Computed Tomography Histogram Approach to Predict Lymph Node Metastasis in Patients With Clinical Stage IA Lung Cancer

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis

BACKGROUND

The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma.

METHODS

Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted.

RESULTS

Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively.

CONCLUSIONS

Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics.

TRIAL REGISTRATION

CRD42022375712.

Distinct impacts of radiological appearance on lymph node metastasis and prognosis based on solid size in clinical T1 non-small cell lung cancer

BACKGROUND

Solid nodules (SN) had more aggressive features and a poorer prognosis than part-solid nodules (PSN). This study aimed to evaluate the specific impacts of nodule radiological appearance (SN vs. PSN) on lymph node metastasis and prognosis based on solid size in cT1 non-small cell lung cancer (NSCLC).

METHODS

Patients with cT1 NSCLC who underwent anatomical resection between 2010 and 2019 were retrospectively screened. Univariable and multivariable logistic regression analyses were adopted to evaluate the associations between nodule radiological appearance and lymph node metastasis. The log-rank test and Cox regression analyses were applied for prognostic evaluation. The cumulative recurrence risk was evaluated by the competing risk model.

RESULTS

There were 958 and 665 NSCLC patients with PSN and SN. Compared to the PSN group, the SN arm had a higher overall lymph node metastasis rate (21.7% vs. 2.7%, P < 0.001), including nodal metastasis at N1 stations (17.7% vs. 2.1%), N2 stations (14.0% vs. 1.6%), and skip nodal metastasis (3.9% vs. 0.6%). However, for cT1a NSCLC, no significant difference existed between SN and PSN (0 vs. 0.4%, P = 1). In addition, the impacts of nodule radiological appearance on lymph node metastasis varied between nodal stations. Solid NSCLC had an inferior prognosis than part-solid patients (5-year disease-free survival: 79.3% vs. 96.2%, P < 0.001). The survival inferiority only existed for cT1b and cT1c NSCLC, but not for cT1a. Strikingly, even for patients with nodal involvement, SN still had a poorer disease-free survival (P = 0.048) and a higher cumulative incidence of recurrence (P < 0.001) than PSN. Specifically, SN had a higher recurrence risk than PSN at each site. Nevertheless, the distribution of recurrences between SN and PSN was similar, except that N2 lymph node recurrences were more frequent in solid NSCLC (28.21% vs. 7.69%, P = 0.041).

CONCLUSION

SN had higher risks of lymph node metastasis and poorer prognosis than PSN for cT1b and cT1c NSCLC, but not for cT1a. SN exhibited a greater proportion of N2 lymph node recurrence than PSN. SN and PSN needed distinct strategies for nodal evaluation and postoperative follow-up.

A Shift in Paradigm: Selective Lymph Node Dissection for Minimizing Oversurgery in Early Stage Lung Cancer

Systematic lymph node dissection has been widely accepted and turned into a standard procedure for lung cancer surgery. In recent years, the concept of "minimal invasive surgery (MIS)" has greatly changed the surgical paradigm of lung cancer. Previous studies revealed that excessive dissection of lymph nodes without metastases had uncertain clinical benefit. Meanwhile, it leads to the elevated risk of postoperative complications including chylothorax and laryngeal nerve injury. In addition, dissection of nonmetastatic lymph nodes may disturb systematic immunity, resulting in the secondary effect on primary tumor or latent metastases. The past decades have witnessed the innovative strategies such as lobe-specific lymph node dissection and selective lymph node dissection. On the basis of evolution of lymph node dissection strategy, we discuss the negative effects of excessive nonmetastatic lymph node dissection and summarize the recent advances in the optimized dissection strategies, hoping to provide unique perspectives on the future directions.

Artificial intelligence-based radiomics for the prediction of nodal metastasis in early-stage lung cancer

We aimed to investigate the value of computed tomography (CT)-based radiomics with artificial intelligence (AI) in predicting pathological lymph node metastasis (pN) in patients with clinical stage 0-IA non-small cell lung cancer (c-stage 0-IA NSCLC). This study enrolled 720 patients who underwent complete surgical resection for c-stage 0-IA NSCLC, and were assigned to the derivation and validation cohorts. Using the AI software Beta Version (Fujifilm Corporation, Japan), 39 AI imaging factors, including 17 factors from the AI ground-glass nodule analysis and 22 radiomics features from nodule characterization analysis, were extracted to identify factors associated with pN. Multivariate analysis showed that clinical stage IA3 (p = 0.028), solid-part size (p < 0.001), and average solid CT value (p = 0.033) were independently associated with pN. The receiver operating characteristic analysis showed that the area under the curve and optimal cut-off values of the average solid CT value relevant to pN were 0.761 and -103 Hounsfield units, and the threshold provided sensitivity, specificity, and negative predictive values of 69%, 65%, and 94% in the entire cohort, respectively. Measuring the average solid-CT value of tumors for pN may have broad applications such as guiding individualized surgical approaches and postoperative treatment.

Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules

Background

Tumor invasiveness plays a key role in determining surgical strategy and patient prognosis in clinical practice. The study aimed to explore artificial-intelligence-based computed tomography (CT) histogram indicators significantly related to the invasion status of lung adenocarcinoma appearing as part-solid nodules (PSNs), and to construct radiomics models for prediction of tumor invasiveness.

Methods

We identified surgically resected lung adenocarcinomas manifesting as PSNs in Peking University People's Hospital from January 2014 to October 2019. Tumors were categorized as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) by comprehensive pathological assessment. The whole cohort was randomly assigned into a training (70%, n=832) and a validation cohort (30%, n=356) to establish and validate the prediction model. An artificial-intelligence-based algorithm (InferRead CT Lung) was applied to extract CT histogram parameters for each pulmonary nodule. For feature selection, multivariate regression models were built to identify factors associated with tumor invasiveness. Logistic regression classifier was used for radiomics model building. The predictive performance of the model was then evaluated by ROC and calibration curves.

Results

In total, 299 AIS/MIAs and 889 IACs were included. In the training cohort, multivariate logistic regression analysis demonstrated that age [odds ratio (OR), 1.020; 95% CI, 1.004-1.037; p=0.017], smoking history (OR, 1.846; 95% CI, 1.058-3.221; p=0.031), solid mean density (OR, 1.014; 95% CI, 1.004-1.024; p=0.008], solid volume (OR, 5.858; 95% CI, 1.259-27.247; p = 0.037), pleural retraction sign (OR, 3.179; 95% CI, 1.057-9.559; p = 0.039), variance (OR, 0.570; 95% CI, 0.399-0.813; p=0.002), and entropy (OR, 4.606; 95% CI, 2.750-7.717; p<0.001) were independent predictors for IAC. The areas under the curve (AUCs) in the training and validation cohorts indicated a better discriminative ability of the histogram model (AUC=0.892) compared with the clinical model (AUC=0.852) and integrated model (AUC=0.886).

Conclusion

We developed an AI-based histogram model, which could reliably predict tumor invasiveness in lung adenocarcinoma manifesting as PSNs. This finding would provide promising value in guiding the precision management of PSNs in the daily practice.

Application of Different Ventilation Modes Combined with AutoFlow Technology in Thoracic Surgery

To investigate the effect of AutoFlow on airway pressure and hemodynamics in mechanical ventilation constant volume-control ventilation mode, 100 patients receiving mechanical ventilation were randomly divided into observation group (SIMV-PSV-PEEP + AutoFlow) and control group (SIMV-PSV-PEEP). The results showed that the peak airway pressure and average airway pressure decreased with different flow rate settings and automatic flow conversion (P < 0.05). The peak airway pressure and mean airway pressure decreased with different resistance settings (P < 0.05). With different compliance settings, the peak airway pressure and average airway pressure decreased after being assisted with an automatic converter (P < 0.05). Adding AutoFlow on the basis of SIMV-PSV mode can significantly reduce peak inspiratory pressure (PIP), mean airway pressure (Pmean), and airway resistance (R). There was no significant difference in hemodynamic monitoring results between the observation group and the control group. It is proved that the SIMV constant volume-controlled ventilation mode combined with AutoFlow can not only ensure tidal volume but also avoid excessive airway pressure, which has little effect on hemodynamics.

Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma

Background

Based on the “seed and soil” theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC).

Methods

Radiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (−) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model’s performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed.

Results

Thirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691–0.927 in the training cohort and 0.700–0.951 in the validation cohort, respectively, thereby indicating good performance.

Conclusion

CT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.