Stromal osseous metaplasia in urothelial carcinoma of the bladder: An unusual and challenging feature. A case report / Carcinoma urotelial de vejiga con metaplasia ósea estromal: una característica inusual y desafiante. Presentación de un caso

A 62-year-old male presented with pain and haematuria starting 3 months before. The computed tomography showed focal and mural bladder thickening with ureteropelvic dilatation. The following transurethral bladder resection revealed a high-grade muscle-invasive urothelial carcinoma. In the subsequent cystoprostatectomy we found the same tumour, but adding focal tumour-associated stromal osseous metaplasia. Ossifying metaplasia is an extremely rare feature in urothelial carcinoma, with a few reported cases and represents a diagnostic challenge, mimicking radiotherapy-induced sarcoma or sarcomatoid carcinoma. (AU)

Varón de 62 años que consulta por dolor y hematuria desde hace 3 meses. En la tomografía computarizada se observó un engrosamiento focal y mural de la vejiga con dilatación ureteropélvica. La resección vesical transuretral reveló un carcinoma urotelial infiltrante de alto grado músculo-invasivo. En la cistoprostatectomía posterior encontramos el mismo tumor, pero añadiendo focos de metaplasia ósea estromal asociada al tumor. La metaplasia osificante es una característica extremadamente rara en el carcinoma urotelial, con algunos casos informados, y representa un desafío diagnóstico, ya que simula un sarcoma inducido por radioterapia o un carcinoma sarcomatoide. (AU)

CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae.

Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.

Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies.

BACKGROUND: Computed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data. METHODS: The current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies. RESULTS: Our findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information. CONCLUSION: The developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.

Prediction of hematologic toxicity in luminal type breast cancer patients receiving neoadjuvant chemotherapy using CT L1 level skeletal muscle index.

This study aims to explore the correlation between the CT-L1 and L3 body composition parameters and analyze the relationship between L1 body composition and hematologic toxicity in luminal-type breast cancer patients undergoing neoadjuvant chemotherapy. Data from 140 luminal-type breast cancer patients who underwent surgical treatment after neoadjuvant chemotherapy were analyzed retrospectively. Spearman analysis was used to assess the correlation between CT-L1 and CT-L3 body composition parameters pre-neoadjuvant chemotherapy. Additionally, univariate and multivariate logistic regression analyses were performed to identify factors influencing hematologic toxicity. CT-L1 body composition parameters were positively correlated with CT-L3 body composition parameters in 34 patients. Severe hematological toxicity occurred in 46 cases among the patient cohort. A skeletal muscle index (SMI) of < 32.91 cm2/m2, initial tumor size ≥ 3.335 cm, and a glucose-to-neutrophil ratio (GLR) ≥ 2.88 were identified as independent risk factors for severe hematologic toxicity during neoadjuvant chemotherapy in luminal-type breast cancer patients. The sample size in this study is small, and the predictive capacity of GLR in hematologic toxicity requires further research for comprehensive validation. CT-L1 analysis represents a viable alternative to CT-L3 analysis for body composition assessment. Patients with a low skeletal muscle index were more prone to experiencing severe hematologic toxicity during neoadjuvant chemotherapy.

Diagnostic Yield of head Computed Tomography in Patients with Syncope: Sex Differences.

BACKGROUND: Syncope is responsible for approximately 1-3% of all emergency department (ED) visits and up to 6% of all hospital admissions in the United States. Although often of no long-term consequence, syncope can be the first presentation of a range of serious conditions such as strokes, tumors, or subarachnoid hemorrhages. Head computed tomography (CT) scanning is therefore commonly ordered in the ED for patients presenting with syncope to rule out any of these conditions, which may present without other associated physical or neurological findings on initial examination. However, the diagnostic yield of head CTs in patients presenting with syncope is unclear. OBJECTIVES: To determine the diagnostic yield of head CT in the ED in patients with syncope. METHODS: We conducted an observational analytical retrospective cross-sectional study on 360 patients diagnosed with syncope who underwent a head CT to determine the diagnostic yield of syncope to determine whether head CT is necessary for every patient presenting with syncope to the ED. RESULTS: The total of new CT findings was 11.4%. Percentages varied between men (12.8%) and women (9.7%), P = 0.353. There were no significant differences between sexes regarding the findings in head CT, yet the incidence increased, especially among elderly males. CONCLUSIONS: Age had a more significant impact on diagnostic yield of syncope than head CT. The use of a head CT scan as a routine diagnosis tool in patients with syncope is unjustifiable unless there is an indication based on medical history or physical examination.

Body composition impacts outcome of bronchoscopic lung volume reduction in patients with severe emphysema: a fully automated CT-based analysis.

Chronic Obstructive Pulmonary Disease (COPD) is characterized by progressive and irreversible airflow limitation, with individual body composition influencing disease severity. Severe emphysema worsens symptoms through hyperinflation, which can be relieved by bronchoscopic lung volume reduction (BLVR). To investigate how body composition, assessed through CT scans, impacts outcomes in emphysema patients undergoing BLVR. Fully automated CT-based body composition analysis (BCA) was performed in patients with end-stage emphysema receiving BLVR with valves. Post-interventional muscle and adipose tissues were quantified, body size-adjusted, and compared to baseline parameters. Between January 2015 and December 2022, 300 patients with severe emphysema underwent endobronchial valve treatment. Significant improvements were seen in outcome parameters, which were defined as changes in pulmonary function, physical performance, and quality of life (QoL) post-treatment. Muscle volume remained stable (1.632 vs. 1.635 for muscle bone adjusted ratio (BAR) at baseline and after 6 months respectively), while bone adjusted adipose tissue volumes, especially total and pericardial adipose tissue, showed significant increase (2.86 vs. 3.00 and 0.16 vs. 0.17, respectively). Moderate to strong correlations between bone adjusted muscle volume and weaker correlations between adipose tissue volumes and outcome parameters (pulmonary function, QoL and physical performance) were observed. Particularly after 6-month, bone adjusted muscle volume changes positively corresponded to improved outcomes (ΔForced expiratory volume in 1 s [FEV1], r = 0.440; ΔInspiratory vital capacity [IVC], r = 0.397; Δ6Minute walking distance [6MWD], r = 0.509 and ΔCOPD assessment test [CAT], r = -0.324; all p < 0.001). Group stratification by bone adjusted muscle volume changes revealed that groups with substantial muscle gain experienced a greater clinical benefit in pulmonary function improvements, QoL and physical performance (ΔFEV1%, 5.5 vs. 39.5; ΔIVC%, 4.3 vs. 28.4; Δ6MWDm, 14 vs. 110; ΔCATpts, -2 vs. -3.5 for groups with ΔMuscle, BAR% < -10 vs. > 10, respectively). BCA results among patients divided by the minimal clinically important difference for forced expiratory volume of the first second (FEV1) showed significant differences in bone-adjusted muscle and intramuscular adipose tissue (IMAT) volumes and their respective changes after 6 months (ΔMuscle, BAR% -5 vs. 3.4 and ΔIMAT, BAR% -0.62 vs. 0.60 for groups with ΔFEV1 ≤ 100 mL vs > 100 mL). Altered body composition, especially increased muscle volume, is associated with functional improvements in BLVR-treated patients.

Feasibility and Diagnostic Performance of Functional SYNTAX Score Derived From Dynamic CT Myocardial Perfusion Imaging.

BACKGROUND: Computed tomography (CT) fractional flow reserve (FFR)-derived functional SYNTAX score (FSSCT-FFR) is a valuable method for guiding treatment strategy in patients with multivessel coronary artery disease. Dynamic CT myocardial perfusion imaging (CT-MPI) demonstrates higher diagnostic accuracy than CT-FFR in identifying hemodynamically significant coronary artery disease. We aimed to evaluate the feasibility of CT-MPI-derived FSS (FSSCT-MPI) with reference to invasive FSS. METHODS: In this retrospective study, patients with multivessel coronary artery disease who underwent dynamic CT-MPI+ coronary CT angiography and invasive coronary angiography or FFR within 4 weeks were consecutively included. Invasive (FSSinvasive) and noninvasive FSS (FSSCT-MPI and FSSCT-FFR) were calculated by an online calculator, which assigned points to lesions with hemodynamic significance (defined as FFRinvasive ≤0.80, invasive coronary angiography diameter stenosis ≥90%, CT-FFR ≤0.80, and myocardial ischemia on CT-MPI). Weighted κ value and net reclassification index were calculated to determine the consistency and incremental discriminatory power of FSSCT-MPI. Receiver operating characteristic curve analysis was used for the comparison of FSSCT-MPI and FSSCT-FFR in detecting intermediate- to high-risk patients. RESULTS: A total of 119 patients (96 men; 64.6±10.6 years) with 305 obstructive lesions were included. The average FSSCT-MPI, FSSCT-FFR, and FSSinvasive were 15.58±13.03, 16.18±13.30, and 13.11±12.22, respectively. The agreement on risk classification based on the FSSCT-MPI tertiles was good (weighted κ, 0.808). With reference to FSSinvasive, FSSCT-MPI correctly reclassified 27 (22.7%) patients from the intermediate- to high SYNTAX score group to the low-score group (net reclassification index, 0.30; P<0.001). In patients with severe calcification, FSSCT-MPI had better diagnostic value than FSSCT-FFR in detecting intermediate- to high-risk patients when compared with FSSinvasive (area under the curve, 0.976 versus 0.884; P<0.001). CONCLUSIONS: Noninvasive FSS derived from CT-MPI is feasible and has strong concordance with FSSinvasive. It allows accurate categorization of FSS in patients with multivessel coronary artery disease, in particular with severe calcification.

Prognostic Impact of Visceral Adipose Tissue Imaging Parameters in Patients with Cholangiocarcinoma after Surgical Resection.

Visceral adiposity is known to be related to poor prognosis in patients with cholangiocarcinoma; however, the prognostic significance of the qualitative features of adipose tissue in cholangiocarcinoma has yet to be well defined. This study investigated the prognostic impact of adipose tissue imaging parameters reflecting the quantity and qualitative characteristics of subcutaneous (SAT) and visceral (VAT) adipose tissue on recurrence-free survival (RFS) and overall survival (OS) in 94 patients undergoing resection of cholangiocarcinoma. The area, mean computed tomography (CT) attenuation, and mean 2-deoxy-2-[18F]fluoro-D-glucose (FDG) uptake of SAT and VAT on positron emission tomography (PET)/CT for staging work-up were measured, and the relationship of these adipose tissue imaging parameters with clinicopathological factors and survival was assessed. TNM stage, histologic grade, lymphovascular invasion, and the size of cholangiocarcinoma showed positive correlations with adipose tissue imaging parameters. Multivariate survival analysis demonstrated that the visceral-to-subcutaneous adipose tissue area ratio (VSR) (p = 0.024; hazard ratio, 1.718) and mean FDG uptake of VAT (p = 0.033; hazard ratio, 9.781) were significant predictors for RFS, but all of the adipose tissue imaging parameters failed to show statistical significance for predicting OS. In addition to visceral adiposity, FDG uptake of VAT might be a promising prognostic parameter for predicting RFS in patients with cholangiocarcinoma.

Lectin-Based Immunophenotyping and Whole Proteomic Profiling of CT-26 Colon Carcinoma Murine Model.

A murine colorectal carcinoma (CRC) model was established. CT26 colon carcinoma cells were injected into BALB/c mice's spleen to study the primary tumor and the mechanisms of cell spread of colon cancer to the liver. The CRC was verified by the immunohistochemistry of Pan Cytokeratin and Vimentin expression. Immunophenotyping of leukocytes isolated from CRC-bearing BALB/c mice or healthy controls, such as CD19+ B cells, CD11+ myeloid cells, and CD3+ T cells, was carried out using fluorochrome-labeled lectins. The binding of six lectins to white blood cells, such as galectin-1 (Gal1), siglec-1 (Sig1), Sambucus nigra lectin (SNA), Aleuria aurantia lectin (AAL), Phytolacca americana lectin (PWM), and galectin-3 (Gal3), was assayed. Flow cytometric analysis of the splenocytes revealed the increased binding of SNA, and AAL to CD3 + T cells and CD11b myeloid cells; and increased siglec-1 and AAL binding to CD19 B cells of the tumor-bearing mice. The whole proteomic analysis of the established CRC-bearing liver and spleen versus healthy tissues identified differentially expressed proteins, characteristic of the primary or secondary CRC tissues. KEGG Gene Ontology bioinformatic analysis delineated the established murine CRC characteristic protein interaction networks, biological pathways, and cellular processes involved in CRC. Galectin-1 and S100A4 were identified as upregulated proteins in the primary and secondary CT26 tumor tissues, and these were previously reported to contribute to the poor prognosis of CRC patients. Modelling the development of liver colonization of CRC by the injection of CT26 cells into the spleen may facilitate the understanding of carcinogenesis in human CRC and contribute to the development of novel therapeutic strategies.

Checklist: Neck computed tomography in non-traumatic emergencies.

Patients attending the emergency department (ED) with cervical inflammatory/infectious symptoms or presenting masses that may involve the aerodigestive tract or vascular structures require a contrast-enhanced computed tomography (CT) scan of the neck. Its radiological interpretation is hampered by the anatomical complexity and pathophysiological interrelationship between the different component systems in a relatively small area. Recent studies propose a systematic evaluation of the cervical structures, using a 7-item checklist, to correctly identify the pathology and detect incidental findings that may interfere with patient management. As a conclusion, the aim of this paper is to review CT findings in non-traumatic pathology of the neck in the ED, highlighting the importance of a systematic approach in its interpretation and synthesis of a structured, complete, and concise radiological report.

Computed tomography multi-planar and 3D image assessment protocol for detailed analysis of inner ear malformations in patients undergoing cochlear implantation counseling.

<b><br>Introduction:</b> Congenital inner ear malformations resulting from embryogenesis may be visualized in radiological scans. Many attempts have been made to describe and classify the defects of the inner ear based on anatomical and radiological findings.</br> <b><br>Aim:</b> The aim was to propose and discuss computed tomography multi-planar and 3D image assessment protocols for detailed analysis of inner ear malformations in patients undergoing cochlear implantation counseling.</br> <b><br>Material and methods:</b> A retrospective analysis of 22 malformed inner ears. CT scans were analyzed using the Multi-Planar Reconstruction (MPR) option and 3D reconstruction.</br> <b><br>Results:</b> The protocol of image interpretation was developed to allow reproducibility for evaluating each set of images. The following malformations were identified: common cavity, cochlear hypoplasia type II, III, and IV, incomplete partition type II and III, and various combinations of vestibule labyrinth malformations. All anomalies have been presented and highlighted in figures with appropriate descriptions for easier identification. Figures of normal inner ears were also included for comparison. 3D reconstructions for each malformation were presented, adding clinical value to the detailed analysis.</br> <b><br>Conclusions:</b> Properly analyzing CT scans in cochlear implantation counseling is a necessary and beneficial tool for appropriate candidate selection and preparation for surgery. As proposed in this study, the unified scans evaluation scheme simplifies the identification of malformations and reduces the risk of omitting particular anomalies. Multi-planar assessment of scans provides most of the necessary details. The 3D reconstruction technique is valuable in addition to diagnostics influencing the decision-making process. It can minimize the risk of misdiagnosis. Disclosure of the inner ear defect and its precise imaging provides detailed anatomical knowledge of each ear, enabling the selection of the appropriate cochlear implant electrode and the optimal surgical technique.</br>.

A rare complication of pediatric liver transplantation: Post-transplant diaphragmatic hernia.

AIM: Acquired post-transplant diaphragmatic hernia (PTDH) is a rare complication of liver transplantation (LT) in children. We aimed to present our experience in PTDH, and a possible causative background is discussed. METHODS: Medical records of patients who had undergone diaphragmatic repair following LT between 2015 and 2023 were reviewed. Demographic information, details of primary diseases necessitating LT, transplantation techniques, and clinical findings associated with PTDH were evaluated. RESULTS: There were seven patients with PTDH. Median age at transplantation was 69 (range: 9-200) months. Five patients received a left lateral sector, one patient had a right lobe, and one had a left lobe graft. Time between LT and PTDH was 9 (2-123) months. One patient who was diagnosed in the postoperative 10th year was asymptomatic. Respiratory distress and abdominal pain were the main symptoms among all. All patients underwent laparotomy, and primary repair was performed in six patients, and one patient required mesh repair because of a large defect. Small intestine herniated in most cases. There were two complicated cases with perforation of the stomach and colonic volvulus. There is no recurrence or long-term complications for the median 60 (20-119) month follow-up period. CONCLUSION: PTDH is a rare but serious complication. Majority of symptomatic cases present within the first postoperative year, whereas some late-presenting cases may not be symptomatic. Inadvertent injury to the inferior phrenic vasculatures due to excessive use of cauterization for control of hemostasis may be a plausible explanation in those cases.

Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography.

Objective.Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols.Approach.Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements.Main result.The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts.Significance.The reduction in model space dimension is shown to increasecomputational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivitytolerates higher noiselevels up to several orders of magnitude larger than standard methods.

Clinical Utility of a CT-based AI Prognostic Model for Segmentectomy in Non-Small Cell Lung Cancer.

Background Currently, no tool exists for risk stratification in patients undergoing segmentectomy for non-small cell lung cancer (NSCLC). Purpose To develop and validate a deep learning (DL) prognostic model using preoperative CT scans and clinical and radiologic information for risk stratification in patients with clinical stage IA NSCLC undergoing segmentectomy. Materials and Methods In this single-center retrospective study, transfer learning of a pretrained model was performed for survival prediction in patients with clinical stage IA NSCLC who underwent lobectomy from January 2008 to March 2017. The internal set was divided into training, validation, and testing sets based on the assignments from the pretraining set. The model was tested on an independent test set of patients with clinical stage IA NSCLC who underwent segmentectomy from January 2010 to December 2017. Its prognostic performance was analyzed using the time-dependent area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for freedom from recurrence (FFR) at 2 and 4 years and lung cancer-specific survival and overall survival at 4 and 6 years. The model sensitivity and specificity were compared with those of the Japan Clinical Oncology Group (JCOG) eligibility criteria for sublobar resection. Results The pretraining set included 1756 patients. Transfer learning was performed in an internal set of 730 patients (median age, 63 years [IQR, 56-70 years]; 366 male), and the segmentectomy test set included 222 patients (median age, 65 years [IQR, 58-71 years]; 114 male). The model performance for 2-year FFR was as follows: AUC, 0.86 (95% CI: 0.76, 0.96); sensitivity, 87.4% (7.17 of 8.21 patients; 95% CI: 59.4, 100); and specificity, 66.7% (136 of 204 patients; 95% CI: 60.2, 72.8). The model showed higher sensitivity for FFR than the JCOG criteria (87.4% vs 37.6% [3.08 of 8.21 patients], P = .02), with similar specificity. Conclusion The CT-based DL model identified patients at high risk among those with clinical stage IA NSCLC who underwent segmentectomy, outperforming the JCOG criteria. © RSNA, 2024 Supplemental material is available for this article.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights.

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.

This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.

Application of artificial intelligence-assisted image diagnosis software based on volume data reconstruction technique in medical imaging practice teaching.

BACKGROUND: In medical imaging courses, due to the complexity of anatomical relationships, limited number of practical course hours and instructors, how to improve the teaching quality of practical skills and self-directed learning ability has always been a challenge for higher medical education. Artificial intelligence-assisted diagnostic (AISD) software based on volume data reconstruction (VDR) technique is gradually entering radiology. It converts two-dimensional images into three-dimensional images, and AI can assist in image diagnosis. However, the application of artificial intelligence in medical education is still in its early stages. The purpose of this study is to explore the application value of AISD software based on VDR technique in medical imaging practical teaching, and to provide a basis for improving medical imaging practical teaching. METHODS: Totally 41 students majoring in clinical medicine in 2017 were enrolled as the experiment group. AISD software based on VDR was used in practical teaching of medical imaging to display 3D images and mark lesions with AISD. Then annotations were provided and diagnostic suggestions were given. Also 43 students majoring in clinical medicine from 2016 were chosen as the control group, who were taught with the conventional film and multimedia teaching methods. The exam results and evaluation scales were compared statistically between groups. RESULTS: The total skill scores of the test group were significantly higher compared with the control group (84.51 ± 3.81 vs. 80.67 ± 5.43). The scores of computed tomography (CT) diagnosis (49.93 ± 3.59 vs. 46.60 ± 4.89) and magnetic resonance (MR) diagnosis (17.41 ± 1.00 vs. 16.93 ± 1.14) of the experiment group were both significantly higher. The scores of academic self-efficacy (82.17 ± 4.67) and self-directed learning ability (235.56 ± 13.50) of the group were significantly higher compared with the control group (78.93 ± 6.29, 226.35 ± 13.90). CONCLUSIONS: Applying AISD software based on VDR to medical imaging practice teaching can enable students to timely obtain AI annotated lesion information and 3D images, which may help improve their image reading skills and enhance their academic self-efficacy and self-directed learning abilities.

A CT-based radiomics nomogram for predicting histologic grade and outcome in chondrosarcoma.

OBJECTIVE: The preoperative identification of tumor grade in chondrosarcoma (CS) is crucial for devising effective treatment strategies and predicting outcomes. The study aims to build and validate a CT-based radiomics nomogram (RN) for the preoperative identification of tumor grade in CS, and to evaluate the correlation between the RN-predicted tumor grade and postoperative outcome. METHODS: A total of 196 patients (139 in the training cohort and 57 in the external validation cohort) were derived from three different centers. A clinical model, radiomics signature (RS) and RN (which combines significant clinical factors and RS) were developed and validated to assess their ability to distinguish low-grade from high-grade CS with area under the curve (AUC). Additionally, Kaplan-Meier survival analysis was applied to examine the association between RN-predicted tumor grade and recurrence-free survival (RFS) of CS. The predictive accuracy of the RN was evaluated using Harrell's concordance index (C-index), hazard ratio (HR) and AUC. RESULTS: Size, endosteal scalloping and active periostitis were selected to build the clinical model. Three radiomics features, based on CT images, were selected to construct the RS. Both the RN (AUC, 0.842) and RS (AUC, 0.835) were superior to the clinical model (AUC, 0.776) in the validation set (P = 0.003, 0.040, respectively). A correlation between Nomogram score (Nomo-score, derived from RN) and RFS was observed through Kaplan-Meier survival analysis in the training and test cohorts (log-rank P < 0.050). Patients with high Nomo-score tumors were 2.669 times more likely to suffer recurrence than those with low Nomo-score tumors (HR, 2.669, P < 0.001). CONCLUSIONS: The CT-based RN performed well in predicting both the histologic grade and outcome of CS.

[Research Progress on Localization of Pulmonary Nodules].

With the widespread adoption of low-dose computed tomography (LDCT) and advancements in computed tomography image resolution, the detection rate of pulmonary nodules, especially smaller ones, has significantly improved. The risk of developing malignant tumors increases with the pulmonary nodule diameter. Video-assisted thoracoscopic surgery (VATS) stands out as the preferred surgical method. The accurate localization of pulmonary nodules is crucial for the success of VATS and remains a significant challenge for thoracic surgeons. Currently, commonly employed localization methods include CT-guided percutaneous positioning, bronchoscope-guided positioning, intraoperative ultrasound positioning, augmented reality (AR), and 3D print-assisted positioning. This review explores recent research progress, highlights the strengths and weaknesses of various pulmonary nodule localization methods. The aim is to provide valuable insights for clinical applications and guide future developments in this field.

Management of incidental pulmonary nodules in CT examination.

Lung cancer is the leading cause of cancer-related death in Denmark and the world. The increase in CT examinations has led to an increase in detection of pulmonary nodules divided into solid and subsolid (including ground glass and part solid). Risk factors for malignancy include age, smoking, female gender, and specific ethnicities. Nodule traits like size, spiculation, upper-lobe location, and emphysema correlate with higher malignancy risk. Managing these potentially malignant nodules relies on evidence-based guidelines and risk stratification. These risk stratification models can standardize the approach for the management of incidental pulmonary findings, as argued in this review.