Version 1
: Received: 27 November 2023 / Approved: 28 November 2023 / Online: 30 November 2023 (02:01:48 CET)
How to cite:
Alanezi, S.T.; Kraśny, M.J.; Kleefeld, C.; Colgan, N. Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models With Tuned Hyperparameters. Preprints2023, 2023111822. https://doi.org/10.20944/preprints202311.1822.v1
Alanezi, S.T.; Kraśny, M.J.; Kleefeld, C.; Colgan, N. Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models With Tuned Hyperparameters. Preprints 2023, 2023111822. https://doi.org/10.20944/preprints202311.1822.v1
Alanezi, S.T.; Kraśny, M.J.; Kleefeld, C.; Colgan, N. Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models With Tuned Hyperparameters. Preprints2023, 2023111822. https://doi.org/10.20944/preprints202311.1822.v1
APA Style
Alanezi, S.T., Kraśny, M.J., Kleefeld, C., & Colgan, N. (2023). Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models With Tuned Hyperparameters. Preprints. https://doi.org/10.20944/preprints202311.1822.v1
Chicago/Turabian Style
Alanezi, S.T., Christoph Kleefeld and Niall Colgan. 2023 "Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models With Tuned Hyperparameters" Preprints. https://doi.org/10.20944/preprints202311.1822.v1
Abstract
We developed a novel machine learning algorithm to augment clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics and novel application of machine learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of greater than 0.91 sensitivity across three separate cohorts. Tumor heterogeneity and prediction between GS cohorts were quantified using two feature selection approaches and two classifiers with tuned hyperparameters (including grid search and randomized search). There was a total of 71 patients analyzed in this study. Multiparametric MRI, incorporating T2WI and the ADC maps, were used to derive radiomics features. Recursive feature elimination (RFE), the least absolute shrinkage and selection operator (LASSO), and two classification approaches, including a support vector machine (SVM) (with grid search) and random forest (RF) (with randomized search), were utilized to differentiate between non-tumor regions versus significant cancer and prediction among Gleason score groups. In T2WI images, the RFE feature selection approach combined with RF and SVM classifiers outperformed LASSO with SVM and RF classifiers regarding performance. The detection precision of the radiomics framework depending on multiple imaging modalities was lower than the diagnostic accuracy of single imaging modality model with every machine learning approach assessed. The best performance was achieved by combining LASSO and SVM into a model that used both T2WI and ADC. This model had an area under the curve (AUC) of 0.91. Radiomic features computed from ADC and T2WI images were used to predict three groups of Gleason score using two kinds of feature selection methods (RFE and LASSO), RF and SVM classifier models with tuned hyperparameters. Using combined sequences (T2WI and ADC map images) and combined radiomics (1st and GLCM features), LASSO, with a feature selection method with RF, was able to predict G3 with the highest sensitivity at a level AUC of 0.92. To predict G3 for single sequence (T2WI images) using GLCM features, LASSO with SVM achieved the highest sensitivity with an AUC of 0.92.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
