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

CT-based radiomics combined with clinical features for invasiveness prediction and pathological subtypes classification of subsolid pulmonary nodules

Purpose

To construct optimal models for predicting the invasiveness and pathological subtypes of subsolid nodules (SSNs) based on CT radiomics and clinical features.

Materials and Methods

This study was a retrospective study involving two centers. A total of 316 patients with 353 SSNs confirmed as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were included from January 2019 to February 2023. Models based on CT radiomics and clinical features were constructed for classification of AAH/AIS and MIA, MIA and IAC, as well as lepidic-predominant adenocarcinoma (LPA) and acinar-predominant adenocarcinoma (APA). Receiver operating characteristic (ROC) curve was used to evaluate the model performance. Finally, the nomograms based on the optimal models were established.

Results

The nomogram based on the combined model (AAH/AIS versus MIA) consisting of lobulation, the GGN-vessel relationship, diameter, CT value, consolidation tumor ratio (CTR) and rad-score performed the best (AUC=0.841), while age, CT value, CTR and rad-score were the significant features for distinguishing MIA from IAC, the nomogram based on these features performed the best (AUC=0.878). There were no significant differences in clinical features between LPA and APA, while the radiomics model based on rad-score showed good performance for distinguishing LPA from APA (AUC=0.926).

Conclusions

The nomograms based on radiomics and clinical features could predict the invasiveness of SSNs accurately. Moreover, radiomics models showed good performance in distinguishing LPA from APA.

Clinicopathological and molecular characterization of resected lung adenocarcinoma: Correlations with histopathological grading systems in Chinese patients

PURPOSE

Driver mutations inform lung adenocarcinoma (LUAD) targeted therapy. Association of histopathological attributes and molecular profiles facilitates clinically viable testing platforms. We assessed correlations between LUAD clinicopathological features, mutational landscapes, and two grading systems among Chinese cases.

METHODS

79 Chinese LUAD patients undergoing resection were subjected to targeted sequencing. 68 were invasive nonmucinous adenocarcinoma (INMA), graded via: predominant histologic pattern-based grading system (P-GS) or novel IASLC grading system (I-GS). Driver mutation distributions were appraised and correlated with clinical and pathological data.

RESULTS

Compared to INMA, non-INMA exhibited smaller, well-differentiated tumors with higher mucin content. INMA grade correlated with size, lymph invasion (P-GS), and driver/EGFR mutations. Mutational spectra varied markedly between grades, with EGFR p.L858R and exon 19 deletion mutations predominating in lower grades; while high-grade P-GS tumors often harbored EGFR copy number variants and complex alterations alongside wild-type cases. I-GS upgrade of P-GS grade 2 to grade 3 was underpinned by ≥20 % high-grade regions bearing p.L858R or ALK fusions. Both systems defined tumors of distinctive phenotypic attributes and molecular genotypes.

CONCLUSIONS

INMA represent larger, mucin-poor, molecularly heterogeneous LUAD with divergent grade-specific mutation profiles. Stronger predictor of clinicopathological attributes and driver mutations, P-GS stratification offers greater accuracy for molecular testing. A small panel encompassing EGFR and ALK captures the majority of P-GS grade 1/2 mutations whereas expanded panels are optimal for grade 3.

[Value of CT Quantitative Parameters in Prediction of Pathological Types of Lung Ground Glass Nodules]

BACKGROUND

The pathological types of lung ground glass nodules (GGNs) show great significance to the clinical treatment. This study was aimed to predict pathological types of GGNs based on computed tomography (CT) quantitative parameters.

METHODS

389 GGNs confirmed by postoperative pathology were selected, including 138 cases of precursor glandular lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], 109 cases of microinvasive adenocarcinoma (MIA) and 142 cases of invasive adenocarcinoma (IAC). The morphological characteristics of nodules were evaluated subjectively by radiologist, as well as artificial intelligence (AI).

RESULTS

In the subjective CT signs, the maximum diameter of nodule and the frequency of spiculation, lobulation and pleural traction increased from AAH+AIS, MIA to IAC. In the AI quantitative parameters, parameters related to size and CT value, proportion of solid component, energy and entropy increased from AAH+AIS, MIA to IAC. There was no significant difference between AI quantitative parameters and the subjective CT signs for distinguishing the pathological types of GGNs.

CONCLUSIONS

AI quantitative parameters were valuable in distinguishing the pathological types of GGNs.

Research in the application of artificial intelligence to lung cancer diagnosis

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.

A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence

PURPOSE

To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance.

METHODS

187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve.

RESULTS

The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05).

CONCLUSION

For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.

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

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.