Photoacoustic computed tomography (PACT), an emerging imaging modality in preclinical cancer research, can provide multiparametric 3D information about structures, physiological functions, and pharmacokinetics. Here, we demonstrate the use of high-definition 3D multiparametric PACT imaging of both primary and metastatic tumors in living mice to noninvasively monitor angiogenesis, carcinogenesis, hypoxia, and pharmacokinetics. The high-definition PACT system with a 1024-element hemispherical ultrasound transducer array provides an isotropic spatial resolution of 380 μm, an effective volumetric field-of-view of 12.8 mm × 12.8 mm × 12.8 mm without scanning, and an acquisition time of <30 s for a whole mouse body. Initially, we monitor the structural progression of the tumor microenvironment (e.g., angiogenesis and vessel tortuosity) after tumor cell inoculation. Then, we analyze the change in oxygen saturation of the tumor during carcinogenesis, verifying induced hypoxia in the tumor\'s core region. Finally, the whole-body pharmacokinetics are photoacoustically imaged after intravenous injection of micelle-loaded IR780 dye, and the in vivo PACT results are validated in vivo and ex vivo by fluorescence imaging. By employing the premium PACT system and applying multiparametric analyses to subcutaneous primary tumors and metastatic liver tumors, we demonstrate that this PACT system can provide multiparametric analyses for comprehensive cancer research.

OBJECTIVE: The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis.
METHODS: Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis.
RESULTS: A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively.
CONCLUSIONS: Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.

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In the realm of cognitive science, the phenomenon of \"successful cognitive aging\" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.

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UNASSIGNED: MicroUS is a new imaging technique that may have potential to reliably monitor prostate disease and therefore release capacity in MRI departments. Firstly, however, it is essential to identify which healthcare staff may be suitable to learn to use this modality. Based on previous evidence, UK sonographers may be well placed to harness this resource.
UNASSIGNED: Currently, there is sparse evidence on the performance of MicroUS for monitoring prostate disease but early findings are encouraging. Although its uptake is increasing, it is believed that only two sites in the UK have MicroUS systems and only one of those uses just sonographers to undertake and interpret this new imaging technique.
UNASSIGNED: UK sonographers have a history of role extension dating back several decades and have proven repeatedly that they are reliable and accurate when measured against a gold standard. We explore the background of UK sonographer role extension and postulate that sonographers are best placed to adopt and embed new imaging techniques and technology into routine clinical practice. This is of particular importance given the dearth of ultrasound focussed radiologists in the UK. To effectively introduce challenging new work streams, multi-professional collaboration in imaging, alongside sonographer role extension, will ensure precious resources are maximised thus ensuring optimum patient care.
UNASSIGNED: UK sonographers have repeatedly demonstrated reliability in many areas of role extension in various clinical settings. Early data indicate that the adoption of MicroUS for use in prostate disease surveillance may be another role suited to sonographers.

Recently, several studies have demonstrated the excellent capabilities of tip-enhanced Raman spectroscopyfor in-depth investigations of structural properties of matter with unprecedented resolution and chemical specificity. These capabilities are utilized here to study the internal structure of core-crosslinked micelles, which are formed by self-assembly of the diblock terpolymer poly(ethylene oxide)-block-poly(furfuryl glycidylether-co-tert-butylglycidyl ether). Supplementing force-volume atomic force microscopy experiments address additionally the nanomechanical properties. Particularly, TERS enables investigating the underlying principles influencing the homogeneity and efficiency of the Diels-Alder core-crosslinking process in the confined hydrophobic core. While the central core region is homogenously crosslinked, a breakdown of the crosslinking reaction is observed in the core-corona interfacial region. The results corroborate that a strong crosslinking efficiency is directly correlated to the formation of a mixed zone of the glycidyl ether and PEO corona blocks reaching ≈5 nm into the core region. Concomitantly a strong exclusion of the encapsulated bismaleimide crosslinker from the interfacial region is observed. It is conceivable that a changed structure, chemical composition and altered nanomechanical properties of this interfacial region may also influence the crosslinking efficiency across the entire core region by a modification of the solubility of the crosslinker in the interfacial core-corona region.

BACKGROUND: Multiparametric imaging has been seen as a route to improved prediction of chemoradiotherapy treatment outcomes. Four-dimensional volumetric perfusion CT (4D PCT) is useful for whole-organ perfusion measurement, as it reflects the heterogeneity of the tumor and its perfusion parameters. However, there has been no study using multiparametric imaging including 4D PCT for the prognostic prediction of chemoradiotherapy. The purpose of this study was to determine whether combining assessments of 4D PCT with diffusion-weighted MRI (DWI) and 18F-fluorodeoxyglucose PET-CT could enhance prognostic accuracy in head and neck squamous cell carcinoma (HNSCC) patients treated with chemoradiotherapy.
METHODS: We examined 53 patients with HNSCC who underwent 4D PCT, DWI and PET-CT before chemoradiotherapy. The imaging and clinical parameters were assessed the relations to locoregional control (LRC) and progression-free survival (PFS) by logistic regression analyses. A receiver operating characteristic (ROC) analysis was performed to assess the accuracy of the significant parameters identified by the multivariate analysis for the prediction of LRC and PFS. We additionally assessed using the scoring system whether these independent parameters could have a complementary role for the prognostic prediction.
RESULTS: The median follow-up was 30 months. In multivariate analysis, blood flow (BF; p = 0.02) and blood volume (BV; p = 0.04) were significant prognostic factors for LRC, and BF (p = 0.03) and skewness of the ADC histogram (p = 0.02) were significant prognostic factors for PFS. A significant positive correlation was found between BF and BV (ρ = 0.6, p < 0.001) and between BF and skewness (ρ = 0.46, p < 0.01). The ROC analysis showed that prognostic accuracy for LRC of BF, BV, and combination of BF and BV were 77.8%, 70%, and 92.9%, and that for PFS of BF, skewness, and combination of BF and skewness were 55.6%, 63.2%, and 77.5%, respectively. The scoring system demonstrated that the combination of higher BF and higher BV was significantly associated with better LRC (p = 0.04), and the combination of lower BF and lower skewness was significantly associated with worse PFS (p = 0.004).
CONCLUSIONS: A combination of parameters derived from 4DPCT and ADC histograms may enhance prognostic accuracy in HNSCC patients treated with chemoradiotherapy.

The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were segmented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature\'s data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the probability and Breast Imaging Reporting and Data System enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.

The study aimed to determine the performance of advanced magnetic resonance imaging (MRI), including a multiparametric MRI-index, for diagnosing and severity grading of chronic pancreatitis (CP) at various functional stages with focus on detection of CP with preserved pancreatic function.
Fifty-four CP patients and 35 healthy controls underwent MRI including assessment of pancreatic volume, main pancreatic duct (MPD) diameter, T1 relaxation time, magnetic resonance elastography (MRE) derived stiffness, and intravoxel incoherent motion (IVIM) diffusion-weighted imaging. Patients were categorized into three subgroups: Preserved pancreatic function (n = 14), partial pancreatic insufficiency (exocrine insufficiency or diabetes, n = 25), and complete pancreatic insufficiency (exocrine insufficiency and diabetes, n = 15). A multiparametric MRI-index was based on ordinal logistic regression analysis. Diagnostic performances of MRI parameters for diagnosing CP at different functional stages were determined using receiver operating characteristic (ROC) analysis.
All MRI parameters differed across CP subgroups and healthy controls (all P < 0.001), except for IVIM. T1 relaxation time (ROC area under the curve (ROC-AUC) 0.82), MRE (ROC-AUC 0.88), and MRI-index (ROC-AUC 0.86) showed the highest performance for detecting patients with preserved pancreatic function (early CP) vs. healthy controls. For detecting preserved pancreatic function vs. partial insufficiency, pancreatic volume, MRI-index, and T1 relaxation time performed best (all ROC-AUC > 0.75), with the MRI-index tending to outperform MRE (ROC-AUC 0.77 vs. 0.63; P = 0.10).
Quantitative assessments of T1 relaxation time and MRE-derived stiffness seem promising for diagnosing CP at different functional stages and may together with multiparametric MRI-index be used for early identification, staging and monitoring of CP.