[Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules]

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

Background

Currently, the computed tomography (CT) radiomics-based models, which can evaluate small (≤ 20 mm) solid pulmonary nodules (SPNs) are lacking. This study aimed to develop a CT radiomics-based model that can differentiate between benign and malignant small SPNs.

Methods

This study included patients with small SPNs between January 2019 and November 2021. The participants were then randomly categorized into training and testing cohorts with an 8:2 ratio. CT images of all the patients were analyzed to extract radiomics features. Furthermore, a radiomics scoring model was developed based on the features selected in the training group via univariate and multivariate logistic regression analyses. The testing cohort was then used to validate the developed predictive model.

Results

This study included 210 patients, 168 in the training and 42 in the testing cohorts. Radiomics scores were ultimately calculated based on 9 selected CT radiomics features. Furthermore, traditional CT and clinical risk factors associated with SPNs included lobulation (P < 0.001), spiculation (P < 0.001), and a larger diameter (P < 0.001). The developed CT radiomics scoring model comprised of the following formula: X = -6.773 + 12.0705×radiomics score+2.5313×lobulation (present: 1; no present: 0)+3.1761×spiculation (present: 1; no present: 0)+0.3253×diameter. The area under the curve (AUC) values of the CT radiomics-based model, CT radiomics score, and clinicoradiological score were 0.957, 0.945, and 0.853, respectively, in the training cohort, while that of the testing cohort were 0.943, 0.916, and 0.816, respectively.

Conclusions

The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy in distinguishing benign and malignant SPNs.

18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

BACKGROUND

Several studies to date have reported on the development of positron emission tomography (PET)/computed tomography (CT)-based models intended to effectively distinguish between benign and malignant pulmonary nodules (PNs). This meta-analysis was designed with the goal of clarifying the utility of these PET/CT-based conventional parameter models as diagnostic tools in the context of the differential diagnosis of PNs.

METHODS

Relevant studies published through September 2023 were identified by searching the Web of Science, PubMed, and Wanfang databases, after which Stata v 12.0 was used to conduct pooled analyses of the resultant data.

RESULTS

This meta-analysis included a total of 13 retrospective studies that analyzed 1,731 and 693 malignant and benign PNs, respectively. The respective pooled sensitivity, specificity, PLR, and NLR values for the PET/CT-based studies developed in these models were 88% (95%CI: 0.86-0.91), 78% (95%CI: 0.71-0.85), 4.10 (95%CI: 2.98-5.64), and 0.15 (95%CI: 0.12-0.19). Of these endpoints, the pooled analyses of model sensitivity (I2 = 69.25%), specificity (I2 = 78.44%), PLR (I2 = 71.42%), and NLR (I2 = 67.18%) were all subject to significant heterogeneity. The overall area under the curve value (AUC) value for these models was 0.91 (95%CI: 0.88-0.93). When differential diagnosis was instead performed based on PET results only, the corresponding pooled sensitivity, specificity, PLR, and NLR values were 92% (95%CI: 0.85-0.96), 51% (95%CI: 0.37-0.66), 1.89 (95%CI: 1.36-2.62), and 0.16 (95%CI: 0.07-0.35), with all four being subject to significant heterogeneity (I2 = 88.08%, 82.63%, 80.19%, and 86.38%). The AUC for these pooled analyses was 0.82 (95%CI: 0.79-0.85).

CONCLUSIONS

These results suggest that PET/CT-based models may offer diagnostic performance superior to that of PET results alone when distinguishing between benign and malignant PNs.

Establishment of a prognostic signature for patients with advanced lung squamous cell carcinoma based on tumor-infiltrating immune cells

Background

With the advancements in the fields of science, technology, and medical therapy, there is an increasing awareness among the general public regarding tumor-infiltrating immune cells. These immune cells have a close association with the prognosis of clinical patients with lung cancer.

Methods

The research used a comprehensive analysis and assessed tumor-infiltrating immune cells in advanced lung squamous cell carcinoma (LUSC) using The Cancer Genome Atlas (TCGA) database and the CIBERSORT algorithm. The research examined 22 types of tumor-infiltrating immune cells and observed notable differences in the infiltration patterns of immune cells between normal tissue and advanced LUSC.

Results

Univariate Cox regression analyses revealed a positive correlation between macrophages M2 and patient prognoses, as well as potential influences on patient prognosis by natural killer (NK) cells resting, monocytes, and activated mast cells. Multivariate Cox regression models were developed, incorporating three types of immune cells. The efficacy of the model was evaluated using a receiver operating characteristic (ROC) curve. Furthermore, the research constructed a nomogram model to individually predict the mortality risk in patients with advanced LUSC. This prediction model serves as a valuable tool for clinicians, enabling them to provide effective guidance based on tumor-infiltrating immune cells for advanced LUSC patients.

Conclusions

The research comprehensively analyzed and evaluated 22 types of tumor-infiltrating immune cells from advanced LUSC, revealing the correlation between immune cell infiltration and overall survival (OS) in clinical patients. Based on the nomogram of NK cells resting, monocytes, and macrophages M2, it can make specific prognostic predictions for advanced LUSC patients.

Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

BACKGROUND

To date, multiple predictive models have been developed with the goal of reliably differentiating between solitary pulmonary nodules (SPNs) that are malignant and those that are benign. The present meta-analysis was conducted to assess the diagnostic utility of these predictive models in the context of SPN differential diagnosis.

METHODS

The PubMed, Embase, Cochrane Library, CNKI, Wanfang, and VIP databases were searched for relevant studies published through August 31, 2021. Pooled data analyses were conducted using Stata v12.0.

RESULTS

In total, 20 retrospective studies that included 5171 SPNs (malignant/benign: 3662/1509) were incorporated into this meta-analysis. Respective pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic score values were 88% (95CI%: 0.84-0.91), 78% (95CI%: 0.74-0.80), 3.91 (95CI%: 3.42-4.46), 0.16 (95CI%: 0.12-0.21), and 3.21 (95CI%: 2.87-3.55), with an area under the summary receiver operating characteristic curve value of 86% (95CI%: 0.83-0.89). Significant heterogeneity among studies was detected with respect to sensitivity (I² = 89.07%), NLR (I² = 87.29%), and diagnostic score (I² = 72.28%). In a meta-regression analysis, sensitivity was found to be impacted by the standard reference in a given study (surgery and biopsy vs. surgery only, P = 0.02), while specificity was impacted by whether studies were blinded (yes vs. unclear, P = 0.01). Sensitivity values were higher when surgery and biopsy samples were used as a standard reference, while unclear blinding status was associated with increased specificity. No significant evidence of publication bias was detected for the present meta-analysis (P = 0.539).

CONCLUSIONS

The results of this meta-analysis demonstrate that predictive models can offer significant diagnostic utility when establishing whether SPNs are malignant or benign.