Unveiling Challenging Microbial Fossil Biosignatures from Rio Tinto with Micro-to-Nanoscale Chemical and Ultrastructural Imaging.

Understanding the nature and preservation of microbial traces in extreme environments is crucial for reconstructing Earth's early biosphere and for the search for life on other planets or moons. At Rio Tinto, southwestern Spain, ferric oxide and sulfate deposits similar to those discovered at Meridiani Planum, Mars, entomb a diversity of fossilized organisms, despite chemical conditions commonly thought to be challenging for life and fossil preservation. Investigating this unique fossil microbiota can elucidate ancient extremophile communities and the preservation of biosignatures in acidic environments on Earth and, potentially, Mars. In this study, we use an innovative multiscale approach that combines the state-of-the-art synchrotron X-ray nanoimaging methods of ptychographic X-ray computed laminography and nano-X-ray fluorescence to reveal Rio Tinto's microfossils at subcellular resolution. The unprecedented nanoscale views of several different specimens within their geological and geochemical contexts reveal novel intricacies of preserved microbial communities. Different morphotypes, ecological interactions, and possible taxonomic affinities were inferred based on qualitative and quantitative 3D ultrastructural information, whereas diagenetic processes and metabolic affinities were inferred from complementary chemical information. Our integrated nano-to-microscale analytical approach revealed previously invisible microbial and mineral interactions, which complemented and filled a gap of spatial resolution in conventional methods. Ultimately, this study contributes to the challenge of deciphering the faint chemical and morphological biosignatures that can indicate life's presence on the early Earth and on distant worlds.

Multi-scale structural characterization of ceramic-based photonic glasses for structural colors.

Structural colors arise from selective light interaction with (nano)structures, which give them advantages over pigmented colors such as resistance to fading and possibility to be fabricated out of traditional low-cost and non-toxic materials. Since the color arises from the photonic (nano)structures, different structural features can impact their photonic response and thus, their color. Therefore, the detailed characterization of their structural features is crucial for further improvement of structural colors. In this work, we present a detailed multi-scale structural characterization of ceramic-based photonic glasses by using a combination of high-resolution ptychographic X-ray computed tomography and small angle X-ray scattering. Our results uncover the structure-processing-properties' relationships of such nanoparticles-based photonic glasses and point out to the need of a review of the structural features used in simulation models concomitantly with the need for further investigations by experimentalists, where we point out exactly which structural features need to be improved.

Reduced dose helical CT scout imaging on next generation wide volume CT system decreases scan length and overall radiation exposure.

Purpose: Traditional CT acquisition planning is based on scout projection images from planar anterior-posterior and lateral projections where the radiographer estimates organ locations. Alternatively, a new scout method utilizing ultra-low dose helical CT (3D Landmark Scan) offers cross-sectional imaging to identify anatomic structures in conjunction with artificial intelligence based Anatomic Landmark Detection (ALD) for automatic CT acquisition planning. The purpose of this study is to quantify changes in scan length and radiation dose of CT examinations planned using 3D Landmark Scan and ALD and performed on next generation wide volume CT versus examinations planned using traditional scout methods. We additionally aim to quantify changes in radiation dose reduction of scans planned with 3D Landmark Scan and performed on next generation wide volume CT. Methods: Single-center retrospective analysis of consecutive patients with prior CT scan of the same organ who underwent clinical CT using 3D Landmark Scan and automatic scan planning. Acquisition length and dose-length-product (DLP) were collected. Data was analyzed by paired t-tests. Results: 104 total CT examinations (48.1 % chest, 15.4 % abdomen, 36.5 % chest/abdomen/pelvis) on 61 individual consecutive patients at a single center were retrospectively analyzed. 79.8 % of scans using 3D Landmark Scan had reduction in acquisition length compared to the respective prior acquisition. Median acquisition length using 3D Landmark Scan was 26.7 mm shorter than that using traditional scout methods (p < 0.001) with a 23.3 % median total radiation dose reduction (245.6 (IQR 150.0-400.8) mGy cm vs 320.3 (IQR 184.1-547.9) mGy cm). CT dose index similarly was overall decreased for scans planned with 3D Landmark and ALD and performed on next generation CT versus traditional methods (4.85 (IQR 3.8-7) mGy vs. 6.70 (IQR 4.43-9.18) mGy, respectively, p < 0.001). Conclusion: Scout imaging using reduced dose 3D Landmark Scan images and Anatomic Landmark Detection reduces acquisition range in chest, abdomen, and chest/abdomen/pelvis CT scans. This technology, in combination with next generation wide volume CT reduces total radiation dose.

Computed Tomography-Based Intratumor Heterogeneity Predicts Response to Immunotherapy Plus Chemotherapy in Esophageal Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: This study explored the intratumor heterogeneity (ITH) of esophageal squamous cell carcinoma (ESCC) using computed tomography (CT) and investigated the value of CT-based ITH in predicting the response to immune checkpoint inhibitor (ICI) plus chemotherapy in patients with ESCC. MATERIALS AND METHODS: This retrospective study included 416 patients with ESCC who received ICI plus chemotherapy at two independent hospitals between January 2019 and July 2022. Multiparametric CT features were extracted from ESCC lesions and screened using hierarchical clustering and dimensionality reduction algorithms. Logistic regression and machine learning models based on selected features were developed to predict treatment response and validated in separate datasets. ITH was quantified using the score calculated by the best-performing model and visualized through feature clustering and feature contribution heatmaps. A gene set enrichment analysis (GSEA) was performed to identify the biological pathways underlying the CT-based ITH. RESULTS: The extreme gradient boosting model based on CT-derived ITH had higher discriminative power, with areas under the receiver operating characteristic curve of 0.864 (95% confidence interval [CI]: 0.774-0.954) and 0.796 (95% CI: 0.698-0.893) in the internal and external validation sets. The CT-based ITH pattern differed significantly between responding and non-responding patients. The GSEA indicated that CT-based ITH was associated with immunity-, keratinization-, and epidermal cell differentiation-related pathways. CONCLUSION: CT-based ITH is an effective biomarker for identifying patients with ESCC who could benefit from ICI plus chemotherapy. Immunity-, keratinization-, and epidermal cell differentiation-related pathways may influence the patient's response to ICI plus chemotherapy.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

OBJECTIVES: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. METHODS: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. RESULTS: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. CONCLUSIONS: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. CLINICAL RELEVANCE STATEMENT: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. KEY POINTS: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

Combined conventional factors and the radiomics signature of coronary plaque texture could improve cardiac risk prediction.

OBJECTIVES: This study aims to investigate how radiomics analysis can help understand the association between plaque texture, epicardial adipose tissue (EAT), and cardiovascular risk. Working with a Photon-counting CT, which exhibits enhanced feature stability, offers the potential to advance radiomics analysis and enable its integration into clinical routines. METHODS: Coronary plaques were manually segmented in this retrospective, single-centre study and radiomic features were extracted using pyradiomics. The study population was divided into groups according to the presence of high-risk plaques (HRP), plaques with at least 50% stenosis, plaques with at least 70% stenosis, or triple-vessel disease. A combined group with patients exhibiting at least one of these risk factors was formed. Random forest feature selection identified differentiating features for the groups. EAT thickness and density were measured and compared with feature selection results. RESULTS: A total number of 306 plaques from 61 patients (mean age 61 years +/- 8.85 [standard deviation], 13 female) were analysed. Plaques of patients with HRP features or relevant stenosis demonstrated a higher presence of texture heterogeneity through various radiomics features compared to patients with only an intermediate stenosis degree. While EAT thickness did not significantly differ, affected patients showed significantly higher mean densities in the 50%, HRP, and combined groups, and insignificantly higher densities in the 70% and triple-vessel groups. CONCLUSION: The combination of a higher EAT density and a more heterogeneous plaque texture might offer an additional tool in identifying patients with an elevated risk of cardiovascular events. CLINICAL RELEVANCE STATEMENT: Cardiovascular disease is the leading cause of mortality globally. Plaque composition and changes in the EAT are connected to cardiac risk. A better understanding of the interrelation of these risk indicators can lead to improved cardiac risk prediction. KEY POINTS: Cardiac plaque composition and changes in the EAT are connected to cardiac risk. Higher EAT density and more heterogeneous plaque texture are related to traditional risk indicators. Radiomics texture analysis conducted on PCCT scans can help identify patients with elevated cardiac risk.

"Interstitial lung abnormalities": translation to and use in Portuguese.

Objective: To conduct a survey on the use of the term "interstitial lung abnormalities" in radiology reports in Brazil, propose an appropriate Portuguese-language translation for the term, and provide a brief review of the literature on the topic. Materials and Methods: A survey was sent via electronic message to various radiologists in Brazil, asking about their familiarity with the term, which translation of the term they use in Portuguese, and whether they use the criteria proposed by the Fleischner Society. Results: A total of 163 responses were received, from all regions of Brazil. Although the vast majority (88%) of the respondents stated that they were familiar with the term "interstitial lung abnormalities", there was considerable variation regarding the equivalent term they used in Portuguese. Conclusion: We suggest that the term "anormalidades pulmonares intersticiais" be used in order to standardize radiology reports and disseminate knowledge of these findings in Brazil.

Objetivo: Fazer um levantamento sobre o uso do termo interstitial lung abnormalities nos laudos radiológicos no Brasil, propor uma tradução para o termo e fazer uma breve revisão sobre o tema. Materiais e Métodos: Foi enviada uma pesquisa, por meio de mensagem eletrônica, para diversos radiologistas de todo o Brasil, questionando sobre a familiarização com o termo, qual tradução em português utilizam e se usam os critérios propostos pela diretriz da Sociedade Fleischner. Resultados: Foram recebidas 163 respostas de todas as regiões do Brasil e a grande maioria dos radiologistas respondeu estar familiarizado com o termo interstitial lung abnormalities (88%), mas houve grande variação em relação ao termo utilizado como tradução para o português. Conclusão: Sugerimos a padronização do termo "anormalidades pulmonares intersticiais", a fim de uniformizar os relatórios radiológicos e difundir esta entidade no País.

Radiological presentation of active pulmonary tuberculosis in kidney transplant recipients: a retrospective study of four cases and a review of the literature.

Although kidney transplantation is the best therapeutic option for patients with chronic kidney disease, the immunosuppression required greatly increases susceptibility to infections that are responsible for high post-transplant mortality. Pulmonary tuberculosis (TB) represents a major cause of such infections, and its early diagnosis is therefore quite important. In view of that, we researched the manifestations of active pulmonary TB in kidney transplant recipients, through chest X-ray and computed tomography (CT), as well as determining the number of cases of active pulmonary TB occurring over a 3.5-year period at our institution. We identified four cases of active pulmonary TB in kidney transplant recipients. The CT scans provided information complementary to the chest X-ray findings in all four of those cases. We compared our CT findings with those reported in the literature. We analyzed our experience in conjunction with an extensive review of the literature that was nevertheless limited because few studies have been carried out in lowand middle-income countries, where the incidence of TB is higher.

Apesar de o transplante renal ser a melhor opção terapêutica para pacientes com doença renal crônica, a imunodepressão decorrente desse tratamento eleva muito a suscetibilidade desses pacientes a infecções, responsáveis por altas taxas de mortalidade pós-operatórias. A tuberculose (TB) pulmonar é uma significativa causa dessas infecções, sendo muito importante o seu diagnóstico precoce. Assim, nós pesquisamos as manifestações da TB pulmonar ativa nessa população de transplantados renais por meio de radiografias simples e tomografia computadorizada (TC) do tórax, também para estabelecer o número de casos de TB pulmonar ativa em nossa instituição após levantamento de 3,5 anos. Encontramos quatro casos de TB pulmonar ativa em pacientes transplantados renais. A TC forneceu informações adicionais em relação às radiografias de tórax em 100% dos casos analisados. Comparamos os nossos achados de TC com os relatados na literatura. Somamos a experiência obtida com extensa revisão da literatura, ainda limitada nessa questão, com poucos estudos realizados em países em desenvolvimento onde a incidência de TB é maior.

Computed tomography and magnetic resonance imaging in Brazil: an epidemiological study on the distribution of equipment and frequency of examinations, with comparisons between the public and private sectors.

Objective: To compare information on highly complex radiological procedures-computed tomography (CT) and magnetic resonance imaging (MRI)-between the public and private health care systems, across the five regions of Brazil, in terms of the numbers of radiological devices and examinations performed, between 2015 and 2021. Materials and Methods: This was a descriptive time series analysis of secondary data in the public domain, available from the Information Technology Department of the Brazilian Unified Health Care System, an entity of the Brazilian National Ministry of Health (NMH) that is responsible for collecting and storing health-related information in Brazil. The analysis included the numbers of CT and MRI scanners; the volumes and types of examinations; the type of institution (public or private); the regions of the country; and the years (2015 to 2021). Results: Progressive increases in the numbers of CT and MRI devices, as well as in the volumes of examinations, were observed over the years in all regions of the country. The private sector showed higher rates of equipment acquisition and of growth in the number of examinations. However, the public health care system did not reach the equipment targets set by the NMH, whereas the private health care system surpassed those targets. A greater number of examinations were performed in the private sector than in the public sector. Conclusion: During the period evaluated, the public health care system did not meet the equipment or examination targets recommended by the NMH, in any of the regions of the country, unlike the private health care system, which exceeded both in all of the regions.

Objetivo: Comparar informações sobre procedimentos radiológicos de alta complexidade ­ tomografia computadorizada (TC) e ressonância magnética (RM) ­, considerando o número de aparelhos e o quantitativo de exames nas esferas pública e privada nas cinco regiões brasileiras entre 2015 e 2021. Materiais e Métodos: Trata-se de um estudo descritivo de série temporal que utilizou dados secundários do Departamento de Informática do Sistema Único de Saúde, órgão do Ministério da Saúde (MS) responsável pela coleta e armazenamento das informações relacionadas à saúde no Brasil. Analisamos os números de aparelhos e de exames de TC e RM, considerando os tipos de aparelhos e exames, instituição (pública ou privada), região brasileira e ano (2015 a 2021). Resultados: Houve aumento de aparelhos e exames de TC e RM em todas as regiões ao longo dos anos. A esfera privada apresentou maior aquisição desses aparelhos e crescimento no número de exames. O sistema público não atingiu o número de aparelhos preconizado pelo MS, enquanto o sistema privado superou a recomendação. Observou-se maior número de exames na esfera privada quando comparada à pública. Conclusão: O sistema público não atendeu aos números de aparelhos e exames realizados preconizados pelo MS, diferentemente da esfera privada, em todas as regiões no período estudado.

Predictors and pathways of in-hospital mortality in active vascular contrast extravasation detected on abdominopelvic CT.

OBJECTIVES: This study aimed to identify factors influencing in-hospital mortality in adult patients with active vascular contrast extravasation (AVCE) on abdominopelvic computed tomography (CT). METHODS: All consecutive patients with AVCE detected on CT between January 2019 and May 2022 were retrospectively included. Their data were compared through uni- and multivariable analyses between patients with and without in-hospital mortality. Path analysis was utilized to clarify the relationships among factors affecting mortality. RESULTS: There were 272 patients (60.2 ± 19.4 years, 150 men) included, of whom 70 experienced in-hospital mortality. Multivariable analysis revealed nonsurgery, chronic kidney disease (CKD) stage 4-5 or dialysis, prolonged partial thromboplastin time (PTT), minimum AVCE length > 8 mm, and a lower rate of packed red cell (PRC) transfusion were identified as independent predictors of in-hospital mortality (p = 0.005-0.048). Path analysis demonstrated direct influences of CKD4-5 or dialysis, prolonged PTT, and minimum AVCE length on mortality (coefficients 0.525-0.616; p = 0.009 to < 0.001). PRC transfusion impacted mortality through nonsurgery (coefficient 0.798, p = 0.003) and intensive care unit (ICU) admission (coefficients 0.025, p = 0.016), leading to subsequent death. Three AVCE spaces (free, loose, and tight) defined on CT were not directly associated with in-hospital mortality. CONCLUSION: In adults with AVCE on CT, AVCE size had a direct independent influence on mortality, highlighting the critical role of radiologists in detecting and characterizing this finding. Additionally, CKD4-5 or dialysis and prolonged PTT also directly influenced mortality, while the lower rate of PRC transfusion impacted mortality through nonsurgery and ICU admission. CLINICAL RELEVANCE STATEMENT: In patients with active vascular contrast extravasation (AVCE) on abdominopelvic CT, larger AVCE directly increased in-hospital mortality. Radiologists' detection and characterization of this finding is crucial, along with recognizing factors like CKD4-5, dialysis, and prolonged PTT to improve patient outcomes. KEY POINTS: Several factors independently predicted in-hospital mortality in patients with abdominopelvic AVCE. Extravasation length > 8 mm was the only imaging marker predictive of in-hospital mortality. Non-imaging factors correlated with in-hospital mortality, and PRC transfusion impacted mortality through nonsurgery and ICU admission pathways.

Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients.

OBJECTIVE: This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images. METHODS: A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan-Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA). RESULTS: BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction. CONCLUSION: This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.

CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study.

Purpose: To construct and validate a computed tomography (CT) radiomics model for differentiating lung neuroendocrine neoplasm (LNEN) from lung adenocarcinoma (LADC) manifesting as a peripheral solid nodule (PSN) to aid in early clinical decision-making. Methods: A total of 445 patients with pathologically confirmed LNEN and LADC from June 2016 to July 2023 were retrospectively included from five medical centers. Those patients were split into the training set (n = 316; 158 LNEN) and external test set (n = 129; 43 LNEN), the former including the cross-validation (CV) training set and CV test set using ten-fold CV. The support vector machine (SVM) classifier was used to develop the semantic, radiomics and merged models. The diagnostic performances were evaluated by the area under the receiver operating characteristic curve (AUC) and compared by Delong test. Preoperative neuron-specific enolase (NSE) levels were collected as a clinical predictor. Results: In the training set, the AUCs of the radiomics model (0.878 [95% CI: 0.836, 0.915]) and merged model (0.884 [95% CI: 0.844, 0.919]) significantly outperformed the semantic model (0.718 [95% CI: 0.663, 0.769], p both<.001). In the external test set, the AUCs of the radiomics model (0.787 [95% CI: 0.696, 0.871]), merged model (0.807 [95%CI: 0.720, 0.889]) and semantic model (0.729 [95% CI: 0.631, 0.811]) did not exhibit statistical differences. The radiomics model outperformed NSE in sensitivity in the training set (85.3% vs 20.0%; p <.001) and external test set (88.9% vs 40.7%; p = .002). Conclusion: The CT radiomics model could non-invasively, effectively and sensitively predict LNEN and LADC presenting as a PSN to assist in treatment strategy selection.

CT evaluation of bowel wall enhancement in pneumatosis intestinalis: preventing non-therapeutic laparotomies.

PURPOSE: To evaluate the diagnostic performance of bowel wall enhancement for diagnosing concomitant bowel ischemia in patients with parietal pneumatosis (PI) diagnosed at abdominal CT. MATERIALS AND METHODS: From January 1, 2012 to December 31, 2021, 226 consecutive patients who presented with PI on abdominal CT from any bowel segment were included. Variables at the time of the CT were retrospectively extracted from medical charts. CT examinations were blindly analyzed by two independent radiologists. The third reader classified all disagreement of bowel enhancement in three categories: (1) normal bowel enhancement; (2) doubtful bowel wall enhancement; (3) absent bowel wall enhancement. Multivariable logistic regression analysis was performed. Concomitant bowel ischemia was defined as requirement of bowel resection specifically due to ischemic lesion in operated patients and death from bowel ischemia in non-operated patients. RESULTS: Overall, 78/226 (35%) patients had PI associated with concomitant bowel ischemia. At multivariate analysis, Only absence or doubtful bowel wall enhancement was associated with concomitant bowel ischemia (OR = 167.73 95%CI [23.39-4349.81], P < 0,001) and acute mesenteric ischemia associated with PP (OR = 67.94; 95%CI [5.18-3262.36], P < 0.009). Among the 82 patients who underwent a laparotomy for suspected bowel ischemia, rate of non-therapeutic laparotomy increased from 15/59 (25%), 2/6 (50%) and 16/17 (94%) when bowel wall enhancement was absent, doubtful and normal respectively. CONCLUSION: Absence of enhancement of the bowel wall is the primary feature associated with concomitant bowel ischemia. It should be carefully assessed when PI is detected to avoid non-therapeutic laparotomy.

New powder reuse schema in laser-based powder bed fusion of polymers.

The laser-based powder bed fusion of polymers (PBF-LB/P) process often utilizes a blend of powders with varying degrees of degradation. Specifically, for polyamide 12, the traditional reuse schema involves mixing post-processed powder with virgin powder at a predetermined ratio before reintroducing it to the process. Given that only about 15% of the powder is utilized in part production, and powders are refreshed in equal proportions, there arises a challenge with the incremental accumulation of material across build cycles. To mitigate the consumption of fresh powder relative to the actual material usage, this study introduces the incorporation of recycled material into the PBF-LB/P process. This new powder reuse schema is presented for the first time, focusing on the laser sintering process. The characteristics of the recycled powder were evaluated through scanning electron microscopy, differential scanning calorimetry, X-ray diffraction, particle size distribution, and dynamic powder flowability assessments. The findings reveal that waste powders can be effectively reused in PBF-LB/P to produce components with satisfactory mechanical properties, porosity levels, dimensional accuracy, and surface quality.

3D airway geometry analysis of factors in airway navigation failure for lung nodules.

BACKGROUND: This study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure. METHODS: We retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit ( http://www.vmtk.org ), simple insight toolkit ( https://sitk.org ), and sci-kit image ( https://scikit-image.org ). We used a machine learning-based approach to explore the utility of these significant factors. RESULTS: Of the 491 patients who were eligible for analysis (mean age, 65 years +/- 11 [standard deviation]; 274 men), the target lesion was reached in 434 and was not reached in 57. Twenty-seven patients in the failure group were matched with 27 patients in the success group based on propensity scores. Bifurcation angle at the target branch, the least diameter of the last section, and the curvature of the last section are the most significant and stable factors for airway navigation failure. The support vector machine can predict airway navigation failure with an average area under the curve of 0.803. CONCLUSIONS: Geometric analysis in 3D space revealed that a large bifurcation angle and a narrow and tortuous structure of the closest bronchus from the lesion are associated with airway navigation failure during R-EBUS. The models developed using quantitative computer tomography scan imaging show the potential to predict airway navigation failure.

Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer.

BACKGROUND: Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning. METHODS: A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS). RESULTS: Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %-90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %-89 %), with no significant difference (p = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (p = 0.054), significant after removing outliers (p < 0.01). Incorporating clinical data improved sensitivity in validation sets (p = 0.029). No significant difference was found between models based on CE or non-CE imaging (p = 0.281) or arterial or venous phase imaging (p = 0.927). CONCLUSION: Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.

Association between higher intermuscular adipose tissue and decreased renal function in patients with systemic lupus erythematosus mediated by insulin resistance.

OBJECTIVES: To quantify the relationship between abdominal computed tomography (CT)-based body composition parameters and renal function in systemic lupus erythematosus (SLE) patients and evaluate the potential effect of insulin resistance on this relationship. METHODS: SLE patients from institutions A and B between January 2017 and August 2023 were enrolled. Areas and attenuation values of subcutaneous adipose tissue, visceral adipose tissue, intermuscular adipose tissue (IMAT), and skeletal muscle index on CT images were measured at the L3 vertebral level. Logistic regression analysis was used to identify risk factors associated with decreased renal function. Linear regression models were used to describe the relationships between body composition parameters and estimated glomerular filtration rate (eGFR). Finally, we used a single-point insulin sensitivity estimator to indirectly reflect the degree of insulin resistance and assess its mediating effect on the association between IMAT area and decreased renal function. RESULTS: Three-hundred thirty-nine SLE patients from institution A (internal dataset) and 114 SLE patients from institution B (external validation dataset) were included. Multivariate logistic regression revealed that IMAT area (odds ratio (OR)institution A: 1.05 (95% confidence intervals (95% CI): 1.01, 1.10), and ORinstitution B: 1.19 (95% CI: 1.03, 1.39)) was an independent risk factor for decreased renal function in SLE patients. In the adjusted linear regression model, high IMAT area was significantly associated with reduced eGFR (ßinstitution A = -1.15, Pinstitution A = 0.005; ßinstitution B = -0.98, Pinstitution B = 0.049). Additionally, insulin resistance contributed a mediating role of 22.8% to the association. CONCLUSION: High IMAT area was associated with decreased renal function in SLE patients and insulin resistance mediated this relationship. CRITICAL RELEVANCE STATEMENT: High intermuscular adipose tissue area is associated with decreased renal function in systemic lupus erythematosus patients mediated by insulin resistance and is correlated with chronicity index in lupus nephritis patients. KEY POINTS: High intramuscular adipose tissue (IMAT) area was associated with decreased renal function in systemic lupus erythematosus (SLE) patients. Insulin resistance mediated the association between IMAT area and eGFR. IMAT area was associated with chronicity index in lupus nephritis patients.

Impact of reconstruction parameters on the accuracy of myocardial extracellular volume quantification on a first-generation, photon-counting detector CT.

BACKGROUND: The potential role of cardiac computed tomography (CT) has increasingly been demonstrated for the assessment of diffuse myocardial fibrosis through the quantification of extracellular volume (ECV). Photon-counting detector (PCD)-CT technology may deliver more accurate ECV quantification compared to energy-integrating detector CT. We evaluated the impact of reconstruction settings on the accuracy of ECV quantification using PCD-CT, with magnetic resonance imaging (MRI)-based ECV as reference. METHODS: In this post hoc analysis, 27 patients (aged 53.1 ± 17.2 years (mean ± standard deviation); 14 women) underwent same-day cardiac PCD-CT and MRI. Late iodine CT scans were reconstructed with different quantum iterative reconstruction levels (QIR 1-4), slice thicknesses (0.4-8 mm), and virtual monoenergetic imaging levels (VMI, 40-90 keV); ECV was quantified for each reconstruction setting. Repeated measures ANOVA and t-test for pairwise comparisons, Bland-Altman plots, and Lin's concordance correlation coefficient (CCC) were used. RESULTS: ECV values did not differ significantly among QIR levels (p = 1.000). A significant difference was observed throughout different slice thicknesses, with 0.4 mm yielding the highest agreement with MRI-based ECV (CCC = 0.944); 45-keV VMI reconstructions showed the lowest mean bias (0.6, 95% confidence interval 0.1-1.4) compared to MRI. Using the most optimal reconstruction settings (QIR4. slice thickness 0.4 mm, VMI 45 keV), a 63% reduction in mean bias and a 6% increase in concordance with MRI-based ECV were achieved compared to standard settings (QIR3, slice thickness 1.5 mm; VMI 65 keV). CONCLUSIONS: The selection of appropriate reconstruction parameters improved the agreement between PCD-CT and MRI-based ECV. RELEVANCE STATEMENT: Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility. KEY POINTS: • CT is increasingly promising for myocardial tissue characterization, assessing focal and diffuse fibrosis via late iodine enhancement and ECV quantification, respectively. • PCD-CT offers superior performance over conventional CT, potentially improving ECV quantification and its agreement with MRI-based ECV. • Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility.

Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. MATERIALS AND METHODS: This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. RESULTS: A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. CONCLUSION: Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.