Search Results (106)

Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. Notably, convolutional neural networks have demonstrated exceptional capabilities in pattern recognition, significantly improving tumor detection, segmentation, and differentiation. This narrative review synthesizes the evolving applications of AI in PBTs, focusing on early tumor detection, imaging analysis, therapy response prediction, and histological classification. AI-driven radiomics and predictive models have yielded promising results in assessing chemotherapy efficacy, optimizing preoperative imaging, and predicting treatment outcomes, thereby advancing the field of precision medicine. Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. Despite these advancements, challenges remain. The rarity of PBTs limits the availability of robust, high-quality datasets for model development and validation, while the lack of standardized imaging protocols complicates reproducibility. Ethical considerations, including data privacy and the interpretability of complex AI algorithms, also warrant careful attention. Future research should prioritize multicenter collaborations, external validation of AI models, and the integration of explainable AI systems into clinical practice. Addressing these challenges will unlock AI’s full potential to revolutionize PBT management, ultimately improving patient outcomes and advancing personalized care. Full article

Background: Gallbladder hypomotility is a key pathogenic factor in cholelithiasis. Non-invasive interventions to enhance gallbladder contractility remain limited. Ultrasound therapy has shown promise in various muscular disorders, but its effects on gallbladder function are unexplored. Methods: This study employed low-intensity pulsed ultrasound (LIPUS) at a 3 MHz frequency and 0.8 W/cm² intensity with a 20% duty cycle to irradiate the gallbladder region of fasting guinea pigs. Gallbladder contractile function was evaluated through multiple complementary approaches: in vivo assessment via two-dimensional/three-dimensional ultrasound imaging to monitor volumetric changes; quantitative functional evaluation using nuclear medicine scintigraphy (^99mTc-HIDA); and ex vivo experiments including isolated gallbladder muscle strip tension measurements, histopathological analysis, α-smooth muscle actin (α-SMA) immunohistochemistry, and intracellular calcium fluorescence imaging. Results: Ultrasound significantly enhanced gallbladder emptying, evidenced by the volume reduction and increased ejection fraction. Scintigraphy confirmed accelerated bile transport in treated animals. Ex vivo analyses demonstrated augmented contractile force, amplitude, and frequency in ultrasound-treated smooth muscle. Histological examination revealed smooth muscle hypertrophy, α-SMA upregulation, and elevated intracellular calcium levels. Extended ultrasound exposure produced sustained functional improvements without tissue damage. Conclusions: Ultrasound effectively enhances gallbladder contractile function through mechanisms involving smooth muscle structural modification and calcium signaling modulation. These findings establish the experimental foundation for ultrasound as a promising non-invasive therapeutic approach to improve gallbladder motility and potentially prevent gallstone formation. Full article

Background: Radiomics is changing clinical practice by providing quantitative information from images to improve diagnosis, prognosis, and treatment planning. This study aims to investigate a radiomics model developed from contrast-enhanced mammography (CEM) images to predict disease-free survival (DFS) and overall survival (OS) in breast cancer (BC) patients. Methods: From January 2013 to December 2015, all consecutive BC patients who underwent CEM before biopsy at a referral center were enrolled. Clinical data included histological results, receptor profiles, and follow-up (DFS and OS). A region of interest (ROI) of the enhancing lesion was selected from recombined CEM images by experienced radiologists, and radiomic features were extracted. A Cox-LASSO model assigned coefficients to the features, generating patient radiomic scores (RSs), which were dichotomized for graphical representation. Model performance was assessed using the C index. Results: The study included 126 BC patients with predominantly “mass”-type lesions (95%) and a median follow-up of 6.88 years (IQR 3.10–8.15). The median age of the patients at the time of examination was 49.2 years (IQR: [42.33–56.98]). Radiomic and clinical–radiomic models showed significant associations between RS, DFS, and OS, with patients with RS below the median showing a better prognosis (p < 0.001). Bootstrap testing confirmed a good model fit for OS prediction, with median C-index values of 0.82 for the clinical model and 0.84 for the clinical–radiomic model. Conclusions: Radiomic analysis of CEM images may predict DFS and OS in BC patients, offering additional prognostic value beyond clinical models alone. Full article

Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the evolving role of radiogenomics and machine learning in improving diagnostic accuracy for bone and soft tissue tumors. We examine integrating quantitative imaging features from MRI, CT, and PET with genomic and transcriptomic data to enable non-invasive tumor profiling. AI-powered platforms employing convolutional neural networks (CNNs) and radiomic texture analysis show promising results in tumor grading, subtype differentiation (e.g., Osteosarcoma vs. Ewing sarcoma), and predicting mutation signatures (e.g., TP53, RB1). Moreover, we highlight the use of liquid biopsy and circulating tumor DNA (ctDNA) as emerging diagnostic biomarkers, coupled with point-of-care molecular assays, to enable early and accurate detection in low-resource settings. The review concludes by discussing translational barriers, including data harmonization, regulatory challenges, and the need for multi-institutional datasets to validate AI-based diagnostic frameworks. This article synthesizes current advancements and provides a forward-looking view of precision diagnostics in musculoskeletal oncology. Full article

Background and Objectives: Innate and adaptive immune responses serve a crucial role in neoplasms. The interaction of immune cells with the neoplastic tissue influences tumor behavior, resulting in either pro-tumorigenic or anti-tumorigenic effects. However, the prognostic significance of the tumor immune microenvironment (TIME) in synovial sarcoma (SS) remains poorly studied. This study aimed to analyze the TIME of SS to determine its impact on the prognosis by examining the intratumoral lymphocytic and macrophagic infiltrate and its potential correlation with survival and recurrence. Methods: We conducted a retrospective observational study of 49 fusion-confirmed SS cases collected from two different institutions. We obtained clinical and follow-up data, and SSs were histologically classified according to WHO criteria. Immunohistochemical analysis, including of CD163, CD68, CD3, CD8, and CD20, was conducted in tissue microarrays using an analog scale. We examined the whole-slide tissue for the 23 cases with sufficient material available and then assessed the positive area by scanning the slides and analyzing the images using QuPath (0.4.4, Belfast, Northern Ireland) to calculate the positive area in an immune hotspot. We correlated the expression of these markers with clinical outcomes. A log-rank test and Kaplan–Meyer curves were used as appropriate (significance: p ≤ 0.05). Results: The most frequent morphological subtype was monophasic (59.6%), followed by biphasic (26.9%) and undifferentiated (7%). The mean disease specific survival (DSS) was 55.3 months, with a median of 33 months. The median overall survival (OS) was 50 months (range: 2–336 months). Both evaluation methods showed a good correlation for all antibodies, with Chi-square values of p < 0.05. All cases showed variable amounts of CD163-positive macrophages. The cases that showed a higher density of CD163-positive macrophages in whole-slide images subjected to digital analysis demonstrated an improved OS and DSS on Kaplan–Meier curves. Cases with lower CD8 and CD3 positivity showed a tendency toward faster progression and a slightly worse prognosis. Conclusions: The tumor immune microenvironment in sarcomas is a complex system that requires further investigation to fully understand its impact on tumorigenesis and patient clinical outcomes. Our results demonstrate that a higher amount of intratumoral CD163-positive macrophage infiltrate is associated with an increased OS and DSS. Our findings show that digital pathology is more precise than subjective quantitative analysis. Full article

Background/Objectives: Lung neuroendocrine neoplasms (NENs) are a heterogeneous group of tumors requiring accurate differentiation from non-small cell lung cancer (NSCLC) for effective treatment. Conventional computed tomography (CT) lacks pathognomonic features to distinguish these subtypes. Radiomics, which extracts quantitative imaging features, offers a potential solution. Methods: This retrospective multicenter study included 301 patients with histologically confirmed lung cancer who underwent native CT scans. The dataset comprised 150 NSCLC cases (75 adenocarcinomas, 75 squamous cell carcinomas) and 151 NENs (75 SCLC, 60 carcinoids, 16 large cell neuroendocrine carcinomas). Tumors were manually segmented, and 107 radiomics features were extracted. Dimensionality reduction and feature selection were performed using Pearson correlation analysis and LASSO regression. Decision tree and random forest classifiers were trained and evaluated using a 70:30 training–testing split. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score. Results: The model differentiating NENs from NSCLC achieved an AUC of 0.988 on the test set, with an accuracy of 97.8%. The model distinguishing SCLC from other NENs attained an AUC of 0.860 and an accuracy of 82.6%. First-order and textural radiomics features were key discriminators. Conclusions: Radiomics-based machine learning models demonstrated high diagnostic accuracy in differentiating lung NENs from NSCLC and in subclassifying NENs. These findings highlight the potential of radiomics as a non-invasive, quantitative tool for lung cancer diagnosis, warranting further validation in larger multicenter studies. Full article

Purpose: We assessed the feasibility of qualitative, semiquantitative, and multiparametric quantitative magnetic resonance imaging (MRI) using a three-dimensional (3D) ultrashort echo time (3D-UTE) sequence together with 2D-T2 and 3D-T1 mapping sequences to evaluate normal and pathological discovertebral complexes (DVCs). We assessed the disc (nucleus pulposus [NP] and annulus fibrosus [AF]), vertebral endplate (cartilage endplate [CEP] and growth plate [GP]), and subchondral bone (SB) using a rat model of degenerative disc disease (DDD). We also assessed whether this complete MRI cartography can improve the monitoring of DDD. Methods: DDD was induced by percutaneous disc trituration and collagenase injection of the tail. Then, the animals were imaged at 4.7T. The adjacent disc served as the control. The MRI protocol was performed at baseline and each week (W) postoperatively for 2 weeks. Visual analysis and signal intensity measurements from the 3D-UTE images, as well as T2 and T1 measurements, were carried out in all DVC portions. Histological analysis with hematoxylin–eosin and Masson trichrome staining was performed following euthanization of the rats at 2 weeks and the results were compared to the MRI findings. Results: Complete qualitative identification of the normal zonal anatomy of the DVC, including the AF, CEP, and GP, was achieved using the 3D-UTE sequence. Quantitative measurements of the signal-to-noise ratio in the AF and NP enabled healthy DVCs to be distinguished from surgery-induced DDD, based on an increase in these values post-surgery. The 2D-T2 mapping results showed a significant increase in the T2 values of the AF and a decrease in the values of the NP between the baseline and W1 and W2 postoperatively (p < 0.001). In the 3D-T1 mapping, there was a significant decrease in the T1 values of the AF and NP between baseline and W1 and W2 postoperatively in immature rats (p < 0.01). This variation in T1 and T2 over time was consistent with the results of the 3D-UTE sequence. Conclusions: Use of the 3D-UTE sequence enabled a complete, robust, and reproducible visualization of DVC anatomy in both immature and mature rats under both normal and pathological conditions. The findings were supported quantitatively by the T2 and T1 mapping sequences and histologically. This sequence is therefore of prime interest in spinal imaging and should be regularly be performed. Full article

Background/Objectives: Although multiple magnetic resonance imaging (MRI) indices are known to be sensitive to the noninvasive assessment of myelin integrity, their relative sensitivities have not been directly compared. This study aimed to identify the most sensitive MRI index for characterizing myelin composition in the spinal cord’s gray matter (GM) and white matter (WM). Methods: MRI was performed on a deer’s ex vivo cervical spinal cord. Quantitative indices known to be sensitive to myelin, including the myelin water fraction (MWF), magnetization transfer ratio (MTR), the signal ratio between T1- and T2-weighted images (T1W/T2W), fractional anisotropy (FA), mean diffusivity (MD), electrical conductivity (σ), and T1, T2, and T1ρ relaxation times were calculated. Their mean values were compared using repeated measures analysis of variance (ANOVA) and post hoc Bonferroni tests or Friedman and post hoc Wilcoxon tests to identify differences across GM and WM columns possessing distinct myelin distributions, as revealed by histological analysis. Relationships among the indices were examined using Spearman’s rank-order correlation analysis. Corrected p < 0.05 was considered statistically significant. Results: All indices except σ differed significantly between GM and all WM columns. Two of the three WM columns had significantly different MWF, FA, MD, and T2, whereas one WM column had significantly different MTR, σ, T1, and T1ρ from the others. A significant moderate to very strong correlation was observed among most indices. Conclusions: The sensitivity of MRI indices in distinguishing spinal cord regions varied. A strategic combination of two or more indices may allow the accurate differentiation of spinal cord regions. Full article

Our prior findings showed that BCL2A1 in neutrophils is highly expressed in the extra-placental membranes (EPMs) of both the human spontaneous preterm-birth (PTB) (i.e., PTL or preterm PROM) and nonhuman-primate PTB model. However, no data exist on whether the intensity of BCL2A1 expression quantitatively increases according to the stage progression of acute histologic chorioamnionitis (acute HCA) in EPM. The objective is to investigate whether the intensity of BCL2A1 expression quantitatively increases according to the stage progression of acute HCA in EPM among spontaneous PTB cases, as measured using QuPath. The study population included 121 singleton PTBs (gestational age [GA] at delivery < 34 weeks) due to either preterm labor or preterm PROM. With digital image analysis, we calculated the percentage of BCL2A1-positive cells in immunohistochemistry according to the stage progression of acute HCA in EPMs as the primary outcome and examined the relationship between the percentage of BCL2A1-positive cells and either the GA at delivery or the amniotic-fluid (AF) WBC count as the secondary outcome. The median percentage of BCL2A1-positive cells progressively increases with the stage progression of acute HCA in EPM (group-1 vs. group-2 vs. group-3 vs. group-4 vs. group-5; 7.62 vs. 5.15 vs. 43.57 vs. 71.07; γ = 0.552, p < 0.000001). The percentage of BCL2A1-positive cells in EPMs and the AFWBC count shows a positive correlation (γ = 0.492, p = 0.000385). Moreover, the percentage of BCL2A1-positive cells in EPMs continuously decreased with increasing GA at delivery (γ = −0.253, p = 0.005148). In conclusion, the intensity of BCL2A1 expression increases according to the stage progression of acute HCA in EPMs and the elevation of AFWBC among spontaneous PTB cases. This finding suggests BCL2A1 in EPMs may be a promising marker and target for acute HCA. Full article

Evaluating intratumor heterogeneity with image texture analysis offers a more sophisticated understanding of sarcoma response to treatment. We examined the effects of inductive moderate hyperthermia (IMH) on sarcoma-45 growth and intratumor heterogeneity across tissue, cellular and molecular levels using magnetic resonance imaging (MRI), ultrasound and histology image analysis. IMH (42 MHz, 20 W) inhibited sarcoma-45 growth kinetics by 34% compared to the untreated control group. T₂-weighted MRI brightness was increased by 42%, reflecting more extensive tumor necrosis, while Young’s modulus increased by 37% due to more pronounced connective tissue replacement in response to IMH. Whereas calculations of Moran’s spatial autocorrelation index revealed distinctions in heterogeneity between tumor core, periphery and capsule regions of interest (ROIs) on MRI, ultrasound and histological examination in the untreated tumor-bearing animals, there was no significant difference between core and periphery after IMH. Exposure to IMH increased overall tumor ROI heterogeneity by 22% on MRI but reduced heterogeneity in the core and periphery on ultrasound and histology images. Ki-67 protein distribution was 25% less heterogeneous on the tumor periphery after IMH. Therefore, this study provides a quantitative characterization of IMH effects on different manifestations of intratumor sarcoma-45 heterogeneity using experimental imaging data. Full article

Natural products have attracted great interest in the development of tissue engineering. Recent studies have demonstrated that unsaturated fatty acids found in natural plant seed oil may exhibit positive osteogenic effects; however, few in vivo studies have focused on the use of plant seed oil for bone regeneration. The aim of this study is to investigate the effects of seed oil found in Sapindus mukorossi (S. mukorossi) on the osteogenic differentiation of mesenchymal stem cells and bone growth in artificial bone defects in vivo. In this study, Wharton-jelly-derived mesenchymal stem cells (WJMSCs) were co-cultured with S. mukorossi seed oil. Cellular osteogenic capacity was assessed using Alizarin Red S staining. Real-time PCR was carried out to evaluate ALP and OCN gene expression. The potential of S. mukorossi seed oil to enhance bone growth was assessed using an animal model. Four 6 mm circular defects were prepared at the parietal bone of New Zealand white rabbits. The defects were filled with hydrogel and hydrogel-S. mukorossi seed oil, respectively. Quantitative analysis of micro-computed tomography (Micro-CT) and histological images was conducted to compare differences in osteogenesis between oil-treated and untreated samples. Although our results showed no significant differences in viability between WJMSCs treated with and without S. mukorossi seed oil, under osteogenic conditions, S. mukorossi seed oil facilitated an increase in mineralized nodule secretion and upregulated the expression of ALP and OCN genes in the cells (p < 0.05). In the animal study, both micro-CT and histological evaluations revealed that new bone formation in artificial bone defects treated with S. mukorossi seed oil were nearly doubled compared to control defects (p < 0.05) after 4 weeks of healing. Based on these findings, it is reasonable to suggest that S. mukorossi seed oil holds promise as a potential candidate for enhancing bone healing efficiency in bone tissue engineering. Full article

Background: Radiomics, an evolving paradigm in medical imaging, involves the quantitative analysis of tumor features and demonstrates promise in predicting treatment responses and outcomes. This study aims to investigate the predictive capacity of radiomics for genetic alterations in non-small cell lung cancer (NSCLC). Methods: This exploratory, observational study integrated radiomic perspectives using computed tomography (CT) and genomic perspectives through next-generation sequencing (NGS) applied to liquid biopsies. Associations between radiomic features and genetic mutations were established using the Area Under the Receiver Operating Characteristic curve (AUC-ROC). Machine learning techniques, including Support Vector Machine (SVM) classification, aim to predict genetic mutations based on radiomic features. The prognostic impact of selected gene variants was assessed using Kaplan–Meier curves and Log-rank tests. Results: Sixty-six patients underwent screening, with fifty-seven being comprehensively characterized radiomically and genomically. Predominantly males (68.4%), adenocarcinoma was the prevalent histological type (73.7%). Disease staging is distributed across I/II (38.6%), III (31.6%), and IV (29.8%). Significant correlations were identified with mutations of ROS1 p.Thr145Pro (shape_Sphericity), ROS1 p.Arg167Gln (glszm_ZoneEntropy, firstorder_TotalEnergy), ROS1 p.Asp2213Asn (glszm_GrayLevelVariance, firstorder_RootMeanSquared), and ALK p.Asp1529Glu (glcm_Imc1). Patients with the ROS1 p.Thr145Pro variant demonstrated markedly shorter median survival compared to the wild-type group (9.7 months vs. not reached, p = 0.0143; HR: 5.35; 95% CI: 1.39–20.48). Conclusions: The exploration of the intersection between radiomics and cancer genetics in NSCLC is not only feasible but also holds the potential to improve genetic predictions and enhance prognostic accuracy. Full article

Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images. Most of previously proposed digital PET phantoms of tumors are static, have an over-simplified morphology, and lack the link to cellular biology that ultimately governs the tumor evolution. In this work, we propose a novel method to investigate the relationship between microscopic tumor parameters and PET image characteristics based on the computational simulation of tumor growth. We use a hybrid, multiscale, stochastic mathematical model of cellular metabolism and proliferation to generate simulated cross-sections of tumors in vascularized normal tissue on a microscopic level. The generated longitudinal tumor growth sequences are converted to PET images with realistic resolution and noise. By changing the biological parameters of the model, such as the blood vessel density and conditions for necrosis, distinct tumor phenotypes can be obtained. The simulated cellular maps were compared to real histology slides of SiHa and WiDr xenografts imaged with Hoechst 33342 and pimonidazole. As an example application of the proposed method, we simulated six tumor phenotypes that contain various amounts of hypoxic and necrotic regions induced by a lack of oxygen and glucose, including phenotypes that are distinct on the microscopic level but visually similar in PET images. We computed 22 standardized Haralick texture features for each phenotype, and identified the features that could best discriminate the phenotypes with varying image noise levels. We demonstrated that “cluster shade” and “difference entropy” are the most effective and noise-resilient features for microscopic phenotype discrimination. Longitudinal analysis of the simulated tumor growth showed that radiomics analysis can be beneficial even in small lesions with a diameter of 3.5–4 resolution units, corresponding to 8.7–10.0 mm in modern PET scanners. Certain radiomics features were shown to change non-monotonically with tumor growth, which has implications for feature selection for tracking disease progression and therapy response. Full article

Background: The prediction of the regrowth potential of pituitary adenomas after surgery is challenging. The genome-wide DNA methylation profiling of pituitary adenomas may separate adenomas into distinct methylation classes corresponding to histology-based subtypes. Specific genes and differentially methylated probes involving regrowth have been proposed, but no study has linked this epigenetic variance with regrowth potential and the clinical heterogeneity of nonfunctioning pituitary adenomas. This study aimed to investigate whether DNA methylation profiling can be useful as a clinical prognostic marker. Methods: A DNA methylation analysis by Illumina’s MethylationEPIC array was performed on 54 pituitary macroadenomas from patients who underwent transsphenoidal surgery during 2007–2017. Twelve patients were excluded due to an incomplete postoperative follow-up, degenerated biobank-stored tissue, or low DNA methylation quality. For the quantitative measurement of the tumor regrowth rate, we conducted a 3D volumetric analysis of tumor remnant volume via annual magnetic resonance imaging. A linear mixed effects model was used to examine whether different DNA methylation clusters had different regrowth patterns. Results: The DNA methylation profiling of 42 tissue samples showed robust DNA methylation clusters, comparable with previous findings. The subgroup of 33 nonfunctioning pituitary adenomas of an SF1-lineage showed five subclusters with an approximately unbiased score of 86%. There were no overall statistically significant differences when comparing hazard ratios for regrowth of 100%, 50%, or 0%. Despite this, plots of correlated survival estimates suggested higher regrowth rates for some clusters. The mixed effects model of accumulated regrowth similarly showed tendencies toward an association between specific DNA methylation clusters and regrowth potential. Conclusion: The DNA methylation profiling of nonfunctioning pituitary adenomas may potentially identify adenomas with increased growth and recurrence potential. Larger validation studies are needed to confirm the findings from this explorative pilot study. Full article

Although discectomy is commonly performed for lumbar intervertebral disc (IVD) herniation, the capacity for tissue repair after surgery is limited, resulting in residual lower back pain, recurrence of IVD herniation, and progression of IVD degeneration. Cell-based therapies, as one-step procedures, are desirable for enhancing IVD repair. This study aimed to investigate the therapeutic efficacy of a combination of newly developed ultra-purified alginate (UPAL) gel and bone marrow aspirate concentrate (BMAC) implantation for IVD repair after discectomy. Prior to an in vivo study, the cell concentration abilities of three commercially available preparation kits for creating the BMAC were compared by measuring the number of bone marrow mesenchymal stem cells harvested from the bone marrow of rabbits. Subsequently, canine-derived BMAC was tested in a canine model using a kit which had the highest concentration rate. At 24 weeks after implantation, we evaluated the changes in the magnetic resonance imaging (MRI) signals as well as histological degeneration grade and immunohistochemical analysis results for type II and type I collagen-positive cells in the treated IVDs. In all quantitative evaluations, such as MRI and histological and immunohistochemical analyses of IVD degeneration, BMAC-UPAL implantation significantly suppressed the progression of IVD degeneration compared to discectomy and UPAL alone. This preclinical proof-of-concept study demonstrated the potential efficacy of BMAC-UPAL gel as a therapeutic strategy for implementation after discectomy, which was superior to UPAL and discectomy alone in terms of tissue repair and regenerative potential. Full article