Imaging Analysis Research Articles

[18F]AlF-NOTA-PCP2: a novel PET/CT tracer for enhanced PD-L1 heterogeneity imaging and comparative analysis with [18F]AlF-NOTA-WL12 in glioblastoma xenografts.

The unsatisfactory efficacy of PD-L1 antibodies in glioblastoma (GBM) is largely due to the temporal and spatial heterogeneity of PD-L1 expression. Molecular imaging can enhance understanding of the tumor immune microenvironment and guide immunotherapy. However, highly sensitive imaging agents capable of effectively visualizing PD-L1 heterogeneity are limited. This study introduces a novel PET tracer, offering improved imaging of PD-L1 heterogeneity in GBM xenografts, with a comparative analysis to [18F]AlF-NOTA-WL12. [18F]AlF-NOTA-PCP2 was synthesized with high purity and its affinity for PD-L1 was characterized using surface plasmon resonance (SPR) and cell binding assays. Its specificity for PD-L1 was evaluated both in vitro using various cell lines and in vivo with GBM xenograft models in NOD/SCID mice. PET/CT imaging was conducted to evaluate the tracer's biodistribution, pharmacokinetics, and ability to quantify tumoral spatial heterogeneity of PD-L1 expression. A focused comparative analysis between [18F]AlF-NOTA-PCP2 and [18F]AlF-NOTA-WL12 was conducted, examining binding affinity, biodistribution, pharmacokinetics, and imaging effectiveness in GBM xenografts. Additionally, human radiation dosimetry estimates compared the safety profiles of both tracers. [18F]AlF-NOTA-PCP2 demonstrated high radiochemical purity (> 95%) and a strong affinity for PD-L1, comparable to [18F]AlF-NOTA-WL12. In vitro and in vivo studies confirmed its specificity for PD-L1, with increased uptake in PD-L1 expressing cells and tumors. Toxicological profiles indicated no significant abnormalities in serum biochemical indicators or major organ tissues. MicroPET/CT imaging showed [18F]AlF-NOTA-PCP2's effectiveness in visualizing PD-L1 expression levels and spatial heterogeneity in GBM xenografts. Comparative studies revealed [18F]AlF-NOTA-PCP2's improved pharmacokinetic properties, including higher tumor-to-blood ratios and lower nonspecific liver uptake, as well as reduced radiation exposure compared to [18F]AlF-NOTA-WL12. [18F]AlF-NOTA-PCP2 distinguishes itself as an exceptionally sensitive PET/CT tracer, adept at non-invasively and accurately quantifying PD-L1 expression and its spatial heterogeneity in tumors, especially in GBM. Its favorable pharmacokinetic properties, safety profile, and high affinity for PD-L1 highlight its potential for enhancing the precision of cancer immunotherapy and guiding individualized treatment strategies. While promising, its clinical translation, especially in brain imaging, necessitates further validation in clinical trials.

The Role of Momentum Partitioning in Covariance Ion Imaging Analysis.

We present results from a covariance ion imaging study, which employs extensive filtering, on the relationship between fragment momenta to gain deeper insight into photofragmentation dynamics. A new data analysis approach is introduced that considers the momentum partitioning between the fragments of the breakup of a molecular polycation to disentangle concurrent fragmentation channels, which yield the same ion species. We exploit this approach to examine the momentum exchange relationship between the products, which provides direct insight into the dynamics of molecular fragmentation. We apply these techniques to extensively characterize the dissociation of 1-iodopropane and 2-iodopropane dications prepared by site-selective ionization of the iodine atom using extreme ultraviolet intense femtosecond laser pulses with a photon energy of 95 eV. Our assignments are supported by classical simulations, using parameters largely obtained directly from the experimental data.

Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.

Acetyl-CoA Carboxylase1 Influences ECERIFERUM2 Activity to Mediate the Synthesis of Very-Long-Chain Fatty Acid Past C28.

Cuticular wax is a protective layer on the aerial surfaces of land plants. In Arabidopsis (Arabidopsis thaliana), cuticular wax is mainly constituted of compounds derived from very-long-chain fatty acids (VLCFAs) with chain lengths longer than C28. CER2-LIKE (ECERIFERUM2-LIKE) proteins interact with CER6/KCS6 (ECERIFERUM6/β-Ketoacyl-CoA Synthase6), the key enzyme of the fatty acid elongase complex, to modify its substrate specificity for VLCFA elongation past C28. However, the molecular regulatory mechanism of CER2-LIKE proteins remains unclear. Arabidopsis eceriferum19 (cer19) mutants display wax-deficient stems caused by loss of waxes longer than C28, indicating that CER19 may participate in the CER2-LIKE-mediated VLCFA elongation past C28. Using positional cloning and genetic complementation, we showed that CER19 encodes Acetyl-CoA Carboxylase1 (ACC1), which catalyzes the synthesis of malonyl-CoA, the essential substrate for the CER6/KCS6-mediated condensation reaction in VLCFA synthesis. We demonstrated that ACC1 physically interacts with CER2-LIKE proteins via split-ubiquitin yeast two-hybrid (SUY2H) and firefly luciferase complementation imaging (LCI) analysis. Additionally, heterologous expression in yeast and genetic analysis in Arabidopsis revealed that ACC1 affects CER2 activity to influence VLCFA elongation past C28. These findings imply that CER2-LIKE proteins might function as a link between ACC1 and CER6/KCS6 and subsequently enhance CER6/KCS6 binding to malonyl-CoA for further utilization in VLCFA elongation past C28. This information deepens our understanding of the complex mechanism of cuticular wax biosynthesis.

Platform-directed allostery and quaternary structure dynamics of SAMHD1 catalysis

SAMHD1 regulates cellular nucleotide homeostasis, controlling dNTP levels by catalysing their hydrolysis into 2’-deoxynucleosides and triphosphate. In differentiated CD4+ macrophage and resting T-cells SAMHD1 activity results in the inhibition of HIV-1 infection through a dNTP blockade. In cancer, SAMHD1 desensitizes cells to nucleoside-analogue chemotherapies. Here we employ time-resolved cryogenic-EM imaging and single-particle analysis to visualise assembly, allostery and catalysis by this multi-subunit enzyme. Our observations reveal how dynamic conformational changes in the SAMHD1 quaternary structure drive the catalytic cycle. We capture five states at high-resolution in a live catalytic reaction, revealing how allosteric activators support assembly of a stable SAMHD1 tetrameric core and how catalysis is driven by the opening and closing of active sites through pairwise coupling of active sites and order-disorder transitions in regulatory domains. This direct visualisation of enzyme catalysis dynamics within an allostery-stabilised platform sets a precedent for mechanistic studies into the regulation of multi-subunit enzymes.

Three-dimensional imaging analysis of CAD/CAM custom-milled versus prefabricated allogeneic block remodelling at 6 months and long-term follow-up of dental implants: A retrospective cohort study.

This retrospective cohort study aimed to volumetrically investigate the bone stability rate of prefabricated allogeneic bone blocks (PBB) and computer-aided design (CAD)/computer-aided manufacturing (CAM) custom-milled allogeneic bone blocks (CCBB) for ridge augmentation. Nineteen patients were treated with 20 allografts: 11 CCBB, 9 PBB; 10 in the maxilla and 10 in the mandible. Clinical treatment history and cone beam computed tomography scans before surgery (t0), directly after graft surgery (t1) and after 6 months of healing prior to implant insertion (t2) were evaluated using a three-dimensional evaluation software for absolute bone volume, stability as well as vertical and horizontal bone gain. Furthermore, the inserted implants were analysed for survival, marginal bone loss (MBL) and complications for a mean follow-up period of 43.75 (±33.94) months. A mean absolute volume of 2228.1 mm3 (±1205) was grafted at t1. The bone stability rate was 87.6% (±9.9) for CCBB and 83.0% (±14.5) for PBB. The stability was higher in the maxilla (91.6%) than in the mandible (79.53%). Surgery time of PBB was longer than for CCBB (mean Δ = 52 min). The survival rate of the inserted implants was 100% with a mean MBL of 0.41 mm (±0.37). The clinical performance of both allograft block designs was equally satisfactory for vertical and horizontal bone grafting prior to implant placement. ClinicalTrials.gov: NCT06027710.

Toward confident prostate cancer detection using ultrasound: a multi-center study.

Deep learning-based analysis of micro-ultrasound images to detect cancerous lesions is a promising tool for improving prostate cancer (PCa) diagnosis. An ideal model should confidently identify cancer while responding with appropriate uncertainty when presented with out-of-distribution inputs that arise during deployment due to imaging artifacts and the biological heterogeneity of patients and prostatic tissue. Using micro-ultrasound data from 693 patients across 5 clinical centers who underwent micro-ultrasound guided prostate biopsy, we train and evaluate convolutional neural network models for PCa detection. To improve robustness to out-of-distribution inputs, we employ and comprehensively benchmark several state-of-the-art uncertainty estimation methods. PCa detection models achieve performance scores up to average AUROC with a 10-fold cross validation setup. Models with uncertainty estimation obtain expected calibration error scores as low as , indicating that confident predictions are very likely to be correct. Visualizations of the model output demonstrate that the model correctly identifies healthy versus malignant tissue. Deep learning models have been developed to confidently detect PCa lesions from micro-ultrasound. The performance of these models, determined from a large and diverse dataset, is competitive with visual analysis of magnetic resonance imaging, the clinical benchmark to identify PCa lesions for targeted biopsy. Deep learning with micro-ultrasound should be further studied as an avenue for targeted prostate biopsy.

Integrating artificial intelligence techniques for advancements in colorectal cancer management: navigating past and predicting future direction.

Artificial Intelligence (AI) in the last few years has emerged as a valuable tool in managing colorectal cancer, revolutionizing its management at different stages. In early detection and diagnosis, AI leverages its prowess in imaging analysis, scrutinizing CT scans, MRI, and colonoscopy views to identify polyps and tumors. This ability enables timely and accurate diagnoses, initiating treatment at earlier stages. AI has helped in personalized treatment planning because of its ability to integrate diverse patient data, including tumor characteristics, medical history, and genetic information. Integrating AI into clinical decision support systems guarantees evidence-based treatment strategy suggestions in multidisciplinary clinical settings, thus improving patient outcomes. This narrative review explores the multifaceted role of AI, spanning early detection of colorectal cancer, personalized treatment planning, polyp detection, lymph node evaluation, cancer staging, robotic colorectal surgery, and training of colorectal surgeons.

How to Identify and Quantify Microplastics and Nanoplastics Using Raman Imaging?

While microplastics and nanoplastics are emerging as a big environmental concern, their characterization is still a challenge, particularly for identification and simultaneous quantification analysis where imaging via a hyper spectrum is generally needed. In the past few years, Raman imaging has been greatly advanced, but the analysis protocol is complicated and not yet standardized because imaging analysis is different from traditional analysis. Herein we provide a step-by-step demonstration of how to employ confocal Raman techniques to image microplastics and nanoplastics.

Ventriculovesical Shunting in a Patient with Leptomeningeal Carcinomatosis: An Alternative to VP Shunts. A Technique Report

AbstractLeptomeningeal carcinomatosis (LC) is a rare but serious complication when cancer cells infiltrate the meninges. It is most commonly associated with breast cancer, but only 5% of breast cancer patients develop it. Leptomeningeal carcinomatosis typically presents with headaches, mainly due to hydrocephalus, and the diagnosis involves a cytological analysis of cerebrospinal fluid (CSF) and/or magnetic resonance imaging (MRI) scans. The treatment of LC consists of a combination of intra-CSF chemotherapy, systemic therapy, radiation therapy, and/or supportive care, including CSF drainage. In the case herein reported, a technique known as ventriculovesical shunting was performed on a female patient with LC and breast cancer who had hydrocephalus due to this condition. This procedure is not as common as ventriculoperitoneal shunts, which can lead, in this case, to serious complications such as peritoneal carcinomatosis.

Comparative analysis of left ventricle function and deformation imaging in short and long axis plane in cardiac magnetic resonance imaging

BackgroundAdvancements in cardiac imaging have revolutionized our understanding of ventricular contraction. While ejection fraction (EF) is still the gold standard parameter to assess left ventricle (LV) function, strain imaging (SI) has provided valuable insights into ventricular mechanics. The lack of an integrative method including SI parameters in a single, validated formula may limit its use. Our aim was to compare different methods for evaluating global circumferential strain (GCS) and their relationship with global longitudinal strain (GLS) and EF in CMR and how the different evaluations fit in the theoretical relationship between EF and global strain.MethodsRetrospective monocenter study. Inclusion of every patient who underwent a CMR during a 15 months period with various clinical indication (congenital heart defect, myocarditis, cardiomyopathy). A minimum of three LV long-axis planes and a stack of short-axis slices covering the LV using classical steady-state free precession cine sequences. A single assessment of GLS on long axis (LAX) slices and a double assessment of GCS and EF with both short axis (SAX) and LAX slices were made by a single experienced CMR investigator.ResultsGCS-SAX and GCS-LAX were correlated (r = 0.77, P < 0.001) without being interchangeable with a high reproducibility for GCS, GLS and EF. EF calculated from LAX images showed an overestimation compared to EF derived from SAX images of 7%. The correlation between calculated EF and theoretical EF derived from SI was high (r = 0.88 with EF-SAX, 0.95 with EF-LAX).Data conclusionThis study highlights the need to integrate strain imaging techniques into clinical by incorporating strain parameters into EF calculations, because it gives a deeper understanding of cardiac mechanics.

Abstract PO1-26-04: Patient-derived organotypic tissue cultures for organoid isolation and probing unique metabolic features of individual breast cancer patients

Abstract Patient-derived organotypic tissue cultures (PD-OTC) are unique models for probing how individual patient’s tumor microenvironment (TME) influences cancer development and treatment responses. They retain the patient tumor architecture and TME, that are difficult to recapitulate in other models, while affording flexible treatment options and comparison of cancer (CA) versus matched non-cancer (NC) tissue responses to treatments. We have developed a culturing method for establishing PD-OTC of breast cancer patients that exhibit CA tissue growth and extensive metabolic reprogramming between CA versus matched NC tissues while enabling patient-derived organoids (PDO) and fibroblasts to be isolated. Pairs of CA and NC tissues freshly resected from six patients bearing early-stage estrogen receptor/progesterone receptor positive ductal carcinoma were sliced at 750 µm, embedded in Matrigel, and cultured in a 6-well plate with a Biopore membrane insert and custom medium at 37°C/5% CO2 with gentle rocking (16-40 mg wet weight; n=3). Excess tissue slices were cryopreserved and kept at -196 °C. Culture media were sampled periodically for metabolite analysis by NMR. Microscopic examination revealed significant tissue, organoid-like, and fibroblast outgrowth after 1-1.5 month of culturing. Two to three days prior to the harvest of two patients’ (CZ16 and 17) OTC, the culture medium was replaced with [U-2H]-glucose + [U-13C,15N]-glutamine-containing medium to allow the two stable isotope tracers to be metabolized, followed by Stable Isotope-Resolved Metabolomic (SIRM) analysis by NMR and ion chromatography coupled with ultra high-resolution Fourier transform mass spectrometry (IC-UHRFTMS). Two separate pieces of the OTC were cut and each was assayed for glucose uptake and mitochondrial membrane potential by live fluorescence spectroscopy via a portable custom-built fluorescence microscope, and by histochemical analysis. Histological analysis showed that both CA and NC OTC of all patients maintained their structural integrity. SIRM analysis of two patients’ OTC revealed active catabolic and anabolic metabolism including glucose uptake/glycolysis, the Krebs cycle/mitochondrial membrane potential, the pentose phosphate pathway (PPP), gluconeogenesis, glycogen synthesis, and purine/pyrimidine/sugar nucleotides synthesis. The CA OTC of both patients showed enhanced glucose uptake, glycolysis, mitochondrial membrane potential/Krebs cycle, PPP, glycogen synthesis, and purine nucleotide synthesis, when compared with the matched NC OTC. Treatment-naïve CA OTC of CZ16 showed much more significant metabolic reprogramming (NAD+ metabolism, in particular) than CA OTC of CZ17 who has undergone neoadjuvant treatment, chemotherapy, and hormone therapy prior to surgery. Distinct metabolic features of breast CA tissues can be exploited as therapeutic targets. Organoids and fibroblasts were isolated from CA and/or NC OTC cultures and their characterization is in progress. When revived, cryopreserved OTC exhibited active metabolism and tissue/organoid/fibroblast outgrowth similarly to fresh OTC. In conclusion, our culturing method enables PD-OTC models to be established efficiently for early-stage breast cancer patients for downstream functional studies including live cell imaging and SIRM analysis. Organoids and fibroblasts can also be derived from < 20 mg of tissues for further studies. Future direction includes therapeutic studies on these patient-derived models to better understand and predict individual patient’s responses to therapy. Citation Format: Teresa Fan, Carlos Goncalves, Jing Yan, Jahid Islam, Penghui Lin, Mohamed Kaddah, Richard Higashi, Andrew Lane, Caigang Zhu. Patient-derived organotypic tissue cultures for organoid isolation and probing unique metabolic features of individual breast cancer patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-26-04.

A hybrid framework for glaucoma detection through federated machine learning and deep learning models

BackgroundGlaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (CNNs), and image processing for accurate glaucoma detection using medical imaging data, surpassing prior research efforts.MethodDeveloping a hybrid glaucoma detection framework using CNNs (ResNet50, VGG-16) and Random Forest. Models analyze pre-processed retinal images independently, and post-processing rules combine predictions for an overall glaucoma impact assessment.ResultThe hybrid framework achieves a significant 95.41% accuracy, with precision and recall at 99.37% and 88.37%, respectively. The F1 score, balancing precision and recall, reaches a commendable 93.52%. These results highlight the robustness and effectiveness of the hybrid framework in accurate glaucoma diagnosis.ConclusionIn summary, our research presents an innovative hybrid framework combining CNNs and traditional ML models for glaucoma detection. Using ResNet50, VGG-16, and Random Forest in an ensemble approach yields remarkable accuracy, precision, recall, and F1 score. These results showcase the methodology’s potential to enhance glaucoma diagnosis, emphasizing its promising role in early detection and preventing irreversible vision loss. The integration of ML and DNNs in medical imaging analysis suggests a valuable path for future advancements in ophthalmic healthcare.

Assessing personalized responses to anti-PD-1 treatment using patient-derived lung tumor-on-chip.

There is a compelling need for approaches to predict the efficacy of immunotherapy drugs. Tumor-on-chip technology exploits microfluidics to generate 3D cell co-cultures embedded in hydrogels that recapitulate simplified tumor ecosystems. Here, we present the development and validation of lung tumor-on-chip platforms to quickly and precisely measure exvivo the effects of immune checkpoint inhibitors on Tcell-mediated cancer cell death by exploiting the power of live imaging and advanced image analysis algorithms. The integration of autologous immunosuppressive FAP+ cancer-associated fibroblasts impaired the response to anti-PD-1, indicating that tumors-on-chips are capable of recapitulating stroma-dependent mechanisms of immunotherapy resistance. For a small cohort of non-small cell lung cancer patients, we generated personalized tumors-on-chips with their autologous primary cells isolated from fresh tumor samples, and we measured the responses to anti-PD-1 treatment. These results support the power of tumor-on-chip technology in immuno-oncology research and open a path to future clinical validations.

Multi-modal fusion deep transfer learning for accurate brain tumor classification using magnetic resonance imaging images

Early identification and treatment of brain tumors depend critically on accurate classification. Accurate brain tumor classification in medical imaging is essential for clinical decisions and individualized treatment plans. This paper introduces a novel method for classifying brain tumors called multimodal fusion deep transfer learning (MMFDTL) using original, contoured, and annotated magnetic resonance imaging (MRI) images to showcase its capabilities. The MMFDTL can capture complex tumor features frequently missed in analyzing individual modalities. The MMFDTL model employs three deep learning models for extracting features VGG16, Inception V3, and ResNet 50. The accuracy rate improves when combined with decision based multimodal fusion. It produces impressive outcomes of sensitivity 92.96%, specificity 98.54%, precision 93.60%, accuracy 98.80%, F1-score 93.26%, and kappa 91.86%. This research can improve medical imaging and brain tumor analysis through its multi modal fusion approach. It could give healthcare practitioners vital insights for personalized treatment plans and informed decision making.

Advanced neuroimaging techniques to explore the effects of motor and cognitive rehabilitation in multiple sclerosis.

Progress in magnetic resonance imaging (MRI) technology and analyses is improving our comprehension of multiple sclerosis (MS) pathophysiology. These advancements, which enable the evaluation of atrophy, microstructural tissue abnormalities, and functional plasticity, are broadening our insights into the effectiveness and working mechanisms of motor and cognitive rehabilitative treatments. This narrative review with selected studies discusses findings derived from the application of advanced MRI techniques to evaluate structural and functional neuroplasticity modifications underlying the effects of motor and cognitive rehabilitative treatments in people with MS (PwMS). Current applications as outcome measure in longitudinal trials and observational studies, their interpretation and possible pitfalls and limitations in their use are covered. Finally, we examine how the use of these techniques could evolve in the future to improve monitoring of motor and cognitive rehabilitative treatments. Despite substantial variability in study design and participant characteristics in rehabilitative studies for PwMS, improvements in motor and cognitive functions accompanied by structural and functional brain modifications induced by rehabilitation can be observed. However, significant enhancements to refine rehabilitation strategies are needed. Future studies in this field should strive to implement standardized methodologies regarding MRI acquisition and processing, possibly integrating multimodal measures. This will help identifying relevant markers of treatment response in PwMS, thus improving the use of rehabilitative interventions at individual level. The combination of motor and cognitive strategies, longer periods of treatment, as well as adequate follow-up assessments will contribute to enhance the quality of evidence in support of their routine use.

Association between quadriceps fat pad edema and patellofemoral osteoarthritis: a quantitative Q-Dixon-based magnetic resonance imaging (MRI) analysis

Association between quadriceps fat pad edema and patellofemoral osteoarthritis: a quantitative Q-Dixon-based magnetic resonance imaging (MRI) analysis

Characterizing Stalagmite Composition using Hyperspectral Imaging

Stalagmites offer nearly continuous records of past climate in continental settings at high temporal resolution. The climatic records preserved in stalagmites are commonly investigated by examining compositional characteristics such as mineralogy, organic content, and lamination patterns. These proxies provide valuable insights into the environmental conditions during stalagmite formation. However, the methods used to obtain information about these proxies are relatively destructive. This study uses hyperspectral imaging, a non-contact technique, to identify mineral composition, organic matter content, and laminations in stalagmite. It is the first wide spectrum imaging analysis in speleothem research, using both visible–near infrared and shortwave infrared wavelengths. Results obtained from hyperspectral imaging were compared by point spectral analysis using an ASD spectroradiometer and a grayscale profile along the growth axis of the stalagmite. Petrographic observation of thin sections and X-ray diffraction (XRD) analyses on selected stalagmite layers were performed to cross-validate the hyperspectral data. A travertine sample was also used to replicate the method on calcite. To automate mineral identification, a machine learning algorithm was developed to map spatial distribution and quantify relative proportions of minerals across the sample. Our findings are in good agreement with traditionally used methods of mineral identification, i.e. XRD and petrography, aiding in the interpretation of paleoclimate proxies, and offer a spatial guide for UTh dating analyses. It also provides insight for future investigations of stalagmite using hyperspectral data and classification through machine learning algorithms.

Comprehensive CT Imaging Analysis of Primary Colorectal Squamous Cell Carcinoma: A Retrospective Study

The aim of this study was to evaluate the findings of CT scans in patients with pathologically confirmed primary colorectal squamous-cell carcinoma (SCC). The clinical presentation and CT findings in eight patients with pathologically confirmed primary colorectal squamous-cell carcinoma were retrospectively reviewed by two gastrointestinal radiologists. Hematochezia was the most common symptom (n = 5). The tumors were located in the rectum (n = 7) and sigmoid colon (n = 1). The tumors showed circumferential wall thickening (n = 4), bulky mass (n = 3), or eccentric wall thickening (n = 1). The mean maximal wall thickness of the involved segment was 29.1 mm ± 13.4 mm. The degree of tumoral enhancement observed via CT was well enhanced (n = 4) or moderately enhanced (n = 4). Necrosis within the tumor was found in five patients. The mean total number of metastatic lymph nodes was 3.1 ± 3.3, and the mean short diameter of the largest metastatic lymph node was 16.6 ± 5.7 mm. Necrosis within the metastatic node was observed in six patients. Invasions to adjacent organs were identified in five patients (62.5%). Distant metastasis was detected in only one patient. In summary, primary SCCs that arise from the colorectum commonly present as marked invasive wall thickening or a bulky mass with heterogeneous well-defined enhancement, internal necrosis, and large metastatic lymphadenopathies.

Investigating cartilage-related diseases by polarization-resolved second harmonic generation (P-SHG) imaging.

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage.