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

Fang, Weiyuan; Zhang, Guorui; Yu, Yanxia; Chen, Hongjie; Liu, Hong

doi:10.1042/bsr20212416

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…Recently, a study reported that 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. Two models that distinguished the pathologic subtypes of LUAD have been developed using AI, and the AUCs of the predictive model for identifying AAH/AIS and MIA and of the model for identifying MIA and IAC were 0.779 and 0.918, respectively ( 22 ). When the diagnostic value of the AI tumor volume was accessed, the AUC of tumor volume based on AI in diagnosing tumor invasiveness was 0.698, which was slightly lower than previously reported findings ( 22 , 24 ).…”

Section: Discussionmentioning

confidence: 99%

“…Two models that distinguished the pathologic subtypes of LUAD have been developed using AI, and the AUCs of the predictive model for identifying AAH/AIS and MIA and of the model for identifying MIA and IAC were 0.779 and 0.918, respectively ( 22 ). When the diagnostic value of the AI tumor volume was accessed, the AUC of tumor volume based on AI in diagnosing tumor invasiveness was 0.698, which was slightly lower than previously reported findings ( 22 , 24 ). Based on the FR + CTC level, tumor invasiveness predictive models have been established.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have reported artificial intelligence (AI) quantitative parameters based on pathologic features or CT images of squamous cell carcinoma, adenocarcinoma and small cell lung carcinoma (19,20). Further studies of AI quantitative parameters were based on CT images of pathological subtypes of LUAD with pure ground glass nodules (GGNs) or early LUAD (21)(22)(23). Others have reported CT-based deep learning models for invasiveness classification of LUAD (24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

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A retrospective diagnostic test study on circulating tumor cells and artificial intelligence imaging in patients with lung adenocarcinoma

Ma¹,

Xu²,

Han³

et al. 2022

Ann Transl Med

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Background: Either tumor volume or folate-receptor-positive circulating tumor cells (FR + CTC) has been proven effective in predicting tumor cell invasion. However, it has yet to be documented to use FR + CTC along with artificial intelligence (AI) tumor volume to differentiate between pathological subtypes of lung adenocarcinoma (LUAD). Therefore, this study is aimed to evaluate the accuracy of FR + CTC and AI tumor volume for classifying the invasiveness of LUAD. Methods: A total of 226 patients who were diagnosed with LUAD were enrolled. The inclusion criteria were: (I) FR + CTC detection and AI imaging before anticancer therapy, and (II) definite histopathologic diagnosis, which is the gold diagnosis of LUAD and its subtypes. Use the CytoploRare ® Detection Kit to quantify FR + CTC and the AI-assisted diagnosis system, ScrynPro, to measure tumor volume. The clinical data were used to construct univariate and multivariate logistic regression models. A nomogram was drawn based on the multivariate logistic regression model. The validity is evaluated by the calibration curve and Hosmer-Lemeshow goodness-of-fit test.Results: The mean age of 146 patients (96 males, 49 females and 1 gender missing) retrospectively enrolled was 56.6. In the cohort, 41 and 105 patients were assigned to adenocarcinoma in situ (AIS) + minimally invasive adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IPA), respectively. There was no significant difference between the sex distribution and smoking history of the two groups (P=0.155 and P=0.442, respectively). In univariate analysis, the nodules type, maximum density, tumor volume and FR + CTC level were statistically significant with the invasiveness of LUAD (P<0.05). The multivariate analysis showed significant differences in FR + CTC and AI tumor volume (P<0.001). The area under the curves (AUCs) of FR + CTC and AI tumor volume in diagnosing tumor invasiveness were 0.659 and 0.698, respectively. A predictive model combining FR + CTC with AI tumor volume showed a sensitivity of 86.89% and a specificity of 70.94%, and the AUC was 0.841. The nomogram had good agreement with actual observation, and the Hosmer-Lemeshow test yielded non-significant goodness-of-fit.Conclusions: FR + CTC and/or AI tumor volume are independent indicators of the invasiveness of LUAD, and the nomogram based on them can be used for the preoperative screening of patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A retrospective diagnostic test study on circulating tumor cells and artificial intelligence imaging in patients with lung adenocarcinoma

Ma¹,

Xu²,

Han³

et al. 2022

Ann Transl Med

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“…Such effective classifications are expected to have applications in clinical practice. Fang et al ( 28 ) analyzed CT images of 224 hairy nodules and GGNs from 210 patients. AI identified GGNs using quantitative parameters and CT signs.…”

Section: Applications Of Ai In Lung Cancer Diagnosismentioning

confidence: 99%

Research in the application of artificial intelligence to lung cancer diagnosis

Liu,

Shen,

Zhang

et al. 2024

Front. Med.

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The morbidity and mortality rates in lung cancer are high worldwide. Early diagnosis and personalized treatment are important to manage this public health issue. In recent years, artificial intelligence (AI) has played increasingly important roles in early screening, auxiliary diagnosis, and prognostic assessment. AI uses algorithms to extract quantitative feature information from high-volume and high-latitude data and learn existing data to predict disease outcomes. In this review, we describe the current uses of AI in lung cancer-focused pathomics, imageomics, and genomics applications.

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“…The CT features, including quantitative and qualitative indicators, were typically used for evaluating the invasiveness of GGNs in previous studies, and have revealed the positive correlations between them [10][11][12]. Among them, the density was more frequently used than others, and both the two-dimensional (2D) and three-dimensional (3D) mean CT values of nodules were effective for further evaluations.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground glass nodules

Xiong,

Fu,

et al. 2024

Preprint

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Background: A positive correlation has been observed between CT value and the invasiveness of neoplastic ground glass nodules (GGNs). However, the traditional mean CT value cannot reflect nodule heterogeneity in density. Purpose: To explore the value of artificial intelligence (AI)-based density proportion analysis in predicting the invasiveness of neoplastic ground glass nodules (GGNs). Methods: Between January 2019 and May 2023, a total of 687 neoplastic GGNs (247 adenocarcinomas in situ[AISs], 231 minimally invasive adenocarcinomas [MIAs], and 209 invasive adenocarcinomas [IACs]) in 654 patients were retrospectively analyzed. AI software was used to obtain the density histograms of nodules, and subsequently the proportions of the components in lesions with higher density at different density thresholds were recorded. The optimal density threshold and the corresponding proportion cutoff value for determining invasive lesions (ILs) (MIAs and IACs) and IACs were respectively explored by conducting receiver operating characteristic curve analysis. Results: For determining the ILs and IACs, the optimal density threshold and the cutoff value for the proportion of components with density higher than the threshold were ≥ −350 HU and 17.22% (area under the curve [AUC]: 0.801, 95% confidence interval (CI): 0.769–0.830, sensitivity: 51.59%, specificity: 93.52%), and ≥ −250HU and 5.64% (AUC: 0.882, 95% CI: 0.855–0.905; sensitivity: 85.65%, specificity: 76.15%), respectively. In combination with these indicators, the AUCs of the morphological features in predicting ILs and IACs increased from 0.794 to 0.849 and from 0.843 to 0.902 (each P < 0.001), respectively. Conclusion: AI-based density analysis has a potential role in determining the invasiveness of neoplastic GGNs.

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

Cited by 9 publications

References 36 publications

A retrospective diagnostic test study on circulating tumor cells and artificial intelligence imaging in patients with lung adenocarcinoma

A retrospective diagnostic test study on circulating tumor cells and artificial intelligence imaging in patients with lung adenocarcinoma

Research in the application of artificial intelligence to lung cancer diagnosis

Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground glass nodules

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