Introduction to radiomics for a clinical audience.

Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.

An optoacoustic imaging feature set to characterise blood vessels surrounding benign and malignant breast lesions.

Combining optoacoustic (OA) imaging with ultrasound (US) enables visualisation of functional blood vasculature in breast lesions by OA to be overlaid with the morphological information of US. Here, we develop a simple OA feature set to differentiate benign and malignant breast lesions. 94 female patients with benign, indeterminate or suspicious lesions were recruited and underwent OA-US. An OA-US imaging feature set was developed using images from the first 38 patients, which contained 14 malignant and 8 benign solid lesions. Two independent radiologists blindly scored the OA-US images of a further 56 patients, which included 31 malignant and 13 benign solid lesions, with a sensitivity of 96.8% and specificity of 84.6%. Our findings indicate that OA-US can reveal vascular patterns of breast lesions that indicate malignancy using a simple feature set based on single wavelength OA data, which is therefore amenable to application in low resource settings for breast cancer management.

AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis.

PURPOSE: To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. METHODS: A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar's test. RESULTS: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). CONCLUSION: A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions.

Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment.

PURPOSE: To evaluate the inter-/intra-observer agreement of BI-RADS-based subjective visual estimation of the amount of fibroglandular tissue (FGT) with magnetic resonance imaging (MRI), and to investigate whether FGT assessment benefits from an automated, observer-independent, quantitative MRI measurement by comparing both approaches. MATERIALS AND METHODS: Eighty women with no imaging abnormalities (BI-RADS 1 and 2) were included in this institutional review board (IRB)-approved prospective study. All women underwent un-enhanced breast MRI. Four radiologists independently assessed FGT with MRI by subjective visual estimation according to BI-RADS. Automated observer-independent quantitative measurement of FGT with MRI was performed using a previously described measurement system. Inter-/intra-observer agreements of qualitative and quantitative FGT measurements were assessed using Cohen's kappa (k). RESULTS: Inexperienced readers achieved moderate inter-/intra-observer agreement and experienced readers a substantial inter- and perfect intra-observer agreement for subjective visual estimation of FGT. Practice and experience reduced observer-dependency. Automated observer-independent quantitative measurement of FGT was successfully performed and revealed only fair to moderate agreement (k = 0.209-0.497) with subjective visual estimations of FGT. CONCLUSION: Subjective visual estimation of FGT with MRI shows moderate intra-/inter-observer agreement, which can be improved by practice and experience. Automated observer-independent quantitative measurements of FGT are necessary to allow a standardized risk evaluation. KEY POINTS: • Subjective FGT estimation with MRI shows moderate intra-/inter-observer agreement in inexperienced readers. • Inter-observer agreement can be improved by practice and experience. • Automated observer-independent quantitative measurements can provide reliable and standardized assessment of FGT with MRI.

[Occipitocervical junction: Aanatomy, craniometry and pathology]. / Okzipitozervikaler Übergang: Anatomie, Kraniometrie und Pathologien.

The occipitocervical junction comprises of the occiput condyles, the atlas, and the axis. The radiological evaluation of this region is supported by craniometric measurement methods which are based on predefined anatomical landmarks. The main pathologies of the occipitocervical junction are traumatic injuries, congenital anomalies or normal variants, infections, arthropathies, and tumors. In this article, the anatomy of the occipitocervical junction as well as the most important craniometric measurement methods are explained. Moreover various pathologies and similar appearing normal variants are presented.

Non-invasive assessment of intratumoral vascularity using arterial spin labeling: A comparison to susceptibility-weighted imaging for the differentiation of primary cerebral lymphoma and glioblastoma.

Using conventional MRI methods, the differentiation of primary cerebral lymphomas (PCNSL) and other primary brain tumors, such as glioblastomas, is difficult due to overlapping imaging characteristics. This study was designed to discriminate tumor entities using normalized vascular intratumoral signal intensity values (nVITS) obtained from pulsed arterial spin labeling (PASL), combined with intratumoral susceptibility signals (ITSS) from susceptibility-weighted imaging (SWI). Thirty consecutive patients with glioblastoma (n=22) and PCNSL (n=8), histologically classified according to the WHO brain tumor classification, were included. MRIs were acquired on a 3T scanner, and included PASL and SWI sequences. nVITS was defined by the signal intensity ratio between the tumor and the contralateral normal brain tissue, as obtained by PASL images. ITSS was determined as intratumoral low signal intensity structures detected on SWI sequences and were divided into four different grades. Potential differences in the nVITS and ITSS between glioblastomas and PCNSLs were revealed using statistical testing. To determine sensitivity, specificity, and diagnostic accuracy, as well as an optimum cut-off value for the differentiation of PCNSL and glioblastoma, a receiver operating characteristic analysis was used. We found that nVITS (p=0.011) and ITSS (p=0.001) values were significantly higher in glioblastoma than in PCNSL. The optimal cut-off value for nVITS was 1.41 and 1.5 for ITSS, with a sensitivity, specificity, and accuracy of more than 95%. These findings indicate that nVITS values have a comparable diagnostic accuracy to ITSS values in differentiating glioblastoma and PCNSL, offering a completely non-invasive and fast assessment of tumoral vascularity in a clinical setting.

Arterial spin-labeling assessment of normalized vascular intratumoral signal intensity as a predictor of histologic grade of astrocytic neoplasms.

BACKGROUND AND PURPOSE: Pulsed arterial spin-labeling is a noninvasive MR imaging perfusion method performed with the use of water in the arterial blood as an endogenous contrast agent. The purpose of this study was to determine the inversion time with the largest difference in normalized intratumoral signal intensity between high-grade and low-grade astrocytomas. MATERIALS AND METHODS: Thirty-three patients with gliomas, histologically classified as low-grade (n = 7) or high-grade astrocytomas (n = 26) according to the World Health Organization brain tumor classification, were included. A 3T MR scanner was used to perform pulsed arterial spin-labeling measurements at 8 different inversion times (370 ms, 614 ms, 864 ms, 1114 ms, 1364 ms, 1614 ms, 1864 ms, and 2114 ms). Normalized intratumoral signal intensity was calculated, which was defined by the signal intensity ratio of the tumor and the contralateral normal brain tissue for all fixed inversion times. A 3-way mixed ANOVA was used to reveal potential differences in the normalized vascular intratumoral signal intensity between high-grade and low-grade astrocytomas. RESULTS: The difference in normalized vascular intratumoral signal intensity between high-grade and low-grade astrocytomas obtained the most statistically significant results at 370 ms (P = .003, other P values ranged from .012-.955). CONCLUSIONS: The inversion time by which to differentiate high-grade and low-grade astrocytomas by use of normalized vascular intratumoral signal intensity was 370 ms in our study. The normalized vascular intratumoral signal intensity values at this inversion time mainly reflect the labeled intra-arterial blood bolus and therefore could be referred to as normalized vascular intratumoral signal intensity. Our data indicate that the use of normalized vascular intratumoral signal intensity values allows differentiation between low-grade and high-grade astrocytomas and thus may serve as a new, noninvasive marker for astrocytoma grading.

Altered likelihood of brain activation in attention and working memory networks in patients with multiple sclerosis: an ALE meta-analysis.

Multiple sclerosis (MS) is a chronic neurological disease, frequently affecting attention and working memory functions. Functional imaging studies investigating those functions in MS patients are hard to compare, as they include heterogeneous patient groups and use different paradigms for cognitive testing. The aim of this study was to investigate alterations in neuronal activation between MS patients and healthy controls performing attention and working memory tasks. Two meta-analyses of previously published fMRI studies investigating attention and working memory were conducted for MS patients and healthy controls, respectively. Resulting maps were contrasted to compare brain activation in patients and healthy controls. Significantly increased brain activation in the inferior parietal lobule and the dorsolateral prefrontal cortex was detected for healthy controls. In contrast, higher neuronal activation in MS patients was obtained in the left ventrolateral prefrontal cortex and the right premotor area. With this meta-analytic approach previous results of investigations examining cognitive function using fMRI are summarized and compared. Therefore a more general view on cognitive dysfunction in this heterogeneous disease is enabled.

[Fetal magnetic resonance imaging of thoracic and abdominal malformations]. / Fetale Magnetresonanztomographie thorakaler und abdomineller Malformationen.

CLINICAL/METHODICAL ISSUE: Diagnosis and differential diagnosis of fetal thoracic and abdominal malformations. STANDARD RADIOLOGICAL METHODS: Ultrasound and magnetic resonance imaging (MRI). METHODICAL INNOVATIONS: In cases of suspected pathologies based on fetal ultrasound MRI can be used for more detailed examinations and can be of assistance in the differential diagnostic process. PERFORMANCE: Improved imaging of anatomical structures and of the composition of different tissues by the use of different MRI sequences. ACHIEVEMENTS: Fetal MRI has become a part of clinical routine in thoracic and abdominal malformations and is the basis for scientific research in this field. PRACTICAL RECOMMENDATIONS: In cases of thoracic or abdominal malformations fetal MRI provides important information additional to ultrasound to improve diagnostic accuracy, prognostic evaluation and surgical planning.

[Indications and technique of fetal magnetic resonance imaging]. / Indikationen und Technik der fetalen Magnetresonanztomographie.

CLINICAL/METHODICAL ISSUE: Evaluation and confirmation of fetal pathologies previously suspected or diagnosed with ultrasound. STANDARD RADIOLOGICAL METHODS: Ultrasound and magnetic resonance imaging (MRI). METHODICAL INNOVATIONS: Technique for prenatal fetal examination. PERFORMANCE: Fetal MRI is an established supplementary technique to prenatal ultrasound. ACHIEVEMENTS: Fetal MRI should only be used as an additional method in prenatal diagnostics and not for routine screening. PRACTICAL RECOMMENDATIONS: Fetal MRI should only be performed in perinatal medicine centers after a previous level III ultrasound examination.