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OBJECTIVE: To demonstrate the value of using 50 keV virtual monochromatic images with deep learning image reconstruction (DLIR) in low-dose dual-energy CT enterography (CTE). METHODS: In this prospective study, 114 participants (62 % M; 41.9 ± 16 years) underwent dual-energy CTE. The early-enteric phase was performed using standard-dose (noise index (NI): 8) and images were reconstructed at 70 keV and 50 keV with 40 % strength ASIR-V (ASIR-V40%). The late-enteric phase used low-dose (NI: 12) and images were reconstructed at 50 keV with ASIR-V40%, and DLIR at medium (DLIR-M) and high strength (DLIR-H). Image standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge-rise-slope (ERS) were computed. The quantitative comb sign score was calculated for the 27 patients with Crohn's disease. The subjective noise, image contrast, display of rectus artery were scored using a 5-point scale by two radiologists blindly. RESULTS: Effective dose was reduced by 50 % (P < 0.001) in the late-enteric phase to 3.26 mSv. The lower-dose 50 keV-DLIR-H images (SD:17.7 ± 0.5HU) had similar image noise (P = 0.97) as the standard-dose 70 keV-ASIR-V40% images (SD:17.7 ± 0.73HU), but with higher (P < 0.001) SNR, CNR, ERS and quantitative comb sign score (5.7 ± 0.17, 1.8 ± 0.12, 156.04 ± 5.21 and 5.05 ± 0.73, respectively). Furthermore, the lower-dose 50 keV-DLIR-H images obtained the highest score in the rectus artery visibility (4.27 ± 0.6). CONCLUSIONS: The 50 keV images in dual-energy CTE with DLIR provides high-quality images, with a 50 % reduction in radiation dose. Images with high contrast and density resolutions significantly enhance the diagnostic confidence of Crohn's disease and are essential for the clinical development of individualized treatment plans.
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Aprendizaje Profundo , Dosis de Radiación , Interpretación de Imagen Radiográfica Asistida por Computador , Imagen Radiográfica por Emisión de Doble Fotón , Tomografía Computarizada por Rayos X , Humanos , Femenino , Masculino , Adulto , Estudios Prospectivos , Tomografía Computarizada por Rayos X/métodos , Imagen Radiográfica por Emisión de Doble Fotón/métodos , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Persona de Mediana Edad , Relación Señal-Ruido , Anciano , Enfermedad de Crohn/diagnóstico por imagenRESUMEN
The escalating health risks posed by warm weather in urban areas have become a pressing global public health issue. This study undertakes a meta-analysis to evaluate the impact of warm weather on health in urban settings. We comprehensively searched PubMed, Embase, Scopus, and Web of Science for literature published before September 6, 2023, evaluating evidence quality using the Navigation Guide Criteria. We included original studies utilizing high temperatures or heatwaves as exposure metrics and employing observational designs. A meta-analysis was carried out to assess the relative risk (RR) of the association between high temperatures (or heatwaves) and disease outcomes. Out of 12,893 studies identified, 188 met the inclusion criteria for meta-analysis. Results demonstrate a statistically significant association between a 1 °C temperature increase and a 2.1 % elevation in disease-related mortality (RR 1.021 [95 % CI 1.018-1.023]), alongside a 1.1 % increase in morbidity (RR 1.011 [95 % CI 1.007-1.016]). Heatwaves also showed associations with increased total mortality (RR 1.224 [95 % CI 1.186-1.264]) and morbidity (RR 1.038 [95 % CI 1.010-1.066]). Subgroup analyses for diseases, sex, age, climatic zones, countries, and time periods consistently indicated heightened disease-related mortality and morbidity linked to high temperatures. Notably, China's urban population faced an elevated mortality risk (RR 1.027 [95 % CI 1.018-1.036]) compared to other countries (RR 1.021 [95 % CI 1.019-1.024]). Mortality associated with high temperatures after 2007 (RR 1.022 [95 % CI 1.015-1.029]) was higher than before 2007 (RR 1.017 [95 % CI 1.013-1.021]), reflecting increased health risks as the global warming accelerates. Our findings underscore the positive association between rising temperatures and/or heatwaves and adverse health outcomes in urban populations. The widespread exposure to high temperatures amplifies health risks across various diseases, demographics, climates, and countries, with potential exacerbation under ongoing global warming. Further research is imperative to delineate factors influencing altered heat exposure impacts.
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Calor , Salud Urbana , Humanos , Salud Urbana/estadística & datos numéricos , Exposición a Riesgos Ambientales/estadística & datos numéricos , Cambio ClimáticoRESUMEN
RATIONALE AND OBJECTIVES: Transarterial chemoembolization (TACE) plus molecular targeted therapies has emerged as the main approach for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). A robust model for outcome prediction and risk stratification of recommended TACE plus molecular targeted therapies candidates is lacking. We aimed to develop an easy-to-use tool specifically for these patients. METHODS: A retrospective analysis was conducted on 384 patients with HCC and PVTT who underwent TACE plus molecular targeted therapies at 16 different institutions. We developed and validated a new prognostic score which called ABPS score. Additionally, an external validation was performed on data from 200 patients enrolled in a prospective cohort study. RESULTS: The ABPS score (ranging from 0 to 3 scores), which involves only Albumin-bilirubin (ALBI, grade 1: 0 score; grade 2: 1 score), PVTT(I-II type: 0 score; III-IV type: 1 score), and systemic-immune inflammation index (SII,<550 × 1012: 0 score; ≥550 × 1012: 1 score). Patients were categorized into three risk groups based on their ABPS score: ABPS-A, B, and C (scored 0, 1-2, and 3, respectively). The concordance index (C-index) of the ABPS scoring system was calculated to be 0.802, significantly outperforming the HAP score (0.758), 6-12 (0.712), Up to 7 (0.683), and ALBI (0.595) scoring systems (all P < 0.05). These research findings were further validated in the external validation cohorts. CONCLUSION: The ABPS score demonstrated a strong association with survival outcomes and radiological response in patients undergoing TACE plus molecular targeted therapy for HCC with PVTT. The ABPS scoring system could serve as a valuable tool to guide treatment selection for these patients.
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OBJECTIVE: To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images. METHODS: In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases. RESULTS: Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05]. CONCLUSIONS: DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.
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Aprendizaje Profundo , Dosis de Radiación , Interpretación de Imagen Radiográfica Asistida por Computador , Imagen Radiográfica por Emisión de Doble Fotón , Tomografía Computarizada por Rayos X , Humanos , Femenino , Estudios Prospectivos , Masculino , Tomografía Computarizada por Rayos X/métodos , Persona de Mediana Edad , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Imagen Radiográfica por Emisión de Doble Fotón/métodos , Adulto , Anciano , Anciano de 80 o más AñosRESUMEN
BACKGROUND AND PURPOSE: To develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) based on OS signature risk stratification. MATERIALS AND METHODS: This study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively. The OS signature was built using a unified fully connected neural network. A deep learning model was developed based on the OS signature. Clinical and combined models were developed and compared with a deep learning model. Additionally, the benefits of PCI were evaluated after stratification using an OS signature. RESULTS: Within the internal and external validation cohorts, the deep learning model (concordance index [C-index] 0.745, 0.733) was far superior to the clinical model (C-index: 0.635, 0.630) in predicting OS, but slightly worse than the combined model (C-index: 0.771, 0.770). Additionally, the deep learning model had excellent calibration, clinical usefulness, and improved accuracy in classifying survival outcomes. Remarkably, patients at high risk had a survival benefit from PCI in both the limited and extensive stages (all P < 0.05), whereas no significant association was observed in patients at low risk. CONCLUSIONS: The CT-based deep learning model exhibited promising performance in predicting the OS of SCLC patients. The OS signature may aid in individualized treatment planning to select patients who may benefit from PCI.
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Irradiación Craneana , Aprendizaje Profundo , Neoplasias Pulmonares , Carcinoma Pulmonar de Células Pequeñas , Tomografía Computarizada por Rayos X , Humanos , Carcinoma Pulmonar de Células Pequeñas/radioterapia , Carcinoma Pulmonar de Células Pequeñas/mortalidad , Carcinoma Pulmonar de Células Pequeñas/diagnóstico por imagen , Carcinoma Pulmonar de Células Pequeñas/patología , Neoplasias Pulmonares/radioterapia , Neoplasias Pulmonares/mortalidad , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico por imagen , Estudios Retrospectivos , Masculino , Femenino , Tomografía Computarizada por Rayos X/métodos , Persona de Mediana Edad , Irradiación Craneana/métodos , Anciano , Tasa de SupervivenciaRESUMEN
This study aims to assess the effectiveness of radiomics signatures obtained from dual-energy computed tomography enterography (DECTE) in the evaluation of mucosal healing (MH) in patients diagnosed with Crohn's disease (CD). In this study, 106 CD patients with a total of 221 diseased intestinal segments (79 with MH and 142 non-MH) from two medical centers were included and randomly divided into training and testing cohorts at a ratio of 7:3. Radiomics features were extracted from the enteric phase iodine maps and 40-kev and 70-kev virtual monoenergetic images (VMIs) of the diseased intestinal segments, as well as from mesenteric fat. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) logistic regression. Radiomics models were subsequently established, and the accuracy of these models in identifying MH in CD was assessed by calculating the area under the receiver operating characteristic curve (AUC). The combined-iodine model formulated by integrating the intestinal and mesenteric fat radiomics features of iodine maps exhibited the most favorable performance in evaluating MH, with AUCs of 0.989 (95% confidence interval (CI) 0.977-1.000) in the training cohort and 0.947 (95% CI 0.884-1.000) in the testing cohort. Patients categorized as high risk by the combined-iodine model displayed a greater probability of experiencing disease progression when contrasted with low-risk patients. The combined-iodine radiomics model, which is built upon iodine maps of diseased intestinal segments and mesenteric fat, has demonstrated promising performance in evaluating MH in CD patients.
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OBJECTIVE: Exploring the efficacy of a Radiological-Clinical (Rad-Clinical) model in predicting prognosis of unresectable hepatocellular carcinoma (HCC) patients after drug eluting beads transcatheter arterial chemoembolization (DEB-TACE) to optimize the targeted sequential treatment. METHODS: In this retrospective analysis, we included 202 patients with unresectable HCC who received DEB-TACE treatment in 17 institutions from June 2018 to December 2022. Progression-free survival (PFS)-related radiomics features were computationally extracted from HCC patients to build a radiological signature (Rad-signature) model with least absolute shrinkage and selection operator regression. A Rad-Clinical model for postoperative PFS was further constructed according to the Rad-signature and clinical variables by Cox regression analysis. It was presented as a nomogram and evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. And further evaluate the application value of Rad-Clinical model in clinical stages and targeted sequential therapy of HCC. RESULTS: Tumor size, Barcelona Clinic Liver Cancer (BCLC) stage, and radiomics score (Rad-score) were found to be independent risk factors for PFS after DEB-TACE treatment for unresectable HCC, with the Rad-Clinical model being the greatest predictor of PFS in these patients (hazard ratio: 2.08; 95% confidence interval: 1.56-2.78; P < 0.001) along with high 6 months, 12 months, 18 months, and 24 months area under the curves of 0.857, 0.810, 0.843, and 0.838, respectively. In addition, compared to the radiomics and clinical nomograms, the Radiological-Clinical nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (45.2%, 95% CI 0.260-0.632, p < 0.05) and integrated discrimination improvement (14.9%, 95% CI 0.064-0.281, p < 0.05). Based on this model, low-risk patients had higher PFS than high-risk patients in BCLC-B and C stages (P = 0.021). Targeted sequential therapy for patients with high and low-risk HCC in BCLC-B stage exhibited significant benefits (P = 0.018, P = 0.012), but patients with high-risk HCC in BCLC-C stage did not benefit much (P = 0.052). CONCLUSION: The Rad-Clinical model may be favorable for predicting PFS in patients with unresectable HCC treated with DEB-TACE and for identifying patients who may benefit from targeted sequential therapy.
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Carcinoma Hepatocelular , Quimioembolización Terapéutica , Neoplasias Hepáticas , Humanos , Carcinoma Hepatocelular/patología , Neoplasias Hepáticas/patología , Nomogramas , Estudios Retrospectivos , Terapia Molecular Dirigida , Resultado del TratamientoRESUMEN
OBJECTIVE: To assess the prognostic value of the Ryan score, Belgian Outcome of Burn Injury (BOBI) score,revised Baux (rBaux) score, and a new model (a Logit(P)-based scoring method created in 2020) for predicting mortality risk in patients with extremely severe burns and to conduct a comparative analysis. METHODS: A retrospective analysis was conducted on 599 burn patients who met the inclusion criteria and were admitted to the burn unit of the First Affiliated Hospital of Nanchang University from 2017 to 2022. Relevant information was collected, and receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were plotted for each of the four models in assessing mortality in these burn patients using both age-stratified and unstratified forms. The ROC curve section was further compared with the area under the curve (AUC), optimal cutoff value, as well as its sensitivity and specificity. Additionally, the quality of the AUC was assessed using the Delong test. RESULT: Among the patients who met the inclusion criteria, 532 were in the survival group and 67 in the death group. Irrespective of age stratification, the novel model exhibited superior performance with an AUC of 0.868 (95% CI: 0.838-0.894) among all four models predicting mortality risk in included patients, and also demonstrated better AUC quality than other models; the calibration curves showed that the accuracy of all four models was good; the DCA curves showed that the clinical utility of the novel model and rBuax score were better. In the comparison of four scoring models across different age groups, the new model demonstrated the largest AUC in both 0-19 years (0.954, 95% CI 0.914-0.979) and 20-59 years groups (0.838, 95% CI 0.793-0.877), while rBuax score exhibited the highest AUC in ≥ 60 years group (0.708, 95% CI of 0.602-0.800). The calibration curves showed that the four models exhibited greater accuracy within the age range of 20-59 years, while the DCA curves indicated that both the novel model and rBuax score scale displayed better prediction in both the 20-59 and ≥ 60 years groups. CONCLUSIONS: All four models demonstrate accurate and effective prognostication for patients with severe burns. Both the novel model and rBaux score exhibit enhanced prediction utility. In terms of the model itself alone, the new model is not simpler than, for example, the rBaux score, and whether it can be applied clinicallyinvolves further study.
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Quemaduras , Humanos , Adulto Joven , Adulto , Persona de Mediana Edad , Recién Nacido , Lactante , Preescolar , Niño , Adolescente , Estudios Retrospectivos , Unidades de Quemados , Hospitalización , Pronóstico , Curva ROCRESUMEN
BACKGROUND: The purpose of this article is to develop a deep learning automatic segmentation model for the segmentation of Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, the radiomics features extracted from the segmented CD lesions will be analyzed and multiple machine learning classifiers will be built to distinguish CD activity. METHODS: This was a retrospective study with 2 sets of CTE image data. Segmentation datasets were used to establish nnU-Net neural network's automatic segmentation model. The classification dataset was processed using the automatic segmentation model to obtain segmentation results and extract radiomics features. The most optimal features were then selected to build 5 machine learning classifiers to distinguish CD activity. The performance of the automatic segmentation model was evaluated using the Dice similarity coefficient, while the performance of the machine learning classifier was evaluated using the area under the curve, sensitivity, specificity, and accuracy. RESULTS: The segmentation dataset had 84 CTE examinations of CD patients (mean age 31 ±â 13 yearsâ , 60 males), and the classification dataset had 193 (mean age 31 ±â 12 yearsâ , 136 males). The deep learning segmentation model achieved a Dice similarity coefficient of 0.824 on the testing set. The logistic regression model showed the best performance among the 5 classifiers in the testing set, with an area under the curve, sensitivity, specificity, and accuracy of 0.862, 0.697, 0.840, and 0.759, respectively. CONCLUSION: The automated segmentation model accurately segments CD lesions, and machine learning classifier distinguishes CD activity well. This method can assist radiologists in promptly and precisely evaluating CD activity.
The automatic segmentation and radiomics of computed tomography enterography images can assist radiologists in accurately and quickly identifying Crohn's disease lesions and evaluating Crohn's disease activity.
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Phased secondary small interfering RNAs (phasiRNAs) in plants play important roles in regulating genome stability, plant development and stress adaption. Camellia sinensis var. assamica has immense economic, medicinal and cultural significance. However, there are still no studies of phasiRNAs and their putative functions in this valuable plant. We identified 476 and 43 PHAS loci which generated 4290 twenty one nucleotide (nt) and 264 twenty four nt phasiRNAs, respectively. Moreover, the analysis of degradome revealed more than 35000 potential targets for these phasiRNAs. We identified several conserved 21 nt phasiRNA generation pathways in tea plant, including miR390 â TAS3, miR482/miR2118 â NB-LRR, miR393 â F-box, miR828 â MYB/TAS4, and miR7122 â PPR in this study. Furthermore, we found that some transposase and plant mobile domain genes could generate phasiRNAs. Our results show that phasiRNAs target genes in the same family in cis- or trans-manners, and different members of the same gene family may generate the same phasiRNAs. The phasiRNAs, generated by transposase and plant mobile domain genes, and their targets, suggest that phasiRNAs may be involved in the inhibition of transposable elements in tea plant. To summarize, these results provide a comprehensive view of phasiRNAs in Camellia sinensis var. assamica.
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BACKGROUND: Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery. METHODS: A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets. RESULTS: The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence. CONCLUSION: The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.
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Carcinoma de Células Renales , Carcinoma de Células Pequeñas , Neoplasias Renales , Neoplasias Pulmonares , Carcinoma Pulmonar de Células Pequeñas , Humanos , Carcinoma de Células Renales/diagnóstico por imagen , Carcinoma de Células Renales/cirugía , Nomogramas , Tomografía Computarizada por Rayos X , Neoplasias Renales/diagnóstico por imagen , Neoplasias Renales/cirugía , Organización Mundial de la Salud , Estudios RetrospectivosRESUMEN
Objectives: To establish a clinical radiomics nomogram that differentiates malignant and non-malignant pleural effusions. Methods: A total of 146 patients with malignant pleural effusion (MPE) and 93 patients with non-MPE (NMPE) were included. The ROI image features of chest lesions were extracted using CT. Univariate analysis was performed, and least absolute shrinkage selection operator and multivariate logistic analysis were used to screen radiomics features and calculate the radiomics score. A nomogram was constructed by combining clinical factors with radiomics scores. ROC curve and decision curve analysis (DCA) were used to evaluate the prediction effect. Results: After screening, 19 radiomics features and 2 clinical factors were selected as optimal predictors to establish a combined model and construct a nomogram. The AUC of the combined model was 0.968 (95% confidence interval [CI] = 0.944-0.986) in the training cohort and 0.873 (95% CI = 0.796-0.940) in the validation cohort. The AUC value of the combined model was significantly higher than those of the clinical and radiomics models (0.968 vs. 0.874 vs. 0.878, respectively). This was similar in the validation cohort (0.873, 0.764, and 0.808, respectively). DCA confirmed the clinical utility of the radiomics nomogram. Conclusion: CT-based radiomics showed better diagnostic accuracy and model fit than clinical and radiological features in distinguishing MPE from NMPE. The combination of both achieved better diagnostic performance. These findings support the clinical application of the nomogram in diagnosing MPE using chest CT.
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PURPOSE: To develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation. METHODS: 558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis. Association of the radiomics signature with PFS was evaluated. A radiomics nomogram was developed based on the radiomics signature, then its calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: In total, 6 CT radiomics features were finally selected. The radiomics signature was significantly associated with PFS (hazard ratio [HR] 4.531, 95% confidence interval [CI] 3.524-5.825, p < 0.001). Incorporating the radiomics signature into the radiomics nomogram resulted in better performance for the estimation of PFS (concordance index [C-index] 0.799) than with the clinical nomogram (C-index 0.629), as well as high 6 months and 12 months area under the curves of 0.885 and 0.846, respectively. Furthermore, the radiomics nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (33.7%, 95% CI 0.216-0.609, p < 0.05) and integrated discrimination improvement (22.7%, 95% CI 0.168-0.278, p < 0.05). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram. CONCLUSION: A CT-based radiomics nomogram exhibited a promising performance for predicting PFS in patients with SCLC, which could provide valuable information for individualized treatment.
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Neoplasias Pulmonares , Carcinoma Pulmonar de Células Pequeñas , Humanos , Nomogramas , Neoplasias Pulmonares/diagnóstico por imagen , Carcinoma Pulmonar de Células Pequeñas/diagnóstico por imagen , Supervivencia sin Progresión , Estudios Retrospectivos , Tomografía Computarizada por Rayos X/métodosRESUMEN
PURPOSE: To assess the efficacy of double-balloon endoscopy (DBE) for the detection of small-bowel strictures in Crohn's disease (CD). METHODS: This tertiary-referral hospital cohort study was conducted between January 2018 and May 2022. CD patients with symptoms of small-bowel stricture were enrolled sequentially. All of the patients were subjected to both computed tomography enterography (CTE) and DBE, and their symptoms of stricture were assessed using the Crohn's Disease Obstructive Score (CDOS). The diagnostic yield of DBE was compared to that of CTE, and the relationship between the DBE findings and CDOS was investigated. The factors influencing the DBE diagnosis were examined using Cox regression analysis. RESULTS: This study included 165 CD patients. The CDOS scores were higher in 95 patients and lower in 70 patients. DBE detected 92.7% (153/165) and CTE detected 85.5% (141/165) of the strictures. The DBE diagnostic yields were 94.7% (90/95) in the high CDOS patients and 91.4% (64/70) in the low CDOS patients (P = 0.13). Patients with a history of abdominal surgery and abscess had a lower diagnosis rate in the multivariate analysis. CONCLUSION: DBE has been demonstrated to be an efficient diagnostic method for detecting small bowel strictures in CD patients. Additionally, there was no difference in the diagnostic yields between patients with low and high obstructive scores.
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Enfermedad de Crohn , Obstrucción Intestinal , Humanos , Enfermedad de Crohn/complicaciones , Enfermedad de Crohn/diagnóstico por imagen , Constricción Patológica/diagnóstico por imagen , Constricción Patológica/etiología , Intestino Delgado/diagnóstico por imagen , Estudios de Cohortes , Obstrucción Intestinal/diagnóstico por imagen , Obstrucción Intestinal/etiología , Endoscopía Gastrointestinal/métodos , Enteroscopía de Doble BalónRESUMEN
[This corrects the article DOI: 10.1016/j.heliyon.2023.e14594.].
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Background: Infliximab (IFX) is the first-line treatment for Crohn's disease (CD). However, the secondary loss of response (LOR) is common in IFX therapy. Therefore, non-invasive assessment of LOR in CD patients is the goal pursued by clinicians. Methods: A multicenter study involving 181 CD patients was conducted, with patients being split into a training cohort (n = 102), testing cohort (n = 45), and validation cohort (n = 34). The study evaluated various clinical factors to establish a clinical model, and a radiomics signature was constructed based on reproducible features from computed tomography enterography (CTE). Logistic regression modeling was used to create models based on the radiomics signature and significant clinical factors, with the receiver operating characteristic curve (ROC) used to compare their performance. Results: The study found that 64 of the 181 CD patients included experienced secondary LOR. The radiomics signature performed well in predicting secondary LOR, showing good discrimination in the training cohort (AUC [area under the curve], 0.947; 95% confidence interval [CI], 0.910-0.976), the testing cohort (AUC, 0.860; 95% CI, 0.768-0.941), and the validation cohort (AUC, 0.921; 95% CI: 0.831-1.000). Decision curve analysis (DCA) demonstrated the clinical value of the radiomics nomogram. Conclusions: The CTE-based radiomics model showed good performance in predicting secondary LOR in CD patients. The nomogram can help clinicians choose alternative biologics early for CD patients.
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RATIONALE AND OBJECTIVES: To develop computed tomography enterography (CTE)-based radiomics models to assess mucosal healing (MH) in patients with Crohn's disease (CD). MATERIALS AND METHODS: CTE images were retrospectively collected from 92 confirmed cases of CD at the post-treatment review. Patients were randomly divided into developing (n = 73) and testing (n = 19) groups. Radiomics features were extracted from the enteric phase images, and the least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection using 5-fold cross-validation on the developing group. The selected features were further identified from the top-ranked features and used to create improved radiomics models. Machine learning models were constructed to compare radiomics models with different radiomics features. The area under the ROC curve (AUC) was calculated to assess the predictive performance for identifying MH in CD. RESULTS: Among the 92 CD patients included in our study, 36 patients achieved MH. The AUC of the radiomics model 1, which was based on the 26 selected radiomics features, was 0.976 for evaluating MH in the testing cohort. The AUCs of radiomics models 2 and 4, based on the top 10 and top 5 positive and negative radiomics features, were 0.974 and 0.952 in the testing cohort, respectively. The AUC of the radiomics model 3, built by removing features with r > 0.5, was 0.956 in the testing cohort. The clinical utility of the clinical radiomics nomogram was confirmed by the decision curve analysis (DCA). CONCLUSION: The CTE-based radiomics models have demonstrated favorable performance in assessing MH in patients with CD. Radiomics features can be used as a promising imaging biomarker for MH.
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Enfermedad de Crohn , Humanos , Enfermedad de Crohn/diagnóstico por imagen , Estudios Retrospectivos , Tomografía Computarizada por Rayos X , Área Bajo la Curva , Aprendizaje Automático , NomogramasRESUMEN
OBJECTIVES: Mucosal healing (MH) is an important goal in the treatment of patients with Crohn's disease (CD). Noninvasive assessment of MH with normalized iodine concentration (NIC) is unknown. METHODS: In this retrospective study, 94 patients with diagnosed CD underwent endoscopy and dual-energy CT enterography (DECTE) at the post-infliximab treatment review. Two radiologists reviewed DECTE images by consensus for assessing diseased bowel segments of the colon or terminal ileum, and the NIC was measured. Patients were divided into transmural healing (TH), MH and non-MH groups. The diagnostic performance of the MH and non-MH groups with clinical factors and NIC was assessed utilizing receiver operating characteristic (ROC) curve analysis. RESULTS: Of the 94 patients included in our study, 8 patients achieved TH, 34 patients achieved MH, and 52 patients did not achieve MH at the post-IFX treatment review. The area under the ROC curve (AUC), sensitivity, specificity, and accuracy values were 0.929 (95% confidence interval [CI] 0.883-0.967), 0.853, 0.827, and 0.837, respectively, for differentiating MHs from non-MHs, and the optimal NIC threshold was 0.448. The AUC of the combined model for distinguishing MHs from non-MHs in CD patients, which was based on the NIC and calprotectin, was 0.964 (95% CI 0.935-0.987). CONCLUSIONS: The normalized iodine concentration measurement in DECTE has good performance in assessment MH in patients with CD. Iodine concentration from DECTE can be used as a radiologic marker for MH.
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BACKGROUND: Alzheimer's disease (AD) is one of the most common causes of dementia, affecting many old people. OBJECTIVES: By designing and synthesizing intracerebral imaging probes, we tried to provide a new solution for the early diagnosis of AD. METHODS: We designed and synthesized bis-iodine-labeled curcumin, and verified its performance through in vivo and in vitro experiments. RESULTS: In this study, bis-iodine-labeled curcumin (7, BICUR) was synthesized. In the in vitro mass spectrum binding assay, Kd values of BICUR with Aß1-40 and Aß1-42 aggregates were 46.29 nM and 64.29 nM, respectively. Aß plaques in AD brain adjacent sections were positively stained by BICUR, which was similar to some other curcumin derivatives. The Log P value of BICUR was 1.45. In the biodistribution experiment, BICUR showed the highest initial brain uptake (5.87% compared to the blood concentration) two minutes after the tail vein injection and rapid clearance from the mouse brain. In the acute toxicity experiment, BICUR showed low toxicity, and the LD50 was >100 mg/kg. Moreover, BICUR showed a high stability in vitro (86.68% unchanged BICUR after incubation for 120min in mouse brain homogenate). Besides, BICUR produced an enhanced CT imaging effect that could be sensitively detected in vitro, but it also showed an obvious differentiation from surrounding tissues after intracerebral injection. CONCLUSION: All results suggested that BICUR could probably act as a targeted CT imaging agent for Aß plaques in the brain.
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Enfermedad de Alzheimer , Curcumina , Yodo , Ratones , Animales , Péptidos beta-Amiloides/metabolismo , Yodo/metabolismo , Placa Amiloide/diagnóstico por imagen , Distribución Tisular , Enfermedad de Alzheimer/metabolismo , Encéfalo/diagnóstico por imagen , Encéfalo/metabolismo , Tomografía Computarizada por Rayos X , Ratones TransgénicosRESUMEN
Purpose: A nomogram was constructed by combining clinical factors and a CT-based radiomics signature to discriminate between high-grade clear cell renal cell carcinoma (ccRCC) and type 2 papillary renal cell carcinoma (pRCC). Methods: A total of 142 patients with 71 in high-grade ccRCC and seventy-one in type 2 pRCC were enrolled and split into a training cohort (n = 98) and a testing cohort (n = 44). A clinical factor model containing patient demographics and CT imaging characteristics was designed. By extracting the radiomics features from the precontrast phase, corticomedullary phase (CMP), and nephrographic phase (NP) CT images, a radiomics signature was established, and a Rad-score was computed. By combining the Rad-score and significant clinical factors using multivariate logistic regression analysis, a clinical radiomics nomogram was subsequently developed. The diagnostic performance of these three models was evaluated by using data from both the training and testing groups using a receiver operating characteristic (ROC) curve analysis. Results: The radiomics signature contained eight validated features from the CT images. The relative enhancement value of CMP (REV1) was an independent risk factor in the clinical factor model. The area under the curve (AUC) value of the clinical radiomics nomogram was 0.974 and 0.952 in the training and testing cohorts, respectively. In the training cohort, the decision curves of the nomogram demonstrated an added overall net advantage compared to the clinical factor model. Conclusion: A noninvasive prediction tool termed radiomics nomogram, combining clinical criteria and the radiomics signature, may accurately predict high-grade ccRCC and type 2 pRCC before surgery. It also has some importance in assisting clinicians in determining future treatment strategies.