Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic‐Based Model vs. PI‐RADS v2

Chen, Tong; Li, Mengjuan; Gu, Yuefan; Zhang, Yue-Yue; Yang, Shuo; Wei, Chaogang; Wu, Jiangfen; Li, Xin; Zhao, Wei; Shen, Jing

doi:10.1002/jmri.26243

Cited by 102 publications

(118 citation statements)

References 39 publications

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“…Since almost all clinically-significant lesions (i.e., 71 out of 73) have a Gleason grade group > 1, we can compare this value with the AUC of 88.8% obtained by our method for the G1 vs all task. Our experiments also confirm results of previous works showing the efficacy of radiomics for analyzing PCa images [40], [50]- [55]. In [56], entropy-based texture features extracted from gray-level co-occurrence matrix (GLCMs) were found to be related to GS, more specifically, that a higher GS is associated with a higher ADC entropy and low ADC energy.…”

Section: Discussionsupporting

confidence: 89%

Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer

et al. 2020

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Convolutional neural networks (CNNs) require large amounts of data for training, beyond what can be acquired for current radiomics models. We hypothesize that deep entropy features (DEFs) derived from existing CNNs can be applied to MRI images of prostate cancers (PCa) to reliably predict the Gleason score (GS) of PCa lesions. In this study, we analyzed 112 lesions acquired from 99 PCa patients, either pre-biopsy or pre-treatment, their associated GS, and multi-parametric MRI (mpMRI) sequences. Our approach is based on the extraction of DEF features produced in individual layers of 9 pre-trained CNN models. We first analyze DEFs from separate CNNs using the Wilcoxon test and Spearman correlation to find significant features associated with GS. In a multivariate analysis, we then use the combined DEFs of all CNNs as input to a random forest (RF) classifier for predicting the Gleason grade group of patients. Among the 9 pre-trained CNNs, the NASNet-mobile architecture offered the features most correlated to GS (ρ=0.47; p<0.05). From the 7,857 combined features, 11 DEFs could differentiate GS < 8 from GS ≥ 8 (corrected p < 0.05). Moreover, the RF classifier discerned GS of 6, 3+4, 4+3, 8 and ≥ 9 with an AUC (%) of 80.08, 85.77, 97.30, 98.20, and 86.51, respectively. Our results suggest that the DEFs can be used to differentiate GS of PCa lesions with the highest accuracy of GS ≥ 8 based on mpMRI. DEFs could improve diagnosis accuracy, reduce the risks of misclassification, help to better assess prognosis, and individualize patient care approaches.

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Section: Discussionsupporting

confidence: 89%

Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer

et al. 2020

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“…The obtained results of AUC, sensitivity, and specificity were extremely high for all the classification tasks tested (PI-RADS 4-5 vs. HT on adv3D, PI-RADS 4-5 vs. HT on adv2D, and PI-RADS 4-5 vs. HT on std3D), reaching values up to 0.99 for AUC and 0.98 for both sensitivity and specificity. These performances were comparable to those obtained by Chen et al [27] in their logistic regression model, which was built by incorporating T2W sequences and ADC maps to classify PCa vs. non-PCa tissues. However, in their work, Chen et al included also shape features, which were deliberately not considered in our models in order to avoid possible biases in feature values introduced by the delineation of HT VOI/ROI which did not follow anatomical boundaries, as in the case of tumor lesions.…”

Section: Discussionsupporting

confidence: 81%

Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol

Monti

Brancato

Costanzo

et al. 2020

Cancers

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Prostate cancer (PCa) is a disease affecting an increasing number of men worldwide. Several efforts have been made to identify imaging biomarkers to non-invasively detect and characterize PCa, with substantial improvements thanks to multiparametric Magnetic Resonance Imaging (mpMRI). In recent years, diffusion kurtosis imaging (DKI) was proposed to be directly related to tissue physiological and pathological characteristic, while the radiomic approach was proven to be a key method to study cancer imaging phenotypes. Our aim was to compare a standard radiomic model for PCa detection, built using T2-weighted (T2W) and Apparent Diffusion Coefficient (ADC), with an advanced one, including DKI and quantitative Dynamic Contrast Enhanced (DCE), while also evaluating differences in prediction performance when using 2D or 3D lesion segmentation. The obtained results in terms of diagnostic accuracy were high for all of the performed comparisons, reaching values up to 0.99 for the area under a receiver operating characteristic curve (AUC), and 0.98 for both sensitivity and specificity. In comparison, the radiomic model based on standard features led to prediction performances higher than those of the advanced model, while greater accuracy was achieved by the model extracted from 3D segmentation. These results provide new insights into active topics of discussion, such as choosing the most convenient acquisition protocol and the most appropriate postprocessing pipeline to accurately detect and characterize PCa.

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“…While ICC values are not directly applicable to other related studies because of differences between subjects in other cohorts, it gives good estimates for comparing between features and between machine learning approaches within the same data set. To study repeatability of classification between significant and insignificant PCa, the 112 patients were split randomly into 70–30% (e.g., Chen et al) development‐testing sets stratifying ratio of cases containing at least 1 lesion with GGG 1, as a trade‐off between amount of training data and statistical power in final evaluations with test data. In test data set, 34 cases was considered sufficient to make reasonable performance estimations with unseen data, while rest of the subjects were left to training to make it possible to compare between numerous individual features, while able to apply machine learning.…”

Section: Methodsmentioning

confidence: 99%

Repeatability of radiomics and machine learning for DWI: Short‐term repeatability study of 112 patients with prostate cancer

Merisaari

Taimen

Shiradkar

et al. 2019

Magnetic Resonance in Med

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Purpose To evaluate repeatability of prostate DWI‐derived radiomics and machine learning methods for prostate cancer (PCa) characterization. Methods A total of 112 patients with diagnosed PCa underwent 2 prostate MRI examinations (Scan1 and Scan2) performed on the same day. DWI was performed using 12 b‐values (0–2000 s/mm2), post‐processed using kurtosis function, and PCa areas were annotated using whole mount prostatectomy sections. A total of 1694 radiomic features including Sobel, Kirch, Gradient, Zernike Moments, Gabor, Haralick, CoLIAGe, Haar wavelet coefficients, 3D analogue to Laws features, 2D contours, and corner detectors were calculated. Radiomics and 4 feature pruning methods (area under the receiver operator characteristic curve, maximum relevance minimum redundancy, Spearman’s ρ, Wilcoxon rank‐sum) were evaluated in terms of Scan1‐Scan2 repeatability using intraclass correlation coefficient (ICC)(3,1). Classification performance for clinically significant and insignificant PCa with Gleason grade groups 1 versus >1 was evaluated by area under the receiver operator characteristic curve in unseen random 30% data split. Results The ICC(3,1) values for conventional radiomics and feature pruning methods were in the range of 0.28–0.90. The machine learning classifications varied between Scan1 and Scan2 with % of same class labels between Scan1 and Scan2 in the range of 61–81%. Surface‐to‐volume ratio and corner detector‐based features were among the most represented features with high repeatability, ICC(3,1) >0.75, consistently high ranking using all 4 feature pruning methods, and classification performance with area under the receiver operator characteristic curve >0.70. Conclusion Surface‐to‐volume ratio and corner detectors for prostate DWI led to good classification of unseen data and performed similarly in Scan1 and Scan2 in contrast to multiple conventional radiomic features.

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Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic‐Based Model vs. PI‐RADS v2

Abstract: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Cited by 102 publications

References 39 publications

Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer

Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer

Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol

Repeatability of radiomics and machine learning for DWI: Short‐term repeatability study of 112 patients with prostate cancer

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