Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

Technical considerations for functional sequencing assays.

Next-generation sequencing technologies need careful design of experiments and evaluation of results to meet field requirements. Here we discuss technical considerations for these high-throughput assays, together with criteria to assess the quality of the results and the necessary validation.

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature. PURPOSE: To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT). MATERIAL AND METHODS: In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland-Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT. RESULTS: The radiologist and DLA measurements exhibited good agreement with a Bland-Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 (P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group (P < 0.001). CONCLUSION: Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.

Single-nucleus RNA-seq identifies divergent populations of FSHD2 myotube nuclei.

FSHD is characterized by the misexpression of DUX4 in skeletal muscle. Although DUX4 upregulation is thought to be the pathogenic cause of FSHD, DUX4 is lowly expressed in patient samples, and analysis of the consequences of DUX4 expression has largely relied on artificial overexpression. To better understand the native expression profile of DUX4 and its targets, we performed bulk RNA-seq on a 6-day differentiation time-course in primary FSHD2 patient myoblasts. We identify a set of 54 genes upregulated in FSHD2 cells, termed FSHD-induced genes. Using single-cell and single-nucleus RNA-seq on myoblasts and differentiated myotubes, respectively, we captured, for the first time, DUX4 expressed at the single-nucleus level in a native state. We identified two populations of FSHD myotube nuclei based on low or high enrichment of DUX4 and FSHD-induced genes ("FSHD-Lo" and "FSHD Hi", respectively). FSHD-Hi myotube nuclei coexpress multiple DUX4 target genes including DUXA, LEUTX and ZSCAN4, and also upregulate cell cycle-related genes with significant enrichment of E2F target genes and p53 signaling activation. We found more FSHD-Hi nuclei than DUX4-positive nuclei, and confirmed with in situ RNA/protein detection that DUX4 transcribed in only one or two nuclei is sufficient for DUX4 protein to activate target genes across multiple nuclei within the same myotube. DUXA (the DUX4 paralog) is more widely expressed than DUX4, and depletion of DUXA suppressed the expression of LEUTX and ZSCAN4 in late, but not early, differentiation. The results suggest that the DUXA can take over the role of DUX4 to maintain target gene expression. These results provide a possible explanation as to why it is easier to detect DUX4 target genes than DUX4 itself in patient cells and raise the possibility of a self-sustaining network of gene dysregulation triggered by the limited DUX4 expression.

A novel regional-scale human health risk assessment model for soil heavy metal(loid) pollution based on empirical Bayesian kriging.

Soil heavy metal(loid)s contamination caused by rapid urbanization and industrialization seriously affects human health and hinders the global sustainable development goals (SDGs). Currently, there is a lack of comprehensive human health risk assessment (HHRA) studies for multiple land use types at the regional scale. We propose a practical risk assessment framework that integrates empirical Bayesian kriging (EBK), pollution level analyses, and modified HHRA modeling. The concentrations of copper industry-related metals (Cu, Ni, Cd, As, and Hg) in 332 topsoil samples from the south bank of the Yangtze River in Tongling were investigated. Obvious enrichment of Cu, Cd, As, and Hg was detected, and the average concentration of Cu was 5.24 times higher than the background values. The distribution of heavy metal(loid) pollution was typically high in the south and east, and low in the north and west. The mean errors of interpolation for Cu, Ni, and Hg were 0.84, 1.29, and 0, respectively, and the root mean square errors of interpolation for Cd and As were 1.29 and 0.86, respectively. Non-carcinogenic risks of soil heavy metal(loid)s were assessed as acceptable throughout the studied area. The hazard index decreased in the order As (0.448) > Ni (0.0729) > Cd (0.0136) > Hg (9.04 ×10-4) > Cu (6.41 ×10-4). Nevertheless, the carcinogenic risks of Ni, Cd, and As in 70-80% of the administrative units (AUs) were between 10-6 to 10-4, considered an unacceptable level. Exposure through the oral ingestion route accounted for 88.0-99.2% of the total three exposure routes. It is worth noting that four AUs were considered to be the priority control units, and Ni and As were identified as the priority control soil heavy metal(loid)s. This case demonstrates the feasibility and scientific validity of the new EBK-HHRA framework, which confirms that EBK can effectively predict the spatial distribution patterns of soil heavy metal(loid)s and that modified HHRA models are conducive to risk integration at the regional scale. The EBK-HHRA approach is generic and provides substantial support for risk source identification and risk management of soil heavy metal(loid)s contamination at the regional scale.

Pollution source identification and abatement for water quality sections in Huangshui River basin, China.

Accurately obtaining the pollution sources and their contribution rates is the basis for refining watershed management. Although many source analysis methods have been proposed, a systematic framework for watershed management is still lacking, including the complete process of pollution source identification to control. We proposed a framework for identification and abatement of pollutants and applied in the Huangshui River Basin. A newer contaminant flux variation method based on a one-dimensional river water quality model was used to calculate the contribution of pollutants. The contributions of various factors to the over-standard parameters of water quality sections at different spatial and temporal scales were calculated. Based on the calculation results, corresponding pollution abatement projects were developed, and the effectiveness of the projects was evaluated through scenario simulation. Our results showed that the large scale livestock and poultry farms and sewage treatment plants were the largest sources of total nitrogen (TP) in Xiaoxia bridge section, with contribution rates of 46.02% and 36.74%, respectively. Additionally, the largest contribution sources of ammonia nitrogen (NH3-N) were sewage treatment plants (36.17%) and industrial sewage (26.33%). Three towns that contributed the most to TP were Lejiawan Town (14.4%), Ganhetan Town (7.3%) and Handong Hui Nationality town (6.6%), while NH3-N mainly from the Lejiawan Town (15.9%), Xinghai Road Sub-district (12.4%) and Mafang Sub-district (9.5%). Further analysis found that point sources in these towns were the main contributor to TP and NH3-N. Accordingly, we developed abatement projects for point sources. Scenario simulation indicated that the TP and NH3-N could be significantly improved by closing down and upgrading relevant sewage treatment plants and building facilities for large scale livestock and poultry farms. The framework adopted in this study can accurately identify pollution sources and evaluate the effectiveness of pollution abatement projects, which is conducive to the refined water environment management.

Dynamics of microRNA expression during mouse prenatal development.

MicroRNAs (miRNAs) play a critical role as posttranscriptional regulators of gene expression. The ENCODE Project profiled the expression of miRNAs in an extensive set of organs during a time-course of mouse embryonic development and captured the expression dynamics of 785 miRNAs. We found distinct organ-specific and developmental stage-specific miRNA expression clusters, with an overall pattern of increasing organ-specific expression as embryonic development proceeds. Comparative analysis of conserved miRNAs in mouse and human revealed stronger clustering of expression patterns by organ type rather than by species. An analysis of messenger RNA expression clusters compared with miRNA expression clusters identifies the potential role of specific miRNA expression clusters in suppressing the expression of mRNAs specific to other developmental programs in the organ in which these miRNAs are expressed during embryonic development. Our results provide the most comprehensive time-course of miRNA expression as part of an integrated ENCODE reference data set for mouse embryonic development.

Spatial-temporal variation, sources and driving factors of organic carbon burial in rift lakes on Yunnan-Guizhou plateau since 1850.

Burial of organic carbon (OC) in rift lakes on plateau is an important part of the global cycle. It is affected by natural and anthropogenic factors. In this study, we selected the sediment records of 7 rift lakes on the Yunnan-Guizhou Plateau to study spatial-temporal variation, sources and driving factors of organic carbon burial since 1850. The analysis of the temporal and spatial trend of carbon burial shows that the TOC concentration, TOC flux, C: N and mass accumulate rate have increased significantly since 1850. Co-occurrence network analysis indicated that a strong correlation between the TOC concentration and silty. TOC concentration were identified as core genera due to their high concentration. Carbon isotope tracing results show that before 1950, endogenous OC input played a dominant role, and after 1950, the proportion of exogenous OC increased. Canonical correlation analysis indicated that after 1950, agriculture intensification and population increase become one of the factors affecting the carbon burial of lakes in this area. The result of this study indicate that anthropogenic factors have become the main factors promoting carbon burial in rift lakes on the plateau.

An integrated assessment system for the carrying capacity of the water environment based on system dynamics.

Implementing an integrated assessment system for the carrying capacity of water environments should include recognizing and eliminating warning signs based on future predictions. However, existing methodologies tend to ignore the warning methods already in place, and current studies fail to quantify water ecology issues adequately. To help solve these problems, the ecological footprint that involves water uses a procedural indicator system, which adopts an early warning methodology system approach. This reconstruction has devised definitions, recognizes hazards, states forecasts status, analyses signs, judges situations, distinguishes levels, and eliminates risks. Based on these procedures, a dynamic system model has been developed, comprising five subsystems with an overarching parent system. These subsystems are population, ecology, water resources, water environment, and water ecology. The simulation involves carrying rates for the water environment, water resource, water ecology, and the level of harmony between society and the environment. All these serve to describe the water environment carrying capacity, i.e., the upper limit of the capacity to supply resources, remove pollutants, and offer sustainable ecological services. To properly quantify the carrying capacity, the water environment carrying rate was assessed by a comprehensive analysis of the water environment, water resources, and water ecological carrying rate. The carrying rates were calculated as the ratios of currently existing pressure to the maximum pressure that can be born. When values are greater than 1, they indicate overload because the actual pressure is greater than the pressure they can bear. The degree of coordination between economy and environment was standardized to range between 0 and 1. The larger the value, the more harmonious the relationship. For this research, the North Canal basin, a basin consisting of several rivers flowing through Beijing, Tianjin, and Hebei in northern China and its surrounding areas, was chosen. The results showed that water environment and resource carrying rates would decline to 2.60 and 0.94, respectively, while the water ecology carrying rate would remain high at 10.98 by the year 2025. In addition, the degree of coordination would increase from 0.65 to 0.79. These statistics mean that the overload statuses will be high for a long time, although they are expected to ease gradually. Besides, the relationship between society and the environment would become more stable. Considering both the overload statuses and the relationship between society and the environment, the warning signs would not vanish. Based on predictions, the measures used were explained from three perspectives, i.e., alleviating pressures, enhancing carrying capacities, and finding a balance between society and the environment. Finally, the effects of the measures were estimated quantitatively.

Bcl11b and combinatorial resolution of cell fate in the T-cell gene regulatory network.

T-cell development from hematopoietic progenitors depends on multiple transcription factors, mobilized and modulated by intrathymic Notch signaling. Key aspects of T-cell specification network architecture have been illuminated through recent reports defining roles of transcription factors PU.1, GATA-3, and E2A, their interactions with Notch signaling, and roles of Runx1, TCF-1, and Hes1, providing bases for a comprehensively updated model of the T-cell specification gene regulatory network presented herein. However, the role of lineage commitment factor Bcl11b has been unclear. We use self-organizing maps on 63 RNA-seq datasets from normal and perturbed T-cell development to identify functional targets of Bcl11b during commitment and relate them to other regulomes. We show that both activation and repression target genes can be bound by Bcl11b in vivo, and that Bcl11b effects overlap with E2A-dependent effects. The newly clarified role of Bcl11b distinguishes discrete components of commitment, resolving how innate lymphoid, myeloid, and dendritic, and B-cell fate alternatives are excluded by different mechanisms.

Scc1 sumoylation by Mms21 promotes sister chromatid recombination through counteracting Wapl.

DNA double-strand breaks (DSBs) fuel cancer-driving chromosome translocations. Two related structural maintenance of chromosomes (Smc) complexes, cohesin and Smc5/6, promote DSB repair through sister chromatid homologous recombination (SCR). Here we show that the Smc5/6 subunit Mms21 sumoylates multiple lysines of the cohesin subunit Scc1. Mms21 promotes cohesin-dependent small ubiquitin-like modifier (SUMO) accumulation at laser-induced DNA damage sites in S/G2 human cells. Cells expressing the nonsumoylatable Scc1 mutant (15KR) maintain sister chromatid cohesion during mitosis but are defective in SCR and sensitive to ionizing radiation (IR). Scc1 15KR is recruited to DNA damage sites. Depletion of Wapl, a negative cohesin regulator, rescues SCR defects of Mms21-deficient or Scc1 15KR-expressing cells. Expression of the acetylation-mimicking Smc3 mutant does not bypass the requirement for Mms21 in SCR. We propose that Scc1 sumoylation by Mms21 promotes SCR by antagonizing Wapl at a step after cohesin loading at DSBs and in a way not solely dependent on Smc3 acetylation.

Identification of conflict between wildlife living spaces and human activity spaces and adjustments in/around protected areas under climate change：A case study in the Three-River Source Region.

Overlap between wildlife living spaces and human activity spaces represents one of the main causes of human-wildlife conflict. It is therefore necessary to identify and adjust the spatial distribution of conflicts because climate change will modify the ranges and locations of species. The MaxEnt model was used to simulate the distribution of wildlife living spaces under current and future climatic conditions (RCP4.5 scenario) based on species distribution data and environmental data from the Three-River Source Region. Furthermore, the logistic-cellular automata model was used to simulate the changes in human activity spaces (construction land and arable land) by 2050. Areas of overlap between wildlife living spaces and human activity spaces were determined by overlay analysis. Then, the areas of construction and arable land that would conflict with wildlife were redistributed outside highly suitable wildlife living spaces. In addition, ecological corridors connecting current and future living spaces were planned for the migration of certain species in response to climate change based on the minimum cumulative resistance model. The results showed that the areas of highly suitable wildlife living spaces will decrease under the influence of climate change if global warming occurs. In the future, the area of overlap between highly suitable wildlife living spaces and construction land will be 125 km2, and that between highly suitable wildlife living spaces and arable land will be 340 km2. The redistribution of these areas will cause human activity spaces to become more concentrated. Moreover, 110 ecological corridors should be constructed across roads in the Three-River Source Region.

An integrated approach to investigate the relationship of coupling coordination between social economy and water environment on urban scale - A case study of Kunming.

With a rapid economic growth and social development in China, the associated problems of water pollution and shortage of water resources would limit the sustainable and coordinated development of socioeconomic and water environmental systems of urban cities. To investigate the relationship of coupling coordination between social economy and water environment on urban scale, we introduced an integrated approach that enables the dynamic evaluation of coupling coordination degree (CCD), which consists of a system dynamics model and a coupling coordination degree model; and applied it to a case study in Kunming in 2016-2025. The business-as-usual (BAU) scenario and five alternative regulating scenarios are simulated to evaluate the effectiveness exerted by various socioeconomic development patterns and water protection efforts in improving CCD. We found that the improvement of CCD could attribute to both the sufficient water protection efforts and the maintaining sustainable speed and scale of socioeconomic development patterns. Under BAU scenario, Kunming would maintain the current state of barely balanced development with CCD at 0.5-0.8, predominantly due to substantial water consumption and pollution. Through the comparison of dynamic evolutions of system indicators and CCD under five alternative regulating scenarios, it is realistic for Kunming to plan its future development in accordance to M-H scenario (Medium-speed socioeconomic development pattern; High-intensity water protection effort). Following this scenario, Kunming's CCD would conform to a steadily increasing trend in 2016-2025 and remain above 0.8 in 2022-2025, tracing a shift in the development stage of that coupling coordination from "barely balanced development" to "highly balanced development" despite the difficulty to cut NH3-N emission significantly. The effective and feasible regulatory measures such as reducing productive or domestic water pollutants and consumptions; improving collection and reuse rates of wastewater, should be prioritized when adjusting coordination development during decision-making.

Single-nucleus RNA-seq of differentiating human myoblasts reveals the extent of fate heterogeneity.

Myoblasts are precursor skeletal muscle cells that differentiate into fused, multinucleated myotubes. Current single-cell microfluidic methods are not optimized for capturing very large, multinucleated cells such as myotubes. To circumvent the problem, we performed single-nucleus transcriptome analysis. Using immortalized human myoblasts, we performed RNA-seq analysis of single cells (scRNA-seq) and single nuclei (snRNA-seq) and found them comparable, with a distinct enrichment for long non-coding RNAs (lncRNAs) in snRNA-seq. We then compared snRNA-seq of myoblasts before and after differentiation. We observed the presence of mononucleated cells (MNCs) that remained unfused and analyzed separately from multi-nucleated myotubes. We found that while the transcriptome profiles of myoblast and myotube nuclei are relatively homogeneous, MNC nuclei exhibited significant heterogeneity, with the majority of them adopting a distinct mesenchymal state. Primary transcripts for microRNAs (miRNAs) that participate in skeletal muscle differentiation were among the most differentially expressed lncRNAs, which we validated using NanoString. Our study demonstrates that snRNA-seq provides reliable transcriptome quantification for cells that are otherwise not amenable to current single-cell platforms. Our results further indicate that snRNA-seq has unique advantage in capturing nucleus-enriched lncRNAs and miRNA precursors that are useful in mapping and monitoring differential miRNA expression during cellular differentiation.

Ring Finger Protein 14 is a new regulator of TCF/ß-catenin-mediated transcription and colon cancer cell survival.

T-cell factor/lymphoid enhancer factor (TCF/LEF) proteins regulate transcription by recruiting ß-catenin and its associated co-regulators. Whether TCF/LEFs also recruit more factors through independent, direct interactions is not well understood. Here we discover Ring Finger Protein 14 (RNF14) as a new binding partner for all TCF/LEF transcription factors. We show that RNF14 positively regulates Wnt signalling in human cancer cells and in an in vivo zebrafish model by binding to target promoters with TCF and stabilizing ß-catenin recruitment. RNF14 depletion experiments demonstrate that it is crucial for colon cancer cell survival. Therefore, we have identified a key interacting factor of TCF/ß-catenin complexes to regulate Wnt gene transcription.

Genetic and epigenetic characteristics of FSHD-associated 4q and 10q D4Z4 that are distinct from non-4q/10q D4Z4 homologs.

Facioscapulohumeral dystrophy (FSHD) is one of the most prevalent muscular dystrophies. The majority of FSHD cases are linked to a decreased copy number of D4Z4 macrosatellite repeats on chromosome 4q (FSHD1). Less than 5% of FSHD cases have no repeat contraction (FSHD2), most of which are associated with mutations of SMCHD1. FSHD is associated with the transcriptional derepression of DUX4 encoded within the D4Z4 repeat, and SMCHD1 contributes to its regulation. We previously found that the loss of heterochromatin mark (i.e., histone H3 lysine 9 tri-methylation (H3K9me3)) at D4Z4 is a hallmark of both FSHD1 and FSHD2. However, whether this loss contributes to DUX4 expression was unknown. Furthermore, additional D4Z4 homologs exist on multiple chromosomes, but they are largely uncharacterized and their relationship to 4q/10q D4Z4 was undetermined. We found that the suppression of H3K9me3 results in displacement of SMCHD1 at D4Z4 and increases DUX4 expression in myoblasts. The DUX4 open reading frame (ORF) is disrupted in D4Z4 homologs and their heterochromatin is unchanged in FSHD. The results indicate the significance of D4Z4 heterochromatin in DUX4 gene regulation and reveal the genetic and epigenetic distinction between 4q/10q D4Z4 and the non-4q/10q homologs, highlighting the special role of the 4q/10q D4Z4 chromatin and the DUX4 ORF in FSHD.

An energy-economy-environment model for simulating the impacts of socioeconomic development on energy and environment.

Many rapidly developing regions have begun to draw the attention of the world. Meanwhile, the energy and environmental issues associated with rapid economic growth have aroused widespread critical concern. Therefore, studying energy, economic, and environmental systems is of great importance. This study establishes a system dynamic model that covers multiple aspects of those systems, such as energy, economy, population, water pollution, air pollution, solid waste, and technology. The model designed here attempts to determine the impacts of socioeconomic development on the energy and environment of Tongzhou District in three scenarios: under current, planning, and sustainable conditions. The results reveal that energy shortages and water pollutions are very serious and are the key issues constraining future social and economic development. Solid waste emissions increase with population growth. The prediction results provide valuable insights into social advancement.

Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer.

OBJECTIVE: To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features. METHODS: We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves. RESULTS: The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839-0.898) and 0.847 (95% CI: 0.787-0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration. CONCLUSION: This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC.

Deep learning-powered 3D segmentation derives factors associated with lymphovascular invasion and prognosis in clinical T1 stage non-small cell lung cancer.

Background: Lymphovascular invasion (LVI) is an invasive biologic behavior that affects the treatment and prognosis of patients with early-stage lung cancer. This study aimed to identify LVI diagnostic and prognostic biomarkers using deep learning-powered 3D segmentation with artificial intelligence (AI) technology. Methods: Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters. Results: Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively). Conclusions: Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.