Tomography, Volume 11, Issue 12 (December 2025) – 13 articles

Cancer-associated fibroblasts (CAFs) are an abundant and diverse cell population within tumor microenvironments of solid tumors. Multiple subtypes of CAFs, defined by molecular and functional markers, have been described in the literature. CAFs contribute to tumor progression by remodeling the extracellular matrix, promoting immune evasion, and supporting angiogenesis and metastasis. Fibroblast activation protein (FAP) is a transmembrane serine protease minimally expressed in normal adult tissues but significantly upregulated in certain subtypes of CAFs across many solid tumors. High levels of FAP have been associated with poor prognosis in various cancers. FAP has increasingly emerged as a promising target for both imaging and therapy. Multiple FAP-targeting strategies, such as small molecules, monoclonal antibodies, drug conjugates, and radiolabeled ligands, are currently being investigated in preclinical and early clinical settings. This review provides a clinically focused overview of CAFs in the tumor microenvironment, highlighting key fibroblast markers, their associations with prognosis across various tumor types, and their utility in radiologic imaging and targeted therapy. We also discuss the potential of non-FAP fibroblast targeting molecules and the clinical rationale for more selective, subtype-specific strategies. By examining fibroblast biology through a radiologist’s lens, we aim to explore the evolving role of stromal targeting in imaging and the treatment of solid tumors. Full article

Background/Objectives: The breast is a highly radiosensitive organ that is directly exposed to ionizing radiation during chest computed tomography (CT) examinations. Excessive radiation exposure increases the risk of radiation-induced malignancies, highlighting the importance of accurate and patient-specific dose estimation. This study aims to estimate the effective radiation dose absorbed by the breast during chest CT examinations using a machine learning (ML)-based personalized prediction approach. Methods: In this retrospective study, a total of 653 female patients who underwent both mammography and chest CT between 2020 and 2024 were included. A structured database was created incorporating demographic and anatomical parameters, including body weight, height, body mass index (BMI), and breast thickness (mm) obtained from mammography, along with dose length product (DLP) values from chest CT scans. Five regression-based ML algorithms—CatBoost, Gradient Boosting, Extra Trees, AdaBoost, and Random Forest—were implemented to predict breast radiation dose. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²). Results: Among the evaluated models, the CatBoost algorithm optimized with Particle Swarm Optimization (CatBoostPSO) achieved the best overall predictive performance, yielding the lowest MSE (0.3795), MAE (0.3846), and MAPE (4.37%), along with the highest R² value (0.9875). CatBoost and Gradient Boosting models demonstrated predictions most closely aligned with ground truth values, indicating that ensemble-based and dynamically optimized models are particularly effective for breast dose estimation. Conclusions: The proposed machine learning framework enables rapid, accurate, and clinically applicable estimation of breast radiation dose during chest CT examinations. This patient-specific approach has strong potential to support personalized radiation dose monitoring and optimization strategies, contributing to improved radiation safety in clinical practice. Full article

Background: Accurate volumetry and imaging characterization of chronic subdural hematoma (cSDH) are essential for prognostication and treatment planning, but manual assessment is time-consuming and therefore underutilized. Methods: We retrospectively analyzed preoperative non-contrast CT (NCCT) scans of 257 patients undergoing first-time surgery for uni- or bilateral cSDH. Hematoma volumes were measured manually using a semi-automated area-outlining tool on every second axial slice and compared with the volumes estimated through the ABC/2 formula. Hematoma attenuation patterns and components were categorized, and interrater reliability was assessed for volume, maximum diameter, and imaging features using intraclass correlation coefficients (ICCs) and Cohen’s κ. Results: A total of 339 hematomas were evaluated. Manual and ABC/2 volume measurements correlated strongly (R² = 0.83, ICC [3, 1] = 0.90). The interrater agreement for manual volumetry was excellent (ICC [2, 1] = 0.96). Agreement was also excellent for maximum diameter (ICC [2, 1] > 0.9) and good for midline shift assessment (κ = 0.81). Agreement was moderate for the identification of fresh clots, trabeculations, and laminations (κ = 0.62–0.72) but poor for general attenuation patterns (κ = 0.44). Conclusions: The manual volumetry of cSDH is feasible and highly reproducible between raters of different experience levels. These results provide a robust reference standard for the validation of automated volumetry tools and support the implementation of quantitative hematoma assessment in future clinical trials and routine care. Full article

Background: Accurate confirmation of nasogastric (NG) tubes is essential for patient safety, but delays and variability in interpretation remain common in clinical practice. Deep learning (DL) models have shown potential for assisting in this task, but real-world performance, particularly in detecting malpositioned tubes, remains insufficiently characterized. Methods: We conducted a pilot evaluation of a previously developed DL model using 135 chest radiographs from Kangwon National University Hospital. Expert physicians established the reference standard. Model performance was assessed and receiver operating characteristic (ROC) curve and precision recall curve (PRC) analyses were performed. Differences between correctly classified and misclassified cases were examined using Wilcoxon rank-sum and Fisher’s exact tests to explore potential clinical or radiographic contributors to model failure. Results: The model correctly classified 129 of 135 cases. The sensitivity was 96.1% (95% confidence interval (CI): 92.2–98.9%), specificity was 85.7% (95% CI: 42.2–97.7%), positive predictive value (PPV) was 99.2% (95% CI: 96.1–99.9%), negative predictive value (NPV) was 54.5% (95% CI: 25.4–80.8%), balanced accuracy was 90.8%, and F1-score was 0.976. The area under the ROC curve was 0.970 (95% CI: 0.929–1.000) and that under the PRC was 0.727 (95% CI: 0.289–1.000), reflecting substantial uncertainty related to the very small number of incomplete cases (n = 6). No statistically significant differences in clinical or radiographic characteristics were observed between correctly classified and misclassified cases. Conclusions: The DL model performed well in identifying correctly positioned NG tubes but demonstrated limited and unstable performance for detecting incomplete placements. Given the safety implications of misclassification, the model should be used only as an assistive tool with mandatory physician oversight. Larger, multi-center studies with greater representation of incomplete cases are required to obtain more reliable estimates and support safe clinical implementation. Full article

Background/Objectives: Aortic valve calcification is commonly evaluated using 3.0 mm true non-contrast (TNC) computed tomography (CT) images. This study evaluates the reproducibility of virtual non-contrast (VNC) reconstructions at different slice intervals using photon-counting detector CT (PCD-CT). Methods: In this retrospective study, we included 279 consecutive patients, who underwent PCD-CT for evaluation of native aortic valve between February 2023 and December 2023 with both TNC and VNC images at 3.0 and 1.5 mm slice intervals. Aortic valve calcium score (AVCS) and aortic valve calcium volume (AVCV) were compared between the two methods using paired t-tests. Agreement for continuous variables was assessed using inter-class coefficients (ICCs). Cohen’s Kappa (κ) was calculated to evaluate the agreement between different modalities in diagnosing severe AV calcification. Results: Compared to the standard, TNC images at 1.5 mm intervals showed higher AVCS (mean difference: −290 ± 418, p < 0.001), with high reproducibility between techniques (CS: ICC 0.969, [IQR 0.962, 0.975]). Compared with reference, VNC showed no significant differences in AVCS at either slice intervals, with excellent reproducibility (3.0 mm, ICC 0.970 [0.963, 0.976]; 1.5 mm, ICC 0.971 [0.964, 0.977]). Compared to TNC 3.0 mm, strong concordance was observed using other reconstruction techniques in assessing severe AV calcification (κ = 0.81 [95% CI: 0.74–0.88], 0.83 [95% CI: 0.76–0.90], and 0.83 [95% CI: 0.76–0.90] for TNC at 1.5 mm, VNC at 3.0 mm, and 1.5 mm, respectively), with low misclassification rates. Conclusions: Our study highlights high reproducibility in the evaluation of AVCS by VNC reconstruction at 3.0 and 1.5 mm intervals compared with reference offering a reliable alternative with an excellent diagnostic accuracy. Full article

Background/Objectives: Breast cancer is one of the most common malignancies in women worldwide. Early diagnosis and accurate classification in breast cancer detection are among the most critical factors determining treatment success and patient survival. In this study, a deep learning-based model was developed that can classify benign, malignant, and normal breast tissues from ultrasound images with high accuracy and achieve better results than the methods commonly used in the literature. Methods: The proposed model was trained on a dataset of breast ultrasound images, and its classification performance was evaluated. The model is designed to effectively learn both local textural features and global contextual relationships by combining Squeeze-and-Excitation (SE) blocks, which emphasize channel-level feature importance, and Convolutional Block Attention Module (CBAM) attention mechanisms, which focus on spatial information, with the MHA structure. The model’s performance is compared with three commonly used convolutional neural networks (CNNs) and three Vision Transformer (ViT) architectures. Results: The developed model achieved an accuracy rate of 96.03% in experimental analyses, outperforming both the six compared models and similar studies in the literature. Additionally, the proposed model was tested on a second dataset consisting of histopathological images and achieved an average accuracy of 99.55%. The results demonstrate that the model can effectively learn meaningful spatial and contextual information from ultrasound data and distinguish different tissue types with high accuracy. Conclusions: This study demonstrates the potential of deep learning-based approaches in breast ultrasound-based computer-aided diagnostic systems, providing a reliable, fast, and accurate decision support tool for early diagnosis. The results obtained with the proposed model suggest that it can significantly contribute to patient management by improving diagnostic accuracy in clinical applications. Full article

Background/Objectives: To identify preoperative MRI features using computer-assisted diagnosis (CAD) that are associated with invasive disease-free survival (IDFS) and distant metastasis-free survival (DDFS) in patients with primarily operable triple-negative breast cancer (TNBC). Methods: This retrospective study was approved by the institutional review board with informed consent was waived. Between January 2012 and December 2014, 74 consecutive women with primary TNBC (mean age, 51 years; range, 29–77 years) who underwent preoperative MRI were included and followed until August 2021. Dynamic contrast-enhanced and T2-weighted images were obtained using 3T scanners. Peritumoral edema and central necrosis were evaluated retrospectively. CAD was used to extract 3D diameters, angiovolume, and kinetic parameters, and kinetic heterogeneity was calculated. Cox proportional hazards models were used to assess associations between MRI features and IDFS and DDFS, adjusting for clinicopathologic factors. Results: During a median follow-up of 80.9 months, 12 patients developed invasive disease, and 8 developed distant metastasis. In multivariable analysis, peak enhancement (hazard ratio [HR], 1.40; 95% confidence interval [CI], 1.06–1.84; p = 0.019) and angiovolume (HR, 2.86; 95% CI, 1.26–6.47; p = 0.012) were independently associated with IDFS, whereas angiovolume (HR, 2.47; 95% CI: 1.28–4.78; p = 0.007) was independently associated with DDFS. Conclusions: Preoperative CAD-derived MRI features, particularly peak enhancement and angiovolume, were associated with IDFS in TNBC patients whereas angiovolume alone was associated with DDFS. Full article

Introduction: Myotonic dystrophy type 1 is the most prevalent muscular dystrophy in adults, characterized by weakness, impaired functional abilities, and myotonia. However, little is known about the relationship between quantitative MRI measures (fat fraction and T₂ relaxation time) and clinical findings of the upper extremity. This study assessed forearm muscle structure in patients with myotonic dystrophy using quantitative MRI and correlated these measures with strength, function, and handgrip myotonia. Materials and Methods: Eighteen adults with myotonic dystrophy type 1 underwent MRI using three-point Dixon and T₂ spin echo imaging of the forearm. Results: The average fat fraction and T₂ relaxation time were greatest in the flexor digitorum profundus (26.7% and 55.6 ms, respectively). Correlations were found between quantitative MRI values and clinical tests of strength (r = −0.61 to −0.92, p < 0.01), function (r = −0.64 to −0.83, p < 0.01), and handgrip myotonia (r = 0.48, p < 0.05). Overall, the anterior forearm fat fraction values showed higher correlations with strength and function compared to those of the posterior forearm. Discussion: Our results support the use of quantitative MRI measures to assess forearm disease pathology and show potential to monitor the effectiveness of therapeutic treatments in patients with myotonic dystrophy type 1. Full article

Background/Objectives: To assess diagnostic accuracy of two-dimensional (2D) projection methods for rapid visual quality control of automated volumetric (3D) lung segmentations compared with slice-based 3D review of segmentation results for application in large-scale studies. Methods: Segmentation of right and left lungs on T1-weighted MRI of 300 participants of the German National Cohort (NAKO) study was performed using the nnU-NET framework. Three variants of 2D projection images of segmentation masks were created: maximum intensity projection (MIP) using pseudo-chromadepth encoding with different color spectra for right and left lung (Colored_MIP) and standard deviation projection of segmentation mask outlines, encoded in black-and-white (Gray_outline) or using color-encoding (Colored_outline). The worst of two ratings by two independent raters conducting slice-based review for segmentation errors on underlying imaging data and review for mislabeling errors served as the standard of reference. All variants were evaluated by five raters each for identification of segmentation errors and the majority rating was used as index test. The time required for review was recorded and diagnostic accuracies were calculated. Results: Sensitivities of Colored_MIP, Colored_outline and Gray_outline were 88.2% [95%-CI 78.7%; 94.4%], 89.5% [80.3%; 95.3%] and 78.9% [68.1%; 87.5%]; specificities were 98.7% [96.1%; 99.7%], 96.4% [93.1%; 98.5%] and 98.7% [96.1%; 99.7%]; and F1-scores were 0.918, 0.895 and 0.863, respectively. Mean time per case and rater required for evaluation was 2.8 ± 0.9 s for Colored_outline, 1.7 ± 0.1 s for Colored_MIP, and 2.0 ± 0.4 s for Gray_outline. Conclusions: The 2D segmentation mask projection images enabled the detection of segmentation errors of automated 3D segmentations of left and right lungs based on MRI with high diagnostic accuracy, especially when using color-encoding. The method enabled evaluation within a matter of seconds per case. Segmentation mask projection images may assist in visual quality control of automated segmentations in large-scale studies. Full article

Objective: Chest computed tomography (CT) is a key component of the diagnostic assessment of people with cystic fibrosis (PwCF) and is increasingly replacing chest radiography. Due to improvements in life expectancy, radiation exposure has become a growing concern in PwCF. Photon-counting CT (PCCT) has the potential to reduce the risk of radiation-induced malignancies while maintaining diagnostic accuracy. This study aimed to compare the radiation dose and image quality of low-dose high-resolution (LD-HR) and ultra-low-dose high-resolution (ULD-HR) CT protocols using PCCT in PwCF. Methods: This retrospective study included 72 PwCF, with 36 undergoing a LD-HR chest CT protocol and 36 receiving an ULD-HR protocol on a PCCT. The radiation dose and image quality were assessed by comparing the effective dose and signal-to-noise ratio (SNR). Three blinded radiologists evaluated the overall image quality, sharpness, noise, and assessability of the bronchi, bronchial wall thickening, and bronchiolitis using a five-point Likert scale. Results: The ULD-HR PCCT protocol reduced radiation exposure by approximately 65% compared with the LD-HR PCCT protocol (median effective dose: 0.19 vs. 0.55 mSv, p < 0.001). While LD-HR images were consistently rated higher than ULD-HR images (p < 0.001), both protocols maintained diagnostic significance (median image quality rating of “4-good”). The average SNR of the lung parenchyma was significantly lower with ULD-HR PCCT compared to LD-HR PCCT (p < 0.001). Conclusions: ULD-HR PCCT significantly reduced radiation exposure while maintaining good diagnostic image quality in PwCF. The effective dose of ULD-HR PCCT is only twice that of a two-plane chest X-ray, making it a viable low-radiation alternative for routine imaging in PwCF. Full article

Background: Ultrasound is the primary imaging modality for evaluating thyroid nodules, with echogenicity and nodule size serving as parameters for malignancy risk stratification. Though the TI-RADS classification system is standardized, interpretation varies among observers due to subjectivity, and can affect diagnostic consistency. This study aimed to evaluate the diagnostic and interobserver agreement of quantitative ultrasound gray-scale analysis and nodule area in differentiating benign from malignant solid thyroid nodules. Methods: This retrospective study reviewed 600 patients who underwent thyroid ultrasound at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, in 2023 and 2024. Of these 600, 107 adult patients with 116 solid thyroid nodules (96 benign and 20 malignant) who subsequently underwent ultrasound-guided fine-needle aspiration were included in the final analysis. From B-mode ultrasound images, the grayscale median (GSM) values of each nodule and adjacent normal thyroid tissue were measured using Adobe Photoshop. The GSM ratio (GSMr) was calculated by dividing nodule GSM by normal tissue GSM. Nodule size, taken as cross-sectional area, was assessed using ImageJ software version 1.53. The Mann–Whitney U test was used to compare GSMr and the area between benign and malignant nodules. Inter-observer agreement was evaluated using the intraclass correlation coefficient (ICC). Results: Malignant nodules had significantly lower GSMr compared to benign nodules (malignant: median 0.76, IQR 0.27; benign: median 0.88, IQR 0.55, p = 0.02). Malignant nodules were also significantly larger than benign nodules (malignant: median 2.77 cm², IQR: 5.08; benign: median 1.78 cm², IQR 1.65, p = 0.02). Inter-observer reproducibility was excellent for both GSMr (ICC = 0.998) and area (ICC = 0.997). Conclusions: Quantitative ultrasound assessment of grayscale echogenicity and nodule area provides valuable diagnostic information for differentiating benign from malignant solid thyroid nodules. These objective measures may enhance diagnostic confidence and support more precise clinical decision-making in thyroid nodule evaluation. Full article

Objectives: Ultra-high dose-rate FLASH radiotherapy has demonstrated strong potential in reducing normal tissue toxicity while maintaining effective tumor control. However, its underlying radiobiological mechanisms remain unclear, highlighting the need for novel approaches to probe the effects of radiation during and immediately after delivery. This study presents the first exploration of 3D PET imaging of positron-emitting nuclei (PENs) generated by a FLASH proton beam. Methods: A home-built 12-panel preclinical small-animal PET system was employed for recording coincidence events. A 142.4 MeV FLASH proton beam with a 100 ms delivery time was directed into a solid water phantom. PET coincidence signals were recorded during the first 1 s and up to 11 min. The system’s capability for 3D localization was also assessed, and Monte Carlo simulations were performed for validation. Results: The PET system successfully recorded coincidence data within the first second, including the 100 ms beam delivery interval. Detector dead-time effects under the high beam flux were observed, leading to underestimated event counts. Following irradiation, the measured activity and decay behavior were consistent with simulations. The PET system accurately reconstructed the spatial distribution of PEN activities, with discrepancies in measured versus calculated line profiles ranging from 3.35–6.85%. Reconstructed PET images enabled reliable 3D localization with sub-millimeter accuracy in both lateral and depth dimensions. Conclusions: Our findings demonstrate that a multi-detector PET system is a promising tool for investigating the radiation effects of FLASH beams. Full article