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Radiology Radiology

Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation

ArXiv Preprint

Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH) is essential for appropriate patient management. This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation with clinical assessment methods. Materials and Methods: In this retrospective study with external validation, 122 patients with pre-capillary PH were used to train (n=83), validate (n=17) and test (n=10 internal test, n=12 external test) a patch based DenseNet-121 classification model. "Normal", "Ground glass", "Ground glass with reticulation", "Honeycombing", and "Emphysema" were classified as per the Fleishner Society glossary of terms. Ground truth classes were segmented by two radiologists with patches extracted from the labelled regions. Proportion of lung volume for each texture was calculated by classifying patches throughout the entire lung volume to generate a coarse texture classification mapping throughout the lung parenchyma. AI output was assessed against diffusing capacity of carbon monoxide (DLCO) and specialist radiologist reported disease severity. Results: Micro-average AUCs for the validation, internal test, and external test were 0.92, 0.95, and 0.94, respectively. The model had consistent performance across parenchymal textures, demonstrated strong correlation with diffusing capacity of carbon monoxide (DLCO), and showed good correspondence with disease severity reported by specialist radiologists. Conclusion: The classification model demonstrates excellent performance on external validation. The clinical utility of its output has been demonstrated. This objective, repeatable measure of disease severity can aid in patient management in adjunct to radiological reporting.

Michael J. Sharkey, Krit Dwivedi, Samer Alabed, Andrew J. Swift

2023-03-20

oncology Oncology

Prognostic risk factor of major salivary gland carcinomas and survival prediction model based on random survival forests.

In Cancer medicine

Salivary gland malignancies are rare and are often acompanied by poor prognoses. So, identifying the populations with risk factors and timely intervention to avoid disease progression is significant. This study provides an effective prediction model to screen the target patients and is helpful to construct a cost-effective follow-up strategy. We enrolled 249 patients diagnosed with salivary gland tumors and analyzed prognostic risk factors using Cox proportional hazard univariable and multivariable regression models. The patients' data were split into training and validation sets on a 7:3 ratio, and the random survival forest (RSF) model was established using the training sets and validated using the validation sets. The maximally selected rank statistics method was used to determine a cut point value corresponding to the most significant relation with survival. Univariable Cox regression suggested age, smoking, alcohol consumption, untreated, neural invasion, capsular invasion, skin invasion, tumors larger than 4 cm, advanced T and N stage, distant metastasis, and non-mucous cell carcinoma were risk factors for poor prognosis, and multivariable analysis suggested that female, aging, smoking, untreated, and non-mucous cell carcinoma were risk factors. The time-dependent ROC curve showed the AUC of the RSF prediction model on 1-, 2-, and 3-year survival were 0.696, 0.779, and 0.765 respectively in the validation sets. Log-rank tests suggested that the cut point 7.42 risk score calculated from the RSF was most effective in dividing patients with significantly different prognoses. The prediction model based on the RSF could effectively screen patients with poor prognoses.

Chen Yufan, Li Guoli, Jiang Wenmei, Nie Rong Cheng, Deng Honghao, Chen Yingle, Li Hao, Chen Yanfeng

2023-Mar-19

machine learning, major salivary gland tumors, prediction model, prognosis, random survival forest

General General

Atrial Fibrillation Ablation Outcome Prediction with a Machine Learning Fusion Framework Incorporating Cardiac Computed Tomography.

In Journal of cardiovascular electrophysiology

BACKGROUND : Structural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation.

METHODS : Consecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification.

RESULTS : 321 patients (64.2 + 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659) or imaging data (AUC 0.764).

CONCLUSION : Our machine learning approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF. This article is protected by copyright. All rights reserved.

Razeghi Orod, Kapoor Ridhima, Alhusseini Mahmood I, Fazal Muhammad, Tang Siyi, Roney Caroline H, Rogers Albert J, Lee Anson, Wang Paul J, Clopton Paul, Rubin Daniel L, Narayan Sanjiv M, Niederer Steven, Baykaner Tina

2023-Mar-19

Machine learning, atrial fibrillation, cardiac imaging, catheter ablation

General General

Deep information-guided feature refinement network for colorectal gland segmentation.

In International journal of computer assisted radiology and surgery

PURPOSE : Reliable quantification of colorectal histopathological images is based on the precise segmentation of glands but precise segmentation of glands is challenging as glandular morphology varies widely across histological grades, such as malignant glands and non-gland tissues are too similar to be identified, and tightly connected glands are even highly possibly to be incorrectly segmented as one gland.

METHODS : A deep information-guided feature refinement network is proposed to improve gland segmentation. Specifically, the backbone deepens the network structure to obtain effective features while maximizing the retained information, and a Multi-Scale Fusion module is proposed to increase the receptive field. In addition, to segment dense glands individually, a Multi-Scale Edge-Refined module is designed to strengthen the boundaries of glands.

RESULTS : The comparative experiments on the eight recently proposed deep learning methods demonstrated that our proposed network has better overall performance and is more competitive on Test B. The F1 score of Test A and Test B is 0.917 and 0.876, respectively; the object-level Dice is 0.921 and 0.884; and the object-level Hausdorff is 43.428 and 87.132, respectively.

CONCLUSION : The proposed colorectal gland segmentation network can effectively extract features with high representational ability and enhance edge features while retaining details to the maximum, dramatically improving the segmentation performance on malignant glands, and better segmentation results of multi-scale and closed glands can also be obtained.

Li Sheng, Shi Shuling, Fan Zhenbang, He Xiongxiong, Zhang Ni

2023-Mar-19

Edge, Feature fusion, Gland segmentation, Refinement, Representation ability

General General

Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering.

In Environmental science & technology ; h5-index 132.0

Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring.

Huang Yen-Hsiang, Wei Hong, Santiago Peter J, Thrift William John, Ragan Regina, Jiang Sunny

2023-Mar-19

SERS, SERStrate, Self-Assembled SERS substrate, quinoline

General General

Identification of patients with epilepsy using automated electronic health records phenotyping.

In Epilepsia

OBJECTIVES : Unstructured data present in electronic health records (EHR) is a rich source of medical information, however its abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy.

METHODS : The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions of anti-seizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression (LR) and an extreme gradient boosting (XGBoost) models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping.

RESULTS : Our study cohort included 3,903 adults drawn from outpatient departments of 9 hospitals between February 2015 and June 2022: mean age 47 ± 18 years, 57% women, 82% White, 84% Non-Hispanic; 70% with epilepsy. The final models included 285 features, including 246 keywords and phrases captured from 8,415 encounters. Both models achieved AUROC and AUPRC of 1 [95% CI 0.99-1.00] in the hold-out testing set.

SIGNIFICANCE : A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.

Fernandes Marta, Cardall Aidan, Jing Jin, Ge Wendong, Moura Lidia M V R, Jacobs Claire, McGraw Christopher, Zafar Sahar F, Westover M Brandon

2023-Mar-19

Electronic medical records (EMR), Neurology, Text mining, Unstructured text