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Endoscopic Ultrasound 2024Endobronchial ultrasound (EBUS) imaging is a valuable tool for predicting lymph node (LN) metastasis in lung cancer patients. This study aimed to develop a risk-scoring...
BACKGROUND AND OBJECTIVES
Endobronchial ultrasound (EBUS) imaging is a valuable tool for predicting lymph node (LN) metastasis in lung cancer patients. This study aimed to develop a risk-scoring model based on EBUS multimodal imaging (grayscale, Doppler mode, elastography) to predict LN metastasis in lung cancer patients.
PATIENTS AND METHODS
This retrospective study analyzed 350 metastatic LNs in 314 patients with lung cancer and 124 reactive LNs in 96 patients with nonspecific inflammation. The sonographic findings were compared with the final pathology results and clinical follow-up. Univariate and multivariate logistic regression analyses were performed to evaluate the independent risk factors of metastatic LNs. According to the coefficients of corresponding indicators in logistic regression analysis, a risk-scoring model was established. Receiver operating characteristic curve was applied to evaluate the predictive capability of model.
RESULTS
Multivariate analysis showed that short axis >10 mm, distinct margin, absence of central hilar structure, presence of necrosis, nonhilar vascularity, and elastography score 4 to 5 were independent predictors of metastatic LNs. Both short axis and margin were scored 1 point, and the rest of independent predictors were scored 2 points. The combination of 3 EBUS modes had the highest area under the receiver operating characteristic and accuracy of 0.884 (95% confidence interval, 0.846-0.922) and 87.55%, respectively. The risk stratification was as follows: 0 to 2 points, malignancy rate of 11.11%, low suspicion; 3 to 10 points, malignancy rate of 86.77%, high suspicion.
CONCLUSIONS
The risk-scoring model based on EBUS multimodal imaging can effectively evaluate metastatic LNs in lung cancer patients to support clinical decision making.
PubMed: 38947743
DOI: 10.1097/eus.0000000000000051 -
Journal of Cancer 2024It's a major public health problem of global concern that malignant gliomas tend to grow rapidly and infiltrate surrounding tissues. Accurate grading of the tumor can...
It's a major public health problem of global concern that malignant gliomas tend to grow rapidly and infiltrate surrounding tissues. Accurate grading of the tumor can determine the degree of malignancy to formulate the best treatment plan, which can eliminate the tumor or limit widespread metastasis of the tumor, saving the patient's life and improving their prognosis. To more accurately predict the grading of gliomas, we proposed a novel method of combining the advantages of 2D and 3D Convolutional Neural Networks for tumor grading by multimodality on Magnetic Resonance Imaging. The core of the innovation lies in our combination of tumor 3D information extracted from multimodal data with those obtained from a 2D ResNet50 architecture. It solves both the lack of temporal-spatial information provided by 3D imaging in 2D convolutional neural networks and avoids more noise from too much information in 3D convolutional neural networks, which causes serious overfitting problems. Incorporating explicit tumor 3D information, such as tumor volume and surface area, enhances the grading model's performance and addresses the limitations of both approaches. By fusing information from multiple modalities, the model achieves a more precise and accurate characterization of tumors. The model I s trained and evaluated using two publicly available brain glioma datasets, achieving an AUC of 0.9684 on the validation set. The model's interpretability is enhanced through heatmaps, which highlight the tumor region. The proposed method holds promise for clinical application in tumor grading and contributes to the field of medical diagnostics for prediction.
PubMed: 38947386
DOI: 10.7150/jca.95987 -
Research Square Jun 2024Objective The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance...
Objective The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. Methods We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. Conclusions We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.
PubMed: 38946976
DOI: 10.21203/rs.3.rs-4482953/v1 -
The American Journal of Psychiatry Jul 2024
Topics: Humans; Brain; Multimodal Imaging; Neuroimaging; Magnetic Resonance Imaging; Brain Mapping; Depressive Disorder, Major; Stress, Psychological
PubMed: 38946274
DOI: 10.1176/appi.ajp.20240400 -
Cell Reports Jun 2024Heterogeneous resistance to immunotherapy remains a major challenge in cancer treatment, often leading to disease progression and death. Using CITE-seq and matched...
Heterogeneous resistance to immunotherapy remains a major challenge in cancer treatment, often leading to disease progression and death. Using CITE-seq and matched 40-plex PhenoCycler tissue imaging, we performed longitudinal multimodal single-cell analysis of tumors from metastatic melanoma patients with innate resistance, acquired resistance, or response to immunotherapy. We established the multimodal integration toolkit to align transcriptomic features, cellular epitopes, and spatial information to provide deeper insights into the tumors. With longitudinal analysis, we identified an "immune-striving" tumor microenvironment marked by peri-tumor lymphoid aggregates and low infiltration of T cells in the tumor and the emergence of MITFSPARCL1 and CENPF melanoma subclones after therapy. The enrichment of B cell-associated signatures in the molecular composition of lymphoid aggregates was associated with better survival. These findings provide further insights into the establishment of microenvironmental cell interactions and molecular composition of spatial structures that could inform therapeutic intervention.
PubMed: 38944836
DOI: 10.1016/j.celrep.2024.114392 -
Journal of Vascular and Interventional... Jun 2024To Describe 6-Month safety, efficacy and multimodal imageability after imageable glass Yttrium-90 radioembolization for unresectable Hepatocellular Carcinoma (HCC) in a...
PURPOSE
To Describe 6-Month safety, efficacy and multimodal imageability after imageable glass Yttrium-90 radioembolization for unresectable Hepatocellular Carcinoma (HCC) in a First-in Human Trial METHODS: Eye90 microspheres® (Eye90), an FDA Breakthrough Designated Device, are glass radiopaque Y-90 microspheres visible on CT and SPECT/CT. Six subjects with unresectable HCC underwent selective (≤ 2 segments) Eye90 treatment in a prospective open-label pilot trial. Key inclusion criteria included liver only HCC, ECOG ≤ 1, total lesion length ≤ 9 cm and Child-Pugh A. Prospective partition dosimetry was utilized. Safety, biochemistry, toxicity, adverse events (AE), multimodal imageability on CT and SPECT/CT and 3 and 6-month MRI local modified RECIST (mRECIST) response was evaluated.
RESULTS
6 subjects with HCC (7 lesions) were treated with Eye90 and followed to 180 days. Administration success was 100%. Eye90 CT radiopacity distribution correlated with SPECT/CT. Target lesion complete response was observed in 3 of 6 subjects (50%) and partial response in 2 (33.3%). Two subjects could not be assessed at 180 days. At 180 days, target lesion complete response was maintained in 3 subjects (50%) and partial response in 1 (16.7%). All subjects reported AEs, and 5 reported AEs related to treatment. There were no treatment related serious AEs.
CONCLUSIONS
Eye90 was safe and effective in six subjects with unresectable HCC up to 6 months. Eye90 was imageable via CT and SPECT/CT with correlation between CT radiopacity and SPECT/CT radioactivity distribution. Eye90 provided previously unavailable CT based tumor targeting information.
PubMed: 38944236
DOI: 10.1016/j.jvir.2024.06.023 -
Nigerian Journal of Clinical Practice Jun 2024Some parameters of 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) can predict tumor chemosensitivity and survival in...
The Prognostic Significance of Tumor SUVmax Value in Pre- and Post-Chemoradiotherapy 18F-FDG PET/CT Imaging in Patients with Localized and Advanced Head and Neck Squamous Cell Carcinoma.
BACKGROUND
Some parameters of 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) can predict tumor chemosensitivity and survival in patients with head and neck squamous cell carcinoma (HNSCC).
AIM
The aim of the study was to investigate the prognostic value of pre- and post-treatment maximum standardized uptake values (SUVmax) in 18F-FDG PET/CT imaging for predicting mortality in patients with HNSCC, as well as its prognostic value in terms of disease progression, overall survival (OS), and progression-free survival (PFS).
METHODS
This retrospective study included 37 patients with a histopathological diagnosis of HNSCCs between 2015 and 2018. In patients with HNSCC, the first 18F-FDG PET/CT imaging was performed for pre-treatment staging, and the second imaging was performed to evaluate post-treatment response. In these imaging studies, SUVmax values of the primary tumor before and after treatment were determined. After the second imaging, patients were re-evaluated and followed up. ROC analysis was used to determine the predictive value of 18F-FDG PET/CT SUVmax parameters in terms of death and progression, and Cox regression analysis was used to investigate the prognostic value in terms of OS and PFS.
RESULTS
Cut-off value 15 for SUVmax1 (pre-treatment) had a significant predictive value for mortality (P = 0.02). Cut-off value 3.1 for SUVmax2 (post-treatment) had a significant predictive value for progression (P = 0.024). In univariate analysis, both SUVmax1 and SUVmax2 values were significant prognostic factors for OS (P = 0.047, P = 0.004). However, for PFS, only the SUVmax2 value was a significant prognostic factor (P = 0.001).
CONCLUSION
SUVmax1 value of the primary tumor at diagnosis in HNSCC patients has a predictive value for mortality and a prognostic value for OS. However, the SUVmax2 value in the primary tumor after treatment is a predictive factor for progression and a prognostic factor for both OS and PFS.
Topics: Humans; Fluorodeoxyglucose F18; Male; Positron Emission Tomography Computed Tomography; Female; Middle Aged; Retrospective Studies; Squamous Cell Carcinoma of Head and Neck; Prognosis; Head and Neck Neoplasms; Aged; Chemoradiotherapy; Adult; Radiopharmaceuticals; Predictive Value of Tests; Disease Progression
PubMed: 38943299
DOI: 10.4103/njcp.njcp_856_23 -
BMC Cardiovascular Disorders Jun 2024The purpose of this study was to review echocardiography-based diagnosis of persistent fifth aortic arch (PFAA) in children.
BACKGROUND
The purpose of this study was to review echocardiography-based diagnosis of persistent fifth aortic arch (PFAA) in children.
METHODS
From January 2015 to December 2022, we retrospectively analyzed the echocardiographic findings and the relevant clinical data during follow-up of patients with PFAA who were treated in the Third Affiliated Hospital of Zhengzhou University. The diagnosis was confirmed by computed tomography angiography or surgery.
RESULTS
Seven PFAA cases included two Weinberg type A and five Weinberg type B. The anatomical details of PFAA were assessed using a combination of the long-axis view of the left ventricular outflow tract (from the left high parasternal window) and the long-axis view of the aortic arch (from the suprasternal window). In Weinberg type A, the distal fifth and fourth aortic arches were connected to the descending aorta, which was associated with aortic coarctation. In Weinberg type B, the upper arch of the fourth aorta was interrupted, and only the lower arch of the fifth aorta was connected to the descending aorta. Surgical repair of PFAA was indicated in five patients with blood flow disruption, among which four had good postoperative results and one refused surgery. Two patients with unobstructed PFAA blood flow required follow-up rather than surgery.
CONCLUSIONS
It is feasible to diagnose PFAA by echocardiography. Combined application of the high parasternal left ventricular outflow tract view and the suprasternal aortic arch view can improve timely detection of different types of PFAA in children.
Topics: Humans; Aorta, Thoracic; Retrospective Studies; Male; Female; Computed Tomography Angiography; Infant; Predictive Value of Tests; Child, Preschool; Aortography; Child; China; Aortic Coarctation; Treatment Outcome; Age Factors; Reproducibility of Results; Echocardiography
PubMed: 38943106
DOI: 10.1186/s12872-024-03999-5 -
Nature Communications Jun 2024Desert locust plagues threaten the food security of millions. Central to their formation is crowding-induced plasticity, with social phenotypes changing from cryptic...
Desert locust plagues threaten the food security of millions. Central to their formation is crowding-induced plasticity, with social phenotypes changing from cryptic (solitarious) to swarming (gregarious). Here, we elucidate the implications of this transition on foraging decisions and corresponding neural circuits. We use behavioral experiments and Bayesian modeling to decompose the multi-modal facets of foraging, revealing olfactory social cues as critical. To this end, we investigate how corresponding odors are encoded in the locust olfactory system using in-vivo calcium imaging. We discover crowding-dependent synergistic interactions between food-related and social odors distributed across stable combinatorial response maps. The observed synergy was specific to the gregarious phase and manifested in distinct odor response motifs. Our results suggest a crowding-induced modulation of the locust olfactory system that enhances food detection in swarms. Overall, we demonstrate how linking sensory adaptations to behaviorally relevant tasks can improve our understanding of social modulation in non-model organisms.
Topics: Animals; Grasshoppers; Odorants; Bayes Theorem; Social Behavior; Smell; Behavior, Animal; Crowding; Feeding Behavior; Olfactory Perception; Male; Female; Cues
PubMed: 38942759
DOI: 10.1038/s41467-024-49719-7 -
Korean Journal of Radiology Jul 2024To develop and validate a preoperative risk score incorporating carbohydrate antigen (CA) 19-9, CT, and fluorine-18-fluorodeoxyglucose (F-FDG) PET/CT variables to...
Predicting Recurrence-Free Survival After Upfront Surgery in Resectable Pancreatic Ductal Adenocarcinoma: A Preoperative Risk Score Based on CA 19-9, CT, and F-FDG PET/CT.
OBJECTIVE
To develop and validate a preoperative risk score incorporating carbohydrate antigen (CA) 19-9, CT, and fluorine-18-fluorodeoxyglucose (F-FDG) PET/CT variables to predict recurrence-free survival (RFS) after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma (PDAC).
MATERIALS AND METHODS
Patients with resectable PDAC who underwent upfront surgery between 2014 and 2017 (development set) or between 2018 and 2019 (test set) were retrospectively evaluated. In the development set, a risk-scoring system was developed using the multivariable Cox proportional hazards model, including variables associated with RFS. In the test set, the performance of the risk score was evaluated using the Harrell C-index and compared with that of the postoperative pathological tumor stage.
RESULTS
A total of 529 patients, including 335 (198 male; mean age ± standard deviation, 64 ± 9 years) and 194 (103 male; mean age, 66 ± 9 years) patients in the development and test sets, respectively, were evaluated. The risk score included five variables predicting RFS: tumor size (hazard ratio [HR], 1.29 per 1 cm increment; < 0.001), maximal standardized uptake values of tumor ≥ 5.2 (HR, 1.29; = 0.06), suspicious regional lymph nodes (HR, 1.43; = 0.02), possible distant metastasis on F-FDG PET/CT (HR, 2.32; = 0.03), and CA 19-9 (HR, 1.02 per 100 U/mL increment; = 0.002). In the test set, the risk score showed good performance in predicting RFS (C-index, 0.61), similar to that of the pathologic tumor stage (C-index, 0.64; = 0.17).
CONCLUSION
The proposed risk score based on preoperative CA 19-9, CT, and F-FDG PET/CT variables may have clinical utility in selecting high-risk patients with resectable PDAC.
Topics: Humans; Male; Fluorodeoxyglucose F18; Female; Positron Emission Tomography Computed Tomography; Middle Aged; Carcinoma, Pancreatic Ductal; Aged; Pancreatic Neoplasms; Retrospective Studies; Radiopharmaceuticals; CA-19-9 Antigen; Tomography, X-Ray Computed; Neoplasm Recurrence, Local; Risk Assessment; Disease-Free Survival; Predictive Value of Tests
PubMed: 38942458
DOI: 10.3348/kjr.2023.1235