Absolute quantification methods for Prostate-Specific antigen by Isotope-Dilution mass spectrometry.

Prostate-specific antigen (PSA) is a diagnostic marker for prostate cancer; however, because it is a macromolecular glycoprotein with complex and diverse isoforms, it is difficult to standardize clinical PSA detection results. To overcome this limitation, herein, naturally extracted PSA was characterized as free PSA (fPSA), and the PSA solution was successfully quantified by amino acid analysis coupled with isotope-dilution mass spectrometry (AAA-IDMS) and enzymatic hydrolysis-IDMS; the results could be traced to the International System of Units (SI) through absolutely quantified amino acids and peptides. After protein hydrolysis or digestion condition optimization, amino acids and signature peptides were detected by liquid chromatography-mass spectrometry with the multiple reaction monitoring mode. The mass concentrations of PSA obtained through AAA-IDMS and enzymatic hydrolysis-IDMS were (75.3 ±â€¯1.5) µg/g (k = 2) and (74.7 ±â€¯1.7) µg/g (k = 2), respectively. The PSA weighted average mass concentration was (75.0 ±â€¯1.6) µg/g (k = 2). The consistency assessment between the two methods was successfully validated, ensuring absolute quantitative accuracy. This study lays the foundation for the development of high-order reference materials for the clinical detection of PSA, which can improve the accuracy, reliability, and consistency of clinical PSA test results.

Commutability assessment of reference materials for homocysteine.

OBJECTIVES: Commutability of reference materials is essential for ensuring the traceability of patient measurement results and the technical basis for the use of reference materials. Commutability is only relevant for matrixed reference material; it is a prerequisite for the accuracy and authenticity of calibration methods. In this study, we evaluated the commutability of reference materials for homocysteine. METHODS: Five conventional measurement methods were applied to simultaneously measure 30 serum samples and seven homocysteine reference materials from the National Institute of Standards and Technology and the National Institute of Metrology. Liquid chromatography tandem-mass spectrometry was used as a reference method. Two methods were used to evaluate the commutability of the seven reference materials according to the Clinical and Laboratory Standards Institute EP30-A and the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) commutability assessment document. RESULTS: Among 35 combinations of the five conventional methods and seven reference materials, after evaluation in accordance with the EP30-A, the seven reference materials passed the commutability assessment, and 34 combinations were commutable. According to the IFCC, the commutability evaluation of 28 combinations was conclusive (commutable or non-commutable), while results for the remaining seven combinations could not be determined. CONCLUSIONS: The homocysteine reference materials showed good commutability. The sensitivity of the measurement procedure, measurement deviation and uncertainty, and differences in the "measurand" selected by different methods may affect the evaluation results. Additionally, different judgment standards for different methods may explain the observed variations in evaluation results.

A Multi-Classification Model for Predicting the Invasiveness of Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules.

Objectives: To establish a multi-classification model for precisely predicting the invasiveness (pre-invasive adenocarcinoma, PIA; minimally invasive adenocarcinoma, MIA; invasive adenocarcinoma, IAC) of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: By the inclusion and exclusion criteria, this retrospective study enrolled 346 patients (female, 297, and male, 49; age, 55.79 ± 10.53 (24-83)) presenting as pGGNs from 1292 consecutive patients with pathologically confirmed lung adenocarcinoma. A total of 27 clinical were collected and 1409 radiomics features were extracted by PyRadiomics package on python. After feature selection with L2,1-norm minimization, logistic regression (LR), extra w(ET) and gradient boosting decision tree (GBDT) were used to construct the three-classification model. Then, an ensemble model of the three algorithms based on model ensemble strategy was established to further improve the classification performance. Results: After feature selection, a hybrid of 166 features consisting of 1 clinical (short-axis diameter, ranked 27th) and 165 radiomics (4 shape, 71 intensity and 90 texture) features were selected. The three most important features are wavelet-HLL_firstorder_Minimum, wavelet-HLL_ngtdm_Busyness and square_firstorder_Kurtosis. The hybrid-ensemble model based on hybrid clinical-radiomics features and the ensemble strategy showed more accurate predictive performance than other models (hybrid-LR, hybrid-ET, hybrid-GBDT, clinical-ensemble and radiomics-ensemble). On the training set and test set, the model can obtain the accuracy values of 0.918 ± 0.022 and 0.841, and its F1-scores respectively were 0.917 ± 0.024 and 0.824. Conclusion: The multi-classification of invasive pGGNs can be precisely predicted by our proposed hybrid-ensemble model to assist patients in the early diagnosis of lung adenocarcinoma and prognosis.

UHR-DeepFMT: Ultra-High Spatial Resolution Reconstruction of Fluorescence Molecular Tomography Based on 3-D Fusion Dual-Sampling Deep Neural Network.

Fluorescence molecular tomography (FMT) is a promising and high sensitivity imaging modality that can reconstruct the three-dimensional (3D) distribution of interior fluorescent sources. However, the spatial resolution of FMT has encountered an insurmountable bottleneck and cannot be substantially improved, due to the simplified forward model and the severely ill-posed inverse problem. In this work, a 3D fusion dual-sampling convolutional neural network, namely UHR-DeepFMT, was proposed to achieve ultra-high spatial resolution reconstruction of FMT. Under this framework, the UHR-DeepFMT does not need to explicitly solve the FMT forward and inverse problems. Instead, it directly establishes an end-to-end mapping model to reconstruct the fluorescent sources, which can enormously eliminate the modeling errors. Besides, a novel fusion mechanism that integrates the dual-sampling strategy and the squeeze-and-excitation (SE) module is introduced into the skip connection of UHR-DeepFMT, which can significantly improve the spatial resolution by greatly alleviating the ill-posedness of the inverse problem. To evaluate the performance of UHR-DeepFMT network model, numerical simulations, physical phantom and in vivo experiments were conducted. The results demonstrated that the proposed UHR-DeepFMT can outperform the cutting-edge methods and achieve ultra-high spatial resolution reconstruction of FMT with the powerful ability to distinguish adjacent targets with a minimal edge-to-edge distance (EED) of 0.5 mm. It is assumed that this research is a significant improvement for FMT in terms of spatial resolution and overall imaging quality, which could promote the precise diagnosis and preclinical application of small animals in the future.

Hybrid Clinical-Radiomics Model for Precisely Predicting the Invasiveness of Lung Adenocarcinoma Manifesting as Pure Ground-Glass Nodule.

RATIONALE AND OBJECTIVES: To identify whether the radiomics features of computed tomography (CT) allowed for the preoperative discrimination of the invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules (pGGNs) and further to develop and compare different predictive models. MATERIALS AND METHODS: We retrospectively included 187 lung adenocarcinomas presenting as pGGNs (66 preinvasive lesions and 121 invasive lesions), which were randomly divided into the training and test sets (8:2). Radiomics features were extracted from non-enhanced CT images. Clinical features, including patient's demographic characteristics, smoking status, and conventional CT features that reflect tumor's morphology and surrounding information were also collected. Intraclass correlation coefficient and ℓ2.1-norm minimization were used to identify influential feature subset which was then used to build three predictive models (clinical, radiomics, and clinical-radiomics models) with the gradient boosting regression tree classifier. The performances of the predictive models were evaluated using the area under the curve (AUC). RESULTS: Of the 1409 radiomics features and 27 clinical feature subtypes, 102 features were selected to construct the hybrid clinical-radiomics model, which achieved the best discriminative power (AUC = 0.934 and 0.929 in training and test set). The radiomics model showed comparable predictive performance (AUC = 0.911 and 0.901 in training and test set) compared to the clinical model (AUC = 0.911 and 0.894 in training and test set). CONCLUSION: The radiomics model showed good predictive performance in discriminating invasive lesions from preinvasive lesions for lung adenocarcinomas presenting as pGGNs. Its performance can be further improved by adding clinical features.