Value Of Features Research Articles

Machine learning-based empirical investigation of user’s perception of digitalisation in pandemic immunisation programs

ABSTRACT This study proposes and validates a framework for tracking post-usage perceptions of digitalisation in pandemic immunisation programmes (PIPs) using machine learning techniques. We categorise PIP digitalisation features into two groups: those offering benefits and those requiring effort/cost. Using consumer value theory (CVT), we examine post-usage perceptions of these features. Online review data from developed and developing economies are analysed, with topic modelling applied to over 46,000 reviews to identify key PIP digitalisation features in two nations. An empirical investigation using dominance and correspondence analysis was conducted to understand their influence on users’ perceptions. The regression coefficient sign indicates the positive or negative perception associated with a feature. This framework helps identify poorly perceived digitalisation features and guides decisions on remedial actions to improve post-usage perception. By drawing on this theoretical framework, we gain insights into how users perceive the value of PIP digitalisation features.

Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson’s disease

BackgroundThe efficacy of levodopa, the most crucial metric for Parkinson’s disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response.MethodsThis retrospective study used two datasets: from the Parkinson’s Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p < 0.001.ResultsFor 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized.ConclusionThe T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction.Critical relevance statementThis study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy.Key PointsThis study investigated the predictive value of T1w features for levodopa response.MedicalNet extractor outperformed all other previously published methods with key region visualization.T1w features are more effective than clinical information in levodopa response prediction.Graphical

Diagnostic and prognostic relevance of plain radiographs for periprosthetic joint infections of the hip: a literature review

Conventional radiography is regularly used to evaluate complications after total hip arthroplasty. In various recent consensus meetings, however, plain radiographs of a potentially infected hip joint have been judged as being only relevant to exclude diagnoses other than infection. Solid data on radiographic presentations of periprosthetic joint infection (PJI) are scarce. As a result, the prognostic value of radiological features in low-grade PJI remains uncertain. The present review article aims to present an overview of the available literature and to develop ideas on future perspectives to define the diagnostic possibilities of radiography in PJIs of the hip. The primary outcome of interest of this systematic review was the radiologic presentation of periprosthetic joint infections of the hip. As secondary outcome of interest served the sensitivity and specificity of the radiologic presentation of periprosthetic joint infections. Of the included articles, 26 were reviews, essays, or case reports and only 18 were clinical studies. Typical radiologic abnormalities of PJI were a periosteal reaction, a wide band of radiolucency at the cement–bone or metal–bone interface, patchy osteolysis, implant loosening, bone resorption around the implant, and transcortical sinus tracts. The frequency of their occurrence is still inadequately defined. A deeper understanding of the underlying causes and the relation between microorganisms to radiologic abnormalities can probably help clinicians in the future to diagnose a PJI. This is why further research shall focus on the radiographic features of PJI.

Analysis of the Value of Quantitative Features in Multimodal MRI Images to Construct a Radio-Omics Model for Breast Cancer Diagnosis.

To analyze the diagnostic value of quantitative features in multimodal magnetic resonance imaging (MRI) images to construct a radio-omics model for breast cancer. Ninety-five patients with breast-related diseases from January 2020 to January 2021 were grouped into the benign group (n=57) and malignant group (n=38) according to the pathological findings. All cases were randomized as the training group (n=66) and validation group (n=29) in a 7:3 ratio based on the examination time. All subjects were examined by T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), dynamic contrast enhancement (DCE), and apparent diffusion coefficient (ADC) multimodality MRI. The MRI findings were analyzed against pathological findings. A diagnostic breast cancer radiomics model was constructed. The diagnostic efficacy of the model in the validation group was analyzed, and the diagnostic efficacy was analyzed via the ROC curve. Fibroadenoma accounted for 49.12% of benign breast diseases, and invasive ductal carcinoma accounted for 73.68% of malignant breast diseases. The sensitivity of T1WI, T2WI, DWI, ADC, and DCE in diagnosing breast cancer was 61.14%, 66.67%, 73.30%, 78.95%, and 85.96%, using the four-fold table method. The area under the curves (AUCs) of T1WI, T2WI, DWI, ADC, and DCE for diagnosing breast cancer were 0.715, 0.769, 0.785, 0.835, and 0.792, respectively. The AUCs of plain scan, diffuse, enhanced, plain scan + diffuse, plain scan + enhanced, enhanced + diffuse, and plain scan + enhanced + diffuse for diagnosing breast cancer were 0.746, 0.798, 0.816, 0.839, 0.890, 0.906, and 0.927, respectively. The construction of a radio-omics model by quantitative features in multimodal MRI images was valuable in the diagnosis of breast cancer. The value of radio-omics models such as plain scan + enhanced + diffuse was higher than the other models in diagnosing breast cancer and could be widely applied in clinical practice.

Association of human interpretable features derived from pancreatic ductal adenocarcinoma pathology slides with disease-free survival.

e16368 Background: Pancreatic ductal adenocarcinoma (PDAC) is predicted to be the second cause of cancer death in the US by 2040. For the minority of patients who are candidates for curative-intent surgical resection, a neoadjuvant approach with chemotherapy or chemotherapy followed by chemoradiotherapy (chemoRT) has become the standard of care. The tumor microenvironment (TME) has been recognized as a key factor in therapeutic resistance and disease progression. This study aims to explore the prognostic value of human-interpretable machine learning image features (HIFs) derived from whole-slide H&amp;E images. Methods: Consecutive PDAC patients who underwent neoadjuvant therapy followed by surgical resection were studied in this single-institution retrospective study. Clinical variables collected include tumor site (head/body/tail), surgery extent, histology, sex, age at diagnosis, grade, treatment response (4-point scale), LVI, PNI, resection margin status, pathological TNM staging, number of lymph nodes removed/positive, neoadjuvant strategy (chemotherapy vs chemoRT), and number of neoadjuvant chemo cycles. These factors were analyzed with a multivariable Cox proportional hazards model for disease-free survival (DFS) after surgery. Additionally, 420 human-interpretable image features (HIFs) derived from a commercial machine learning software were analyzed with a univariable Cox model on a binary outcome of 1-yr DFS, applying a Bonferroni correction for multiple comparisons to identify significant predictors. Results: Increased neoadjuvant chemotherapy cycles (HR = 0.62, p = 0.001, [median: 8, IQR: 6-12]) and receipt of combination neoadjuvant therapy (HR = 0.15, p = 0.020, [29% chemo, 71% +chemoRT]) exhibited a protective effect in the multivariable DFS model. 3 HIFs quantifying immune cells and macrophages within the cancerous tissue and its stroma were associated with an improved probability of 1-year DFS. Conclusions: In addition to clinical factors, HIFs may offer valuable insights into the tumor microenvironment's influence on PDAC progression, paving the way for improved prognostic models and therapeutic strategies. Future work will integrate clinical predictors with HIFs in a combined model.

Interpretable artificial intelligence-based analysis for morphologic classification of neuroblastic tumors.

10023 Background: Rapid and accurate identification of morphologic features of neuroblastic tumors (NTs) is critical for risk stratification and therapeutic decision making. The prognostic value of features like neuroblast differentiation, mitosis-karyorrhexis index (MKI), and Schwannian stromal presence is well established. Deep learning permits objective histopathological analysis, streamlining workflows for pathologists, notably in rare cancers. In rare cancers, our method minimizes bias and optimizes limited data using transfer and self-supervised learning (SSL) for feature extraction, with improved explainability. Here, we used an artificial intelligence-based model to morphologically classify NT tumors and MYCN-amplification. Methods: Annotated H&amp;E-stained slides of diagnostic NT tumor biopsies from the University of Chicago and the Children’s Oncology Group were digitalized. Pathologists defined three binarized measures including diagnostic category (ganglioneuroblastoma/neuroblastoma), grade (differentiating/poorly differentiating), and MKI (low and intermediate/high). MYCN status was abstracted from patient records (amplified/non-amplified). Using Slideflow, our open-source pipeline, we developed an attention-based multiple instance learning model with features extracted by CTransPath, a SSL model pretrained on pan-cancer images from The Cancer Genome Atlas. For each measure, model performance was evaluated using 5-fold cross validation by aggregating k-fold model predictions across multiple metrics. Patients were excluded from a model if the measure of interest was unknown. Feature significance was assessed visually using Class Activation Mapping (Grad-CAM). Results: The mean age of the study cohort (n = 172) was 3.66 years. Of patients with clinical information, 84 of 138 (60.2%) had metastatic disease and 94 of 133 (70.7%) were high-risk. Of the 148 tumors with a diagnostic category of neuroblastoma, 93.2% were poorly differentiated and 25% had high MKI. Of the 135 tumors with known MYCN status, 40 were amplified (29.6%). The final models excelled across all outcomes, performing best for diagnostic category, grade, and MYCN status (Table 1). Physician review of the attention-based heatmaps for all measures highlighted biologically relevant regions such as neuropil. Conclusions: We created a deep learning pipeline for auto-characterization of digitized H&amp;E-stained NT pathology slides. Our approach may also aid in identifying molecular features including MYCN-amplification. Review of heatmaps showed pertinent biological tissue, boosting model reliability.[Table: see text]

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis.

Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.

This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

Cross-Regional Crop Classification Based on Sentinel-2

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.

(Late)nt? Exploring the latent potential for participatory heritage conservation in makhanda, South Africa

There is an increasing call for heritage management to decentralise and involve stakeholders outside of the sphere of governance. This research focuses on participatory conservation of heritage resources and the value of narrative in heritage conservation and tourism development. The case study is situated in the city of Makhanda, which was established in 1812 and possesses a significant number of individual built environment heritage resources and historic streetscapes. These urban features have been identified as assets to the city by various stakeholders and institutions, but they remain under threat. Utilising semi-structured interviews with owners of historic residential properties in the city, the paper argues that homeowners are an underutilised resource in the management of the historic urban fabric. The study found that homeowners held an appreciation for and identified the value of the historic features of their properties, and it argues that this appreciation can be harnessed in heritage management. Interviews revealed, however, that homeowners faced many challenges in managing and maintaining their historic properties and received little support in this regard. Citizen participation in heritage conservation was limited and a top-down approach to heritage resource management predominated.

Beyond the aggregated paradigm: phenology and structure in mutualistic networks

Mutualistic relationships, where species interact to obtain mutual benefits, constitute an essential component of natural ecosystems. The use of ecological networks to represent the species and their ecological interactions allows the study of structural and dynamic patterns common to different ecosystems. However, by neglecting the temporal dimension of mutualistic communities, relevant insights into the organization and functioning of natural ecosystems can be lost. Therefore, it is crucial to incorporate empirical phenology -the cycles of species’ activity within a season- to fully understand the impact of temporal variability on network architecture. In this paper, by using empirical datasets together with a set of synthetic models, we propose a framework to characterize the phenology of plant-pollinator communities and assess how it reshapes their portrayal as a network. Analyses of three empirical cases reveal that non-trivial information is missed when representing the network of interactions as static, which leads to overestimating the value of fundamental structural features. We discuss the implications of our findings for mutualistic relationships and intra-guild competition for common resources. We show that recorded interactions and species’ activity duration are pivotal factors in accurately replicating observed patterns within mutualistic communities. Furthermore, our exploration of synthetic models underscores the system-specific character of the mechanisms driving phenology, increasing our understanding of the complexities of natural ecosystems.

Value of magnetic resonance imaging radiomics features in predicting histologic grade of invasive ductal carcinoma of the breast.

Breast cancer has the second highest mortality rate of all cancers and occurs mainly in women. To investigate the relationship between magnetic resonance imaging (MRI) radiomics features and histological grade of invasive ductal carcinoma (IDC) of the breast and to evaluate its diagnostic efficacy. The two conventional MRI quantitative indicators, i.e. the apparent diffusion coefficient (ADC) and the initial enhancement rate, were collected from 112 patients with breast cancer. The breast cancer lesions were manually segmented in dynamic contrast-enhanced MRI (DCE-MRI) and ADC images, the differences in radiomics features between Grades I, II and III IDCs were compared and the diagnostic efficacy was evaluated. The ADC values (0.77 ± 0.22 vs 0.91 ± 0.22 vs 0.92 ± 0.20, F= 4.204, p< 0.01), as well as the B_sum_variance (188.51 ± 67.803 vs 265.37 ± 77.86 vs 263.74 ± 82.58, F= 6.040, p< 0.01), L_energy (0.03 ± 0.02 vs 0.13 ± 0.11 vs 0.12 ± 0.14, F= 7.118, p< 0.01) and L_sum_average (0.78 ± 0.32 vs 16.34 ± 4.23 vs 015.45 ± 3.74, F= 21.860, p< 0.001) values of patients with Grade III IDC were significantly lower than those of patients with Grades I and II IDC. The B_uniform (0.15 ± 0.12 vs 0.11 ± 0.04 vs 0.12 ± 0.03, F= 3.797, p< 0.01) and L_SRE (0.85 ± 0.07 vs 0.78 ± 0.03 vs 0.79 ± 0.32, F= 3.024, p< 0.01) values of patients with Grade III IDC were significantly higher than those of patients with Grades I and II IDC. All differences were statistically significant (p< 0.05). The ADC radiomics signature model had a higher area-under-the-curve value in identifying different grades of IDC than the ADC value model and the DCE radiomics signature model (0.869 vs 0.711 vs 0.682). The accuracy (0.812 vs 0.647 vs 0.710), specificity (0.731 vs 0.435 vs 0.342), positive predictive value (0.815 vs 0.663 vs 0.669) and negative predictive value (0.753 vs 0.570 vs 0.718) of the ADC radiomics signature model were all significantly better than the ADC value model and the DCE radiomics signature model. ADC values and breast MRI radiomics signatures are significant in identifying the histological grades of IDC, with the ADC radiomics signatures having greater value.

In-situ reaction modification of isocyanate derivatives with hole-transport units on perovskite film surface for efficient and stable solar cells

FAPbI3 perovskite solar cells (PSCs) have attracted widespread research attention. The interfacial properties play a crucial role in perovskite defect passivation and interfacial carrier transport, significantly influencing the power conversion efficiency (PCE) and stability of devices. Here, a novel interface modification strategy of in-situ reaction on perovskite surface is proposed. A molecule N,N′-bis(4-isocyanatophenyl)-N,N′-di-p-tolyl-[1,1′-biphenyl]-4,4′-diamine (TPD-NCO), involving a hole-transporting unit TPD was synthesized. The highly reactive isocyanate (NCO) groups addition-reacted with formamidine ions (FA+) from the FAPbI3 surface, forming urea with firm covalent bonds. The resulting stable modification smoothened the interface, and its urea groups coordinated with Pb2+ and formed hydrogen bonds with I-, thereby passivating the surface defects and inhibiting ion migration. Moreover, the hole-transporting units were firstly anchored through this covalent modification as a p-type dopant to lower the interface energy barrier for hole extraction. The PCE of target devices increased from 22.82% to 24.46%, which also remained 93% for 5460 h under high humidity (65% RH), 87% for 1060 h under 75 °C, and 77% for 420 h under maximum power point (MPP) operation at 50 °C, indicating both excellent efficiency and long-term stabilities. This work displays the value of both firm and hole-conductive features in in-situ reactive interface modification.

Person re-identification algorithm by image from video surveilance sistem using a neural network compound descriptor

For people re-identification in distributed video surveillance systems, it is important to have an algorithm that ensures efficiency when a person is blocked by other people or objects. Therefore, for this task, an algorithm has been developed that involves the compound descriptor formation, which includes a global features vector of a person’s image and three local ones for its upper, middle and down parts. The regions of interest selection is carried out based on the detecting key points results in the image of the human body. If a person's image part is occluded by other people or objects, then it is classified as invisible. The hidden part person image is not used in the formation of the local feature. To obtain it, the average value of the k-nearest neighbors such features of a person’s image is calculated. The experiments performed indicate an increase in re-identification accuracy for the Market-1501, DukeMTMC-ReID, MSMT17 and PolReID1077 datasets.

Multi-scale relational graph convolutional network for multiple instance learning in histopathology images

Graph convolutional neural networks have shown significant potential in natural and histopathology images. However, their use has only been studied in a single magnification or multi-magnification with either homogeneous graphs or only different node types. In order to leverage the multi-magnification information and improve message passing with graph convolutional networks, we handle different embedding spaces at each magnification by introducing the Multi-Scale Relational Graph Convolutional Network (MS-RGCN) as a multiple instance learning method. We model histopathology image patches and their relation with neighboring patches and patches at other scales (i.e., magnifications) as a graph. We define separate message-passing neural networks based on node and edge types to pass the information between different magnification embedding spaces. We experiment on prostate cancer histopathology images to predict the grade groups based on the extracted features from patches. We also compare our MS-RGCN with multiple state-of-the-art methods with evaluations on several source and held-out datasets. Our method outperforms the state-of-the-art on all of the datasets and image types consisting of tissue microarrays, whole-mount slide regions, and whole-slide images. Through an ablation study, we test and show the value of the pertinent design features of the MS-RGCN.

Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study.

To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment. We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively. In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively). Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

The Relationships of Oral Reading Fluency at Word, Sentence, and Passage Levels and Reading Comprehension in Chinese.

This study aims to expand our understanding of the relations of oral reading fluency at word, sentence, and passage levels to reading comprehension in Chinese-speaking secondary school-aged students. In total, 80 participants (46 males and 34 females) ranging from 13 to 15 years old joined this study and were tested on tasks of oral reading fluency at three levels, reading comprehension, and nonverbal IQ. Our results showed a clear relationship from fluency at the level of the word to the sentence and then the passage in oral reading fluency as well as both the direct and indirect importance of word-level oral reading fluency in reading comprehension. Only the indirect effect from word-level oral reading fluency to reading comprehension through passage-level oral reading fluency was significant. Our findings suggest that sentence-level oral reading fluency is the crucial component to reading comprehension in Chinese. Additionally, recognition of the potential value of unique features, such as syntactic awareness and word segment accuracy, that happen at the sentence level should be integrated into instructional activities for reading.

Artificial Intelligence–Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome

BackgroundA lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. ObjectivesThis study sought to investigate the additive value of artificial intelligence–enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). MethodsAmong ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. ResultsAmong 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. ConclusionsAI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328)

Predictors of treatment response in lymphogenic metastasized papillary thyroid cancer: a histopathological study.

Lymph node metastases in papillary thyroid cancer (PTC) increase the risk for persistent and recurrent disease. Data on the predictive value of histopathological features of lymph node metastases, however, are inconsistent. The aim of this study was to evaluate the prognostic significance of known and new histopathological features of lymph node metastases in a well-defined cohort of PTC patients with clinically evident lymph node metastases. A total of 1042 lymph node metastases, derived from 129 PTC patients, were reexamined according to a predefined protocol and evaluated for diameter, extranodal extension, cystic changes, necrosis, calcifications, and the proportion of the lymph node taken up by tumor cells. Predictors for a failure to achieve a complete biochemical and structural response to treatment were determined. The presence of more than 5 lymph node metastases was the only independent predictor for a failure to achieve a complete response to treatment (odds ratio [OR] 3.39 [95% CI, 1.57-7.33], P < .05). Diameter nor any of the other evaluated lymph node features were significantly associated with the response to treatment. Detailed reexamination of lymph nodes revealed that only the presence of more than 5 lymph node metastases was an independent predictor of failure to achieve a complete response to treatment. No predictive value was found for other histopathological features, including the diameter of the lymph node metastases. These findings have the potential to improve risk stratification in patients with PTC and clinically evident lymph node metastases.

NET-related gene signature for predicting AML prognosis

Acute Myeloid Leukemia (AML) is a malignant blood cancer with a high mortality rate. Neutrophil extracellular traps (NETs) influence various tumor outcomes. However, NET-related genes (NRGs) in AML had not yet received much attention. This study focuses on the role of NRGs in AML and their interaction with the immunological microenvironment. The gene expression and clinical data of patients with AML were downloaded from the TCGA-LAML and GEO cohorts. We identified 148 NRGs through the published article. Univariate Cox regression was used to analyze the association of NRGs with overall survival (OS). The least absolute shrinkage and selection operator were utilized to assess the predictive efficacy of NRGs. Kaplan–Meier plots visualized survival estimates. ROC curves assessed the prognostic value of NRG-based features. A nomogram, integrating clinical information and prognostic scores of patients, was constructed using multivariate logistic regression and Cox proportional hazards regression models. Twenty-seven NRGs were found to significantly impact patient OS. Six NRGs—CFTR, ENO1, PARVB, DDIT4, MPO, LDLR—were notable for their strong predictive ability regarding patient survival. The ROC values for 1-, 3-, and 5-year survival rates were 0.794, 0.781, and 0.911, respectively. In the training set (TCGA-LAML), patients in the high NRG risk group showed a poorer prognosis (p < 0.001), which was validated in two external datasets (GSE71014 and GSE106291). The 6-NRG signature and corresponding nomograms exhibit superior predictive accuracy, offering insights for pre-immune response evaluation and guiding future immuno-oncology treatments and drug selection for AML patients.