Tomography, Volume 11, Issue 3 (March 2025) – 20 articles

Cardiac computed tomography (CT) has rapidly advanced, becoming an invaluable tool for diagnosing and prognosticating various cardiovascular diseases. While echocardiography and cardiac magnetic resonance imaging (CMR) remain the gold standards for myocardial assessment, modern CT technologies offer enhanced spatial resolution, making it an essential tool in clinical practice. Cardiac CT has expanded beyond coronary artery disease evaluation, now playing a key role in assessing cardiomyopathies and structural heart diseases. Innovations like photon-counting CT enable precise estimation of myocardial extracellular volume, facilitating the detection of infiltrative disorders and myocardial fibrosis. Additionally, CT-based myocardial strain analysis allows for the classification of impaired myocardial contractility, while quantifying cardiac volumes and function remains crucial in cardiomyopathy evaluation. This review explores the emerging role of cardiac CT in cardiomyopathy phenotyping, emphasizing recent technological advancements. Full article

Background: Predictive models like Residual Neural Networks (ResNets) can use Magnetic Resonance Imaging (MRI) data to identify cervix tumors likely to recur after radiotherapy (RT) with high accuracy. However, there persists a lack of insight into model selections (explainability). In this study, we explored whether model features could be used to generate simulated images as a method of model explainability. Methods: T2W MRI data were collected for twenty-seven women with cervix cancer who received RT from the TCGA-CESC database. Simulated images were generated as follows: [A] a ResNet model was trained to identify recurrent cervix cancer; [B] a model was evaluated on T2W MRI data for subjects to obtain corresponding feature maps; [C] most important feature maps were determined for each image; [D] feature maps were combined across all images to generate a simulated image; [E] the final image was reviewed by a radiation oncologist and an initial algorithm to identify the likelihood of recurrence. Results: Predictive feature maps from the ResNet model (93% accuracy) were used to generate simulated images. Simulated images passed through the model were identified as recurrent and non-recurrent cervix tumors after radiotherapy. A radiation oncologist identified the simulated images as cervix tumors with characteristics of aggressive Cervical Cancer. These images also contained multiple MRI features not considered clinically relevant. Conclusion: This simple method was able to generate simulated MRI data that mimicked recurrent and non-recurrent cervix cancer tumor images. These generated images could be useful for evaluating the explainability of predictive models and to assist radiologists with the identification of features likely to predict disease course. Full article

Background/Objectives: This study aimed to investigate the predictive power of integrated longitudinal amyloid positron emission tomography (PET) and brain magnetic resonance imaging (MRI) data for determining the likelihood of conversion to Alzheimer’s disease (AD) in patients with mild cognitive impairment (MCI). Methods: We included 180 patients with MCI from the Alzheimer’s Disease Neuroimaging Initiative, with baseline and 2-year follow-up scans obtained using F-18 florbetapir PET and MRI. Patients were categorized as converters (progressing to AD) or nonconverters based on a 6-year follow-up. Quantitative analyses included the calculation of amyloid burden using the standardized uptake value ratio (SUVR), brain amyloid smoothing scores (BASSs), brain atrophy indices (BAIs), and their integration into shape features. Longitudinal changes and receiver operating characteristic analyses assessed the predictive power of these biomarkers. Results: Among 180 patients with MCI, 76 (42.2%) were converters, who exhibited significantly higher baseline and 2-year follow-up values for SUVR, BASS, BAI, and shape features than nonconverters (p < 0.001). Shape features demonstrated the highest predictive accuracy for conversion, with areas under the curve of 0.891 at baseline and 0.898 at 2 years. Percent change analyses revealed significant increases in brain atrophy; amyloid deposition changes showed a paradoxical decrease in converters. Additionally, strong associations were observed between longitudinal changes in shape features and neuropsychological test results. Conclusions: The integration of amyloid PET and MRI biomarkers enhances the prediction of AD progression in patients with MCI. These findings support the potential of combined imaging approaches for early diagnosis and targeted interventions in AD. Full article

Purpose: In cancers imaged using contrast-enhanced protocols, such as hepatocellular carcinoma (HCC), formal guidelines rely on measurements of lesion size (in mm) and radiographic density (in Hounsfield units [HU]) to evaluate response to treatment. However, the variability of these measurements across different contrast enhancement phases remains poorly understood. This limits the ability of clinicians to discern whether measurement changes are accurate. Methods: In this study, we investigated the variability of maximal lesion diameter and mean lesion density of HCC lesions on CT scans across four different contrast enhancement phases: non-contrast-enhanced phase (NCE), early arterial phase (E-AP), late arterial phase (L-AP), and portal venous phase (PVP). HCC lesions were independently segmented by two expert radiologists. For each pair of a lesion’s scan timepoints, one was selected randomly as the baseline measurement and the other as the repeat measurement. Both absolute and relative differences in measurements were calculated, as were the coefficients of variance (CVs). Analysis was further stratified by both contrast enhancement phase and lesion diameter. Results: Lesion diameter was found to have a CV of 5.11% (95% CI: 4.20–6.01%). About a fifth of the measurement’s relative changes were greater than 10%. Although there was no significant difference in diameter measurements across different phases, there was a significant negative correlation (R = −0.303, p-value = 0.030) between lesion diameter and percent difference in diameter measurement. Lesion density measurements varied significantly across all phases, with the greatest relative difference of 47% in the late arterial phase and a CV of 22.84% (21.48–24.20%). The overall CV for lesion density measurements was 26.19% (24.66–27.72%). Conclusions: Changes in tumor diameter measurements within 10% may simply be due to variability, and lesion density is highly sensitive to contrast timing. This highlights the importance of paying attention to these two variables when evaluating tumor response in both clinical trials and practice. Full article

Background/Objectives: The risk of hemorrhage during CT-guided lung biopsy has not been systematically studied in cases where ground-glass opacities (GGO) are present in the access route or when biopsies are performed in highly perfused, dependent lung areas. While patient positioning has been studied for pneumothorax prevention, its role in minimizing hemorrhage risk remains unexplored. This study aimed to determine whether GGOs in the access route and biopsies in dependent lung areas are risk factors for pulmonary hemorrhage during CT-guided lung biopsy. Methods: A retrospective analysis was conducted on 115 CT-guided lung biopsies performed at a single center (2020–2023). Patients were categorized based on post-interventional hemorrhage exceeding 2 cm (Grade 2 or higher). We evaluated the presence of GGOs in the access route and biopsy location (dependent vs. non-dependent areas) using chi square, Fisher’s exact, and Mann–Whitney U tests. Univariate and multivariate logistic regression analyses were conducted to evaluate risk factors for pulmonary hemorrhage. Results: Pulmonary hemorrhage beyond 2 cm occurred in 30 of 115 patients (26%). GGOs in the access route were identified in 67% of these cases (p < 0.01), and hemorrhage occurred more frequently when biopsies were performed in dependent lung areas (63% vs. 40%, p = 0.03). Multivariable analysis showed that GGOs in the access route (OR 5.169, 95% CI 1.889–14.144, p = 0.001) and biopsies in dependent areas (OR 4.064, 95% CI 1.477–11.186, p < 0.001) independently increased hemorrhage risk. Conclusions: GGOs in the access route and dependent lung area biopsies are independent risk factors for hemorrhage during CT-guided lung biopsy. Full article

Background/Objectives: Holotomography is an advanced imaging technique that enables high-resolution, three-dimensional visualization of microscopic specimens without the need for fixation or staining. Here we aim to apply holotomography technology to image live Hypsibius exemplaris in their native state, avoiding invasive sample preparation procedures and phototoxic effects associated with other imaging modalities. Methods: We use a low concentration of 7% ethanol for tardigrade sedation and sample preparation. Holotomographic images were obtained and reconstructed using the Tomocube HT-X1 system, enabling high-resolution visualization of tardigrade anatomical structures. Results: We captured detailed, label-free holotomography images of both external and internal structures of tardigrade, including the digestive tract, brain, ovary, claws, salivary glands, and musculature. Conclusions: Our findings highlight holotomography as a complementary high-resolution imaging modality that effectively addresses the challenges faced with traditional imaging techniques in tardigrade research. Full article

Rationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care. Objective: Our objective was to develop predictive measures of tumor response to NAC prior to starting for LABC using machine learning and textural computed tomography (CT) features in different level of frequencies. Materials and Methods: A total of 851 textural biomarkers were determined from CT images and their wavelet coefficients for 117 patients with LABC to evaluate the response to NAC. A machine learning pipeline was designed to classify response to NAC treatment for patients with LABC. For training predictive models, three models including all features (wavelet and original image features), only wavelet and only original-image features were considered. We determined features from CT images in different level of frequencies using wavelet transform. Additionally, we conducted a comparison of feature selection methods including mRMR, Relief, Rref QR decomposition, nonnegative matrix factorization and perturbation theory feature selection techniques. Results: Of the 117 patients with LABC evaluated, 82 (70%) had clinical–pathological response to chemotherapy and 35 (30%) had no response to chemotherapy. The best performance for hold-out data splitting was obtained using the KNN classifier using the Top-5 features, which were obtained by mRMR, for all features (accuracy = 77%, specificity = 80%, sensitivity = 56%, and balanced-accuracy = 68%). Likewise, the best performance for leave-one-out data splitting could be obtained by the KNN classifier using the Top-5 features, which was obtained by mRMR, for all features (accuracy = 75%, specificity = 76%, sensitivity = 62%, and balanced-accuracy = 72%). Conclusions: The combination of original textural features and wavelet features results in a greater predictive accuracy of NAC response for LABC patients. This predictive model can be utilized to predict treatment outcomes prior to starting, and clinicians can use it as a recommender system to modify treatment. Full article

Background: Digital tomosynthesis (DTS) is an advanced imaging modality that enhances diagnostic accuracy by offering three-dimensional visualization from two-dimensional projections, which is particularly beneficial in breast and lung imaging. However, this increased imaging capability raises concerns about patient exposure to ionizing radiation. Methods: This study explores the energy and angular dependence of thermoluminescent dosimeters (TLDs), specifically TLD100H, to improve the accuracy of organ dose assessment during DTS. Using a comprehensive experimental approach, organ doses were measured in both DTS and traditional RX modes. Results: The results showed lung doses of approximately 3.21 mGy for the left lung and 3.32 mGy for the right lung during DTS, aligning with the existing literature. In contrast, the RX mode yielded significantly lower lung doses of 0.33 mGy. The heart dose during DTS was measured at 2.81 mGy, corroborating findings from similar studies. Conclusions: These results reinforce the reliability of TLD100H dosimetry in assessing radiation exposure and highlight the need for optimizing imaging protocols to minimize doses. Overall, this study contributes to the ongoing dialogue on enhancing patient safety in diagnostic imaging and advocates for collaboration among medical physicists, radiologists, and technologists to establish best practices. Full article

Background: The discovery of novel antibody–drug conjugates for low-expression human epidermal growth factor receptor 2 (HER2-low) breast cancer highlights the inadequacy of the conventional binary classification of HER2 status as either negative or positive. Identification of HER2-low breast cancer is crucial for selecting patients who may benefit from targeted therapies. This study aims to determine whether qualitative and quantitative magnetic resonance imaging (MRI) features can effectively reflect low-HER2-expression breast cancer. Methods: Pre-treatment breast MRI images from 232 patients with pathologically confirmed breast cancer were retrospectively analyzed. Both clinicopathologic and MRI features were recorded. Qualitative MRI features included Breast Imaging Reporting and Data System (BI-RADS) descriptors from dynamic contrast-enhanced MRI (DCE-MRI), as well as intratumoral T2 hyperintensity and peritumoral edema observed in T2-weighted imaging (T2WI). Quantitative features were derived from diffusion kurtosis imaging (DKI) using multiple b-values and included statistics such as mean, median, 5th and 95th percentiles, skewness, kurtosis, and entropy from apparent diffusion coefficient (ADC), D_app, and K_app histograms. Differences in clinicopathologic, qualitative, and quantitative MRI features were compared across groups, with multivariable logistic regression used to identify significant independent predictors of HER2-low breast cancer. The discriminative power of MRI features was assessed using receiver operating characteristic (ROC) curves. Results: HER2 status was categorized as HER2-zero (n = 60), HER2-low (n = 91), and HER2-overexpressed (n = 81). Clinically, estrogen receptor (ER), progesterone receptor (PR), hormone receptor (HR), and Ki-67 levels significantly differed between the HER2-low group and others (all p < 0.001). In MRI analyses, intratumoral T2 hyperintensity was more prevalent in HER2-low cases (p = 0.009, p = 0.008). Mass lesions were more common in the HER2-zero group than in the HER2-low group (p = 0.038), and mass shape (p < 0.001) and margin (p < 0.001) significantly varied between the HER2 groups, with mass shape emerging as an independent predictive factor (HER2-low vs. HER2-zero: p = 0.010, HER2-low vs. HER2-over: p = 0.012). Qualitative MRI features demonstrated an area under the curve (AUC) of 0.763 (95% confidence interval [CI]: 0.667–0.859) for distinguishing HER2-low from HER2-zero status. Quantitative features showed distinct differences between HER2-low and HER2-overexpression groups, particularly in non-mass enhancement (NME) lesions. Combined variables achieved the highest predictive accuracy for HER2-low status, with an AUC of 0.802 (95% CI: 0.701–0.903). Conclusions: Qualitative and quantitative MRI features offer valuable insights into low-HER2-expression breast cancer. While qualitative features are more effective for mass lesions, quantitative features are more suitable for NME lesions. These findings provide a more accessible and cost-effective approach to noninvasively identifying patients who may benefit from targeted therapy. Full article

Background/Objectives: While ancillary tests for brain death diagnosis are not routinely recommended in guidelines, they may be necessary in specific clinical scenarios. Computed tomography angiography (CTA) is particularly advantageous in pediatric patients due to its noninvasive nature, accessibility, and rapid provision of anatomical information. This study aims to assess the diagnostic sensitivity of a revised venous system (ICV-SPV) utilizing a 4-point scoring system in children clinically diagnosed with brain death. Materials and Methods: A total of 43 pediatric patients clinically diagnosed with brain death who underwent CTA were retrospectively analyzed. Imaging was performed using a standardized brain death protocol. Three distinct 4-point scoring systems (A20-V60, A60-V60, ICV-SPV) were utilized to assess vessel opacification in different imaging phases. To evaluate age-dependent sensitivity, patients were categorized into three age groups: 26 days–1 year, 2–6 years, and 6–18 years. The sensitivity of each 4-point scoring system in diagnosing brain death was calculated for all age groups. Results: The revised venous scoring system (ICV-SPV) demonstrated the highest overall sensitivity in confirming brain death across all age groups, significantly outperforming the reference 4-point scoring systems. Furthermore, the ICV-SPV system exhibited the greatest sensitivity in patients with cranial defects. Conclusions: The revised 4-point venous CTA scoring system, which relies on the absence of ICV and SPV opacification, is a reliable tool for confirming cerebral circulatory arrest in pediatric patients with clinical brain death. Full article

Objectives: Prostate-specific membrane antigen (PSMA)-targeted radioligands are promising diagnostic tools for the targeted positron emission tomography (PET) imaging of prostate cancer (PCa). In present work, we aimed to develop a novel PSMA tracer to provide an additional option for prostate cancer diagnosis. Methods: Our team designed a new structure of the PSMA tracer and evaluated it with cellular experiments in vitro to preliminarily verify the targeting and specificity of ⁶⁸Ga-NOTA-GC-PSMA. PET/CT imaging of PSMA-positive xenograft-bearing models in vivo to further validate the in vivo specificity and targeting of the radiotracer. Pathological tissue sections from prostate cancer patients were compared with pathological immunohistochemistry and pathological tissue staining results by radioautography experiments to assess the targeting-PSMA of ⁶⁸Ga-NOTA-GC-PSMA on human prostate cancer pathological tissues. Results: The novel tracer showed high hydrophilicity and rapid clearance rate. Specific cell binding and micro-PET imaging experiments showed that ⁶⁸Ga-NOTA-GC-PSMA displayed a high specific LNCaP tumor cell uptake (1.70% ± 0.13% at 120 min) and tumor-to-muscle (T/M) and tumor-to-kidney (T/K) ratio (13.87 ± 11.20 and 0.20 ± 0.08 at 60 min, respectively). Conclusions: The novel tracer ⁶⁸Ga-NOTA-GC-PSMA is promising radionuclide imaging of PCa. Full article

Calcified lung nodules are frequently encountered on chest imaging, often as incidental findings. While calcifications are typically associated with benign conditions, they do not inherently exclude malignancy, making accurate differentiation essential. The primary diagnostic challenge lies in distinguishing benign from malignant nodules based solely on imaging features. Various calcification patterns, including diffuse, popcorn, lamellated and eccentric, provide important diagnostic clues, though overlap among different conditions may persist. A comprehensive diagnostic approach integrates clinical history with multimodal imaging, including magnetic resonance and nuclear medicine, when necessary, to improve accuracy. When imaging findings remain inconclusive, tissue sampling through biopsy may be required for definitive characterization. This review provides an overview of the imaging features of calcified lung nodules, emphasizing key diagnostic challenges and their clinical implications. Full article

Background: Temporomandibular joint (TMJ) disorders are a significant cause of orofacial pain. Artificial intelligence (AI) has been successfully applied to other imaging modalities but remains underexplored in ultrasonographic evaluations of TMJ. Objective: This study aimed to develop and validate an AI-driven method for the automatic and reproducible measurement of TMJ space width from ultrasonographic images. Methods: A total of 142 TMJ ultrasonographic images were segmented into three anatomical components: the mandibular condyle, joint space, and glenoid fossa. State-of-the-art architectures were tested, and the best-performing 2D Residual U-Net was trained and validated against expert annotations. The algorithm for joint space width measurement based on TMJ segmentation was proposed, calculating the vertical distance between the superior-most point of the mandibular condyle and its corresponding point on the glenoid fossa. Results: The segmentation model achieved high performance for the mandibular condyle (Dice: 0.91 ± 0.08) and joint space (Dice: 0.86 ± 0.09), with notably lower performance for the glenoid fossa (Dice: 0.60 ± 0.24), highlighting variability due to its complex geometry. The TMJ space width measurement algorithm demonstrated minimal bias, with a mean difference of 0.08 mm and a mean absolute error of 0.18 mm compared to reference measurements. Conclusions: The model exhibited potential as a reliable tool for clinical use, demonstrating accuracy in TMJ ultrasonographic analysis. This study underscores the ability of AI-driven segmentation and measurement algorithms to bridge existing gaps in ultrasonographic imaging and lays the foundation for broader clinical applications. Full article

Background: Functional tumor volume (FTV), measured from dynamic contrast-enhanced MRI, is an imaging biomarker that can predict treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The FTV-based predictive model, combined with core biopsy, informed treatment decisions of recommending patients with excellent responses to proceed to surgery early in a large NAC clinical trial. Methods: In this retrospective study, we constructed models using FTV measurements. We analyzed performance tradeoffs when a probability threshold was used to identify excellent responders through the prediction of pathology complete response (pCR). Individual models were developed within cohorts defined by the hormone receptor and human epidermal growth factor receptor 2 (HR/HER2) subtype. Results: A total of 814 patients enrolled in the I-SPY 2 trial between 2010 and 2016 were included with a mean age of 49 years (range: 24 to 77). Among these patients, 289 (36%) achieved pCR. The area under the ROC curve (AUC) ranged from 0.68 to 0.74 for individual HR/HER2 subtypes. When probability thresholds were chosen based on minimum positive predictive value (PPV) levels of 50%, 70%, and 90%, the PPV-sensitivity tradeoff varied among subtypes. The highest sensitivities (100%, 87%, 45%) were found in the HR−/HER2+ sub-cohort for probability thresholds of 0, 0.62, and 0.72; followed by the triple-negative sub-cohort (98%, 52%, 4%) at thresholds of 0.13, 0.58, and 0.67; and HR+/HER2+ (78%, 16%, 8%) at thresholds of 0.34, 0.57, and 0.60. The lowest sensitivities (20%, 0%, 0%) occurred in the HR+/HER2− sub-cohort. Conclusions: Predictive models developed using imaging biomarkers, alongside clinically validated probability thresholds, can be incorporated into decision-making for precision oncology. Full article

Background/Objectives: The accurate detection of microcalcifications in mammograms is critical for the early detection of breast cancer. However, the variability between different manufacturers is significant, particularly with digital breast tomosynthesis (DBT). Manufacturers have many design differences, including sweep angles, detector types, reconstruction techniques, filters, and focal spot construction. This study outlined the development of an innovative phantom model using crystallizations to improve the accuracy of imaging microcalcifications in DBT. The goal of these models was to achieve consistent evaluations, thereby reducing the variability between different scanners. Methods: We created a novel phantom model that simulates different types of breast tissue densities with calcifications. Furthermore, these crystalline-grown phantoms can more accurately represent the physiological shapes and compositions of microcalcifications than do other available phantoms for calcifications and can be evaluated on different systems. Microcalcification patterns were generated using the evaporation of sodium chloride, transplantation of calcium carbonate crystals, and/or injection of hydroxyapatite. These patterns were embedded in multiple layers within the wax to simulate various depths and distributions of calcifications with the ability to generate a large variety of patterns. Results: The tomosynthesis imaging revealed phantoms that utilized calcium carbonate crystals showed demonstrable visualization differences between the 3D DBT reconstructions and the magnification/2D view, illustrating the model’s value. The phantom was able to highlight changes in the contrast and resolution, which is crucial for accurate microcalcification evaluation. Conclusions: Based on the crystalline growth, this phantom model offers an important new standardized target for evaluating DBT systems. By promoting standardization, especially through the development of advanced breast calcification phantoms, this work and design aimed to contribute to improving earlier and more accurate breast cancer detection. Full article

Lung cancer is the leading cause of cancer-related deaths globally and ranks among the most common cancer types. Given its low overall five-year survival rate, early diagnosis and timely treatment are essential to improving patient outcomes. In recent years, advances in computer technology have enabled artificial intelligence to make groundbreaking progress in imaging-based lung cancer diagnosis. The primary aim of this study is to develop a computer-aided diagnosis (CAD) system for lung cancer using endobronchial ultrasonography (EBUS) images and deep learning algorithms to facilitate early detection and improve patient survival rates. We propose ${\mathrm{M}}^3\text{-}\mathrm{Net}$, which is a multi-branch framework that integrates multiple features through an attention-based mechanism, enhancing diagnostic performance by providing more comprehensive information for lung cancer assessment. The framework was validated on a dataset of 95 patient cases, including 13 benign and 82 malignant cases. The dataset comprises 1140 EBUS images, with 540 images used for training, and 300 images each for the validation and test sets. The evaluation yielded the following results: accuracy of 0.76, F1-score of 0.75, AUC of 0.83, PPV of 0.80, NPV of 0.75, sensitivity of 0.72, and specificity of 0.80. These findings indicate that the proposed attention-based multi-feature fusion framework holds significant potential in assisting with lung cancer diagnosis. Full article

Background: X-ray computed tomography (CT) imaging technology provides high-precision anatomical visualization of patients and has become a standard modality in clinical diagnostics. A widely adopted strategy to mitigate radiation exposure is sparse-view scanning. However, traditional iterative approaches require manual design of regularization priors and laborious parameter tuning, while deep learning methods either heavily depend on large datasets or fail to capture global image correlations. Methods: Therefore, this paper proposes a combination of model-driven and data-driven methods, using the ADMM iterative algorithm framework to constrain the network to reduce its dependence on data samples and introducing the CNN and Transformer model to increase the ability to learn the global and local representation of images, further improving the accuracy of the reconstructed image. Results: The quantitative and qualitative results show the effectiveness of our method for sparse-view reconstruction compared with the current most advanced reconstruction algorithms, achieving a PSNR of 42.036 dB, SSIM of 0.979, and MAE of 0.011 at 32 views. Conclusions: The proposed algorithm has effective capability in sparse-view CT reconstruction. Compared with other deep learning algorithms, the proposed algorithm has better generalization and higher reconstruction accuracy. Full article

Purpose: Dual-energy computed tomography (DECT) allows for the measurement of iodine concentration, a component for the synthesis of thyroid hormones. DECT can create virtual non-contrast (VNC) images, potentially reducing radiation exposure. This study explores the correlations between thyroid function and iodine concentration, as well as the relationship between thyroid densities in true non-contrast (TNC) and virtual non-contrast (VNC) images and thyroid function. Methods: The study involved 87 patients undergoing 4D-CT imaging with single and dual-energy scans for diagnosing primary hyperparathyroidism. Thyroid densities and iodine concentrations were measured across all scanning phases. These measurements were correlated with thyroid function, indicated by TSH and FT4 levels. Differences in thyroid density between post-contrast phases and TNC phases (ΔHU) were analyzed for correlations with thyroid function and iodine concentrations. Results: Positive correlations between iodine concentrations and TSH were found, with Spearman’s coefficients (R) of 0.414, 0.361, and 0.349 for non-contrast, arterial, and venous phases, respectively. Thyroid density on TNC showed significant positive correlations with TSH levels (R = 0.436), consistently across both single- (R = 0.435) and dual-energy (R = 0.422) scans. Thyroid densities on VNC images did not correlate with TSH or FT4. Differences in density between contrast and non-contrast scans (ΔHU) negatively correlated with TSH (p = 0.002). Conclusions: DECT-derived iodine concentrations and thyroid densities in non-contrast CT scans demonstrated positive correlations with thyroid function, in contrast to thyroid densities on VNC scans. This indicates that VNC images are unsuitable for this purpose. Correlations between ΔHU and TSH suggest a potential link between the thyroid’s structural properties to capture iodine and its hormonal function. This study underscores the potential value of (DE-) CT imaging for evaluating thyroid function as an additional benefit in head and neck scans. Full article

Background/Objective: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop therapies. Magnetic resonance imaging (MRI) of these tumors is an invaluable tool for monitoring tumor growth and characterizing the tumors as well. Methods: In this work, a pipeline for automating the segmentation of xenografts in mouse models was developed. T₂-weighted (T2-wt) MRI images from mice implanted with six different prostate cancer patient-derived xenografts (PDX) in the kidneys, liver, and tibia were used. The segmentation pipeline included a slice classifier to identify the slices that had tumors and subsequent training and validation using several U-Net-based segmentation architectures. Multiple combinations of the algorithm and training images for different sites were evaluated for inference quality. Results and Conclusions: The slice classifier network achieved 90% accuracy in identifying slices containing tumors. Among the various segmentation architectures tested, the dense residual recurrent U-Net achieved the highest performance in kidney tumors. When evaluated across the kidneys, tibia, and liver, this architecture performed the best when trained on all data as compared to training on only data from a single site (and inferring on a multi-site tumor images), achieving a Dice score of 0.924 across the test set. Full article

Background/Objectives: Radiomic features exhibit a correlation with tumor size on pretreatment images. However, on post-treatment images, this association is influenced by treatment efficacy and varies between responders and non-responders. This study introduces a novel model, called baseline-referenced Delta radiomics, which integrates the association between radiomic features and tumor size into Delta radiomics to predict chemotherapy response in liver metastases from breast cancer (BC) and colorectal cancer (CRC). Materials and Methods: A retrospective study analyzed contrast-enhanced computed tomography (CT) scans of 83 BC patients and 84 CRC patients. Among these, 57 BC patients with 106 liver lesions and 37 CRC patients with 109 lesions underwent post-treatment imaging after systemic chemotherapy. Radiomic features were extracted from up to three lesions per patient following manual segmentation. Tumor response was assessed by measuring the longest diameter and classified according to RECIST 1.1 criteria as progressive disease (PD), partial response (PR), or stable disease (SD). Classification models were developed to predict chemotherapy response using pretreatment data only, Delta radiomics, and baseline-referenced Delta radiomics. Model performance was evaluated using confusion matrix metrics. Results: Baseline-referenced Delta radiomics performed comparably or better than established radiomics models in predicting tumor response in chemotherapy-treated patients with liver metastases. The sensitivity, specificity, and balanced accuracy in predicting response ranged from 0.66 to 0.97, 0.81 to 0.97, and 80% to 90%, respectively. Conclusions: By integrating the relationship between radiomic features and tumor size into Delta radiomics, baseline-referenced Delta radiomics offers a promising approach for predicting chemotherapy response in liver metastases from breast and colorectal cancer. Full article