Diagnostics最新文献

{"title":"Use of Ultrasound for Body Composition in Assessment in Pediatric Patients: Are There Still Challenges?","authors":"Patricia Miranda Farias, Amanda Matos Lima Melo, Aryanne Almeida da Costa, Valter Aragão do Nascimento, Arnildo Pott, Rita de Cássia Avellaneda Guimarães, Karine de Cássia Freitas","doi":"10.3390/diagnostics15192472","DOIUrl":"10.3390/diagnostics15192472","url":null,"abstract":"<p><p>Patients who present nutritional risk upon hospital admission are more likely to have worse clinical outcomes. Evaluating muscle thickness with ultrasound is a predictor of muscle mass loss in pediatric patients. We reviewed the muscle mass loss detection through ultrasound to assess the body composition of pediatric patients. We found an association between muscle reduction, as detected by ultrasound, and the duration of mechanical ventilation, nutritional deficits in energy and protein intake, and the age-related skeletal muscle atrophy of the limbs. All studies reported a reduction in muscle thickness of more than 10% during hospitalization. There is a lack of standardization in muscle mass assessment protocols and established cut-off points in critically ill hospitalized children. Further studies are needed to establish an accurate and standardized analysis for monitoring muscle changes using ultrasound.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DNA Hypermethylation at the Invasive Front of Oral Squamous Cell Carcinoma Confers Poorly Differentiated Characteristics and Promotes Migration of Cancer Cells.","authors":"Li-Po Wang, Chien-Ya Li, Yu-Hsueh Wu, Meng-Yen Chen, Yi-Ping Hsieh, Tze-Ta Huang, Tse-Ming Hong, Yuh-Ling Chen","doi":"10.3390/diagnostics15192477","DOIUrl":"10.3390/diagnostics15192477","url":null,"abstract":"<p><p><b>Background/Objectives:</b> Oral squamous cell carcinoma (OSCC) is a common and aggressive oral cancer with high recurrence and mortality rates, largely due to late diagnosis and metastasis. Epigenetic regulation, particularly aberrant DNA methylation, plays a critical role in cancer progression. Altered methylation patterns disrupt cancer-related gene regulation. Our previous study found that oral cancer patients exhibit increased synthesis of S-adenosyl-L-methionine, a key methyl donor for cytosine methylation. Therefore, the aim of this study was to explore the relationship between global DNA methylation and OSCC progression and to evaluate the impact of DNA methylation heterogeneity on oral cancer cells. <b>Methods:</b> Immunohistochemistry (IHC) and immunofluorescence (IF) staining were used to examine 5-methylcytosine (5-mC) expression in OSCC clinical specimens and oral cancer cells. The DNA methyltransferase inhibitor 5-Aza-dC was used to assess the effects of DNA methylation on cell function and gene expression. RNA sequencing was used to identify key differentially expressed genes affected by 5-Aza-dC treatment. Cell migration was assessed using a wound closure assay. Protein and gene expression were analyzed using Western blotting and quantitative PCR. <b>Results:</b> An inverse relationship was found between 5-mC levels and cancer differentiation-poorly differentiated OSCC exhibited higher 5-mC levels. Additionally, higher 5-mC staining was observed at the invasion front of oral cancer tissues. In OSCC cells, 5-mC content correlated with migration ability. Furthermore, conditioned medium from cancer-associated fibroblasts enhanced both methylation levels and migration of OSCC cells. Treatment with 5-Aza-dC significantly increased epithelial differentiation, reduced epithelial-to-mesenchymal transition and cell adhesion-related genes, and inhibited OSCC cell migration. <b>Conclusions:</b> The findings highlight the critical role of DNA hypermethylation in OSCC progression, particularly in regulating differentiation, migration, and EMT. The interplay between the tumor microenvironment and epigenetic modifications underscores the complexity of OSCC biology and opens avenues for innovative therapeutic strategies.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145298775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Model and Knowledge Graph-Driven AJCC Staging of Prostate Cancer Using Pathology Reports.","authors":"Eunbeen Jo, Tae Il Noh, Hyung Joon Joo","doi":"10.3390/diagnostics15192474","DOIUrl":"10.3390/diagnostics15192474","url":null,"abstract":"<p><p><b>Background/Objectives</b>: To develop an automated American Joint Committee on Cancer (AJCC) staging system for radical prostatectomy pathology reports using large language model-based information extraction and knowledge graph validation. <b>Methods</b>: Pathology reports from 152 radical prostatectomy patients were used. Five additional parameters (Prostate-specific antigen (PSA) level, metastasis stage (M-stage), extraprostatic extension, seminal vesicle invasion, and perineural invasion) were extracted using GPT-4.1 with zero-shot prompting. A knowledge graph was constructed to model pathological relationships and implement rule-based AJCC staging with consistency validation. Information extraction performance was evaluated using a local open-source large language model (LLM) (Mistral-Small-3.2-24B-Instruct) across 16 parameters. The LLM-extracted information was integrated into the knowledge graph for automated AJCC staging classification and data consistency validation. The developed system was further validated using pathology reports from 88 radical prostatectomy patients in The Cancer Genome Atlas (TCGA) dataset. <b>Results</b>: Information extraction achieved an accuracy of 0.973 and an F1-score of 0.986 on the internal dataset, and 0.938 and 0.968, respectively, on external validation. AJCC staging classification showed macro-averaged F1-scores of 0.930 and 0.833 for the internal and external datasets, respectively. Knowledge graph-based validation detected data inconsistencies in 5 of 150 cases (3.3%). <b>Conclusions</b>: This study demonstrates the feasibility of automated AJCC staging through the integration of large language model information extraction and knowledge graph-based validation. The resulting system enables privacy-protected clinical decision support for cancer staging applications with extensibility to broader oncologic domains.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145298886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pilot Study of Exploring miRNA-Protein Interaction Networks in Pancreatic Ductal Adenocarcinoma Patients: Implications for Diagnosis and Prognosis.","authors":"Sena Şen, Merve Çiğdem Özgel, Şeref Buğra Tunçer, Hamza Uğur Bozbey, Senem Karabulut, Didem Taştekin","doi":"10.3390/diagnostics15192479","DOIUrl":"10.3390/diagnostics15192479","url":null,"abstract":"<p><p><b>Background:</b> Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies for which there are few effective biomarkers for diagnosis, prognosis, and treatment monitoring. Given the paucity of data in the literature, this study aimed to evaluate the biomarker potential of selected miRNAs (miR-222-3p, miR-3154, miR-3945, miR-4534, and miR-4742) and their protein targets in the context of PDAC. <b>Methods:</b> The expression levels of miRNA candidates were quantified by real-time quantitative PCR in lymphocyte samples from 46 PDAC patients and 50 healthy controls. In silico analyses were performed to identify potential target genes and proteins. ELISA was used to measure protein expression in both groups. Statistical analyses included ROC curve analysis, linear regression, and correlation analyses. In addition, correlations between miRNA/protein expression and clinicopathologic characteristics, including survival, were investigated. <b>Results:</b> miR-222-3p and miR-3154 were significantly downregulated in PDAC patients compared to controls (<i>p</i> < 0.001). Among the dual miRNA combinations, miR-222-3p and miR-4534 showed the highest discriminatory power (AUC = 0.629, <i>p</i> = 0.022). The miR-222-3p expression was significantly increased in patients with a history of alcohol consumption (<i>p</i> = 0.02). Significant correlations were observed between miR-3154 expression and T-stage (<i>p</i> = 0.01) and between perineural invasion and miR-222-3p levels (<i>p</i> = 0.02). Survival analysis showed that high miR-3945 expression was significantly associated with shorter overall survival (<i>p</i> = 0.001). Elevated levels of ESR1, HCFC1, and EPC1 were significantly associated with lymphatic invasion (<i>p</i> < 0.05), while high KCNA1 expression correlated with shorter survival (<i>p</i> = 0.006), indicating its potential as a negative prognostic biomarker. Linear regression analysis revealed a significant positive correlation between miR-3945 and KCNA1 expression (β = 0.259, <i>p</i> = 0.038), indicating a possible regulatory interaction. A borderline correlation was also found between miR-4742 and EPC1 expression (<i>p</i> = 0.055). <b>Conclusions:</b> This study identifies several miRNAs and associated proteins with diagnostic and prognostic significance in PDAC. The results emphasize the clinical relevance of integrating multi-layered analyses of miRNA-protein interactions. The observed associations highlight the role of these molecular markers in tumor progression and patient survival and offer promising opportunities for future research and clinical application in precision oncology.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145298949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heart Murmur Detection in Phonocardiogram Data Leveraging Data Augmentation and Artificial Intelligence.","authors":"Melissa Valaee, Shahram Shirani","doi":"10.3390/diagnostics15192471","DOIUrl":"10.3390/diagnostics15192471","url":null,"abstract":"<p><p><b>Background/Objectives</b>: With a 17.9 million annual mortality rate, cardiovascular disease is the leading global cause of death. As such, early detection and disease diagnosis are critical for effective treatment and symptom management. Cardiac auscultation, the process of listening to the heartbeat, often provides the first indication of underlying cardiac conditions. This practice allows for the identification of heart murmurs caused by turbulent blood flow. In this exploratory research paper, we propose an AI model to streamline this process to improve diagnostic accuracy and efficiency. <b>Methods</b>: We utilized data from the 2022 George Moody PhysioNet Heart Sound Classification Challenge, comprising phonocardiogram recordings of individuals under 21 years of age in Northeast Brazil. Only patients who had recordings from all four heart valves were included in our dataset. Audio files were synchronized across all recordings and converted to Mel spectrograms before being passed into a pre-trained Vision Transformer, and finally a MiniROCKET model. Additionally, data augmentation was conducted on audio files and spectrograms to generate new data, extending our total sample size from 928 spectrograms to 14,848. <b>Results</b>: Compared to the existing methods in the literature, our model yielded significantly enhanced quality assessment metrics, including Weighted Accuracy, Sensitivity, and F-Score, and resulted in a fast evaluation speed of 0.02 s per patient. <b>Conclusions</b>: The implementation of our method for the detection of heart murmurs can supplement physician diagnosis and contribute to earlier detection of underlying cardiovascular conditions, fast diagnosis times, increased scalability, and enhanced adaptability.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Feature Selection, Machine Learning, and SHAP Explainability to Predict Severe Acute Pancreatitis.","authors":"İzzet Ustaalioğlu, Rohat Ak","doi":"10.3390/diagnostics15192473","DOIUrl":"10.3390/diagnostics15192473","url":null,"abstract":"<p><p><b>Background/Objectives</b>: Severe acute pancreatitis (SAP) carries substantial morbidity and resource burden, and early risk stratification remains challenging with conventional scores that require serial observations. The aim of this study was to develop and compare supervised machine-learning (ML) pipelines-integrating feature selection and SHAP-based explainability-for early prediction of SAP at emergency department (ED) presentation. <b>Methods</b>: This retrospective, single-center cohort was conducted in a tertiary-care ED between 1 January 2022 and 1 January 2025. Adult patients with acute pancreatitis were identified from electronic records; SAP was classified per the Revised Atlanta criteria (persistent organ failure ≥ 48 h). Six feature-selection methods (univariate AUROC filter, RFE, mRMR, LASSO, elastic net, Boruta) were paired with six classifiers (kNN, elastic-net logistic regression, MARS, random forest, SVM-RBF, XGBoost) to yield 36 pipelines. Discrimination, calibration, and error metrics were estimated with bootstrapping; SHAP was used for model interpretability. <b>Results</b>: Of 743 patients (non-SAP 676; SAP 67), SAP prevalence was 9.0%. Compared with non-SAP, SAP patients more often had hypertension (38.8% vs. 27.1%) and malignancy (19.4% vs. 7.2%); they presented with lower GCS, higher heart and respiratory rates, lower systolic blood pressure, and more frequent peripancreatic fluid (31.3% vs. 16.9%) and pleural effusion (43.3% vs. 17.5%). Albumin was lower by 4.18 g/L, with broader renal-electrolyte and inflammatory derangements. Across the best-performing models, AUROC spanned 0.750-0.826; the top pipeline (RFE-RF features + kNN) reached 0.826, while random-forest-based pipelines showed favorable calibration. SHAP confirmed clinically plausible contributions from routinely available variables. <b>Conclusions</b>: In this study, integrating feature selection with ML produced accurate and interpretable early prediction of SAP using data available at ED arrival. The approach highlights actionable predictors and may support earlier triage and resource allocation; external validation is warranted.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TurkerNeXtV2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification.","authors":"Omer Esmez, Gulnihal Deniz, Furkan Bilek, Murat Gurger, Prabal Datta Barua, Sengul Dogan, Mehmet Baygin, Turker Tuncer","doi":"10.3390/diagnostics15192478","DOIUrl":"10.3390/diagnostics15192478","url":null,"abstract":"<p><p><b>Background/Objectives:</b> Lightweight CNNs for medical imaging remain limited. We propose TurkerNeXtV2, a compact CNN that introduces two new blocks: a pooling-based attention with an inverted bottleneck (TNV2) and a hybrid downsampling module. These blocks improve stability and efficiency. The aim is to achieve transformer-level effectiveness while keeping the simplicity, low computational cost, and deployability of CNNs. <b>Methods:</b> The model was first pretrained on the Stable ImageNet-1k benchmark and then fine-tuned on a collected plantar-pressure OA dataset. We also evaluated the model on a public blood-cell image dataset. Performance was measured by accuracy, precision, recall, and F1-score. Inference time (images per second) was recorded on an RTX 5080 GPU. Grad-CAM was used for qualitative explainability. <b>Results:</b> During pretraining on Stable ImageNet-1k, the model reached a validation accuracy of 87.77%. On the OA test set, the model achieved 93.40% accuracy (95% CI: 91.3-95.2%) with balanced precision and recall above 90%. On the blood-cell dataset, the test accuracy was 98.52%. The average inference time was 0.0078 s per image (≈128.8 images/s), which is comparable to strong CNN baselines and faster than the transformer baselines tested under the same settings. <b>Conclusions:</b> TurkerNeXtV2 delivers high accuracy with low computational cost. The pooling-based attention (TNV2) and the hybrid downsampling enable a lightweight yet effective design. The model is suitable for real-time and clinical use. Future work will include multi-center validation and broader tests across imaging modalities.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Diagnosis, Classification, and Segmentation of Abdominal Aortic Aneurysm and Dissection from Computed Tomography Images.","authors":"Hakan Baltaci, Sercan Yalcin, Muhammed Yildirim, Harun Bingol","doi":"10.3390/diagnostics15192476","DOIUrl":"10.3390/diagnostics15192476","url":null,"abstract":"<p><p><b>Background/Objectives</b>: Diagnosis of abdominal aortic aneurysm and abdominal aortic dissection (AAA and AAD) is of strategic importance as cardiovascular disease has fatal implications worldwide. This study presents a novel deep learning-based approach for the accurate and efficient diagnosis of abdominal aortic aneurysms (AAAs) and aortic dissections (AADs) from CT images. <b>Methods</b>: Our proposed convolutional neural network (CNN) architecture effectively extracts relevant features from CT scans and classifies regions as normal or diseased. Additionally, the model accurately delineates the boundaries of detected aneurysms and dissections, aiding in clinical decision-making. A pyramid scene parsing network has been built in a hybrid method. The layer block after the classification layer is divided into two groups: whether there is an AAA or AAD region in the abdominal CT image, and determination of the borders of the detected diseased region in the medical image. <b>Results</b>: In this sense, both detection and segmentation are performed in AAA and AAD diseases. Python programming has been used to assess the accuracy and performance results of the proposed strategy. From the results, average accuracy rates of 83.48%, 86.9%, 88.25%, and 89.64% were achieved using ResDenseUNet, INet, C-Net, and the proposed strategy, respectively. Also, intersection over union (IoU) of 79.24%, 81.63%, 82.48%, and 83.76% have been achieved using ResDenseUNet, INet, C-Net, and the proposed method. <b>Conclusions</b>: The proposed strategy is a promising technique for automatically diagnosing AAA and AAD, thereby reducing the workload of cardiovascular surgeons.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12524272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Relationship Between Non-Invasive Tests and Digital Pathology for Quantifying Liver Fibrosis in MASLD.","authors":"Xiaodie Wei, Lixia Qiu, Xinxin Wang, Chen Shao, Jing Zhao, Qiang Yang, Jun Chen, Meng Yin, Richard L Ehman, Jing Zhang","doi":"10.3390/diagnostics15192475","DOIUrl":"10.3390/diagnostics15192475","url":null,"abstract":"<p><p><b>Background:</b> It is crucial to evaluate liver fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD). Digital pathology, an automated method for quantitative fibrosis measurement, provides valuable support to pathologists by providing refined continuous metrics and addressing inter-observer variability. Although non-invasive tests (NITs) have been validated as consistent with manual pathology, the relationship between digital pathology and NITs remains unexplored. <b>Methods:</b> This study included 99 biopsy-proven MASLD patients. Quantitative-fibrosis (Q-Fibrosis) used second-harmonic generation/two-photon excitation fluorescence microscopy (SHG/TPEF) to quantify fibrosis parameters (q-FPs). Correlations between eight NITs and q-FPs were analyzed. <b>Results:</b> Using manual pathology as standard, Q-Fibrosis exhibited excellent diagnostic performance in fibrosis stages assessment with area under the receiver operating characteristic curves (AUCs) ranging from 0.924 to 0.967. In addition, magnetic resonance elastography (MRE) achieved the highest diagnostic accuracy (AUC: 0.781-0.977) among the eight NITs. Furthermore, MRE-assessed liver stiffness measurement (MRE-LSM) showed the strongest correlation with q-FPs, particularly adjusted by string length, string width, and the number of short and thick strings within the portal region. <b>Conclusions:</b> Both MRE and digital pathology demonstrated excellent diagnostic accuracy. MRE-LSM was primarily determined by collagen extent, location and pattern, which provide a new perspective for understanding the relationship between the change in MRE and histological fibrosis reverse.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12524164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morphological Variations of the Pterygomaxillary Suture According to Skeletal Patterns.","authors":"Tuğçe Akın, Hacer Eberliköse, Berin Tuğtağ Demir, Burak Bilecenoğlu, Hakan Alpay Karasu","doi":"10.3390/diagnostics15192467","DOIUrl":"10.3390/diagnostics15192467","url":null,"abstract":"<p><p><b>Background:</b> The posterosuperior maxillary region poses a challenge in orthognathic surgery due to its complex three-dimensional anatomy. The pterygomaxillary suture (PMS) is a key landmark for various procedures. Understanding its anatomical relationships is essential to improving surgical precision. <b>Methods:</b> A retrospective analysis of CBCT images from 120 patients aged 18-70 years at Ankara Medipol University was conducted. Patients were categorized into skeletal Classes I, II, and III according to the ANB angle. Linear and angular measurements of the PMS and adjacent structures were performed. The statistical analysis included the Shapiro-Wilk, Independent <i>t</i>-test, Mann-Whitney U test, and regression analysis (<i>p</i> < 0.05). <b>Results:</b> There were clear differences between the skeletal groups. Class II and III patients had a lesser lateral PMS-baseline intersection distance (IV-VI) and Class II had a lesser medial PMS-baseline perpendicular distance (VV') compared to Class I (<i>p</i> < 0.05). Additionally, the angle V-IV-VI was significantly narrower in Class II and III groups, indicating altered PMS orientation in these skeletal patterns. <b>Conclusions:</b> PMS morphology, including thickness, width, and angulation, is influenced by skeletal pattern. A preoperative CBCT assessment and individualized surgical planning are essential to ensure the safety and accuracy of Le Fort I osteotomies, especially in Class II and III patients.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 19","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}