T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results

Nketiah, Gabriel Addio; Elschot, Mattijs; Kim, Eugene; Teruel, Jose R.; Scheenen, Tom W. J.; Bathen, Tone Frost; Selnæs, Kirsten Margrete

doi:10.1007/s00330-016-4663-1

Cited by 121 publications

(89 citation statements)

References 50 publications

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“…In recent years a number of studies have used a radiomics approach for the assessment for aggressiveness in prostatic carcinoma . These studies have demonstrated the feasibility of radiomics features for characterizing PCa habitats, where the mp‐MRI platform consisting of T 2 WI, DWI, and/or DCE was exploited in differentiating between Gleason score (GS) 3 + 4 and GS 4 + 3, reflecting cancer aggressiveness with a higher accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, many studies have applied the emerging radiomics technique to improve the diagnostic or predictive performance of PCa research . As an extension of texture analysis, radiomics enable medical images to be converted into high‐dimensional, mineable, and quantitative features by using high‐throughput extraction algorithms of these characterizations .…”

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confidence: 99%

“…Previous studies on breast cancer, colorectal cancer, and lung cancer have shown superior performance for characterizing or grading the tumors with the assistance of radiomics. For prostate neoplasms, additional values of radiomics features were also reported on PCa detection, aggressiveness assessment, and survival analysis in comparison with the conventional morphologic predictors . We hypothesized that a radiomics signature incorporating a panel of stable radiomics features might provide information about morphology.…”

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confidence: 99%

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MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Xie

Wang

et al. 2019

Magnetic Resonance Imaging

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Background Radiomics approaches based on multiparametric MRI (mp‐MRI) have shown high accuracy in prostate cancer (PCa) management. However, there is a need to apply radiomics to the preoperative prediction of extracapsular extension (ECE). Purpose To develop and validate a radiomics signature to preoperatively predict the probability of ECE for patients with PCa, compared with the radiologists' interpretations. Study Type Retrospective. Population In total, 210 patients with pathology‐confirmed ECE status (101 positive, 109 negative) were enrolled. Field Strength/Sequence T2‐weighted imaging (T2WI), diffusion‐weighted imaging, and dynamic contrast‐enhanced imaging were performed on two 3.0T MR scanners. Assessment A radiomics signature was constructed to predict the probability of ECE prior to radical prostatectomy (RP). In all, 17 stable radiomics features of 1619 extracted features based on T2WI were selected. The same images were also evaluated by three radiologists. The predictive performance of the radiomics signature was validated and compared with radiologists' interpretations. Statistical Tests A radiomics signature was developed by a least absolute shrinkage and selection operator (LASSO) regression algorithm. Samples enrolled were randomly divided into two groups (143 for training and 67 for validation). Discrimination, calibration, and clinical usefulness were validated by analysis of the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve, respectively. The predictive performance was then compared with visual assessments of three radiologists. Results The radiomics signature yielded an AUC of 0.902 and 0.883 in the training and validation cohort, respectively, and outperformed the visual assessment (AUC: 0.600–0.697) in the validation cohort. Pairwise comparisons demonstrated that the radiomics signature was more sensitive than the radiologists (75.00% vs. 46.88%–50.00%, all P < 0.05), but obtained comparable specificities (91.43% vs. (88.57%–94.29%); P ranged from 0.64–1.00). Data Conclusion A radiomics signature was developed and validated that outperformed the radiologists' visual assessments in predicting ECE status. Level of Evidence: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1914–1925.

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MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Xie

Wang

et al. 2019

Magnetic Resonance Imaging

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“…Accurate classification of the different Gleason score patterns on MRI is still a challenge and requires objective, reproducible, quantitative analysis methods. Texture analysis, which is a promising technique, could potentially distinguish between these different PCa patterns [5].…”

Section: Advances In Mrimentioning

confidence: 99%

Editorial Comment: Advances in MRI and PET of the prostate: concurrence or complementarity?

2018

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This Editorial Comment refers to the articles "Diagnostic evaluation of magnetization transfer and diffusion kurtosis imaging for prostate cancer detection in a re-biopsy population" by Barrett T et al., Eur Radiol. 2017 Prostate cancer (PCa) is the most common cancer in men. Although many of these tumours may be indolent and do not require treatment, prostate cancer is the third cause of mortality in Europe and the USA. Recent advances in management of PCa have become possible with improved staging and restaging, with multimodality imaging being instrumental. A rapid survey of recent issues of European Radiology confirms the vitality of the clinical research in modern noninvasive imaging of prostate cancer.

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“…These features are tested against a multitude of different genomic variables. Metrics, such as the false discovery rate, are often implemented to identify meaningful prospective variables in the setting of multiple hypotheses testing . A different exploratory method is hierarchical clustering, which is used to identify similarities in large genetic datasets.…”

Section: Introduction To Radiogenomicsmentioning

confidence: 99%

Background, current role, and potential applications of radiogenomics

Pinker

Shitano

Sala

et al. 2017

Magnetic Resonance Imaging

157

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T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results

Abstract: • T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.

Cited by 121 publications

References 50 publications

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Editorial Comment: Advances in MRI and PET of the prostate: concurrence or complementarity?

Background, current role, and potential applications of radiogenomics

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