Computed Tomography-based Radiomics for Risk Stratification in Prostate Cancer

Osman, Sarah; Leijenaar, Ralph T.H.; Cole, Aidan; Lyons, C.; Hounsell, Alan R.; Prise, Kevin M.; O’Sullivan, Joe M.; Lambin, Philippe; McGarry, Conor K.; Jain, Suneil

doi:10.1016/j.ijrobp.2019.06.2504

Cited by 47 publications

(59 citation statements)

References 33 publications

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“…After incorporation into prediction models, such features can be used effectively to predict prognosis, histological properties, and molecular aberrations [18][19][20][21]. In PC, previous studies could demonstrate successful prediction of Gleason score and survival using radiomic models [22,23].…”

Section: Introductionmentioning

confidence: 99%

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

Peeken

Shouman

Kroenke

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)–positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs). Methods Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling. Results Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node–specific decision curve analysis, there was a clinical net benefit above LN short diameter. Conclusion The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features. Electronic supplementary material The online version of this article (10.1007/s00259-020-04864-1) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

Peeken

Shouman

Kroenke

et al. 2020

Eur J Nucl Med Mol Imaging

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“…This study corroborates studies that have found radiomic features to correlate with prognosis among lung, 12 breast, 13 and prostate cancer patients. 6 To address the issue of combining multi-focal data at a patient-level, prior studies have implemented a weighted average of features of all metastases 14 while others have included all tumors from a specific patient assigned to either a training or validation set to avoid cluster-correlation biases. 15 While tumor-level data may be useful for certain tasks like primary-site prediction, there is a need for aggregation of patient-level data for overall outcomes like survival or recurrence.…”

Section: Discussionmentioning

confidence: 99%

“…Although radiomic features have shown the ability to risk stratify cancer patients, 5,6 radiomic analysis has largely been limited to the evaluation of individual tumor volumes. There is no established methodology regarding the best way to combine radiomic features for patients with multifocal sites of disease to establish a patient-level correlate of clinical outcomes.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors

Chang

Joel

Chang

et al. 2020

Preprint

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Background: Radiomic feature analysis has been shown to be effective at modeling cancer outcomes. It has not yet been established how to best combine these radiomic features in patients with multifocal disease. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognostication to better inform treatment. Methods: We compared six mathematical methods of combining radiomic features of 3596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Results: Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model. Conclusions: Radiomic features can be effectively combined to establish patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and disease sites.

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“…Radiomics and CNNs offers an effective and non-invasive way to predict oncological outcomes [16]- [20]. For PCa, multiple studies have identified imaging features that correlate with GS [21]- [23]. However, a common limitation of radiomics approaches is the requirement of having enough high-quality data to both train and validate the model.…”

Section: Introductionmentioning

confidence: 99%

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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Computed Tomography-based Radiomics for Risk Stratification in Prostate Cancer

Abstract: Dr Lambin is inventor of two patents on radiomics and one non patentable invention (software), licensed to Oncoradiomics and has (minority) shares in the company Oncoradiomics. Dr.

Cited by 47 publications

References 33 publications

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors

Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer

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