Apparent Diffusion Coefficient as a Method for Quantifying DWI of the Prostate,” American Journal of Roentgenology vol 205, (2015): 585-593. Doi: 10.2214/AJR.15.14338.
  5. J. C. Barsce, J. A. Palombarini and E. C. Martínez, “Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning,” CoRR vol abs/2112.08094, (2021)
  6. J. F. F. Bray, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “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 (2018): 394-424.
  7. J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, “Algorithms for hyperparameter optimization,” Advances in neural information processing systems vol. 24 (2011): 394-424.
  8. J. Bergstra, B. Komer, C. Eliasmith, D. Yamins and D. D. Cox, “Hyperopt: a python library for model selection and hyperparameter optimization,” Computational Science & Discovery, 8(1) p.014008 (2015).
  9. W. Chang and et al, “Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost,” Diagnostics, 11(5) (2021) p.792
  10. A. Chatterjee and et al, “Performance of ultrafast DCE-MRI for diagnosis of prostate cancer,” Academic radiology, 25(3) (2018): 349-358.
  11. T. Chen, and C.Guestrin, “XGBoost: A Scalable Tree Boosting System,” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM (2016): 785–794
  12. F. Chollet, and et al, “Keras,” GitHub. (2015).
  13. R. Cuocolo and et al, “Machine learning applications in prostate cancer magnetic resonance imaging,” European radiology experimental. 3(1), (2019):1-8.
  14. C. De Vente, P. Vos, M. Hosseinzadeh, J. Pluim, and M. Veta, “Deep learning regression for prostate cancer detection and grading in biparametric MRI,” IEEE Transactions on Biomedical Engineering. 68(2), (2020):374-383.
  15. R. Fraiman and G. Muniz, “Trimmed means for functional data,” Test 10. (2001): 419–440. http://dx.doi.org/10.1007/BF02595706.
  16. G. N. Dimitrakopoulos, A. G. Vrahatis, V. Plagianakos, and K. Sgarbas, “Pathway analysis using XGBoost classification in Biomedical Data,” In Proceedings of the 10th Hellenic Conference on Artificial Intelligence (2018): 1-6
  17. J. H. Hayes, and M. J. Barry, “Screening for prostate cancer with the prostate-specific antigen test: a review of current evidence,” Jama 311(11) (2014): pp.1143-1149.
  18. A. M. Hotker and et al, “Assessment of Prostate Cancer Aggressiveness by Use of the Combination of Quantitative DWI and Dynamic Contrast-Enhanced MRI. AJR,” American journal of roentgenology vol. 206,4 (2016): 756-63. http://dx.doi.org/10.2214/AJR.15.14912.
  19. J. Jucevičius and et al, “Automated 2D Segmentation of Prostate in T2-weighted MRI Scans,” International journal of computers communication & control, [S.l.], v. 12, n. 1, (2016): 53-60. ISSN 1841-9844.
  20. B. Liu and et al, “Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI,” Clinical radiology, 74(11) (2019): 896-e1.
  21. J. Liu and et al, “Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model,” Plos one. (2021);16(2):e0246306.
  22. S. López-Pintado and J. Romo, “On the Concept of Depth for Functional Data” Journal of the American Statistical Association v. 104, n. 486 (2009): 718-734. http://dx.doi.org/10.1198/jasa.2009.0108.
  23. R. N. Low, D. B. Fuller, and N. Muradyan, “Dynamic gadolinium-enhanced perfusion MRI of prostate cancer: assessment of response to hypofractionated robotic stereotactic body radiation therapy,” American Journal of Roentgenology 197.4 (2011): 907-915.
  24. T. O’Malley and et al, “Keras Tuner,” https://github.com/keras-team/keras-tuner (2019).
  25. F. Pedregosa and et al, “Scikit-learn: Machine learning in Python,” Journal of machine learning research vol. 12, (2011): 2825-2830.
  26. I. Reda and et al, “Deep learning role in early diagnosis of prostate cancer,” Technology in cancer research & treatment, 17, (2018) p.1533034618775530.
  27. A. Vaitulevičius and et al, “DCE MRI Modality Investigation for Cancerous Prostate Region Detection: Case Analysis ,” unpublished.
  28. F. Wilcoxon, “Individual Comparisons by Ranking Methods,” Biometrics Bulletin vol. 1, (1945): 80-83. http://dx.doi.org/10.2307/3001968.
  29. C. J. Wu and et al, “DWI-associated entire-tumor histogram analysis for the differentiation of low-grade prostate cancer from intermediate-high-grade prostate cancer,” Abdom Imaging 40, 3214-3221 (2015).