A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging

Giannini, Valentina; Mazzetti, Simone; Vignati, A.; Russo, Filippo; Bollito, Enrico; Porpiglia, Francesco; Stasi, M.; Regge, Daniele

doi:10.1016/j.compmedimag.2015.09.001

Cited by 61 publications

(51 citation statements)

References 35 publications

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“…For instance, traditional CADx systems have been used on ultrasonography images to diagnose cervical cancer in lymph nodes, where they have been found to improve the performance of particularly inexperienced radiologists as well as reduce variability among them 61 . Other application areas include prostate cancer in multiparametric MRI, where a malignancy probability map is first calculated for the entire prostate, followed by automated segmentation for candidate detection 62 .…”

Section: Impact On Oncology Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Section: Impact On Oncology Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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“…The limitation of the non-quantitative value of pixel intensities in MR images can be reduced by the application of some preprocessing steps, as for example, the correction of magnetic field inhomogeneities 60,61 and the intensity normalization across intersubject or intrasubject acquisitions. 62 MR images can be used for the estimation of quantitative parameters that describe functional properties.…”

Section: Mr Imagesmentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

133

108

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The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

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“…In this study of 85 patients, MRI had 33% sensitivity and 96% specificity in the characterization of extracapsular cancer manifestation versus 58% and 98% for multiparametric MRI [16]. In the assessment of the T-stage of prostate cancer, multiparametric MRI with use of a fully automated computer system has been reported to have a median sensitivity of 84% (1 st to 3 rd quartile, 77% to 93%) and a median specificity of 86% (1 st to 3 rd quartile, 76% to 95%) and a lesion-based sensitivity of 96% [17]. Studies comparing MRI with PET/CT in the characterization of primary prostate cancer show better sensitivity for MRI with use of diffusion-weighted imaging (MRI) and determination of the apparent diffusion coefficient (ADC).…”

Section: Discussionmentioning

confidence: 99%