A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions

Hou, Ying; Bao, Meiling; Wu, Chen‐Jiang; Zhang, Jing; Zhang, Yudong; Shi, Haibin

doi:10.1007/s00261-020-02678-1

Cited by 33 publications

(36 citation statements)

References 28 publications

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“…Hou et al. ( 21 ) developed T2WI, DWI, and ADC radiomics machine learning models to predict CsPCa in PI-RADS 3 lesions with AUC of 0.89. The results of the above studies are quite different, maybe because the MRI sequence used in their studies is different.…”

Section: Discussionmentioning

confidence: 99%

“…Some radiomics studies have differentiated malignant from benign lesions and assessed the aggressiveness, survival, and treatment response in prostate lesions ( 15 – 17 ). However, there is limited research ( 18 – 21 ) applying radiomics analysis to detect CsPCa in equivocal PI-RADS 3 lesions and no validation data to verify their findings.…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of a Radiomics Nomogram for Predicting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions

Sun

et al. 2022

Front. Oncol.

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PurposeTo develop and validate a radiomics nomogram for the prediction of clinically significant prostate cancer (CsPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) category 3 lesions.MethodsWe retrospectively enrolled 306 patients within PI-RADS 3 lesion from January 2015 to July 2020 in institution 1; the enrolled patients were randomly divided into the training group (n = 199) and test group (n = 107). Radiomics features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast-enhanced (DCE) imaging. Synthetic minority oversampling technique (SMOTE) was used to address the class imbalance. The ANOVA and least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection and radiomics signature building. Then, a radiomics score (Rad-score) was acquired. Combined with serum prostate-specific antigen density (PSAD) level, a multivariate logistic regression analysis was used to construct a radiomics nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate radiomics signature and nomogram. The radiomics nomogram calibration and clinical usefulness were estimated through calibration curve and decision curve analysis (DCA). External validation was assessed, and the independent validation cohort contained 65 patients within PI-RADS 3 lesion from January 2020 to July 2021 in institution 2.ResultsA total of 75 (24.5%) and 16 (24.6%) patients had CsPCa in institution 1 and 2, respectively. The radiomics signature with SMOTE augmentation method had a higher area under the ROC curve (AUC) [0.840 (95% CI, 0.776–0.904)] than that without SMOTE method [0.730 (95% CI, 0.624–0.836), p = 0.08] in the test group and significantly increased in the external validation group [0.834 (95% CI, 0.709–0.959) vs. 0.718 (95% CI, 0.562–0.874), p = 0.017]. The radiomics nomogram showed good discrimination and calibration, with an AUC of 0.939 (95% CI, 0.913–0.965), 0.884 (95% CI, 0.831–0.937), and 0.907 (95% CI, 0.814–1) in the training, test, and external validation groups, respectively. The DCA demonstrated the clinical usefulness of radiomics nomogram.ConclusionThe radiomics nomogram that incorporates the MRI-based radiomics signature and PSAD can be conveniently used to individually predict CsPCa in patients within PI-RADS 3 lesion.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Radiomics Nomogram for Predicting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions

Sun

et al. 2022

Front. Oncol.

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“…Hou et al [59] in 2020 developed a model integrating data extracted from T2W, DWI and ADC maps images of 271 patients. This model achieved promising performance in improving diagnostic accuracy in PI-RADS 3 by allowing clinically significant PCa to be differentiated from indolent and normal cases.…”

Section: Prediction Of Gleason Score and Pi-radsmentioning

confidence: 99%

Radiomics and Prostate MRI: Current Role and Future Applications

et al. 2021

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Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.

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“…Some studies investigated the role of machine learning-based classifiers in detecting clinically significant PCa with PI-RADS score 3 lesions. Results predicted by the classifier may be an important reference for clinical decision making and will help in increasing the prostate-positive biopsy rate in PI-RADS 3 while decreasing unnecessary biopsies [43][44][45]. Giambelluca et al [30] showed that predictive models based on texture features extracted through a texture analysis software (MaZda 4.6) had a good performance for the diagnosis of clinically significant PCa among PI-RADS 3 lesions on T2W (AUROC = 0.77) and ADC map (AUROC = 0.81) images.…”

Section: Radiomics and Pi-rads Scorementioning

confidence: 99%

Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature

et al. 2021

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Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naïve patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to increase the features available to detect PCa, to avoid unnecessary biopsies, to define tumor aggressiveness, and to monitor post-treatment recurrence of PCa. The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as guide clinical decisions and patient management. The purpose of this review is to describe the current research applications of radiomics in PCa on MR images.

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A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions

Cited by 33 publications

References 28 publications

Development and Validation of a Radiomics Nomogram for Predicting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions

Development and Validation of a Radiomics Nomogram for Predicting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions

Radiomics and Prostate MRI: Current Role and Future Applications

Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature

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