Quality control and whole-gland, zonal and lesion annotations for the PROSTATEx challenge public dataset

Cuocolo, Renato; Stanzione, Arnaldo; Castaldo, Anna; Lucia, Davide Raffaele De; Imbriaco, Massimo

doi:10.1016/j.ejrad.2021.109647

Cited by 54 publications

(40 citation statements)

References 18 publications

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“…The present study describes 60 stable, uncorrelated and non-invariant radiomics features, extracted from MRI images, which previously underwent a quality assessment [ 16 ], and used to distinguish significant from non-significant prostate cancer lesions through an ML approach. Firstly, a univariate statistical analysis was performed to prove that these 60 features were useful in distinguishing the lesions by themselves (16 of them were revealed to be statistically significant).…”

Section: Discussionmentioning

confidence: 99%

“…A total of 299 verified prostate lesions were included in this study. Specifically, the lesion annotation masks were obtained from an online open repository ( , accessed on 1 July 2020) and coupled with the source MRI images, which can be found in the PROSTATEx training dataset ( , accessed on 1 July 2020) [ 16 , 17 ]. The ground-truth of the public dataset is obtained with a manual annotation.…”

Section: Methodsmentioning

confidence: 99%

“…As a result, the aim of the present study is to evaluate the ability of the combined radiomics and ML approach using several ML algorithms (tree-based, instance-based and based on the a priori probability theory) on a publicly available dataset of MRI images, elaborated by Cuocolo et al [ 16 ], in differentiating a clinically significant from a clinically non-significant prostate lesion.…”

Section: Introductionmentioning

confidence: 99%

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A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

et al. 2021

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Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Naïve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

et al. 2021

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“…Regrettably, to the best of our knowledge, there are no public and annotated repositories for other pelvic cancer types (MRI or CT) and RT target volumes. Whilst global and institutional efforts are necessary to initiate public repositories, appropriate quality control and external expert auditing need to be in place to ensure data are of high quality [179,180].…”

Section: Discussionmentioning

confidence: 99%

Automatic Segmentation of Pelvic Cancers using Deep Learning: State-of-the-Art Approaches and Challenges

Kalantar

Lin

Winfield

et al. 2021

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The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides ground for technology development for computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive and clinically-oriented overview of DL-based segmentation studies for bladder, prostate, cervical and rectal cancers, highlighting the key findings, challenges and limitations.

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“…Availability of high-quality public databases including imaging, histopathologic and genomic data could be a solution, allowing researchers to reproduce and replicate their results on larger scale [ 85 ]. However, these are challenging to collect and share, and still require quality checks to ensure their validity and representativeness of the general population over time [ 86 , 87 ]. Moreover, the lack of standardization in AI pipelines, imaging acquisition parameters and segmentation methods also negatively impact reproducibility and comparability between studies.…”

Section: Limitations and Future Perspectivesmentioning

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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Quality control and whole-gland, zonal and lesion annotations for the PROSTATEx challenge public dataset

Cited by 54 publications

References 18 publications

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

Automatic Segmentation of Pelvic Cancers using Deep Learning: State-of-the-Art Approaches and Challenges

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

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