Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

Tsougos, Ioannis; Vamvakas, Alexandros; Kappas, C.; Fezoulidis, Ioannis; Vassiou, Katerina

doi:10.1155/2018/7417126

Cited by 25 publications

(15 citation statements)

References 69 publications

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“…In this setting, several authors have correlated TA features calculated on MRI to predict pCR [99]. Braman et al [100] used DCE-MRI imaging extracted from both tumoral and peritumoral regions, obtaining an AUC of 78% for the training dataset and 74% for the validation dataset.…”

Section: Texture Analysis and Prognosis-focus On Breast Cancermentioning

confidence: 99%

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

et al. 2021

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The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

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Section: Texture Analysis and Prognosis-focus On Breast Cancermentioning

confidence: 99%

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

et al. 2021

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“…With the advances in computer technology, extracting large data from medical images using automatic algorithms becomes more feasible; and "radiomics," which allows high‐throughput extraction of tremendous amounts of quantitative information from radiographic images, emerged . Texture and histogram features based on MR images have the potential to provide noninvasive imaging biomarkers to aid in breast cancer diagnosis, prognosis, and treatment response evaluation . The radiomics signatures are also related to molecular biomarkers and subtypes, and can aid in patients' management using a precision medicine approach …”

mentioning

confidence: 99%

Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

Zhou

Zhang

Chang

et al. 2019

Magnetic Resonance Imaging

144

123

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Background: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fullyautomatic diagnosis using deep learning is rarely reported. Purpose: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deeplearning methods, by taking peritumor tissues into consideration. Study Type: Retrospective. Population: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). Field Strength/Sequence: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. Assessment: 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connectedcomponent labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. Statistical Tests: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. Results: In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the perlesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. Data Conclusion: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. Level of Evidence: 3 Technical Efficacy: Stage 2

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“…CDSSs are becoming essential tools for health care providers as Big Data technologies have enabled powerful instruments for acquisition and analysis of large amounts of comprehensive, heterogeneous data for clinical care, administration, and research 6 . Recent years have witnessed significant advances in Big Data tools and techniques for clinical decision support in various domains such as radiology, 13 breast cancer, 14 and neurosurgery 15 . Long‐term evidence of utility and user acceptability is beginning to emerge, 16 and randomized clinical trials of CDSSs suggest efficacy 17,18 .…”

Section: Clinical Decision Support Systems Interpretability and Adamentioning

confidence: 99%

Can Big Data guide prognosis and clinical decisions in epilepsy?

Cui

Zhang

et al. 2021

Epilepsia

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Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of “‐omics” domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence‐based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision‐making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.

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Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

Cited by 25 publications

References 69 publications

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

Can Big Data guide prognosis and clinical decisions in epilepsy?

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