Use of relative CT values to evaluate the invasiveness of pulmonary subsolid nodules in patients with emphysema

Clinicopathological and computed tomography features associated with recurrence-free survival of patients with small-sized peripheral invasive lung adenocarcinoma after sublobectomy

Background

Sublobar resection is gradually becoming a standard treatment for small-sized (≤2 cm) peripheral non-small cell lung cancer (NSCLC), with lung adenocarcinoma (LADC) being the most frequent histologic subtype. However, the prognostic predictors for preoperatively determining whether sublobectomy is feasible for patients with early LADC have not yet been well identified. Therefore, this study aimed to investigate the clinicopathological and computed tomography (CT) features associated with the recurrence-free survival (RFS) of patients with small-sized invasive LADC (SILADC) after sublobar resection.

Methods

This retrospective cohort study analyzed 107 patients with SILADC who underwent preoperative chest CT scan and sublobar resection from December 2012 to March 2019. The Kaplan-Meier survival was used to analyze the relationship between clinicopathological characteristics, preoperative chest CT findings, and RFS. The Cox proportional hazards regression was used to identify independent prognostic factors of poor RFS.

Results

For clinicopathological characteristics, RFS was shorter in patients aged ≥70 years, smokers, and those with micropapillary/solid-predominant adenocarcinomas (all P values <0.05). For preoperative CT features, RFS was shorter in patients with tumor size ≥1.4 cm, solid component size ≥1.1 cm, proportion of solid component ≥72%, solid density, spiculation, vascular convergence sign, peripheral fibrosis, and type II pleural tag (all P values <0.05). Multivariate analysis showed proportion of solid component ≥72% [hazard ratio (HR): 5.920; P=0.006; 95% confidence interval (CI): 1.686-20.794], spiculation (HR: 5.026; P=0.001; 95% CI: 2.008-12.581), and type II pleural tag (HR: 4.638; P=0.002; 95% CI: 1.773-12.136) were independent risk factors for poor prognosis in patients with SILADC after sub-lobectomy.

Conclusions

Clinicopathological and CT characteristics are helpful for predicting the RFS of patients with SILADC after sublobar resection and can be used as an auxiliary tool for thoracic surgeons to choose the best surgical mode.

A machine learning approach for preoperatively assessing pulmonary function with computed tomography in patients with lung cancer

Background

It is clinically important to accurately assess the pulmonary function of patients with lung cancer, especially before surgery. This knowledge can help clinicians to monitor patients pre- and post-surgery, predict the impact of surgery on pulmonary function, and help to optimize postsurgical recovery. We used a deep learning approach for assessing pulmonary function on computed tomography (CT) scans in patients with lung cancer before they underwent surgery.

Methods

A total of 188 patients with lung cancer whose diagnoses had been pathologically confirmed were enrolled in this study. We used a software to automatically delineate regions of interest (ROIs) throughout the airways, lobes, and the whole lungs. We then used AK software to extract radiomics features of the 3 types of ROIs. We randomly separated these cases into a training cohort and a test cohort at a ratio of 7:3. We next constructed a logistic regression model to assess pulmonary function from the radiomics features. The machine learning outcomes were compared with established clinical criteria for pulmonary function. including forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), FVC, and maximum vital capacity (VCmax) to evaluate the accuracy of the machine learning model.

Results

In the ROIs of the lobes, our results showed that the machine learning model had good performance in predicting FVC and VCmax, attaining a Spearman correlation r value of 0.714 with P<0.001 for FVC and a r value of 0.687 with P<0.001 for VCmax. Using the airway ROIs, our model achieved a r of 0.603 with P=0.001 for VCmax. Using the whole lung ROIs, our model achieved a r of 0.704 with P<0.001 for FVC and a r of 0.693 with P<0.001 for VCmax.

Conclusions

Preoperative CT may provide a means for evaluating pulmonary function in patients with lung cancer. With radiomics features extracted from the airway, lobes, and the whole lung region, and a properly trained machine learning model, it is possible to obtain accurate estimation for metrics used in clinical criteria and to offer clinicians imaging-based indicators for the status of pulmonary functions.

Quantitative evaluation of density variability in the lesion-lung boundary zone to differentiate pulmonary subsolid nodules

Background

Transition of the CT values from nodule to peripheral normal lung is related to pathological changes and may be a potential indicator for differential diagnosis. This study investigated the significance of the standard deviation (SD) values in the lesion-lung boundary zone when differentiating between benign and neoplastic subsolid nodules (SSNs).

Methods

From January 2012 to July 2021, a total of 229 neoplastic and 84 benign SSNs confirmed by pathological examination were retrospectively and nonconsecutively enrolled in this study. The diagnostic study was not registered with a clinical trial platform, and the study protocol was not published. Computed tomography (CT) values of the ground-glass component (CT1), adjacent normal lung tissue (CT2), and lesion-lung boundary zone (CT3) were measured consecutively. The SD of CT3 was recorded to assess density variability. The CT1, CT2, CT3, and SD values were compared between benign and neoplastic SSNs.

Results

No significant differences in CT1 and CT2 were observed between benign and neoplastic SSNs (each P value >0.05). CT3 (-736.1±51.0 vs. -792.6±73.9; P<0.001) and its SD (135.6±29.6 vs. 83.6±20.6; P<0.001) in neoplastic SSNs were significantly higher than those in benign SSNs. Moreover, the SD increased with the invasiveness degree of neoplastic SSNs (r=0.657; P<0.001). The receiver operating characteristic (ROC) curve revealed that the area under the curve was 0.927 (95% CI: 0.896-0.959) when using the SD (cutoff value =106.98) as a factor to distinguish SSNs, which increased to 0.966 (95% CI: 0.934-0.985) when including nodules with a CT1 of ≥-715 Hounsfield units (HU) only (cutoff of SD 109.9, sensitivity 0.930, and specificity 0.914).

Conclusions

The SD as an objective index is valuable for differentiating SSNs, especially for those with a CT1 of ≥-715 HU, which have a higher possibility of neoplasm if the SD is >109.9.

Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening

BACKGROUND

Due to different management strategy and prognosis of different subtypes of lung adenocarcinomas appearing as pure ground-glass nodules (pGGNs), it is important to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) during lung cancer screening. The aim of this study was to develop and validate the qualitative and quantitative models to predict the invasiveness of lung adenocarcinoma appearing as pGGNs based on low-dose computed tomography (LDCT) and compare their diagnostic performance with that of intraoperative frozen section (FS).

METHODS

A total of 223 consecutive pathologically confirmed pGGNs from March 2018 to December 2020 were divided into a primary cohort (96 IAs and 64 AIS/MIAs) and validation cohort (39 IAs and 24 AIS/MIAs) according to scans (Brilliance iCT and Somatom Definition Flash) performed at Sichuan Cancer Hospital and Institute. The following LDCT features of pGGNs were analyzed: the qualitative features included nodule location, shape, margin, nodule-lung interface, lobulation, spiculation, pleural indentation, air bronchogram, vacuole, and vessel type, and the quantitative features included the diameter, volume, and mean attenuation. Multivariate logistic regression analysis was used to build a qualitative model, quantitative model, and combined qualitative and quantitative model. The diagnostic performance was assessed according to the following factors: the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy.

RESULTS

The AUCs of the qualitative model, quantitative model, combined qualitative and quantitative model, and the FS diagnosis were 0.854, 0.803, 0.873, and 0.870, respectively, in the primary cohort and 0.884, 0.855, 0.875, and 0.946, respectively, in the validation cohort. No significant difference of the AUCs was found among the radiological models and the FS diagnosis in the primary or validation cohort (all corrected P>0.05). Among the radiological models, the combined qualitative and quantitative model consisting of vessel type and volume showed the highest accuracy in both the primary and validation cohorts (0.831 and 0.889, respectively).

CONCLUSIONS

The diagnostic performances of the qualitative and quantitative models based on LDCT to differentiate IA from AIS/MIA in pGGNs are equivalent to that of intraoperative FS diagnosis. The vessel type and volume can be preoperative and non-invasive biomarkers to assess the invasive risk of pGGNs in lung cancer screening.

Benign and malignant pulmonary part-solid nodules: differentiation via thin-section computed tomography

BACKGROUND

Pulmonary part-solid nodules (PSNs) reportedly have a high possibility of malignancy, while benign PSNs are common. This study aimed to reveal the differences between benign and malignant PSNs by comparing their thin-section computed tomography (CT) features.

METHODS

Patients with PSNs confirmed by postoperative pathological examination or follow-up (at the same period) were retrospectively enrolled from March 2016 to January 2020. The clinical data of patients and CT features of benign and malignant PSNs were reviewed and compared. Binary logistic regression analysis was performed to reveal the predictors of malignant PSNs.

RESULTS

A total of 119 PSNs in 117 patients [age (mean ± standard deviation), 56±11 years; 70 women] were evaluated. Of the 119 PSNs, 44 (37.0%) were benign, and 75 (63.0%) were malignant (12 adenocarcinomas in situ, 22 minimally invasive adenocarcinomas, and 41 invasive adenocarcinomas). There were significant differences in the patients' age and smoking history between benign and malignant PSNs. In terms of CT characteristics, malignant and benign lesions significantly differed in the following CT features: whole nodule, internal solid component, and peripheral ground-glass opacity. The binary logistic regression analysis revealed that well-defined border [odds ratio (OR), 4.574; 95% confidence interval (CI), 1.186-17.643; P=0.027] and lobulation (OR, 61.739; 95% CI, 5.230-728.860; P=0.001) of the nodule, as well as irregular shape (OR, 9.502; 95% CI, 1.788-50.482; P=0.008) and scattered distribution (OR, 13.238; 95% CI, 1.359-128.924; P=0.026) of the internal solid components were significant independent predictors distinguishing malignant PSNs. However, the lesion shape, density, and margin were similar between malignant and benign lesions.

CONCLUSIONS

Well-defined and lobulated PSNs with irregular and scattered solid components are highly likely to be malignant.

Predicting the histological invasiveness of pulmonary adenocarcinoma manifesting as persistent pure ground-glass nodules by ultra-high-resolution CT target scanning in the lateral or oblique body position

Background

Ultra-high-resolution computed tomography (U-HRCT) has improved image quality for displaying the detailed characteristics of disease states and lung anatomy. The purpose of this study was to retrospectively examine whether U-HRCT target scanning in the lateral or oblique body position (protocol G scan) could predict histological invasiveness of pulmonary adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).

Methods

From January 2015 to December 2016, 260 patients with 306 pathologically confirmed pGGNs who underwent preoperative protocol G scans were retrospectively reviewed and analyzed. The U-HRCT findings of preinvasive lesions [atypical adenomatous hyperplasias (AAH) and adenocarcinomas in situ (AIS)] and invasive pulmonary adenocarcinomas [minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC)] were manually compared and analyzed using orthogonal multiplanar reformation (MPR) images. The logistic regression model was established to determine variables that could predict the invasiveness of pGGNs. Receiver operating characteristic (ROC) curve analysis was performed to evaluate their diagnostic performance.

Results

There were 213 preinvasive lesions (59 AAHs and 154 AISs) and 93 invasive pulmonary adenocarcinomas (53 MIAs and 40 IACs). Compared with the preinvasive lesions, invasive adenocarcinomas exhibited a larger diameter (13.5 vs. 9.3 mm, P=0.000), higher mean attenuation (-571 vs. -613 HU, P=0.002), higher representative attenuation (-475 vs. -547 HU, P=0.000), lower relative attenuation (-339 vs. -292 HU, P=0.000) and greater frequencies of heterogeneity (P=0.001), air bronchogram (P=0.000), bubble lucency (P=0.000), and pleural indentation (P=0.000). Multiple logistic analysis revealed that larger diameter [odds ratio (OR), 1.328; 95% CI: 1.208-1.461; P=0.000] and higher representative attenuation (OR, 1.005; 95% CI: 1.003-1.007; P=0.000) were significant predictive factors of invasive pulmonary adenocarcinomas from preinvasive lesions. The optimal cut-off value of the maximum diameter for invasive pulmonary adenocarcinomas was larger than 10 mm (sensitivity, 66.7%; specificity, 72.8%).

Conclusions

The imaging features based on protocol G scanning can effectively help predict the histological invasiveness of pGGNs. The maximum diameter and representative attenuation are important parameters for predicting invasiveness.