Radiomics

Starmans, Martijn P.A.; Voort, Sebastian R. van der; Tovar, Jose M. Castillo; Veenland, Jifke F.; Klein, Stefan; Niessen, Wiro J.

doi:10.1016/b978-0-12-816176-0.00023-5

Cited by 39 publications

(23 citation statements)

References 34 publications

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“…Regarding the correlation of the tumors on MRI with the pathology reference, the most common practice is to perform a segmentation by an expert radiologist. The region of interest (ROI) delineation is a factor that has a direct influence on the feature computation [38]. Therefore, studying the robustness of the features to the segmentation is a factor that authors should consider when validating their methods.…”

Section: Discussionmentioning

confidence: 99%

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

Arif

Niessen

et al. 2020

Cancers

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Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range. Results: From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77–0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial. Conclusions: To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.

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Section: Discussionmentioning

confidence: 99%

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

Arif

Niessen

et al. 2020

Cancers

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“…Different modalities of medical imaging (CT [ 12 ], MRI, PET) are utilized for radiomics and radiogenomics studies. Before feature extraction can be carried out, the definition of a region/volume of interest (ROI/VOI) must be realized.…”

Section: Radiomicsmentioning

confidence: 99%

“…Fully automated segmentation methods, supervised and unsupervised, are generally built on basic image processing of pixel intensities and/or textural features, with the most promising methods relying on deep learning by training a U-net type structure [ 17 ]. Supervised techniques are considered to be more accurate but interobserver variability will still be present, as the manual part of the segmentation and the settings of the algorithm influence the result [ 12 , 18 ]. Unsupervised segmentation techniques commonly rely on labeled atlases and have been shown to be less accurate than the supervised techniques [ 19 ].…”

Section: Radiomicsmentioning

confidence: 99%

Radiogenomics in Colorectal Cancer

Badic

Tixier

Rest

et al. 2021

Cancers

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The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.

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“…In recent years, radiomics, i.e., the use of a large number of quantitative medical imaging features to predict clinical outcomes, has been successfully used in various clinical areas (9)(10)(11). In liver cancer, this has been mostly based on computed tomography to make predictions such as survival, prognosis, and recurrence (12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

Automated differentiation of malignant and benign primary solid liver lesions on MRI: an externally validated radiomics model

Starmans

Miclea

Vilgrain

et al. 2021

Preprint

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Background & Aims: Distinguishing malignant from benign primary solid liver lesions is highly important for treatment planning. However, diagnosis on radiological imaging is challenging. In this study, we developed a radiomics model based on magnetic resonance imaging (MRI) to distinguish the most common malignant and benign primary solid liver lesions, and externally validated the model in two centers. Approach & Results: Datasets were retrospectively collected from three tertiary referral centers (A, B and C) including data from affiliated hospitals sent for revision. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. For each patient, only a T2-weighted MRI was included. A radiomics model was developed on dataset A using a combination of machine learning approaches, and internally evaluated on dataset A through cross-validation. Next, the model was externally validated on datasets B and C, and compared to scoring by two experienced abdominal radiologists on dataset C. In the resulting dataset, in total, 486 patients were included (A: 187, B: 98 and C: 201). Despite substantial MRI acquisition heterogeneity, the radiomics model developed on dataset A had a mean area under the receiver operating characteristic curve (AUC) of 0.78 in the internal validation on dataset A, and a similar AUC in the external validations (B: 0.74, C: 0.76). In dataset C, the two radiologists showed moderate agreement (Cohens κ: 0.61) and achieved AUCs of 0.86 and 0.82, respectively. Conclusions: Our radiomics model using T2-weighted MRI only can non-invasively distinguish malignant from benign primary solid liver lesions. External validation indicated that our model is generalizable despite substantial differences in the acquisition protocols.

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Radiomics

Cited by 39 publications

References 34 publications

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

Radiogenomics in Colorectal Cancer

Automated differentiation of malignant and benign primary solid liver lesions on MRI: an externally validated radiomics model

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