Tomography最新文献

{"title":"Radiomic Features of Mesorectal Fat as Indicators of Response in Rectal Cancer Patients Undergoing Neoadjuvant Therapy.","authors":"Francesca Treballi, Ginevra Danti, Sofia Boccioli, Sebastiano Paolucci, Simone Busoni, Linda Calistri, Vittorio Miele","doi":"10.3390/tomography11040044","DOIUrl":"https://doi.org/10.3390/tomography11040044","url":null,"abstract":"<p><strong>Background: </strong>Rectal cancer represents a major cause of mortality in the United States. Management strategies are highly individualized, depending on patient-specific factors and tumor characteristics. The therapeutic landscape is rapidly evolving, with notable advancements in response rates to both radiotherapy and chemotherapy. For locally advanced rectal cancer (LARC, defined as up to T3-4 N+), the standard of care involves total mesorectal excision (TME) following neoadjuvant chemoradiotherapy (nCRT). Magnetic resonance imaging (MRI) has emerged as the gold standard for local tumor staging and is increasingly pivotal in post-treatment restaging.</p><p><strong>Aim: </strong>In our study, we proposed an MRI-based radiomic model to identify characteristic features of peritumoral mesorectal fat in two patient groups: good responders and poor responders to neoadjuvant therapy. The aim was to assess the potential presence of predictive factors for favorable or unfavorable responses to neoadjuvant chemoradiotherapy, thereby optimizing treatment management and improving personalized clinical decision-making.</p><p><strong>Methods: </strong>We conducted a retrospective analysis of adult patients with LARC who underwent pre- and post-nCRT MRI scans. Patients were classified as good responders (Group 0) or poor responders (Group 1) based on MRI findings, including tumor volume reduction, signal intensity changes on T2-weighted and diffusion-weighted imaging (DWI), and alterations in the circumferential resection margin (CRM) and extramural vascular invasion (EMVI) status. Classification criteria were based on the established literature to ensure consistency. Key clinical and imaging parameters, such as age, TNM stage, CRM involvement, and EMVI presence, were recorded. A radiomic model was developed using the LASSO algorithm for feature selection and regularization from 107 extracted radiomic features.</p><p><strong>Results: </strong>We included 44 patients (26 males and 18 females) who, following nCRT, were categorized into Group 0 (28 patients) and Group 1 (16 patients). The pre-treatment MRI analysis identified significant features (out of 107) for each sequence based on the Mann-Whitney test and <i>t</i>-test. The LASSO algorithm selected three features (shape_Sphericity, shape_Maximum2DDiameterSlice, and glcm_Imc2) for the construction of the radiomic logistic regression model, and ROC curves were subsequently generated for each model (AUC: 0.76).</p><p><strong>Conclusions: </strong>We developed an MRI-based radiomic model capable of differentiating and predicting between two groups of rectal cancer patients: responders and non-responders to neoadjuvant chemoradiotherapy (nCRT). This model has the potential to identify, at an early stage, lesions with a high likelihood of requiring surgery and those that could potentially be managed with medical treatment alone.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055445","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":"Anatomical Variations and Morphometry of Carotid Sinus: A Computed Tomography Study.","authors":"Noor Fazaldad, Srinivasa Rao Sirasanagandla, Anwar Al-Shuaili, Sreenivasulu Reddy Mogali, Ramya Chandrasekaran, Humoud Al Dhuhli, Eiman Al-Ajmi","doi":"10.3390/tomography11040045","DOIUrl":"https://doi.org/10.3390/tomography11040045","url":null,"abstract":"<p><strong>Background: </strong>The radiological evaluation of the carotid sinus (CS) anatomy and its morphometry is essentially important for various surgical procedures involving the carotid bifurcation and the CS itself. Despite its tremendous clinical significance, studies dealing with the CS anatomy are seldom reported. Hence, the present study aimed to evaluate the frequencies of the CS positional variants and their morphometry and correlate them with age and body mass index (BMI).</p><p><strong>Methods: </strong>In this retrospective cross-sectional study, a total of 754 disease-free carotid arteries were examined using computed tomography angiography scans to determine the CS positional variations (such as types I to III) and its morphometry, including the CS diameter and length. Additionally, the association between these parameters and factors such as sex, age, and body mass index were explored using appropriate statistical tests. The inter-rater agreement of the collected dataset was evaluated using Cohen's Kappa.</p><p><strong>Results: </strong>The CS type I was observed in 87.67% of the cases, and type II and type III were observed at lower frequencies with 9.02% and 3.32%, respectively. There were statistically significant (<i>p</i> < 0.001) differences observed in the mean diameter and length of the sinus between the sex and the type I CS variations. However, there was no significant and strong correlation between the age and BMI factors with sinus length and sinus diameter. The kappa values for inter-rater agreement ranged from 0.77 to 0.99 for all parameters.</p><p><strong>Conclusions: </strong>In type I, the CS length and carotid vessel's diameter is significantly different between the sexes. However, age and BMI do not affect the CS anatomy in radiologically disease-free carotid arteries. Knowledge of the CS variant anatomy is clinically significant as it influences the patients' surgical and physiological outcomes.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025600","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":"The Role of Monochromatic Superb Microvascular Index to Predict Malignancy of Solid Focal Lesions: Correlation Between Vascular Index and Histological Bioptic Findings.","authors":"Francesco Giurazza, Luigi Basile, Felice D'Antuono, Fabio Corvino, Antonio Borzelli, Claudio Carrubba, Raffaella Niola","doi":"10.3390/tomography11040043","DOIUrl":"https://doi.org/10.3390/tomography11040043","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to assess the potential role of the ultrasound (US) monochromatic Superb Microvascular Index (mSMI) to predict malignancy of solid focal lesions, correlating the vascular index (VI) with bioptic histological results.</p><p><strong>Methods: </strong>In this single-center retrospective analysis, patients undergoing percutaneous US-guided biopsy of solid lesions were considered. Biopsy indication was given by a multidisciplinary team evaluation based on clinical radiological data. Exclusion criteria were: unfeasible SMI evaluations due to poor respiratory compliance, locations not appreciable with the SMI, previous antiangiogenetic chemo/immunotherapies, and inconclusive histological reports. The mSMI examination was conducted in order to visualize extremely low-velocity flows with a high resolution and high frame rate; the VI was semi-automatically calculated. All bioptic procedures were performed under sole US guidance using 16G or 18G needles, immediately after mSMI assessment.</p><p><strong>Results: </strong>Forty-four patients were included (mean age: 64 years; 27 males, 17 females). Liver (15/43), kidneys (9/43), and lymph nodes (6/43) were the most frequent targets. At histopathological analysis, 7 lesions were benign and 37 malignant, metastasis being the most represented. The VI calculated in malignant lesions was statistically higher compared to benign lesions (35.45% and 11% in malignant and benign, respectively; <i>p</i>-value 0.013). A threshold VI value of 15.4% was identified to differentiate malignant lesions. The overall diagnostic accuracy of the VI with the mSMI was 0.878, demonstrating a high level of diagnostic accuracy.</p><p><strong>Conclusions: </strong>In this study, the mSMI analysis of solid focal lesions undergoing percutaneous biopsy significantly correlated with histological findings in terms of malignant/benign predictive value, reflecting histological vascular changes in malignant lesions.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144038874","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":"Variability Between Radiation-Induced Cancer Risk Models in Estimating Oncogenic Risk in Intensive Care Unit Patients.","authors":"Emilio Quaia, Chiara Zanon, Riccardo Torchio, Fabrizio Dughiero, Francesca De Monte, Marta Paiusco","doi":"10.3390/tomography11040042","DOIUrl":"10.3390/tomography11040042","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the variability of oncogenic risk related to radiation exposure in patients frequently exposed to ionizing radiation for diagnostic purposes, specifically ICU patients, according to different risk models, including the BEIR VII, ICRP 103, and US EPA models.</p><p><strong>Methods: </strong>This was an IRB-approved observational retrospective study. A total of 71 patients (58 male, 13 female; median age, 66 years; interquartile range [IQR], 65-71 years) admitted to the ICU who underwent X-ray examinations between 1 October 2021 and 28 February 2023 were included. For each patient, the cumulative effective dose during a single hospital admission was calculated. Lifetime attributable risk (LAR) was estimated based on the BEIR VII, ICRP 103, and US EPA risk models to calculate additional oncogenic risk related to radiation exposure. The Friedman test for repeated-measures analysis of variance was used to compare risk values between different models. The intraclass correlation coefficient (ICC) was used to assess the consistency of risk values between different models.</p><p><strong>Results: </strong>Different organ, leukemia, and all-cancer risk values estimated according to different oncogenic risk models were significantly different, but the intraclass correlation coefficient revealed a good (>0.75) or even excellent (>0.9) agreement between different risk models. The ICRP 103 model estimated a lower all-cancer (median 69.05 [IQR 30.35-195.37]) and leukemia risk (8.22 [3.02-27.93]) compared to the US EPA (all-cancer: 139.68 [50.51-416.16]; leukemia: 23.34 [3.47-64.37]) and BEIR VII (all-cancer: 162.08 [70.6-371.40]; leukemia: 24.66 [12.9-58.8]) models.</p><p><strong>Conclusions: </strong>Cancer risk values were significantly different between risk models, though inter-model agreement in the consistency of risk values was found to be good, or even excellent.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12030842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055450","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":"Rosette Trajectory MRI Reconstruction with Vision Transformers.","authors":"Muhammed Fikret Yalcinbas, Cengizhan Ozturk, Onur Ozyurt, Uzay E Emir, Ulas Bagci","doi":"10.3390/tomography11040041","DOIUrl":"https://doi.org/10.3390/tomography11040041","url":null,"abstract":"<p><strong>Introduction: </strong>An efficient pipeline for rosette trajectory magnetic resonance imaging reconstruction is proposed, combining the inverse Fourier transform with a vision transformer (ViT) network enhanced with a convolutional layer. This method addresses the challenges of reconstructing high-quality images from non-Cartesian data by leveraging the ViT's ability to handle complex spatial dependencies without extensive preprocessing.</p><p><strong>Materials and methods: </strong>The inverse fast Fourier transform provides a robust initial approximation, which is refined by the ViT network to produce high-fidelity images.</p><p><strong>Results and discussion: </strong>This approach outperforms established deep learning techniques for normalized root mean squared error, peak signal-to-noise ratio, and entropy-based image quality scores; offers better runtime performance; and remains competitive with respect to other metrics.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042899","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":"Assessing Acute DWI Lesions in Clinically Diagnosed TIA: Insights from a Cohort Study in Cluj, Romania.","authors":"Khaled Abu Arif, Ioan Stefan Florian, Alexandru Ioan Florian, Alina Vasilica Blesneag, Enola Maer, Răzvan Mircea Cherecheș","doi":"10.3390/tomography11040040","DOIUrl":"10.3390/tomography11040040","url":null,"abstract":"<p><strong>Background: </strong>The updated definition of a TIA emphasizes tissue characteristics rather than symptom duration, defining a TIA as a transient neurological episode without ischemic lesions in brain imaging, including in DWI. If imaging reveals a lesion, even in patients with transient symptoms, the event is reclassified as a minor ischemic stroke.</p><p><strong>Objective: </strong>This retrospective observational study aimed to determine the prevalence of ischemic lesions in DWI in patients with a TIA diagnosis.</p><p><strong>Results: </strong>Adults aged 18-90 years, diagnosed with a TIA by a neurologist and who underwent MRI-DWI at CMT hospital within the first week after symptom onset (May 2023-July 2024), were included. Ethical approval was obtained. Descriptive statistics summarized patient demographics, clinical features, Fazekas scale grades, and imaging findings.</p><p><strong>Conclusions: </strong>Among the 26 patients clinically diagnosed with TIAs, 7 (26.9%) exhibited ischemic lesions in DWI, reclassifying these cases as minor ischemic strokes under the updated definition. The prevalence of ischemic lesions was notably higher in patients with comorbidities such as hypertension and diabetes. These findings highlight the importance of early MRI-DWI to accurately distinguish TIAs from minor ischemic strokes. Routine urgent DWI within the first week of TIA symptoms enhances diagnosis and risk stratification and can guide targeted stroke prevention strategies, particularly when combined with the ABCD2 score.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059531","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":"Utility of Cardiac CT for Cardiomyopathy Phenotyping.","authors":"Ramzi Ibrahim, Mahmoud Abdelnabi, Girish Pathangey, Juan Farina, Steven J Lester, Chadi Ayoub, Said Alsidawi, Balaji K Tamarappoo, Clinton Jokerst, Reza Arsanjani","doi":"10.3390/tomography11030039","DOIUrl":"10.3390/tomography11030039","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732856","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":"Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer.","authors":"Destie Provenzano, Jeffrey Wang, Sharad Goyal, Yuan James Rao","doi":"10.3390/tomography11030038","DOIUrl":"10.3390/tomography11030038","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732504","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":"Variability of HCC Tumor Diameter and Density Measurements on Dynamic Contrast-Enhanced Computed Tomography.","authors":"Siddharth Guha, Abdalla Ibrahim, Pengfei Geng, Qian Wu, Yen Chou, Oguz Akin, Lawrence H Schwartz, Chuan-Miao Xie, Binsheng Zhao","doi":"10.3390/tomography11030036","DOIUrl":"10.3390/tomography11030036","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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, <i>p</i>-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%).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732857","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":"Longitudinal Analysis of Amyloid PET and Brain MRI for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease: Findings from the ADNI Cohort.","authors":"Do-Hoon Kim","doi":"10.3390/tomography11030037","DOIUrl":"10.3390/tomography11030037","url":null,"abstract":"<p><strong>Background/objectives: </strong>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).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>p</i> < 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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11945403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732777","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}