Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores

Winkel, David; Breit, Hanns‐Christian; Shi, Bibo; Boll, Daniel T.; Seifert, Hans‐Helge; Wetterauer, Christian

doi:10.21037/qims.2020.03.08

Cited by 19 publications

(27 citation statements)

References 48 publications

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“…In current study, the combination of texture features and machine learning based on ADC maps was performed to provide tissue information and analyze GG upgrading. Machine learning analysis based on varied biomarkers has been successfully applied in PCa detection and evaluation ( 39 , 44 – 46 ). In the study by Nitta S et al., the age of the patients, PSA level, prostate volumes, and white blood cell count in urinalysis were used as input data for the machine learning methods, reaching the higher AUCs than the AUCs of the PSA level, PSA density and PSA velocity ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

“…found that the dynamic contrast-enhanced (DCE)-MRI original image-derived features integrated with machine learning methods could predict PCa invasiveness non-invasively with high accuracy ( 45 ). In a recent study by Winkel D J et al., using quantitative imaging parameters as input, machine learning models outperformed PI-RADS assessment scores in the prediction of PCa ( 46 ). All these studies indicated that machine learning methods could help to evaluate the heterogeneity and aggressiveness of PCa.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

Xie

Min

et al. 2021

Front. Oncol.

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ObjectiveTo evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2).Materials and methodsFifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann−Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter features. Four supervised machine learning methods were employed. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The statistical comparison between AUCs was performed.ResultsSix texture features were selected for the machine learning models building. These texture features were significantly different between GG upgrading group and GG non-upgrading group (P < 0.05). The six features had no significant difference between GG1 and GG2 based on the final pathology of radical prostatectomy. All machine learning methods had satisfactory predictive efficacy. The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750−0.994), 0.967, and 0.778, respectively. The AUC, sensitivity, and specificity of SVM were 0.861 (95%CI: 0.732−0.991), 1.000, and 0.722, respectively. There had no significant difference between AUCs in the test cohort.ConclusionA combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

Xie

Min

et al. 2021

Front. Oncol.

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“…Thus, the diagnoses in our study were defined as no cancer, low-risk PCa (GS ≤3+4), and high-risk PCa (GS ≥4+3). As DRE Other research has found evidence of the high diagnostic accuracy of MP-MRI (26)(27)(28). A prostate biopsy under US/ MP-MRI fusion can greatly improve the detection rate of clinically SPCa and contribute to a multivariate PCa prediction model, but no one has combined the EQS with MRI and clinical factors to build a PCa prediction model before, despite MRI contributing significantly to existing models (10).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram based on multiparametric magnetic resonance imaging and elastography-derived data for the stratification of patients with prostate cancer

Ding

Song

Wu³

et al. 2021

Quant Imaging Med Surg

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Background: This study sought to develop and validate a nomogram combining the elastographic Q-analysis score (EQS), the Prostate Imaging Reporting and Data System (PI-RADS) score, and clinical parameters for the stratification of patients with prostate cancer (PCa). Methods: A retrospective study was conducted of 375 patients with 375 lesions who underwent volumenavigation transrectal ultrasound (TRUS) and multiparametric magnetic resonance imaging (MP-MRI)fusion targeted biopsies between April 2017 and January 2020. Based on a multivariate logistic regression model, a nomogram was created to assess any PCa and high-risk PCa [Gleason score (GS) ≥4+3] using data from patients diagnosed between April 2017 and June 2019 (n=271), and was validated in patients diagnosed after July 2019 (n=104). The nomogram's performance was evaluated based on its discrimination, calibration, and clinical usefulness.Results: The areas under the curve (AUCs) of the nomogram for predicting any PCa and high-risk PCa were 0.949 [95% confidence interval (CI), 0.921 to 0.978] and 0.936 (95% CI, 0.906 to 0.965), respectively, in the training cohort, and 0.946 (95% CI, 0.894 to 0.997) and 0.971 (95% CI, 0.9331 to 1), respectively, in the validation cohort. The nomogram was well calibrated, and no significant difference was found between the predicted and observed probabilities. A decision curve analysis (DCA) for the nomogram with and without the EQS showed that the threshold probability of for any PCa was <67%. Conclusions:The nomogram that combined elastography-derived and MP-MRI data was more clinically useful than the model based on PI-RADS and clinical parameters alone. Our nomogram could aid urologists to make decisions and avoid unnecessary biopsies.

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“…The average sensitivity was 82–92% at an average specificity of 43–76% with an area under the curve (AUC) of 0.65 to 0.89 for several lesion volumes ranging from >0.03 to >0.5 cc. In addition, supervised ML classifiers have been used to successfully predict clinically significant cancer prostate cancer utilizing a group of quantitative image-features and comparing them with conventional PI-RADS v2 assessment scores [ 205 ].…”

Section: Selected Examples On Mri Biomarkers In Solid Tumorsmentioning

confidence: 99%

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

et al. 2021

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The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

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Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores

Cited by 19 publications

References 48 publications

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

Development and validation of a nomogram based on multiparametric magnetic resonance imaging and elastography-derived data for the stratification of patients with prostate cancer

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

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