Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis

Cuocolo, Renato; Cipullo, Maria Brunella; Stanzione, Arnaldo; Romeo, Valeria; Green, Roberta; Cantoni, Valeria; Ponsiglione, Andrea; Ugga, Lorenzo; Imbriaco, Massimo

doi:10.1007/s00330-020-07027-w

Cited by 73 publications

(70 citation statements)

References 47 publications

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“…While the QUADAS-2 analysis presented some unclear elements, no high-risk sources of bias were identified. Study heterogeneity was high, but this is in line with other machine learning meta-analyses and diagnostic meta-analyses in general [21,57,58]. Finally, not all articles were specified if the WHO 2016 classification of central nervous system tumors was used.…”

Section: Discussionmentioning

confidence: 62%

“…As previously reported, the presentation of accuracy metrics in radiomics and ML studies is often inconsistent and incomplete [21]. Due to this situation, our meta-analysis could only employ AUC values as these were the most commonly reported.…”

Section: Discussionmentioning

confidence: 99%

“…The QUADAS-2 evaluates the risk of bias in different domains ("patient selection," "index test," "reference standard," and "flow and timing") and can be personalized according to the specific research question [21]. It was assessed in consensus by two readers for each of the studies selected for the meta-analysis.…”

Section: Data Collection and Study Evaluationmentioning

confidence: 99%

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Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

et al. 2021

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Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice.

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Section: Discussionmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 99%

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Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

et al. 2021

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“…Recent technological developments and research breakthroughs have led to a wider introduction of advanced techniques for data and image analysis, aiming to surpass the limitations of current clinical practice. This is reflected by the growing number of radiomics and ML studies that have been published across medicine, and in oncology and radiology specifically, sometimes showing results that are competitive or surpass expert radiologists [ 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Radiomics Machine Learning and Deep Learningmentioning

confidence: 99%

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

et al. 2021

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The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.

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“…Many automatic prostate segmentation methodologies have been developed in ultrasound, MRI and computed tomography images [ 5 , 6 ] to reduce subjectivity in delineating tissue boundaries. For example, Cuocolo et al [ 7 ] performed a meta-analysis of machine learning approaches for diagnostic purposes using MRI. They concluded that these pipelines showed good accuracy results but need further investigation, better standardisation in design and proper reporting of the results.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation

et al. 2020

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The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.

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Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis

Cited by 73 publications

References 47 publications

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation

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