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.

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.

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.

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.

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.

A chest CT-based nomogram for predicting survival in acute myeloid leukemia.

BACKGROUND: The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). METHODS: 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. RESULTS: Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011). CONCLUSIONS: In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection.

Lung cancer is one of the most deadly diseases in the world. Lung cancer detection can save the patient's life. Despite being the best imaging tool in the medical sector, clinicians find it challenging to interpret and detect cancer from Computed Tomography (CT) scan data. One of the most effective ways for the diagnosis of certain malignancies like lung tumours is Positron Emission Tomography (PET) imaging. So many diagnosis models have been implemented nowadays to diagnose various diseases. Early lung cancer identification is very important for predicting the severity level of lung cancer in cancer patients. To explore the effective model, an image fusion-based detection model is proposed for lung cancer detection using an improved heuristic algorithm of the deep learning model. Firstly, the PET and CT images are gathered from the internet. Further, these two collected images are fused for further process by using the Adaptive Dilated Convolution Neural Network (AD-CNN), in which the hyperparameters are tuned by the Modified Initial Velocity-based Capuchin Search Algorithm (MIV-CapSA). Subsequently, the abnormal regions are segmented by influencing the TransUnet3+. Finally, the segmented images are fed into the Hybrid Attention-based Deep Networks (HADN) model, encompassed with Mobilenet and Shufflenet. Therefore, the effectiveness of the novel detection model is analyzed using various metrics compared with traditional approaches. At last, the outcome evinces that it aids in early basic detection to treat the patients effectively.

Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.

BACKGROUND: To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. METHODS: A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. RESULTS: Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. CONCLUSION: The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.

The sensitivity of a neck CT scan in detecting pneumothoraces in nonpenetrating trauma.

BACKGROUND: The trauma-related pneumothorax is a common intrathoracic injury and can go undetected with detrimental outcomes. Chest computed tomography (CT) investigation in low- to middle-income countries (LMIC) is not always readily available during emergency situations and increased workloads. However, alternative investigations all have limitations in including pneumothoraces. Patients may have trauma indications for CT of the head and neck. The neck CT scan may hold an extra potential advantage in pneumothorax detection. This study aimed to assess its sensitivity in pneumothorax detection in nonpenetrating trauma. METHODS: A retrospective study was conducted from 1 January 2016 to 31 December 2021. All adult patients sustaining nonpenetrating injuries, and investigated with chest and neck CT scans were included. The chest CT scan was the gold standard against,which the neck CT scan was compared to determine the accuracy of pneumothorax detection. Stata version 16 was used for descriptive statistical analysis, and a p-value of 0.05 was considered statistically significant. RESULTS: One thousand ninety three were eligible for evaluation; 204 (18.66%) pneumothoraces were detected on chest CT scans, 200 (98.0%) of which were also detected on the neck CT scan, producing a sensitivity of 98.0% (95% CI 95.1-99.5%) and a specificity of 100% (95% CI 99.6-100%). Most pneumothoraces were detected at T1 level (45.09%), followed by T2 (43.62%). CONCLUSION: The neck CT scan has demonstrated high sensitivity and specificity in pneumothorax detection. Thus, it may be used as an additional tool for those who could not receive or do not need a formal chest CT but have an indication for neck CT scans.

A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study.

BACKGROUND: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC. METHODS: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data. RESULTS: Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression. CONCLUSION: This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.

Massive left pulmonary artery aneurysm with a co-existing patent ductus arteriosus in a five-year-old female child: A case report.

Pulmonary Artery Aneur ysm (PAA), whether congenital or acquired, is a rare diagnostic find ing com pare d to aor tic aneur ysms. There have been fe w cases where PA As were documented as a complication of untreated Patent Ductus Ar teriosus (PDA) due to long-standing Pulmonary Arterial H ypertension (PAH). However, it is quite rare for a case of PAA to be reported with co-existing PDA without PAH. This report highlights a case of a five -year-old girl who was presented with palpitations, easy fatigability, fever, c yanos is, and vomiting. A Chest X-ray s howed mo derate cardiomega ly. A PDA of 6 mm was diagnosed on Transthoracic E chocardiog rap hy ( TTE ) and a large cavity con necte d with LPA raised suspicion of a possible LPA aneur ysm. A Chest CT scan confirm ed the diagnosis of a saccular aneurysm, originating from the distal part of the main Left Pulmonary Artery (LPA) just proximal to the point of bifurcation into lobar branches, measuring 7.5x6.5 cm. During surgery, the aneurysm was opened, emptied with suction and closed without resecting the aneur ysmal walls. The patient had an uneventful post-op course and is doing well during regular interval follow up visits.

Krukenburg tumour: Role of 18F-FDG PET/CT in identifying the primary site.

Krukenberg tumours are a rare form of metastatic tumours of the ovary. They primary site is usually the gastro-intestinal system with the most common being gastric cancer. We present the case of a 35-year-old female coming in with a large pelvi-abdominal mass for investigation. This pelvic mass showed mild to moderate metabolic activity. 18F-FDG PET-CT was able to identify the primary gastric carcinoma. Subsequent histopathology confirmed this to be gastric adenocarcinoma with metastases to the ovary.