Multivariate Statistical Methods Research Articles

Anchorage and lap capacity of plain surface and square twisted bars in existing R.C. Structures: A comprehensive approach

AbstractAlthough plain round or square section reinforcement is no longer used in new construction, there are many older structures still in service built with such bars. In recognition of the need for guidance on the assessment of load‐carrying capacity of structures reinforced with such bars, design codes are now introducing or re‐introducing provisions to assess the capacity of laps and anchorages of such reinforcement. The authors have contributed to the introduction of design rules in the fib Model Code 2020. This paper describes the derivation of these provisions. Starting from the form of expression proposed earlier by the authors, the data from which these proposals are derived is specified and modifications in interpretation preparatory to a statistical analysis are outlined. A rigorous multivariate statistical procedure is then employed firstly to determine a mean strength expression for the capacity of anchorages and laps which is then validated against test data. Further statistical analysis is subsequently used to determine a design expression using the approach outlined in EN 1990, taking appropriate account scatter in test data. Finally, some comparisons are presented between the assessment expressions proposed and design provisions that were in place when these types of bars were in common use. Areas where earlier design provisions may be non‐conservative are identified.

Two-Part Mixed Effects Mixture Model for Zero-Inflated Longitudinal Compositional Data

Compositional data (CD) is mostly analyzed using ratios of components and log-ratio transformations to apply known multivariable statistical methods. Therefore, CD where some components equal zero represents a problem. Furthermore, when the data is measured longitudinally, and appear to come from different sub-populations, the analysis becomes highly complex. Our objective is to build a statistical model addressing structural zeros in longitudinal CD and apply it to the analysis of radiation-induced lung damage (RILD) over time. We propose a two-part mixed-effects model extended to the case where the non-zero components of the vector might come from a two-component mixture population. Maximum likelihood estimates for fixed effects and variance components were calculated by an approximate Fisher scoring procedure base on sixth-order Laplace approximation. The expectation-maximization (EM) algorithm estimates the mixture model’s probability. This model was used to analyze the radiation therapy effect on tissue change in one patient with non-small cell lung cancer (NSCLC), utilizing five CT scans over 24 months. Instead of using voxel-level data, voxels were grouped into larger subvolumes called patches. Each patch’s data is a CD vector showing proportions of dense, hazy, or normal tissue. Proposed method performed reasonably for estimation of the fixed effects, and their variability. However, the model produced biased estimates of the nuisance parameters in the model.

The Influence of Wildfire Climate on Wildfire Incidence: The Case of Portugal

Although the influence of climate on the fire regime is unanimously recognized, most publications and studies on this influence are on a global scale. Therefore, this study aims to demonstrate the role of climate in wildfire incidence at the country and regional scale using multivariate statistical analysis and machine learning methods (clustering and classification algorithms). Mainland Portugal was chosen as a case study due to its climate and because it is the European region most affected by wildfires. The results demonstrate the climate signature in the spatial and temporal distribution of the wildfire incidence. The conclusions of the study include (i) the existence of two pyro-regions, with different types of climate (Csb and Csa) composed of NUTS II regions: the northern region composed of the Norte and Centro regions and the southern region composed of Alentejo and Algarve; (ii) the intra-annual variability in the wildfire incidence, characterized by two peaks, one in the spring and the other in the summer, are a consequence of the country’s type of climate; and (iii) how the annual cycle of wildfire incidence varies over the years depends on the weather conditions throughout each year. These results are of fundamental importance for wildfire managers, especially in the context of climate change.

Regional fertility predictors based on socioeconomic determinants in Slovakia

The study's primary purpose was to recognise the effects of determinants on the level of fertility and thereby explain the differences in trends in the regions of Slovakia. At the turn of the century, the differences in fertility in regions increased, but the total fertility rate decreased. Multivariate statistical methods clarified the regional effects of the level and nature of fertility. Initial regression surveys indicated weak effects between regions, which led to applying factor and cluster analysis to establish regional types. Comprehensive regression analysis was then applied. The strength and nature of regional relationships differed at the inter- and intra-regional levels. Research has demonstrated significant differences in fertility rates dependent on the socioeconomic environment, as regional types uniquely link to determinants. Moreover, each determinant has specific spatial patterns with unequal regression coefficients at different regional levels, which cannot be evaluated constantly. Knowing how spatial variation in fecundity occurs will enable future studies to elucidate the processes involved. Finally, fertility is vital for social assessment and policy formulation, the study’s findings could inform local decision-makers and planners in identifying the socioeconomic conditions underlying fertility at the regional level and planning appropriate intervention strategies.

Effectiveness of Compound Dilong Capsules in Patients with Asymptomatic Cerebral Carotid Artery Stenosis: A Retrospective Case-Control Study.

Asymptomatic cerebral carotid artery stenosis (ACCAS) benefits from secondary prevention via statins and antiplatelets; nonetheless, the impact of medication alone is often limited. Evidence has suggested enhanced therapeutic outcomes when Chinese patent medicine-specifically, compound dilong capsules (CDC)-is integrated with conventional secondary prevention measures. We retrospectively analyzed 319 ACCAS patients from January 2018 to December 2022 at Xuanwu Hospital, Capital Medical University. Depending on the clinical outcomes-improvement or stabilization versus progression-patients were classified into effective or ineffective treatment groups. Patient medical records and questionnaire responses were the primary data sources. The study accounted for demographic variables, clinical history, and medication details, with the primary focus on CDC use and its duration. Treatment outcomes were gauged alongside Transcranial color-coded sonography and Carotid Doppler ultrasonography findings. We employed both univariate and multivariate statistical methods to assess the data. CDC administration (aOR=2.51, 95% CI 1.39-4.54, P=0.002) and extended usage beyond six months (aOR=3.54, 95% CI 1.71-7.32, P=0.001) demonstrate a statistically significant correlation with treatment efficacy. Gender (aOR=2.54, 95% CI 1.30-5.00, P=0.007), hypertension management (aOR=0.56, 95% CI 0.33-0.95, P=0.031), and antiplatelet therapy with aspirin (aOR=9.53, 95% CI 1.15-78.89, P=0.037) or clopidogrel (aOR=9.97, 95% CI 1.10-90.12, P=0.041) also influenced the therapeutic outcome significantly. Incorporating CDC as part of a secondary prevention strategy for over six months can beneficially modulate and limit the progression of vascular stenosis in ACCAS. These findings underscore the value of combining traditional Chinese medicine with modern pharmacological interventions in ACCAS management.

Retrospective Examination of Risk Factors Affecting Iron Deficiency Anemia Using Machine Learning Methods

Iron deficiency anemia is one of the most common types of anemia worldwide. In recent years, new developments in the field of medicine have offered early diagnosis and treatment opportunities for anemia patients. In the field of data science, in parallel with the developments in medicine, significant developments are taking place in subjects such as data collection, storage, processing, and reporting. Interdisciplinary joint studies positively contribute to patients’ quality of life and lifespan. In this study, the accuracy of the statistical results was tested with Machine Learning Method (MLM) while investigating the factors that affect the correct prediction of Iron Deficiency Anemia (IDA) diagnosis. In the first stage, the relationships between all variables in the data set and their effects on the differentiation of disease groups were investigated using univariate and multivariate statistical methods. In the second step, the data set was analyzed in detail using four different methods with Artificial Neural Network (ANN) classifier. Weka 3.8 application was preferred for these operations. In the last stage, the results obtained in both stages were compared. Accordingly, it has been observed that hemoglobin (Hb), mean cell volume (MCV), iron (Fe), and ferritin (FERR) have more effects on IDA. ANN (98.06%) is a better discriminator with a correct classification rate

Methodological Approach to Assessing International Logistics Management in Commercial Activities in The Recreational Field: Innovations in Modeling

The main purpose of the study is to optimize the logistics systems of our chosen recreational area in the context of attracting new international tourist flows in accordance with the principles of sustainable development. The object of the study is the logistics system of our chosen recreational area - the Prague Zoo. The scientific task of the study is to determine the logical potential of the recreational zone, including indicators that influence the sustainable development of the recreational zone. The research methodology includes the use of a multivariate mathematical and statistical method. As a result of the study, a strategy for optimizing logistics systems to attract international tourists was proposed, while adhering to the principles of sustainable development. Despite this, the work has its limitations. Most of them are due to the fact that the Prague Zoo was chosen as the object of study, its commerce, logistics system and the impact of the latter on sustainable development. All these characteristics may be good for other recreational areas. The prospect of further research will be to expand the analysis of other recreational areas, and their commercial and logistics systems. In addition, further research will be aimed at more detailed development of measures to attract international tourists to recreational areas, while maintaining the implementation of the principles of sustainable development.

Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations

Explanatory factor analysis (EFA) is a multivariate statistical method frequently used in quantitative research and has begun to be used in many fields such as social sciences, health sciences and economics. With EFA, researchers focus on fewer items that explain the structure, instead of considering too many items that may be unimportant and carry out their studies by placing these items into meaningful categories (factors). However, for over sixty years, many researchers have made different recommendations about when and how to use EFA. Differences in these recommendations confuse the use of EFA. The main topics of discussion are sample size, number of items, item extraction methods, factor retention criteria, rotation methods and general applicability of the applied procedures. The abundance of these discussions and opinions in the literature makes it difficult for researchers to decide which procedures to follow in EFA. For this reason, it would be beneficial for researchers to gather different information about the general procedures (sample number, rotation methods, etc.) in the use of EFA. This paper aims to provide readers with an overview of what procedures to follow when implementing EFA and share practical information about the latest developments in methodological decisions in the EFA process. It is considered that the study will be an important guide for the researchers in the development of clear decision paths in the use of EFA, with the aspect of presenting the most up-to-date information collectively.

Quality evaluation and health risk assessment of karst groundwater in Southwest China

Groundwater in karst regions is of immense value due to its vital support for regional ecosystems and residents' livelihoods. However, it is simultaneously threatened by multi-source pollution from agricultural non-point sources, industrial and domestic point sources, and mining activities. This study focuses on the Guangxi of China, which features typical karst topography, aiming to thoroughly assess the groundwater quality and related health risks in Guangxi, especially identifying the impacts of various key pollution sources on the groundwater environment. A total of 1912 groundwater samples were collected, covering an area of approximately 237,600 km2. The spatial distribution of pollutants was analysed using the Nemeroww index method and Kriging interpolation, while multivariate statistical and cluster analysis methods were employed to identify the main types of pollution sources. Furthermore, based on the human health risk assessment model of the U.S. Environmental Protection Agency (US EPA), a risk assessment was conducted for key pollutants. The results revealed widespread heavy metal contamination in Guangxi's groundwater, particularly with concentrations of Mn, As, Al, Pb reaching up to 9.4 mg/L, 2.483 mg/L, 37.95 mg/L, 4.761 mg/L, respectively, significantly exceeding China's national Class III groundwater quality standards. Cluster analysis indicated that mining and industrial activities are the primary sources of pollution. The health risk assessment demonstrated that these activities pose a significant risk to public health. The aim of this study is to provide a scientific basis for the protection of the groundwater environment in Guangxi and other karst areas, the formulation of pollution prevention and control strategies, and the optimization of urban and industrial land use layouts. Future research should focus on advanced isotopic and molecular biological techniques to trace pollution sources more precisely and evaluate the effectiveness of pollution control measures.

Effect of Needle Age and Location on the Volatile Composition of Essential Oils from Juniperus communis L.: A Chemometric Study

AbstractProfiles of the volatile organic compound (VOC) content of essential oils (EOs) extracted from young and old Juniperus communis L. needles were generated via gas chromatography combined with mass spectrometry (GC‐MS) and flame ionization detection (GC‐FID) and subsequently processed using multivariate statistical methods. These were then used to differentiate the EOs according to needle age and geographical origin. From a total 49 of VOCs identified, statistically significant differences (p<0.05) between young‐ and old‐needle EOs were found for 21 VOCs. Most of the EOs from the young needles showed higher (p<0.05) contents of α‐ and β‐pinene and sesquiterpenes (β‐ and γ‐elemene, germacrene D and B, α‐muurolene, and δ‐cadinene) and their oxygenated derivatives (α‐ and τ‐cadinol), while most of the monoterpenes (α‐thujene, sabinene, terpinolene, β‐myrcene, β‐phellandrene, and p‐cymene) were present in higher quantities (p<0.05) in the EOs from the old needles. The volatiles germacrene D, α‐ and β‐pinene, α‐cadinol, and sabinene were identified via stepwise and canonical discriminant analysis as the most valuable for EO discrimination according to needle age and geographical origin. Recognition and prediction ability tests of the EOs achieved 100 % accuracy in EO classification according to the above mentioned criterion.

Variability and driving effect of aquatic gross primary productivity across long-distance inter-basin water diversion project

Long-distance water diversion projects significantly affect the water resource cycle and allocation of regions. However, many unknowns still exist in water ecosystem functionality and energy flow in large-scale inter-basin water diversion projects. This study focused on the gross primary production (GPP) in the Middle-Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the world's longest inter-basin water diversion project. The spatiotemporal distribution, driving factors, and pathways of GPP were comprehensively analyzed based on four years of high-frequency water quality monitoring and satellite re-analysis data from 11 national stations, coupling the Bayesian hierarchical models and multivariate statistical methods. The results showed that the daily average GPP in the main canal of the MRSNWDPC over the years was 2.650 g O2 m−2 d−1, with seasonal peak GPP occurring in summer and generally increasing with the distance along the canal. Five structural equation modeling (SEM) were built to analyze the variations of GPP, revealing the surface pressure (PS) and surface carbon dioxide concentrations (CO2) and pH value were the main driving factors in the main canal. The surface pressure showed significant negative impacts on GPP changes in the canal, while the CO2 and pH showed different direction effects in different sections. The daily average carbon equivalent GPPs in the MRSNWDPC is 0.828 g C m−2 d−1, ranging from 0.600 to 1.028 g C m−2 d−1, and close to the Yangtze River and the East Sea of China. This study provides a scientific basis for a deeper understanding and analysis of the energy flow mechanisms in water ecosystems of mega inter-basin water diversion projects.

Analysis of the Volatile Components in Different Parts of Three Species of the Genus Amomum via Combined HS-SPME-GC-TOF-MS and Multivariate Statistical Analysis.

The study used headspace solid-phase microextraction coupled with gas chromatography-time-of-flight mass spectrometry (HS-SPME-GC-TOF-MS) to analyze volatile compounds in leaves and fruits of Amomum tsaoko, Amomum paratsaoko, and Amomum koenigii. The composition and aroma of distinct metabolites were analyzed using multivariate statistical methods. A total of 564 volatile compounds were identified from three species of the genus Amomum, which were further divided into nine categories: terpenoids, carboxylic acids, alcohols, hydrocarbons, aldehydes, ketones, phenols, ethers, and other compounds. Terpenoids and alcohols were the most abundant. The content and types of compounds vary in A. tsaoko, A. paratsaoko, and A. koenigii, so mixing or substituting them is not advisable. We selected 45 metabolites based on the criteria of the variable importance in projection values (VIP > 1.5) and one-way ANOVA (p < 0.05). The top 19 metabolites with the most significant VIP values were chosen. Interestingly, (Z)-2-decenal was only found in Amomum koenigii, while nitroethane and nonanal were only present in cultivated A. tsaoko. Additionally, linalool, cineole, and (D)-limonene were the main components affecting the aroma of three species of the genus Amomum. The volatile components identified in this study provide a theoretical basis for analyzing the unique flavor of A. tsaoko, A. paratsaoko, and A. koenigii.

Enhancing postgraduate education quality: a comprehensive analysis of factors influencing students’ satisfaction in university-business cooperation

ABSTRACT This study investigates the critical factors influencing students’ satisfaction with university-business cooperation (UBC) in postgraduate education, focusing on Chinese universities of applied sciences. Employing a comprehensive research model, we utilised multivariate statistical analysis methods, including canonical correlation analysis, partial correlation analysis, principal component analysis (PCA), and cluster analysis, to validate our measurement and hypotheses. Our findings reveal that teaching, tutor supervision, practical relevance, and individual development are primary determinants of students’ satisfaction with UBC in postgraduate education. Additionally, significant correlations exist between these factors, consistent with our proposed research model. The study emphasises the importance of fostering effective UBC for enhancing postgraduate education quality. Practical implications and recommendations for stakeholders are discussed, contributing both to the academic understanding of UBC and offering actionable insights for educational institutions and businesses. Future research directions in this field are also suggested.

Quantitative assessment of irrigation water and organic/inorganic amendment on biometric growth profiles of Abelmoschus esculentus and Solanum lycopersicum and their varieties

ABSTRACT In recent decades, the use of chemical fertilizers has been recklessly provoked to meet the increased food needs of the rapidly growing population. However, there is some disagreement about the use of chemical fertilizers in agriculture. Hence, the appropriate nitrogen, phosphate, and potassium ratios must be determined before their application in agricultural practices. This study explored three distinct sources of nutrients to support healthy seed germination and reduce nutrient loss: chemical fertilizers, vermicompost, and nutrient-laden irrigation water supply. A sustainable, affordable, and green petri plate seed germination experiment was used to analyze the biometric growth patterns of two plant species (Abelmoschus esculentus and Solanum lycopersicum). To quantify the effects of different irrigation water sources (groundwater, river water), their combinations with chemical fertilizers and vermicompost (3 ton/ha), multivariate statistical methods such as correlation, principal component analysis, and deep neural networks were used. The purpose of this research was to find the optimal nutrient delivery technique for encouraging healthy plant growth while minimizing the environmental stress of excessive nutrient application.

Morphological variation in blackthorn (&lt;i&gt;Prunus spinosa&lt;/i&gt; L.) populations in the Northwestern part of the Balkan Peninsula - Absence of geographical and environmental structure

Blackthorn (&lt;i&gt;Prunus spinosa&lt;/i&gt; L., Rosaceae) is a deciduous, insect-pollinated, and animal-dispersed shrub or a small tree that naturally occurs throughout Europe. The aims of this study were to describe fruit morphological variability of natural populations in the northwestern part of the Balkan Peninsula and to determine the impact of geographical and environmental conditions on shaping the species’ variability. In total, ten fruit and stone morphological traits were studied using data from twelve natural populations. e obtained data was analyzed using descriptive and multivariate statistical methods. High morphological variability was determined both among and within the studied populations. However, our study revealed no clear eco-geographical differentiation of the studied populations. High morphological variability of the populations, but also the absence of geographical and environmental structuring of populations, could be attributed to the animal-mediated dispersal of fruits over greater distances, combined with the vegetative regeneration and high levels of seed flow among populations, as well as the influence of founder effect. Based on the conducted research, valuable information on intra- and interpopulation variability of fruits’ morphological traits was obtained, which can be useful in creating the guidelines for conservation, breeding, and afforestation programs.

Method of thromboembolism prediction in advanced atherosclerosis

Aim. To develop a method for thromboembolism prediction in patients with ad­vanced atherosclerosis.Material and methods. The study involved 170 patients with advanced atheroscle­rosis and 110 patients with local atherosclerotic lesions. At the first stage, speciali­zed experts generated signs characterizing the severity of prothrombotic conditions. To determine whether patients belonged to the first or second group, the multivariate statistics method was used. Decision rules (DRs) was developed based on soft computing (SC), as well as the methodology for the synthesis of hybrid fuzzy DRs.Results. According to the discriminant function coefficient (DFC), the division into classes was made with the formation of an intersection area, which affected the diagnostic sensitivity of this method (=86%). Due to difficulties of separating more heterogeneous groups with a small sample, RP was synthesized according to the atherosclerosis severity classification using SC technology. Atherosclerosis severity classification was adopted according to fuzzy DRs: initiation stage (up to 20%); reverse stage (21-40%); progressive stage (41-65%); critical stage (more than 65%). As­ses­sment of thromboembolism risk in advanced atherosclerosis in the examined pati­ents showed the following trends: progressive stage — 55% of patients; critical stage — 21% of patients; reverse stage — 16% of patients; initiation stage — 7% of patients.Conclusion. DFC for DRs makes it possible to identify groups with high and low thromboembolism risk. The resulting final fuzzy DRs make it possible to differentiate the atherothrombotic condition in advanced atherosclerosis by severity, which can help to timely determine preventive and therapeutic measures.

PROCESS MONITORING IN HYBRID ELECTRIC VEHICLES BASED ON DYNAMIC NONLINEAR METHOD

Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose a multivariate statistical method based on dynamic nonlinear improvement, namely dynamic neural component analysis (DNCA). This method does not require the establishment of precise analytical models; instead, it only necessitates acquiring data from HEV powertrains. Through numerical simulation and real HEV experiments, we demonstrate the effectiveness of this approach in monitoring highway third-level faults. The testing outcomes demonstrate that DNCA outperforms traditional dynamic methods like dynamic principal component analysis (DPCA), conventional nonlinear methods such as kernel PCA (KPCA) and NCA, as well as traditional dynamic nonlinear methods like DKPCA.

Economic Impact and Adaptive Responses in the Global Hotel Industry During the COVID-19 Pandemic: A Comparative Analysis

Abstract The COVID-19 pandemic has become a challenge of unprecedented scale for various industries, and the hotel industry has not escaped the waves of economic uncertainty. Economic indicators in the hotel industry, which once reflected growth and prosperity, abruptly turned into indicators of crisis and the need for adaptation. The main aim of the presented article is the evaluation, categorization, and comparison of hotel-type accommodation facilities in selected countries based on their economic indicators in the pandemic period. Comparison and categorization of countries will be carried out using a multivariate statistical method – cluster analysis. All investigated indicators were obtained by the method of sociological inquiry in the form of a questionnaire. The application of cluster analysis identified the economic importance and potential in the countries in question using the investigated economic indicators in the hotel industry. The results showed a significant economic difference between the countries. The analysis divided the requested countries into five clusters – categories from the lowest (Turkey) to the highest impact of the COVID-19 pandemic on the economic situation in the hotel industry (Slovak Republic). Since the prevention of crises in the hotel industry is a key aspect that can minimize negative consequences and ensure the sustainability of operations, at the end of the presented article we have outlined the measures that accommodation establishments should apply in their normal practice.

Application of the structural equation modeling-partial least square method to analyze factors affecting the quality of life of hypertension patients

Abstract Hypertension is a serious health issue in Pidie Regency, Indonesia. This research utilizes Structural Equation Modeling - Partial Least Squares (SEM-PLS) method to analyze the factors contributing to hypertension in Pidie Regency. Primary data were was gathered via surveys and discussions held with inhabitants from the area. One of the multivariate statistical methods that can be used to explain these complex relationships is SEM-PLS. The direct effects of SEM-PLS analysis indicate the hidden demographic factors notably diminish the concealed variable of quality of life. The concealed indicators of medication adherence showcase a positive impact but lack statistical significance on the hidden variable of quality of life. Additionally, the latent activity variable has a positive and significant effect on quality of life. The research findings revealed an R2 value of 0.234, signifying that 23.4% of the variance in the latent quality of life variable can be explained by latent demographic, medication adherence, and activity variables.

The clinical, radiological, postoperative pathological, and genetic features of nodular lung adenocarcinoma: a real-world single-center data.

The preoperative differential diagnosis of nodular lung adenocarcinoma has long been a challenging issue for thoracic surgeons. This study aimed to explore differential diagnosis of nodular lung adenocarcinoma by comprehensively analyzing its clinical, computed tomography (CT) imaging, and postoperative pathological and genetic features. The clinical, CT imaging, and postoperative pathological features of different classifications of nodular lung adenocarcinoma were retrospectively analyzed through univariate and multivariate statistical methods. There were 132 patients with nodular lung adenocarcinoma enrolled. Firstly, compared with ground-glass nodular lung adenocarcinoma, solid nodular lung adenocarcinoma was more common in women [odds ratio (OR), 3.662; 95% confidence interval (CI): 1.066-12.577] and older adults (OR, 1.061; 95% CI: 1.007-1.119), and CT signs were mostly lobulation (OR, 4.957; 95% CI: 1.714-14.337) and spiculation (OR, 8.214; 95% CI: 2.740-24.621); the mean CT (CTm) value of solid nodular lung adenocarcinoma was significantly higher than that of ground-glass nodular lung adenocarcinoma, and the optimal diagnostic threshold was -267.5 Hounsfield units (HU). Secondly, the maximum diameter of nodule size (NSmax) of invasive adenocarcinoma (IAC) was significantly greater than that of minimally IAC (MIA; OR, 6.306; 95% CI: 1.191-33.400) or atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS; OR, 189.539; 95% CI: 4.720-7,610.476), and the optimal diagnostic threshold between IAC and MIA was 1.35 cm; the CTm value of IAC was significantly higher than that of MIA, and the optimal diagnostic threshold was -460.75 HU. Thirdly, lepidic-predominant adenocarcinoma (LPA) manifest more commonly as pure ground-glass nodule (pGGN; OR, 6.252; 95% CI: 1.429-27.358) or mixed ground-glass nodule (mGGN; OR, 4.224; 95% CI: 1.223-14.585). Moreover, the mutation rate of epidermal growth factor receptor (EGFR) in IAC was 70.69% (41/58). The EGFR mutation rates of mGGNs (OR, 8.794; 95% CI: 1.489-51.933) and solid nodules (SNs; OR, 12.912; 95% CI: 1.597-104.383) were significantly higher than that of pGGNs. Furthermore, compared with those of micropapillary-predominant adenocarcinoma (MPA), solid-predominant adenocarcinoma (SPA), or invasive mucinous adenocarcinoma (IMA), there were significantly higher EGFR mutation rates in acinar-predominant adenocarcinoma/papillary-predominant adenocarcinoma (APA/PPA; OR, 55.925; 95% CI: 4.045-773.284) and LPA (OR, 38.265; 95% CI: 2.307-634.596). Different classifications of nodular lung adenocarcinoma have their own clinicopathological and CT imaging features, and the latter is the main predictor.