To improve the model’s robustness and generalization performance, we investigate effective test-time augmentation-based ensemble prediction methods and evaluate the effectiveness of various ensemble prediction techniques in combination. In the training phase, we generate an optimized predictive model using a multi-modality regression network. The prediction is then determined through ensemble average voting with augmented test images generated by diverse data augmentation methods, including affine transformation, Mixup, Cutout, CutMix, and their combinations. Our experimentation reveal that all ensemble prediction methods demonstrated the ability to address issues through regularization, such as averaging errors on images subjected to random modifications. Notably, the use of Affine significantly improves over the baseline, with a 18.3% increase in accuracy and a 12.2% increase in AUC. The adoption of CutMix, maintains stability in both sensitivity and specificity, resulting in a higher balanced accuracy than Mixup and Cutout.
Assessment of prostate cancer aggressiveness is important because the effectiveness of treatment vary depending on the aggressiveness. The use of multi-parametric MR imaging prior to biopsy is recommended for accurate prostate cancer aggressiveness assessment but suffers from similar visual appearance of tumors between adjacent grades. To improve the predictive performance of prostate cancer aggressiveness, this study proposes a deep regression model involving size-normalized patch generation and multiple losses. First, we generate two types of input patches such as tumor-centered patch and size-normalized patch to effectively learn the characteristics of small tumors. Second, we propose a multiple loss functions consisting of triplet loss, mean squared error, and cross-entropy ordinal loss to increase the ability to discriminate between tumors with similar visual appearance and different aggressiveness. As a result, the proposed model trained with the size-normalized ADC map showed the highest performance with an accuracy of 78.85%, specificity of 89.66%, and AUC of 0.77. The ensemble model of tumor-centered T2w image and size-normalized ADC map improved sensitivity by 8.69% and showed the best performance with accuracy of 78.85%.
The use of quantitative radiomic features of MRI to predict the aggressiveness of prostate cancer has attracted increasing amounts of attention due to its potential as a non-invasive biomarker for prostate cancer. Although clinical studies have shown that apparent diffusion coefficient (ADC) values correlate with the aggressiveness of prostate cancer, most studies on radiomic features have been performed only with T2-weighted MR (T2wMR). Therefore, we investigate the usefulness of radiomic features of T2wMR and ADC to predict prostate cancer aggressiveness. To define the prostate cancer region of T2wMR based on ground truth pathology, a radiologist manually segmented prostate cancer referring to a fusion result of registration of histopathology image and T2wMR. The prostate cancer region of the ADC is then defined as the same region as the T2wMR through registration of the ADC on the T2wMR. To extract radiomic features to predict prostate cancer aggressiveness, total 68 features are calculated for each region of T2wMR and ADC. To predict the aggressiveness of prostate cancer, a random forest classifier is trained for each region in T2wMR and ADC. The prostate cancer regions were categorized as G1 (GS <= 3+4) and G2 (GS <= 4+3). As results, the prediction model of ADC was provided high performance than that of T2wMR, and the area under the curves of the receiver operating characteristic (ROC) were 0.70 and 0.74 in T2wMR and ADC. Experiment results showed that the possibility of determining the aggressiveness of prostate cancer through the quantitative radiomic features of T2wMR and ADC.
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