BackgroundIn this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).MethodsA total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2−) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.ResultsAmong all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2− group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2− breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.ConclusionsThrough a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0846-1) contains supplementary material, which is available to authorized users.

Purpose: To develop a high isotropic-resolution sequence to evaluate intracranial vessels at 3.0 Tesla (T).Materials and Methods: Thirteen healthy volunteers and 4 patients with intracranial stenosis were imaged at 3.0T using 0.5-mm isotropic-resolution three-dimensional (3D) Volumetric ISotropic TSE Acquisition (VISTA; TSE, turbo spin echo), with conventional 2D-TSE for comparison. VISTA was repeated for 6 volunteers and 4 patients at 0.4-mm isotropic-resolution to explore the trade-off between SNR and voxel volume. Wall signal-to-noise-ratio (SNR wall ), wall-lumen contrast-to-noise-ratio (CNR wall-lumen ), lumen area (LA), wall area (WA), mean wall thickness (MWT), and maximum wall thickness (maxWT) were compared between 3D-VISTA and 2D-TSE sequences, as well as 3D images acquired at both resolutions. Reliability was assessed by intraclass correlations (ICC).Results: Compared with 2D-TSE measurements, 3D-VISTA provided 58% and 74% improvement in SNR wall and CNR wall-lumen , respectively. LA, WA, MWT and maxWT from 3D and 2D techniques highly correlated (ICCs of 0.96, 0.95, 0.96, and 0.91, respectively). CNR wall-lumen using 0.4-mm resolution VISTA decreased by 27%, compared with 0.5-mm VISTA but with reduced partial-volume-based overestimation of wall thickness. Reliability for 3D measurements was good to excellent. Conclusion:The 3D-VISTA provides SNR-efficient, highly reliable measurements of intracranial vessels at high isotropic-resolution, enabling broad coverage in a clinically acceptable time.

Key Points Question Can quantitative imaging features extracted from the tumor and tumor environment on breast magnetic resonance imaging characterize tumor biological features relevant to outcome of targeted therapy? Findings In this diagnostic study of 209 patients, among HER2 ( ERBB2 )-positive breast cancers, an intratumoral and peritumoral imaging signature capable of discriminating the response-associated HER2 -enriched molecular subtype was identified. When evaluated among recipients of HER2 -targeted therapy, this signature was found to be associated with response to neoadjuvant chemotherapy. Meaning Quantitative analysis of the tumor and its surroundings may provide valuable cues into breast cancer biological features and likelihood of response to targeted therapy.

Detector-based spectral computed tomography is a novel dual-energy CT technology that employs two layers of detectors to simultaneously collect low- and high-energy data in all patients using standard CT protocols. In addition to the conventional polyenergetic images created for each patient, projection-space decomposition is used to generate spectral basis images (photoelectric and Compton scatter) for creating multiple spectral images, including material decomposition (iodine-only, virtual non-contrast, effective atomic number) and virtual monoenergetic images, on-demand according to clinical need. These images are useful in multiple clinical applications, including- improving vascular contrast, improving lesion conspicuity, decreasing artefacts, material characterisation and reducing radiation dose. In this article, we discuss the principles of this novel technology and also illustrate the common clinical applications. Teaching points • The top and bottom layers of dual-layer CT absorb low- and high-energy photons, respectively.• Multiple spectral images are generated by projection-space decomposition.• Spectral images can be generated in all patients scanned in this scanner.