The present work aims to review the clinical evidence of survival outcome after treatment of colorectal cancer liver metastases using yttrium-90 radioembolization, existence, and relevance of clinical, imaging, and genomic predictors of treatment efficacy and the amount of administered activity. Publications listed in PubMed between July 2016 and May 2021 were screened. Predictors of overall survival were reported and distinguished in clinical, imaging, and genomic variables. Administered activity is reported as median and mean value; overall survival is presented as a median value from the treatment. Fourteen papers resulted to be eligible for this systematic review, 11 retrospective, and 3 prospective studies. Ten studies reported administered activity data, with an average mean administered activity of 1.63 GBq and an average median administered activity of 1.53 GBq. Many clinical, imaging and genomic variables have been identified or defined as predictors of efficacy, leading to the possibility of improvements in patient selection criteria. The overall survival resulted to be about 9 months. The clinical evidence on the application of radioembolization with yittrium-90 resumed in this work underlines the importance to analyze several clinical, imaging, and genomic variables to predict the outcome of the therapy. The overall survival has not improved significantly with respect to older studies. Further developments on treatment planning and patient selection could lead to better clinical outcomes.

This study compares various methodologies for lung dosimetry in radioembolization using Monte Carlo (MC) simulations. A voxelized anthropomorphic phantom, created from a real patient’s CT scan, preserved the actual density distribution of the lungs. Lung dosimetry was evaluated for five lung-shunt (LS) cases using traditional methods: the mono-compartmental organ-level approach (MIRD), local energy deposition (LED), and convolution with voxel S-values, either with local density corrections (SVOX_L) or without (SVOX_ST). Additionally, a novel voxel S-value (VSV) kernel for lung tissue with an ICRU density of $0.296\mathrm{g}/\mathrm{c}{\mathrm{m}}^3$ was developed. Calculations were performed using either the ICRU lung density (Lung_296), the average lung density of the phantom (Lung_221), or the local density (Lung_L). The comparison revealed significant underestimations in the mean absorbed dose (AD) for the classical approaches: approximately −$40\%$ for MIRD, −$27\%$ for LED, −$28\%$ for SVOX_L, and −$88\%$ for SVOX_ST. Similarly, calculations with the lung VSV kernel showed underestimations of about −$62\%$ for Lung_296, −$50\%$ for Lung_221, and −$35\%$ for Lung_L. Given the high heterogeneity of lung tissue, traditional dosimetric methods fail to provide accurate estimates of the mean AD for the lungs. Therefore, MC dosimetry based on patient images is recommended as the preferred method for precise assessment of lung AD during radioembolization.

This study compares dosimetric approaches for lung dosimetry in ¹⁶⁶ radioembolization (Ho-TARE) with direct Monte Carlo (MC) simulations on a voxelized anthropomorphic phantom derived from a real patient’s CT scan, preserving the patient’s lung density distribution. Lung dosimetry was assessed for five lung shunt (LS) scenarios with conventional methods: the mono-compartmental organ-level approach (MIRD), voxel S-value convolution for soft tissue (kST, ICRU soft tissue with 1.04 g/cm³) and lung tissue (kLT, ICRU lung tissue with 0.296 g/cm³), local density rescaling (kST_L and kLT_L, respectively, for soft tissue and lung tissue), or global rescaling for a lung mean density of 0.221 g/cm³ (kLT₂₂₁). Significant underestimations in the mean absorbed dose (AD) were observed, with relative differences with respect to the reference (MC) of −64% for MIRD, −93% for kST, −56% for kST_L, −76% for kLT, −68% for kLT₂₂₁, and −60% for kLT_L. Given the high heterogeneity of lung tissue, standard dosimetric approaches cannot accurately estimate the AD. Additionally, MC results for ¹⁶⁶Ho showed notable spatial absorbed dose inhomogeneity, highlighting the need for tailored lung dosimetry in Ho-TARE accounting for the patient-specific lung density distribution. MC-based dosimetry thus proves to be essential for safe and effective radioembolization treatment planning in the presence of LS.

Cerebrovascular dysfunction is an early and underrecognized contributor to cognitive decline. Standard measures such as cerebrovascular reactivity (CVR) during hypercapnia capture only the amplitude of flow responses, providing limited insight into the timing of vascular adaptation. Temporal features, such as delay (onset latency) and time constant (rate of adjustment), together with gain (response amplitude) may serve as more sensitive indicators of vascular health, but cannot be directly obtained from conventional imaging. Here, we investigated cerebral blood flow (CBF), cerebral blood volume (CBV), and blood oxygenation level dependent (BOLD) signal dynamics during hypercapnic challenge in healthy aging. Using a physiologically validated computational model, we estimated delay, time constant, and gain by optimizing the mapping of end-tidal gases to their arterial counterparts in a region-of-interest framework. Once parametrized using CBF, the model successfully predicted CBV and BOLD responses in independent experimental sessions. Across subjects, aging was associated with widespread heterogeneous region-specific changes in delay and substantial reductions in gain and time constant, indicating that cerebrovascular responses become weaker and less adaptable with age. These results demonstrate that calibrated simulations have the ability to track vascular aging, allowing the extraction of parameters that may represent novel biomarkers of cerebrovascular dysfunction. Unlike conventional CVR, temporal hemodynamic parameters capture the dynamics of vascular adaptation, providing a complementary dimension for early detection and therapeutic monitoring in aging and disease.