MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Sushentsev, Nikita; Rundo, Leonardo; Blyuss, Oleg; Gnanapragasam, Vincent; Sala, Evis; Barrett, Tristan

doi:10.1038/s41598-021-92341-6

Cited by 21 publications

(18 citation statements)

References 86 publications

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“…Moreover, gaining access to larger cohorts will help evaluate the utility of the proposed cut-offs to predict histopathological progression between risk groups rather than individual grade groups, which may improve patient survival. 43 Finally, combining the proposed cut-offs with other quantitative MRI features such as those derived from radiomics, 44,45 alongside standard clinical biomarkers of disease progression, e.g. PSA and PSA density, may further improve their diagnostic performance and help objectivise serial MRI assessment in AS.…”

Section: Discussionmentioning

confidence: 99%

Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression

Sushentsev

Caglič

Rundo

et al. 2021

The British Journal of Radiology

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Objective: To analyse serial changes in MRI-derived tumour measurements and apparent diffusion coefficient (ADC) values in prostate cancer (PCa) patients on active surveillance (AS) with and without histopathological disease progression. Methods: This study included AS patients with biopsy-proven PCa with a minimum of two consecutive MR examinations and at least one repeat targeted biopsy. Tumour volumes, largest axial two-dimensional (2D) surface areas, and maximum diameters were measured on T2 weighted images (T2WI). ADC values were derived from the whole lesions, 2D areas, and small-volume regions of interest (ROIs) where tumours were most conspicuous. Areas under the ROC curve (AUCs) were calculated for combinations of T2WI and ADC parameters with optimal specificity and sensitivity. Results: 60 patients (30 progressors and 30 non-progressors) were included. In progressors, T2WI-derived tumour volume, 2D surface area, and maximum tumour diameter had a median increase of +99.5%,+55.3%, and +21.7% compared to +29.2%,+8.1%, and +6.9% in non-progressors (p < 0.005 for all). Follow-up whole-volume and small-volume ROIs ADC values were significantly reduced in progressors (−11.7% and −9.5%) compared to non-progressors (−6.1% and −1.6%) (p < 0.05 for both). The combined AUC of a relative increase in maximum tumour diameter by 20% and reduction in small-volume ADC by 10% was 0.67. Conclusion: AS patients show significant differences in tumour measurements and ADC values between those with and without histopathological disease progression. Advances in knowledge: This paper proposes specific clinical cut-offs for T2WI-derived maximum tumour diameter (+20%) and small-volume ADC (−10%) to predict histopathological PCa progression on AS and supplement subjective serial MRI assessment.

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Section: Discussionmentioning

confidence: 99%

Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression

Sushentsev

Caglič

Rundo

et al. 2021

The British Journal of Radiology

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“…Accordingly, we produced three versions for each ROI (i.e., original, opening, and closing). This procedure simulates ROI variations through consideration of intra-/inter-reader dependence during manual contouring 44 . The optimal number of bins was selected after the ROI perturbation process, by considering the rebinning with the highest number of robust features.…”

Section: Methodsmentioning

confidence: 99%

Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features

Farias

Noia

Han

et al. 2021

Sci Rep

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Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies. Therefore, we propose a Generative Adversarial Network (GAN)-based lesion-focused framework for Computed Tomography (CT) image Super-Resolution (SR); for the lesion (i.e., cancer) patch-focused training, we incorporate Spatial Pyramid Pooling (SPP) into GAN-Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). At $$2\times $$ 2 × SR, the proposed model achieved better perceptual quality with less blurring than the other considered state-of-the-art SR methods, while producing comparable results at $$4\times $$ 4 × SR. We also evaluated the robustness of our model’s radiomic feature in terms of quantization on a different lung cancer CT dataset using Principal Component Analysis (PCA). Intriguingly, the most important radiomic features in our PCA-based analysis were the most robust features extracted on the GAN-super-resolved images. These achievements pave the way for the application of GAN-based image Super-Resolution techniques for studies of radiomics for robust biomarker discovery.

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“…In parallel with working on further iterations of PRECISE, developing quantitative tools to make serial MRI assessment more objective may help further improve its performance and limit variance to achieve consistent expert-level quality [ 6 ]. Pilot studies have adopted artificial intelligence (AI) techniques to devise MRI-derived radiomics models for predicting PCa progression on AS both at baseline [ 11 ] and at follow-up [ 12 ]. However, in the multiple time point follow-up setting, the only established methodological framework for analysing temporal radiomic patterns is delta-radiomics (DR), which only measures change between two time points [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance

et al. 2023

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Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78–0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64–0.87]; p = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76–0.93]; p = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. Key Points •LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. •Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.

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MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Cited by 21 publications

References 86 publications

Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression

Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression

Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features

Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance

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