scholarly journals Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs

2022 ◽  
Author(s):  
Weiyuan Fang ◽  
Guorui Zhang ◽  
Yali Yu ◽  
Hongjie Chen ◽  
Hong Liu
Keyword(s):  
Artificial Intelligence ◽  
Lung Adenocarcinoma ◽  
Predictive Model ◽  
Adenocarcinoma In Situ ◽  
Invasive Adenocarcinoma ◽  
Diagnostic Efficacy ◽  
Ct Value ◽  
Quantitative Parameters ◽  
Mean Diameter ◽  
Pathological Subtypes

Objective: To explore the value of quantitative parameters of artificial intelligence and computed tomography (CT) signs in identifying pathological subtypes of lung adenocarcinoma appearing as ground-glass nodules (GGNs). Methods: CT images of 224 GGNs from 210 individuals were collected retrospectively and pathologically classified into atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups. Artificial intelligence was used to identify GGNs and to obtain quantitative parameters, and CT signs were recognized manually. The mixed predictive model based on logistic multivariate regression was evaluated. Results: Of the 224 GGNs, 55, 93, and 76 were AAH/AIS, MIA, IAC, respectively. In terms of artificial intelligence parameters, from AAH/AIS to MIA, and IAC, there was a gradual increase in two-dimensional mean diameter, three-dimensional mean diameter, mean CT value, maximum CT value, and volume of GGNs (all P < 0.0001). Except for the CT signs of the location, and the tumor-lung interface, there were significant differences among the three groups in the density type, shape, vacuole signs, air bronchogram, lobulation, spiculation, pleural indentation, and vascular convergence signs (all P < 0.05). The areas under the curve (AUC) of predictive model 1 for identifying the AAH/AIS and MIA and model 2 for identifying MIA and IAC were 0.779 and 0.918, respectively, which were greater than the quantitative parameters independently (all P < 0.05). Conclusion: Artificial intelligence parameters are valuable for identifying subtypes of early lung adenocarcinoma, and when combined with CT signs to improve its diagnostic efficacy.

Artificial intelligence-assisted CT characterizations and quantitative analysis for differentiating pre-invasive lesions from invasive adenocarcinomas in pulmonary subsolid nodules ≤ 2cm.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21614-e21614
Author(s):  
Bingyu Zhang ◽  
Fenglei Yu ◽  
Muyun Peng
Keyword(s):  
Artificial Intelligence ◽  
Quantitative Analysis ◽  
Regression Model ◽  
Characteristic Curve ◽  
Maximum Diameter ◽  
Adenocarcinoma In Situ ◽  
Invasive Adenocarcinoma ◽  
Ct Value ◽  
Significant Difference ◽  
Preinvasive Lesions

e21614 Background: The use of artificial intelligence (AI) in medical imaging has dramatically improved the quality of segmentation including accuracy, efficiency and reproducibility. This study sought to determine whether AI-assisted computed tomography (CT) features and quantitative analysis of pulmonary subsolid nodules (SSNs) under 2cm could be used to differentiate preinvasive lesions from invasive adenocarcinomas. Methods: Clinical data and CT images of 297 preinvasive lesions and early invasive lung adenocarcinomas confirmed by surgery pathology with CT manifestations of SSNs under 2cm were retrospectively analysed. The nodules were divided into two groups: the preinvasive lesions (PILs, N = 115) including 7 cases of atypical adenomatous hyperplasia (AAH), 30 cases of adenocarcinoma in situ (AIS) and 78 cases of minimally invasive adenocarcinoma (MIA), and the invasive adenocarcinomas (IACs, N = 182). All CTs were processed by AI and the volume, mean CT value, consolidation-to-tumor ratio (CTR), mass and maximum diameter of each SSN were obtained. Results: The volume, mean CT value, CTR, maximum diameter and mass of nodules showed significant difference between the two groups (Table). Multivariate analysis was determined by logistic regression. The regression model between the two groups was logit(p) = -1.439-2.927Volume +0.0005(mean CT value)-0.463(CTR > 0.5) +0.238(maximum diameter)+6.298(mass).The receiver operating characteristic curve (ROC) showed that the mass can do the best prediction among all the independent factors with the areas under the curve(AUC) 0.748 at a cut-off value of 0.154, with the sensitivity of 70.9% and specificity of 70.4% .The AUC of the ROC using the regression probabilities of regression model was 0.769. Conclusions: AI-assisted CT characterizations may be promising tools to predict if SSNs under 2 cm have invaded. [Table: see text]

A Clinicopathological Study of Small Lung Adenocarcinoma 1 cm or Less in Size: Emphasis on Histological Subtypes Associated With Lymph Node Metastasis and Recurrence

2017 ◽  
Vol 26 (1) ◽  
pp. 4-11 ◽  
Author(s):  
Wei Zhao ◽  
Hui Wang ◽  
Jun Xie ◽  
Bo Tian
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lung Adenocarcinoma ◽  
Who Classification ◽  
Limited Resection ◽  
Adenocarcinoma In Situ ◽  
Invasive Adenocarcinoma ◽  
Node Metastasis ◽  
Minimally Invasive Adenocarcinoma

Background. The aim of this study was to assess the prognostic significance of the newly proposed 2015 World Health Organization (WHO) lung adenocarcinoma classification for patients undergoing resection for small (≤1 cm) lung adenocarcinoma. We also investigated whether lobectomy offers prognostic advantage over limited resection for this category of tumors. Methods. A retrospective study of resected pulmonary adenocarcinomas (n = 83) in sizes 1 cm or less was carried out in which comprehensive histologic subtyping was assessed according to the 2015 WHO classification on all consecutive patients who underwent lobectomy or limited resection between 1998 and 2012. Correlation between clinicopathologic parameters and the difference in recurrence between lobectomy and limited resection group was evaluated. Results. Our data show that the proposed 2015 WHO classification identifies histological subsets of small lung adenocarcinomas with significant differences in prognosis. No recurrence was noted for patients with adenocarcinoma in situ and minimally invasive adenocarcinoma. Invasive adenocarcinomas displayed high heterogeneity and the presence of micropapillary component of 5% or greater in adenocarcinomas was significantly related to lymph node involvement and recurrence ( P < .001). Stage IA patients who underwent limited resection had a higher risk of recurrence than did those treated by lobectomy ( P < .05). Conclusions. Application of the 2015 WHO classification identifies patients with adenocarcinoma in situ and minimally invasive adenocarcinoma had excellent prognosis. Micropapillary pattern was associated with high risk of lymph node metastasis and recurrence.

Features Based on Thin-section Computed Tomography Identify the Typeof lung Adenocarcinoma

2020 ◽  
Author(s):  
Zhiqiang Li ◽  
Hongwei Zheng ◽  
Shanshan Liu ◽  
Xinhua Wang ◽  
Lei Xiao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Adenocarcinoma ◽  
Thin Section ◽  
Ground Glass Opacity ◽  
Smoking History ◽  
Ground Glass ◽  
Quantitative Aspect ◽  
Adenocarcinoma In Situ ◽  
Invasive Adenocarcinoma ◽  
Pleural Invasion

Abstract Background: To investigate whether thin-section computed tomography (TSCT) features may efficiently guide the invasiveness basedclassification of lung adenocarcinoma. Methods: Totally, 316 lung adenocarcinoma patients (from 2011-2015) were divided into three groups: 56 adenocarcinoma in situ (AIS), 98 minimally invasive adenocarcinoma (MIA), and 162 invasive adenocarcinoma (IAC) according their pathological results. Their TSCT features, including nodule pattern, shape, pleural invasion, solid proportion, border, margin, vascular convergence, air bronchograms, vacuole sign, pleural indentation, diameter, solid diameter, and CT values of ground-glass nodules (GGN) were analyzed. Pearson’s chi-square test, Fisher’s exact test and One-way ANOVA were adopted tocomparebetweengroups. Receiver operating characteristic (ROC) analysis wereperformedto assess its value for prediction and diagnosis. Results: Patients with IAC were significantly elder than those in AIS or MIA group,and more MIA patients had a smoking history than AIS and IAC. No recurrence happened in the AIS and MIA groups, while 4.3% recurrences were confirmed in the IAC group. As for TSCT variables, we found AIS group showed dominantly higher 91.07%PGGN pattern and 87.50% round/oval nodules than that in MIA and IAC group. In contrast, MIA group showed more cases with undefined border and vascular convergence than AIS and IAC group. Importantly, IAC group uniquely showed higher frequency of pleural invasion compared with MIA and AIS group. The majority of patients (82.1%) in IAC group showed ≥ 50% solid proportion. We found diameter and solid diameter of the lesions were notably larger in the IAC group compared with AIS and MIA groupin quantitative aspect. In addition, for MGGNs, the CT values of ground-glass opacity (GGO) and ground-glass opacity solid portion (GGO-solid) were both higher in the IAC group than AIS and MIA. Finally, we also observed that smooth margin took a dominant proportion in the AIS group while most cases in the IAC group had a lobulate margin. Patients in MIA and IAC group shared higher level of air bronchograms and vacuole signs than AIS group. Conclusions: The unique features in different groups identified by TSCT had diagnosis value for lung adenocarcinoma.

scholarly journals Features Based on Thin-section Computed Tomography Identify the Type of lung Adenocarcinoma

2020 ◽  
Author(s):  
Zhiqiang Li ◽  
Hongwei Zheng ◽  
Shanshan Liu ◽  
Xinhua Wang ◽  
Lei Xiao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Adenocarcinoma ◽  
Thin Section ◽  
Ground Glass Opacity ◽  
Smoking History ◽  
Ground Glass ◽  
Quantitative Aspect ◽  
Adenocarcinoma In Situ ◽  
Invasive Adenocarcinoma ◽  
Pleural Invasion

Abstract Background: To investigate whether thin-section computed tomography (TSCT) features may efficiently guide the invasiveness basedclassification of lung adenocarcinoma. Methods: Totally, 316 lung adenocarcinoma patients (from 2011-2015) were divided into three groups: 56 adenocarcinoma in situ (AIS), 98 minimally invasive adenocarcinoma (MIA), and 162 invasive adenocarcinoma (IAC) according their pathological results. Their TSCT features, including nodule pattern, shape, pleural invasion, solid proportion, border, margin, vascular convergence, air bronchograms, vacuole sign, pleural indentation, diameter, solid diameter, and CT values of ground-glass nodules (GGN) were analyzed. Pearson’s chi-square test, Fisher’s exact test and One-way ANOVA were adopted tocomparebetweengroups. Receiver operating characteristic (ROC) analysis wereperformedto assess its value for prediction and diagnosis. Results: Patients with IAC were significantly elder than those in AIS or MIA group,and more MIA patients had a smoking history than AIS and IAC. No recurrence happened in the AIS and MIA groups, while 4.3% recurrences were confirmed in the IAC group. As for TSCT variables, we found AIS group showed dominantly higher 91.07%PGGN pattern and 87.50% round/oval nodules than that in MIA and IAC group. In contrast, MIA group showed more cases with undefined border and vascular convergence than AIS and IAC group. Importantly, IAC group uniquely showed higher frequency of pleural invasion compared with MIA and AIS group. The majority of patients (82.1%) in IAC group showed ≥ 50% solid proportion. We found diameter and solid diameter of the lesions were notably larger in the IAC group compared with AIS and MIA groupin quantitative aspect. In addition, for MGGNs, the CT values of ground-glass opacity (GGO) and ground-glass opacity solid portion (GGO-solid) were both higher in the IAC group than AIS and MIA. Finally, we also observed that smooth margin took a dominant proportion in the AIS group while most cases in the IAC group had a lobulate margin. Patients in MIA and IAC group shared higher level of air bronchograms and vacuole signs than AIS group. Conclusions: The unique features in different groups identified by TSCT had diagnosis value for lung adenocarcinoma.

scholarly journals Genomic and immune profiling of pre-invasive lung adenocarcinoma

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Haiquan Chen ◽  
Jian Carrot-Zhang ◽  
Yue Zhao ◽  
Haichuan Hu ◽  
Samuel S. Freeman ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Lung Adenocarcinoma ◽  
Copy Number ◽  
Adenocarcinoma In Situ ◽  
Copy Number Alterations ◽  
Invasive Adenocarcinoma ◽  
Minimally Invasive Adenocarcinoma ◽  
Invasive Lung Adenocarcinoma ◽  
Hla Loss

AbstractAdenocarcinoma in situ and minimally invasive adenocarcinoma are the pre-invasive forms of lung adenocarcinoma. The genomic and immune profiles of these lesions are poorly understood. Here we report exome and transcriptome sequencing of 98 lung adenocarcinoma precursor lesions and 99 invasive adenocarcinomas. We have identified EGFR, RBM10, BRAF, ERBB2, TP53, KRAS, MAP2K1 and MET as significantly mutated genes in the pre/minimally invasive group. Classes of genome alterations that increase in frequency during the progression to malignancy are revealed. These include mutations in TP53, arm-level copy number alterations, and HLA loss of heterozygosity. Immune infiltration is correlated with copy number alterations of chromosome arm 6p, suggesting a link between arm-level events and the tumor immune environment.

scholarly journals Evolution of DNA methylome from precancerous lesions to invasive lung adenocarcinomas

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xin Hu ◽  
Marcos R. Estecio ◽  
Runzhe Chen ◽  
Alexandre Reuben ◽  
Linghua Wang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
T Regulatory Cells ◽  
Precancerous Lesions ◽  
Adenocarcinoma In Situ ◽  
Neoplastic Progression ◽  
Invasive Adenocarcinoma ◽  
Reduced Representation ◽  
Dna Methylome ◽  
Lung Adenocarcinomas ◽  
Early Carcinogenesis

AbstractThe evolution of DNA methylome and methylation intra-tumor heterogeneity (ITH) during early carcinogenesis of lung adenocarcinoma has not been systematically studied. We perform reduced representation bisulfite sequencing of invasive lung adenocarcinoma and its precursors, atypical adenomatous hyperplasia, adenocarcinoma in situ and minimally invasive adenocarcinoma. We observe gradual increase of methylation aberrations and significantly higher level of methylation ITH in later-stage lesions. The phylogenetic patterns inferred from methylation aberrations resemble those based on somatic mutations suggesting parallel methylation and genetic evolution. De-convolution reveal higher ratio of T regulatory cells (Tregs) versus CD8 + T cells in later-stage diseases, implying progressive immunosuppression with neoplastic progression. Furthermore, increased global hypomethylation is associated with higher mutation burden, copy number variation burden and AI burden as well as higher Treg/CD8 ratio, highlighting the potential impact of methylation on chromosomal instability, mutagenesis and tumor immune microenvironment during early carcinogenesis of lung adenocarcinomas.

scholarly journals Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meng Nie ◽  
Ke Yao ◽  
Xinsheng Zhu ◽  
Na Chen ◽  
Nan Xiao ◽  
...  
Keyword(s):  
Bile Acid ◽  
Lung Adenocarcinoma ◽  
Acid Metabolism ◽  
Predictive Biomarkers ◽  
Metabolic Reprogramming ◽  
Plasma Metabolites ◽  
Bile Acid Metabolism ◽  
Adenocarcinoma In Situ ◽  
Invasive Adenocarcinoma ◽  
Metabolic Evolution

AbstractMetabolic reprogramming evolves during cancer initiation and progression. However, thorough understanding of metabolic evolution from preneoplasia to lung adenocarcinoma (LUAD) is still limited. Here, we perform large-scale targeted metabolomics on resected lesions and plasma obtained from invasive LUAD and its precursors, and decipher the metabolic trajectories from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC), revealing that perturbed metabolic pathways emerge early in premalignant lesions. Furthermore, three panels of plasma metabolites are identified as non-invasive predictive biomarkers to distinguish IAC and its precursors with benign diseases. Strikingly, metabolomics clustering defines three metabolic subtypes of IAC patients with distinct clinical characteristics. We identify correlation between aberrant bile acid metabolism in subtype III with poor clinical features and demonstrate dysregulated bile acid metabolism promotes migration of LUAD, which could be exploited as potential targetable vulnerability and for stratifying patients. Collectively, the comprehensive landscape of the metabolic evolution along the development of LUAD will improve early detection and provide impactful therapeutic strategies.

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