Tomography最新文献

{"title":"Preclinical Evaluation of a Novel PSMA-Targeted Agent ⁶⁸Ga-NOTA-GC-PSMA for Prostate Cancer Imaging.","authors":"Wenjin Li, Yihui Luo, Yuqi Hua, Qiaoling Shen, Liping Chen, Yu Xu, Haitian Fu, Chunjing Yu","doi":"10.3390/tomography11030029","DOIUrl":"10.3390/tomography11030029","url":null,"abstract":"<p><p><b>Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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). <b>Conclusions:</b> The novel tracer ⁶⁸Ga-NOTA-GC-PSMA is promising radionuclide imaging of PCa.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calcified Lung Nodules: A Diagnostic Challenge in Clinical Daily Practice.","authors":"Elisa Baratella, Marianna Carbi, Pierluca Minelli, Antonio Segalotti, Barbara Ruaro, Francesco Salton, Roberta Polverosi, Maria Assunta Cova","doi":"10.3390/tomography11030028","DOIUrl":"10.3390/tomography11030028","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology.","authors":"Julia Lasek, Karolina Nurzynska, Adam Piórkowski, Michał Strzelecki, Rafał Obuchowicz","doi":"10.3390/tomography11030027","DOIUrl":"10.3390/tomography11030027","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>This study aimed to develop and validate an AI-driven method for the automatic and reproducible measurement of TMJ space width from ultrasonographic images.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Phantom for Standardized Microcalcification Detection Developed Using a Crystalline Growth System.","authors":"Dee H Wu, Caroline Preskitt, Natalie Stratemeier, Hunter Lau, Sreeja Ponnam, Supriya Koya","doi":"10.3390/tomography11030025","DOIUrl":"10.3390/tomography11030025","url":null,"abstract":"<p><strong>Background/objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11945459/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of Lung Cancer Using Endobronchial Ultrasonography Image Based on Multi-Scale Image and Multi-Feature Fusion Framework.","authors":"Huitao Wang, Takahiro Nakajima, Kohei Shikano, Yukihiro Nomura, Toshiya Nakaguchi","doi":"10.3390/tomography11030024","DOIUrl":"10.3390/tomography11030024","url":null,"abstract":"<p><p>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 M3-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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11945964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-Based Model for Personalizing Neoadjuvant Treatment in Breast Cancer.","authors":"Wen Li, Natsuko Onishi, Jessica E Gibbs, Lisa J Wilmes, Nu N Le, Pouya Metanat, Elissa R Price, Bonnie N Joe, John Kornak, Christina Yau, Denise M Wolf, Mark Jesus M Magbanua, Barbara LeStage, Laura J van 't Veer, Angela M DeMichele, Laura J Esserman, Nola M Hylton","doi":"10.3390/tomography11030026","DOIUrl":"10.3390/tomography11030026","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>Predictive models developed using imaging biomarkers, alongside clinically validated probability thresholds, can be incorporated into decision-making for precision oncology.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ADMM-TransNet: ADMM-Based Sparse-View CT Reconstruction Method Combining Convolution and Transformer Network.","authors":"Sukai Wang, Xueqin Sun, Yu Li, Zhiqing Wei, Lina Guo, Yihong Li, Ping Chen, Xuan Li","doi":"10.3390/tomography11030023","DOIUrl":"10.3390/tomography11030023","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Four-Dimensional Dual-Energy Computed Tomography-Derived Parameters and Their Correlation with Thyroid Gland Functional Status.","authors":"Max H M C Scheepers, Zaid J J Al-Difaie, Nicole D Bouvy, Bas Havekes, Alida A Postma","doi":"10.3390/tomography11030022","DOIUrl":"10.3390/tomography11030022","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>p</i> = 0.002).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts.","authors":"Satvik Nayak, Henry Salkever, Ernesto Diaz, Avantika Sinha, Nikhil Deveshwar, Madeline Hess, Matthew Gibbons, Sule Sahin, Abhejit Rajagopal, Peder E Z Larson, Renuka Sriram","doi":"10.3390/tomography11030021","DOIUrl":"10.3390/tomography11030021","url":null,"abstract":"<p><strong>Background/objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results and conclusions: </strong>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer.","authors":"Nicolò Gennaro, Moataz Soliman, Amir A Borhani, Linda Kelahan, Hatice Savas, Ryan Avery, Kamal Subedi, Tugce A Trabzonlu, Chase Krumpelman, Vahid Yaghmai, Young Chae, Jochen Lorch, Devalingam Mahalingam, Mary Mulcahy, Al Benson, Ulas Bagci, Yuri S Velichko","doi":"10.3390/tomography11030020","DOIUrl":"10.3390/tomography11030020","url":null,"abstract":"<p><p><b>Background/Objectives</b>: 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). <b>Materials and Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11945686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}