Correlation between histological invasiveness and the computed tomography value in pure ground-glass nodules

Qualitative and quantitative CT analyses of the solid component in lung adenocarcinoma for predicting invasiveness

PURPOSE

This study aimed to evaluate the CT findings of lung adenocarcinoma with solid components and to determine the difference between adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) with invasive adenocarcinoma (IAC).

MATERIALS AND METHODS

A total of 54 cases were included in this study. The diagnoses of lung adenocarcinoma consisted of AIS or MIA (n = 20) and IAC (n = 34). The following factors were evaluated on CT images: part-solid nodule or solid nodule, presence of air bronchogram, air space, calcification within the tumor, presence of interstitial pneumonia and emphysema, diameters of the tumor and solid component, and CT values of the solid component. The volume and CT number histograms, including the 50th, 75th, and 100th percentiles of solid component were obtained using a software program. The CT criteria were compared between AIS, MIA, and IAC, and an indicator of differentiation was considered.

RESULTS

Part-solid nodules were observed more frequently in AIS and MIA (85.0%) than in IAC (55.9%). All criteria for quantitative analysis showed significant differences between AIS or MIA and IAC, and the diameter of the solid component in the mediastinal window was an indicator of differentiation (p = 0.0006; odds ratio, 1.4; 95% confidence interval, 1.2-1.8).

CONCLUSION

The diameter of the solid component on the mediastinal window was considered an indicator of differentiation between AIS, MIA, and IAC.

CONDENSED ABSTRACT

Quantitative data of solid component, including both manual measurements and evaluation using CT software, are correlated with pathological invasiveness. Diameter of the solid component in the mediastinal window would be an indicator of IAC.

Exploration of CT-based discrimination and diagnosis of various pathological types of ground glass nodules in the lungs

PURPOSE

This study aims to examine the diagnostic usefulness of CT imaging in distinguishing between various pathological forms of lung ground-glass nodules (GGNs).

METHODS

We conducted a retrospective analysis on 210 patients with lung ground-glass nodules (GGNs) who received diagnosis and treatment at our hospital between January 2021 and May 2024. Every patient had comprehensive imaging and pathology investigations. Lesion size, three-dimensional ratio, two-dimensional ratio, size of solid components, form, spiculation, lobulation, and cavitation were studied across several pathological kinds of pulmonary ground-glass nodules (GGNs).

RESULTS

Of the 210 patients, 51 were diagnosed with benign conditions, while 159 had malignant lesions distributed across AIS, MIA, and IAC. The imaging data revealed that pulmonary ground-glass nodules (GGNs) exhibiting spiculation, lobulation, cavitation, pleural indentation, irregular shape, and fuzzy borders were considerably more prevalent in the inflammatory group, atypical adenomatous hyperplasia (AAH) group, adenocarcinoma in situ (AIS) group, minimally invasive adenocarcinoma (MIA) group, and invasive adenocarcinoma (IAC) group. These differences were statistically significant (P < 0.05). Significant variations in lesion size and size of solid components were observed among the groups, with the inflammatory group having the smallest size, followed by the AAH group, AIS group, MIA group, and finally the IAC group (P < 0.05). Nevertheless, there were no statistically significant disparities in the three-dimensional ratio and two-dimensional ratio across the five groups (P > 0.05). The calculated areas under the curve for distinguishing pre-invasive lesions from MIA and MIA from IAC, depending on the size of solid components, were 0.705 and 0.814, respectively. These values indicate a high diagnostic accuracy.

CONCLUSION

A thorough examination of the CT imaging characteristics of ground-glass nodules is crucial for accurately distinguishing between various pathological forms of pulmonary GGNs.

PMILACG Model: A Predictive Model for Identifying Invasiveness of Lung Adenocarcinoma Based on High-Resolution CT-Determined Ground Glass Nodule Features

The morphology of ground-glass nodule (GGN) under high-resolution computed tomography (HRCT) has been suggested to indicate different histological subtypes of lung adenocarcinoma (LUAD); however, existing studies only include the limited number of GGN characteristics, which lacks a systematic model for predicting invasive LUAD. This study aimed to construct a predictive model based on GGN features under HRCT for LUAD. A total of 1,189 surgical LUAD patients were enrolled, and their GGN-related features were assessed by 2 individual radiologists. The pathological diagnosis of the invasive LUAD was established by pathologic examination following surgery (including 1,073 invasive and 526 non-invasive LUAD). After adjustment by multivariate logistic regression, GGN diameter (OR = 1.382, 95% CI: 1.300-1.469), mean CT attenuation (OR = 1.007, 95% CI: 1.006-1.009), heterogeneous uniformity of density (OR = 2.151, 95% CI: 1.587-2.915), not defined nodule-lung interface (OR = 1.915, 95% CI: 1.384-2.651), GGN with spiculation (OR = 2.097, 95% CI: 1.519-2.896), type I (OR = 1.678, 95% CI: 1.216-2.371), and type II (OR = 3.577, 95% CI: 1.153-11.097) vessel changes were independent risk factors for invasive LUAD. In addition, a predictive model integrating these six independent GGN features was established (named as invasion of lung adenocarcinoma by GGN features (ILAG)), and receiver-operating characteristic curve illustrated that the ILAG model presented good predictive value for invasive LUAD (AUC: 0.905, 95% CI: 0.890-0.919). In conclusion, The ILAG predictive model, which integrates imaging features of GGN via HRCT, including diameter, mean CT attenuation, heterogeneous uniformity of density, not defined nodule-lung interface, GGN with spiculation, type I, and type II vessel changes, shows great potential for early estimation of LUAD invasiveness.

Site-specific follow-up strategy for surgically resected patients with NSCLC based on ten-year follow-up data

OBJECTIVES

There is currently no consensus regarding the optimal site-specific postoperative follow-up duration for patients with completely resected non-small cell lung cancer. Long-term surveillance for recurrence may lead to psychological distress or economic burdens for patients.We aimed to propose an appropriate site-specific follow-up strategy for patients with non-small cell lung cancer based on 10-year follow-up data.

MATERIALS AND METHODS

We analyzed recurrence patterns in 2,359 patients with non-small cell lung cancer who underwent surgical resection from 2008 to 2013. We established potential site-specific follow-up endpoints when the subsequent recurrence rates fell below 5% and proposed a corresponding follow-up strategy.

RESULTS

With a median follow-up of 111.0 months, postoperative recurrences were observed in 985 patients (41.8 %). We identified several factors associated with site-specific recurrence recurrence patterns, including ground-glass opacity component, sex, histology type, and pathological TNM stage. No recurrence was observed in patients with pure ground-glass nodules, a consolidation-to-tumor ratio less than 0.5, or a pathological type classified as lepidic pattern-predominant adenocarcinoma. In thorax, brain and bone, patients with non-squamous cell carcinoma exhibited higher recurrence rates than those with squamous cell carcinoma. In abdomen and neck, male patients have a higher recurrence rate than female patients, particularly within the pathological stage III group.

CONCLUSIONS

The follow-up strategy was developed based on the recurrence patterns analyzed from ten-year follow-up data. The online tool may assist in determining the optimal site-specific duration for surveillance based on clinicopathologic features.

Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study

BACKGROUND

Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + PTV (GPTV), for predicting the pathological invasiveness of pure ground-glass nodules present in lung adenocarcinoma.

METHODS

This was a retrospective, cross-sectional, bicentric study with data collected from January 1, 2018, to June 1, 2022. We divided the dataset into a training cohort (n = 88) from one center and an external validation cohort (n = 59) from another center. Radiomic signatures (rad-scores) were obtained after features were selected through correlation and least absolute shrinkage and selection operator analysis. Three machine learning models, a support vector machine model, a random forest model, and a generalized linear model, were then applied to build radiomic models.

RESULTS

Invasive adenocarcinoma had a higher rad-score (P<0.001) in the GTV and GPTV. The area under the curves (AUC) of GTV, PTV, and GPTV were 0.839, 0.809, and 0.855 in the training cohort and 0.755, 0.777, and 0.801 in the external validation cohort, respectively. The GPTV model had higher AUCs for predicting pathological invasiveness. The random forest model had the best validity and fit for the proposed machine learning approach, suggesting that it may be the most appropriate model.

CONCLUSIONS

GPTV had the highest diagnostic efficiency for predicting pathological invasiveness in patients with pure ground-grass nodules, and the random forest model outperformed other predictive models.

The value of interlobar fissure semilunar sign based on multifactor joint analysis in predicting the invasiveness of ground glass nodules with interlobar fissure attachment in the lungs

BACKGROUND

This study explores the value of interlobar fissure semilunar sign(IFSS) based on multifactor joint analysis in predicting the invasiveness of ground glass nodules(GGNs) with interlobar fissure attachment in the lungs.

METHODS

This was a retrospective analysis of clinical data and CT images of 203 GGNs attached to the interlobar fissures confirmed by surgery and pathology. According to pathological results, those GGNs were divided into three groups: glandular precursor lesion (atypical adenomatous hyperplasia/adenocarcinoma in situ), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). Various quantitative and qualitative parameters were analyzed.

RESULTS

Patient age, maximum diameter, mean size, maximum CT value, and mean CT value differed significantly among the three groups and between with the other group (P < 0.05). The types of GGNs, IFSS, lobulation, spiculation, cavity sign, air bronchogram sign, bronchial changes, and vascular changes had varying degrees of significance in the comparison of each group of lesions. Logistic regression analysis showed that IFSS is one of the important factors in predicting whether GGN is invasive. The regression model I was Logit (P) 1 = -3.578 + 0.272 × 2 + 2.253 × 5, with the area under curve (AUC) for diagnosis of MIA = 0.762. Model III was Logit (P) 3 = -4.494 + 0.376 × 2 + 2.363 × 5, with the AUC for diagnosis of MIA/IAC = 0.881. The sensitivity and specificity of IFSS in model III were 0.961 and 0.458, respectively.

CONCLUSIONS

The absence of IFSS in GGNs attached to the interlobar fissure suggests noninvasive lesions. The logistic regression model based on multi factor joint analysis IFSS and maximum diameter can better predict whether the GGN attached to the interlobar fissure pleura is invasive.

The effect of radio-frequency ablation in treating pulmonary ground glass nodule patients and its influence on pulmonary function

Background

With widespread application of low-dose thin-section chest CT screening, the detection rate of patients with subsolid nodules (SNs) as CT findings has increased remarkably.

Objective

To clarify effect of radio-frequency ablation (RFA) in treating pulmonary ground glass nodule (GGN) patients and its impact on pulmonary function.

Methods

A total of 100 patients diagnosed with pulmonary GGN in our hospital from January 2019 to December 2021 by pulmonary thin-section CT + enhanced imaging examination and all underwent simultaneous needle biopsy were enrolled and randomly divided into control group and observation group, with 50 cases each. The control group received treatment with traditional surgery. The observation group received treatment with RFA. The surgical indicators, surgical treatment efficacy, levels of inflammatory cytokines, complications and pulmonary function indicators were assessed between two groups.

Results

The operation time, hospital stay and intraoperative blood loss in observation group were markedly shorter/less than controls (P < 0.05). No residual recurrence was observed in total 84 nodules of 50 cases in observation group. The size of nodules was observed by CT 1, 3 and 6 months after operation. With prolongation of postoperative time, nodules gradually shrank, after 6 months, fibrous cord scar residue gradually formed, with statistical significance relative to those before operation (P < 0.05). The postoperative IL-6, CRP and TNF-α in both groups presented elevation, whereas postoperative changes in IL-6, CRP and TNF-α in observation group presented depletion relative to control group, with statistical significance (P < 0.05). The complication rate in observation group presented depletion relative to control group, with statistical significance (P < 0.05). The FVC, FEV1, FEV1%, MVV and PEF in both groups presented depletion 1 month after operation and presented statistical significance relative to those before operation except for FEV1% (P < 0.05), and changes in observation group presented depletion relative to control group. Six months after operation, FVC, FEV1, FEV1%, MVV and PEF in both groups had recovered to preoperative levels and presented no difference relative to preoperative level (P > 0.05), and presented no difference between two groups (P > 0.05).

Conclusion

RFA for the therapy of pulmonary GGN is safe and effective, without surgical scar, and is less traumatic to the body, which has a good application prospect.

The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA

Objective

This study aims to analyze the quantitative parameters and morphological indices of three-dimensional (3D) visualization to differentiate lepidic predominant adenocarcinoma (LPA) from non-LPA subtypes, which include acinar predominant adenocarcinoma (APA), papillary predominant adenocarcinoma (PPA), micropapillary predominant adenocarcinoma (MPA), and solid predominant adenocarcinoma (SPA).

Methods

A group of 178 individuals diagnosed with lung adenocarcinoma were chosen and categorized into two groups: the LPA group and the non-LPA group, according to their pathological results. Quantitative parameters and morphological indexes such as 3D volume, solid proportion, and vascular cluster sign were obtained using 3D visualization and reconstruction techniques.

Results

Significant differences were observed in the vascular cluster sign, spiculation, shape, air bronchogram, bubble-like lucency, margin, pleural indentation, lobulation, maximum tumor diameter, 3D mean CT value, 3D volume, 3D mass, 3D density, and solid proportion between two groups (P<0.05). The optimal cut-off values for diagnosing non-LPA were a 3D mean CT value of -445.45 HU, a 3D density of 0.56 mg·mm-3, and a solid proportion reaching 27.95 %. Multivariate logistic regression analysis revealed that 3D mean CT value, lobulation, and margin characteristics independently predicted stageⅠinvasive lung adenocarcinoma. The combination of three indicators significantly improved prediction accuracy (AUC=0.881).

Conclusion

The utilization of 3D visualization technology in a systematic approach enables the acquisition of 3D quantitative parameters and morphological indicators of thin-slice CT lesions. These efforts significantly contribute to the identification of histopathological subtypes for stageⅠinvasive lung adenocarcinoma. When integrated with pertinent clinical data, this offers essential guidance for developing various surgical techniques and treatment plans.

Lung adenocarcinoma: characteristic radiological presentations

Radiology, mainly computed tomography, has a fundamental role in diagnosis, staging and follow-up of lung adenocarcinoma, the most common type of pulmonary cancer. Within its broad spectrum of presentation, the pathological, clinical and morphological characteristics of this neoplasm allow, in an appropriate clinical context, to suggest certain histological subtypes among which are mucinous adenocarcinoma, lepidic growth adenocarcinoma or associated with cystic lung lesions. The objective of this review is to describe the pathologic, clinical and radiological features of those characteristic forms of lung carcinoma that can be diagnosed radiologically with fair accuracy.

Discrimination of invasive lung adenocarcinoma from Lung-RADS category 2 nonsolid nodules through visual assessment: a retrospective study

OBJECTIVES

Invasive adenocarcinomas (IADs) have been identified among nonsolid nodules (NSNs) assigned as Lung Imaging Reporting and Data System (Lung-RADS) category 2. This study used visual assessment for differentiating IADs from noninvasive lesions (NILs) in this category.

METHODS

This retrospective study included 222 patients with 242 NSNs, which were resected after preoperative computed tomography (CT)-guided dye localization. Visual assessment was performed by using the lung and bone window (BW) settings to classify NSNs into BW-visible (BWV) and BW-invisible (BWI) NSNs. In addition, nodule size, shape, border, CT attenuation, and location were evaluated and correlated with histopathological results. Logistic regression was performed for multivariate analysis. A p value of < 0.05 was considered statistically significant.

RESULTS

A total of 242 NSNs (mean diameter, 7.6 ± 2.8 mm), including 166 (68.6%) BWV and 76 (31.4%) BWI NSNs, were included. IADs accounted for 31% (75) of the nodules. Only 4 (5.3%) IADs were identified in the BWI group and belonged to the lepidic-predominant (n = 3) and acinar-predominant (n = 1) subtypes. In univariate analysis for differentiating IADs from NILs, the nodule size, shape, CT attenuation, and visual classification exhibited statistical significance. Nodule size and visual classification were the significant predictors for IAD in multivariate analysis with logistic regression (p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of visual classification in IAD prediction were 94.7%, 43.1%, 42.8%, and 94.7%, respectively.

CONCLUSIONS

The window-based visual classification of NSNs is a simple and objective method to discriminate IADs from NILs.

CLINICAL RELEVANCE STATEMENT

The present study shows that using the bone window to classify nonsolid nodules helps discriminate invasive adenocarcinoma from noninvasive lesions.

KEY POINTS

• Evidence has shown the presence of lung adenocarcinoma in Lung-RADS category 2 nonsolid nodules. • Nonsolid nodules are classified into the bone window-visible and the bone window-invisible nonsolid nodules, and this classification differentiates invasive adenocarcinoma from noninvasive lesions. • The Lung-RADS category 2 nonsolid nodules are unlikely invasive adenocarcinoma if they show nonvisualization in the bone window.

A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma

PURPOSE

Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients.

MATERIALS AND METHODS

Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models.

RESULTS

In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability.

CONCLUSION

Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.

A clinical-radiological predictive model for solitary pulmonary nodules and the relationship between radiological features and pathological subtype

AIM

To develop a clinical-radiological model to predict the malignancy of solitary pulmonary nodules (SPNs) and to evaluate the accuracy of chest computed tomography imaging characteristics of SPN in diagnosing pathological type.

MATERIALS AND METHODS

The predictive model was developed using a retrospective cohort of 601 SPN patients (Group A) between July 2015 and July 2020. The established model was tested using a second retrospective cohort of 124 patients between August 2020 and August 2021 (Group B). The radiological characteristics of all adenocarcinomas in two groups were analysed to determine the correlation between radiological and pathological characteristics.

RESULTS

Malignant nodules were found in 78.87% of cases and benign in 21.13%. Two clinical characteristics (age and gender) and four radiological characteristics (calcification, vascular convergence, pleural retraction sign, and density) were identified as independent predictors of malignancy in patients with SPN using logistic regression analysis. The area under the receiver operating characteristic curve (0.748) of the present model was greater than the other two reported models. Diameter, spiculation, lobulation, vascular convergence, and pleural retraction signs differed significantly among pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma. Only diameter and density were significantly different among invasive adenocarcinoma subtypes.

CONCLUSIONS

Older age, male gender, no calcification, vascular convergence, pleural contraction sign, and lower density were independent malignancy predictors of SPNs. Furthermore, the pathological classification can be clarified based on the radiological characteristics of SPN, providing a new option for the prevention and treatment of early lung cancer.

Histopathologic fate of resected pulmonary pure ground glass nodule: a systematic review and meta-analysis

Background

Pure ground glass nodules (GGNs) have been increasingly detected through lung cancer screening programs. However, there were limited reports about pathologic characteristics of pure GGN. Here we presented a meta-analysis of the histologic outcome and proportion analysis of pure GGN.

Methods

This study included previous pathological reports of pure GGN published until June 14, 2022 following a systematic search. A meta-analysis estimated the summary effects and between-study heterogeneity for pathologic diagnosis of invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and atypical adenomatous hyperplasia (AAH).

Results

This study incorporated 24 studies with 3,845 cases of pure GGN that underwent surgery. Among them, sublobar resection was undertaken in 60% of the patients [95% confidence interval (CI): 38-78%, I2=95%]. The proportion of IA in cases of resected pure GGN was 27% (95% CI: 18-37%, I2=95%), and 50% of IA had non-lepidic predominant patterns (95% CI: 35-65%, I2=91%). The pooled proportions of MIA, AIS, and AAH were 24%, 36%, and 11%, respectively. Among nine studies with available clinical outcomes, no recurrences or metastases was observed other than one study.

Conclusions

The portion of IA in cases of pure GGN is significantly larger that expected. More than half of them owned invasiveness components if MIA and IA were combined. Furthermore, there were quite number of lesions with aggressive histologic patterns other than the lepidic subtype. Therefore, further attempts are necessary to differentiate advanced histologic subtype among radiologically favorable pure GGN.

Randomized comparison of the four-hook anchor device and hook-wire use for the preoperative localization of pulmonary nodules

OBJECTIVE

To compare the efficacy and safety of preoperative localization of small pulmonary nodules (SPNs) with 4-hook anchor device and hook-wire before video-assisted thoracoscopic surgery.

METHODS

Patients with SPNs scheduled for computed tomography-guided nodule localization before video-assisted thoracoscopic surgery between May 2021 and June 2021 at our center were randomized to either 4-hook anchor group or hook-wire group. The primary end point was intraoperative localization success.

RESULTS

After randomization, 28 patients with 34 SPNs were assigned to the 4-hook anchor group and 28 patients with 34 SPNs to the hook-wire group. The operative localization success rate was significantly greater in the 4-hook anchor group than in the hook-wire group (94.1% [32/34] vs 64.7% [22/34]; P = .007). All lesions in the 2 groups were successfully resected under thoracoscopy, but 4 patients in the hook-wire group who required transition from wedge resection to segmentectomy or lobectomy because of unsuccessful localization. Total localization-related complication rate was significantly lower in the 4-hook anchor group than in the hook-wire group (10.3% [3/28] vs 50.0% [14/28]; P = .004). The rate of chest pain requiring analgesia after the localization procedure was significantly lower in the 4-hook anchor group than in the hook-wire group (0 vs 5/28, 17.9%; P = .026). There were no significant differences in localization technical success rate, operative blood loss, hospital stay length and hospital cost between the 2 groups (all P > .05).

CONCLUSIONS

The use of the 4-hook anchor device for SPN localization offers advantages over the traditional hook-wire technique.

The value of CT shape quantification in predicting pathological classification of lung adenocarcinoma

OBJECTIVE

To evaluate whether quantification of lung GGN shape is useful in predicting pathological categorization of lung adenocarcinoma and guiding the clinic.

METHODS

98 patients with primary lung adenocarcinoma were pathologically confirmed and CT was performed preoperatively, and all lesions were pathologically ≤ 30 mm in size. On CT images, we measured the maximum area of the lesion's cross-section (MA). The longest diameter of the tumor (LD) was marked with points A and B, and the perpendicular diameter (PD) was marked with points C and D, which was the longest diameter perpendicular to AB. and D, which was the longest diameter perpendicular to AB. We took angles A and B as big angle A (BiA) and small angle A (SmA). We measured the MA, LD, and PD, and for analysis we derived the LD/PD ratio and the BiA/SmA ratio. The data were analysed using the chi-square test, t-test, ROC analysis, and binary logistic regression analysis.

RESULTS

Precursor glandular lesions (PGL) and microinvasive adenocarcinoma (MIA) were distinguished from invasive adenocarcinoma (IAC) by the BiA/SmA ratio and LD, two independent factors (p = 0.007, p = 0.018). Lung adenocarcinoma pathological categorization was indicated by the BiA/SmA ratio of 1.35 and the LD of 11.56 mm with sensitivity of 81.36% and 71.79%, respectively; specificity of 71.79% and 74.36%, respectively; and AUC of 0.8357 (95% CI: 0.7558-0.9157, p < 0.001), 0.8666 (95% CI: 0.7866-0.9465, p < 0.001), respectively. In predicting the pathological categorization of lung adenocarcinoma, the area under the ROC curve of the BiA/SmA ratio combined with LD was 0.9231 (95% CI: 0.8700-0.9762, p < 0.001), with a sensitivity of 81.36% and a specificity of 89.74%.

CONCLUSIONS

Quantification of lung GGN morphology by the BiA/SmA ratio combined with LD could be helpful in predicting pathological classification of lung adenocarcinoma.

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.

Meta-analysis of the correlation between CT-based features and invasive properties of pure ground-glass nodules

Several studies have revealed that computed tomography (CT) features can make a distinction in the invasive properties of pure ground-glass nodules (pGGNs). However, imaging parameters related to the invasive properties of pGGNs are unclear. This meta-analysis was designed to decipher the correlation between the invasiveness of pGGNs and CT-based features, and ultimately to be conducive to making rational clinical decisions. We searched a series of databases, including PubMed, Embase, Web of Science, Cochrane Library, Scopus, wanfang, CNKI, VIP, as well as CBM databases, until September 20, 2022, for the eligible publications only in Chinese or English. This meta-analysis was implemented with the Stata 16.0 software. Ultimately, 17 studies published between 2017 and 2022 were included. According to the meta-analysis, we observed a larger maximum size of lesions in invasive adenocarcinoma (IAC) versus that in preinvasive lesions (PIL) [SMD = 1.37, 95% CI (1.07-1.68), P < 0.05]. Meanwhile, there were also increased mean CT values of IAC [SMD = 0.71, 95% CI (0.35, 1.07), P < 0.05], the incidence of pleural traction sign [OR = 1.94, 95% CI (1.24, 3.03), P < 0.05], the incidence of IAC spiculation [OR = 1.55, 95% CI (1.05, 2.29), P < 0.05] in comparison to those of PIL. Nevertheless, IAC and PIL exhibited no significant differences in vacuole sign, air bronchogram, regular shape, lobulation and vascular convergence sign (all P > 0.05). Therefore, IAC and PIL manifested different CT features of pGGNs. The maximum diameter of lesions, mean CT value, pleural traction sign and spiculation are important indicators to distinguish IAC and PIL. Reasonable use of these features can be helpful to the treatment of pGGNs.

Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods

PURPOSE

To investigate the differences of size and density measurements in assessing pure ground-glass nodules (pGGNs) growth, and compare the growth rates and growth proportions of the two methods during follow-up period.

METHODS

Ninety patients with at least 3 consecutive thin-section chest CTs and confirmed 103 pGGNs on baseline CT were enrolled retrospectively. Using the two definitions of size and density to evaluate pGGNs growth with semi-automated segmentation. Then, the two methods were compared to assess differences in pGGNs growth.

RESULTS

For the size and density methods to assess nodule growth, 50.5% and 26.2% showed interval growth at the last CT (p < 0.001). Among the 19 nodules that grew in both size and density, the volume doubling time (VDT) of solid component (mean, 317.1; standard deviation, 224.8 days) was shorter than total VDT (median, 942.8; range, 400.1-2315.9 days) (p < 0.001). Of the 27 growth pGGNs assessed by the density method, the growth rates at years 1 and 2 were 25.9% and 63.0%, while the growth rates of 52 growing nodules assessed by size method were 11.5% and 48.1%, respectively. Twenty of 103 (19.4%) nodules were classified into category 4A lesions, and 7 (6.8%) were 4B lesions.

CONCLUSION

Compared to size measurements, observed density increases have a higher proportion of early growth and faster growth rates in growing nodules. Clinicians need to pay close attention to the nodules of new solid components and make timely decision management.

Ten-Year Follow-up Results of Pure Ground-Glass Opacity-Featured Lung Adenocarcinomas After Surgery

BACKGROUND

Previously, we have demonstrated that the 5-year recurrence-free survival after surgery of pure ground-glass opacity (GGO)-featured lung adenocarcinoma is 100%. This study aimed to reveal the long-term outcomes of these patients 10 years after surgery.

METHODS

Lung adenocarcinoma patients who underwent surgery between December 2007 and December 2013 were reviewed. Patients with pure GGO-featured lung adenocarcinoma were enrolled. Postoperative survival and the risk of developing second primary lung cancer were analyzed.

RESULTS

Overall, 308 cases of pure GGO-featured lung adenocarcinomas were included. Of these patients, 226 (73.4%) were female, 268 (87.0%) were nonsmokers, and 187 (60.7%) underwent sublobar resection. The median follow-up period after surgery was 112 months. The 10-year recurrence-free survival rate of these patients was 100%, and 10-year overall survival rate was 96.9%. Both 5-year and 10-year lung cancer-specific survival were 100%. There was no difference in 10-year recurrence-free survival rates between patients who underwent lobectomy or sublobar resection (P = .697). EGFR mutations were detected in 55.6% (84 of 151) of patients who underwent mutational analysis. The risk of developing secondary primary lung cancer for pure GGO-featured lung adenocarcinoma patients at 10 years after resection was 2.4%, and was not correlated with EGFR mutation status (P = .452).

CONCLUSIONS

No recurrence was observed in patients with pure GGO-featured lung adenocarcinomas 10 years after surgery, even when pathologically evaluated as invasive adenocarcinoma. Pure GGO can be cured by surgery. Surgery is recommended for the appropriate time window with the view to cure. Our study emphasizes that radiologic pure GGO-featured lung adenocarcinomas should be distinguished from other lung adenocarcinomas.

Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas

OBJECTIVE

The purpose of the study is to investigate the performance of radiomics-based analysis in prediction of pure ground-glass nodule (pGGN) lung adenocarcinomas invasiveness using thin-section computed tomography images.

METHODS

A total of 382 patients surgically resected single pGGN and pathologically confirmed were enrolled in the retrospective study. The pGGN cases were divided into two groups: the noninvasive group and the invasive adenocarcinoma (IAC) group. 330 patients were randomly assigned to the training and testing cohorts with a ratio of 7:3 (245 noninvasive lesions, 85 IAC lesions), while 52 patients (30 noninvasive lesions, 22 IAC lesions) were assigned to the external validation cohort. A model, radiomics model, and combined clinical-radiographic-radiomic model were built using the LASSO and multivariate backward stepwise regression analysis on the basis of the selected and radiomics features. The area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate and compare the model performance for invasiveness discrimination among the three cohorts.

RESULTS

Three clinical-radiographic features (including age, gender and the mean CT value) and three radiomics features were selected for model building. The combined model and radiomics model performed better than the clinical-radiographic model. The AUCs of the combined model in the training, testing, and validation cohorts were 0.856, 0.859, and 0.765, respectively. The DCA demonstrated the radiomics signatures incorporating clinical-radiographic feature was clinically useful in predicting pGGN invasiveness.

CONCLUSIONS

The proposed radiomics-based analysis incorporating the clinical-radiographic feature could accurately predict pGGN invasiveness, providing a noninvasive biomarker for the individualized and precise medical treatment of patients.

Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study

To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (n = 143) or test set (n = 60). The areas under the curve of the predictive models were 0.743 (95% confidence interval CI [0.661-0.824]) for the logistic regression, 0.828 (95% CI [0.76-0.896]) for the support vector machine, and 0.917 (95% CI [0.869-0.965]) for the XGBoost classifier models in the training set, and 0.803 (95% CI [0.694-0.913]), 0.726 (95% CI [0.598-0.854]), and 0.874 (95% CI [0.776-0.972]) in the test set, respectively. In addition, the decision curve showed that the XGBoost model added more net benefit within the range of 0.06 to 0.93.

Establishment and validation a prediction model for discrimination of invasive adenocarcinomas for patients with peripheral pulmonary subsolid nodules

Background

The optimal management of patients with subsolid pulmonary nodules is of growing clinical concern. This study sought to develop and validate a more precise predictive model to evaluate the pathological invasiveness of patients with lung peripheral subsolid nodules (SSNs).

Methods

The data of 1,140 patients with peripheral SSNs who underwent surgical resection at Shanghai Renji Hospital from January 2014 to December 2018 were retrospectively analyzed. The patients were randomly assigned to a training and validation cohort (at a ratio of 2 to 1). Clinical parameters and imaging features were collected to estimate the independent predictors of pathological invasiveness of SSNs. A nomogram model was developed and applied to the validation cohort. The predictive performance of the nomogram model was evaluated by a calibration curve analysis, an area under the receiver operating characteristic curve (AUC) analysis, and a decision curve analysis (DCA), which was also compared with other diagnostic models.

Results

In the multivariate analysis, the nodule diameter (P<0.001), solid component size (P<0.001), mean CT attenuation (P=0.001), spiculation (P<0.001), and pleura indentation (P=0.011) were identified as independent predictors of the pathological invasiveness of SSNs. A nomogram model based on the results of the multivariate analysis was developed and showed a robust discrimination in the validation cohort, with an AUC of [0.890; 95% confidence interval (CI), 0873-0.907], which was higher than another two reported models. The calibration curve showed optimal agreement between the pathological invasive probability as predicted by the nomogram and the actual probability.

Conclusions

We developed and validated a nomogram model to evaluate the risk of the pathological invasiveness for patients with lung SSNs. The AUC of this nomogram model was higher than another two reported models. Our nomogram model may help clinicians to make individualized treatment more precisely.

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists’ readings

Background

Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature.

Purpose

To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT).

Material and Methods

In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland–Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT.

Results

The radiologist and DLA measurements exhibited good agreement with a Bland–Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 ( P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group ( P < 0.001).

Conclusion

Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

Purpose

To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification.

Methods

A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves.

Results

Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit.

Conclusions

A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00483-1.

A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma

Objective

The morphology of ground-glass nodule (GGN) under high-resolution computed tomography (HRCT) has been suggested to indicate different histological subtypes of lung adenocarcinoma (LUAD); however, existing studies only include the limited number of GGN characteristics, which lacks a systematic model for predicting invasive LUAD. This study aimed to construct a predictive model based on GGN features under HRCT for LUAD.

Methods

A total of 301 surgical LUAD patients with HRCT-confirmed GGN were enrolled, and their GGN-related features were assessed by 2 individual radiologists. The pathological diagnosis of the invasive LUAD was established by pathologic examination following surgery (including 171 invasive and 130 non-invasive LUAD patients).

Results

GGN features including shorter distance from pleura, larger diameter, area and mean CT attenuation, more heterogeneous uniformity of density, irregular shape, coarse margin, not defined nodule-lung interface, spiculation, pleural indentation, air bronchogram, vacuole sign, vessel changes, lobulation were observed in invasive LUAD patients compared with non-invasive LUAD patients. After adjustment by multivariate logistic regression model, GGN diameter (OR = 1.490, 95% CI, 1.326-1.674), mean CT attenuation (OR = 1.007, 95% CI, 1.004-1.011) and heterogeneous uniformity of density (OR = 3.009, 95% CI, 1.485-6.094) were independent risk factors for invasive LUAD. In addition, a predictive model integrating these three independent GGN features was established (named as invasion of lung adenocarcinoma by GGN features (ILAG)), and receiver-operating characteristic curve illustrated that the ILAG model presented good predictive value for invasive LUAD (AUC: 0.919, 95% CI, 0.889-0.949).

Conclusions

ILAG predictive model integrating GGN diameter, mean CT attenuation and heterogeneous uniformity of density via HRCT shows great potential for early estimation of LUAD invasiveness.

Radiomics for identifying lung adenocarcinomas with predominant lepidic growth manifesting as large pure ground-glass nodules on CT images

Purpose

To explore the value of radiomics in the identification of lung adenocarcinomas with predominant lepidic growth in pure ground-glass nodules (pGGNs) larger than 10 mm.

Methods

We retrospectively analyzed CT images of 204 patients with large pGGNs (≥ 10 mm) pathologically diagnosed as minimally invasive adenocarcinomas (MIAs), lepidic predominant adenocarcinomas (LPAs), and non-lepidic predominant adenocarcinomas (NLPAs). All pGGNs in the two groups (MIA/LPA and NLPA) were randomly divided into training and test cohorts. Forty-seven patients from another center formed the external validation cohort. Baseline features, including clinical data and CT morphological and quantitative parameters, were collected to establish a baseline model. The radiomics model was built with the optimal radiomics features. The combined model was developed using the rad_score and independent baseline predictors. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. The differential diagnosis performance of the models was compared with three radiologists (with 20+, 10+, and 3 years of experience) in the test cohort.

Results

The radiomics (training AUC: 0.833; test AUC: 0.804; and external validation AUC: 0.792) and combined (AUC: 0.849, 0.820, and 0.775, respectively) models performed better for discriminating than the baseline model (AUC: 0.756, 0.762, and 0.725, respectively) developed by tumor location and mean CT value of the whole nodule. The DeLong test showed that the AUCs of the combined and radiomics models were significantly increased in the training cohort. The highest AUC value of the radiologists was 0.600.

Conclusion

The application of CT radiomics improved the identification performance of lung adenocarcinomas with predominant lepidic growth appearing as pGGNs larger than 10 mm.

Adenocarcinoma in situ and minimally invasive adenocarcinoma in lungs of smokers: image feature differences from those in lungs of non-smokers

PURPOSE

We aimed to examine the characteristics of imaging findings of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) in the lungs of smokers compared with those of non-smokers.

MATERIALS AND METHODS

We included seven cases of AIS and 20 cases of MIA in lungs of smokers (pack-years ≥ 20) and the same number of cases of AIS and MIA in lungs of non-smokers (pack-years = 0). We compared the diameter of the entire lesion and solid component measured on computed tomography (CT) images, pathological size and invasive component diameter measured from pathological specimens, and CT values of the entire lesion and ground-glass opacity (GGO) portions between the smoker and non-smoker groups.

RESULTS

The diameters of AIS and MIA on CT images and pathological specimens of the smoker group were significantly larger than those of the non-smoker group (p = 0.036 and 0.008, respectively), whereas there was no significant difference in the diameter of the solid component on CT images or invasive component of pathological specimens between the two groups. Additionally, mean CT values of the entire lesion and GGO component of the lesions in the smoker group were significantly lower than those in the non-smoker group (p = 0.036 and 0.040, respectively).

CONCLUSION

AIS and MIA in smoker's lung tended to have larger lesion diameter and lower internal CT values compared with lesions in non-smoker's lung. This study calls an attention on smoking status in CT-based diagnosis for early stage adenocarcinoma.

Hybrid Clinical-Radiomics Model for Precisely Predicting the Invasiveness of Lung Adenocarcinoma Manifesting as Pure Ground-Glass Nodule

RATIONALE AND OBJECTIVES

To identify whether the radiomics features of computed tomography (CT) allowed for the preoperative discrimination of the invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules (pGGNs) and further to develop and compare different predictive models.

MATERIALS AND METHODS

We retrospectively included 187 lung adenocarcinomas presenting as pGGNs (66 preinvasive lesions and 121 invasive lesions), which were randomly divided into the training and test sets (8:2). Radiomics features were extracted from non-enhanced CT images. Clinical features, including patient's demographic characteristics, smoking status, and conventional CT features that reflect tumor's morphology and surrounding information were also collected. Intraclass correlation coefficient and ℓ_2.1-norm minimization were used to identify influential feature subset which was then used to build three predictive models (clinical, radiomics, and clinical-radiomics models) with the gradient boosting regression tree classifier. The performances of the predictive models were evaluated using the area under the curve (AUC).

RESULTS

Of the 1409 radiomics features and 27 clinical feature subtypes, 102 features were selected to construct the hybrid clinical-radiomics model, which achieved the best discriminative power (AUC = 0.934 and 0.929 in training and test set). The radiomics model showed comparable predictive performance (AUC = 0.911 and 0.901 in training and test set) compared to the clinical model (AUC = 0.911 and 0.894 in training and test set).

CONCLUSION

The radiomics model showed good predictive performance in discriminating invasive lesions from preinvasive lesions for lung adenocarcinomas presenting as pGGNs. Its performance can be further improved by adding clinical features.

Can computed tomography differentiate adenocarcinoma in situ from minimally invasive adenocarcinoma?

BACKGROUND

Given the subtle pathological signs of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), effective differentiation between the two entities is crucial. However, it is difficult to predict these conditions using preoperative computed tomography (CT) imaging. In this study, we investigated whether histological diagnosis of AIS and MIA using quantitative three-dimensional CT imaging analysis could be predicted.

METHODS

We retrospectively analyzed the images and histopathological findings of patients with lung cancer who were diagnosed with AIS or MIA between January 2017 and June 2018. We used Synapse Vincent (v. 4.3) (Fujifilm) software to analyze the CT attenuation values and performed a histogram analysis.

RESULTS

There were 22 patients with AIS and 22 with MIA. The ground-glass nodule (GGN) rate was significantly higher in patients with AIS (p < 0.001), whereas the solid volume (p < 0.001) and solid rate (p = 0.001) were significantly higher in those with MIA. The mean (p = 0.002) and maximum (p = 0.025) CT values were significantly higher in patients with MIA. The 25th, 50th, 75th, and 97.5th percentiles (all p < 0.05) for the CT values were significantly higher in patients with MIA.

CONCLUSIONS

We demonstrated that quantitative analysis of 3D-CT imaging data using software can help distinguish AIS from MIA. These analyses are useful for guiding decision-making in the surgical management of early lung cancer, as well as subsequent follow-up.

Ultra-low-dose CT reconstructed with ASiR-V using SmartmA for pulmonary nodule detection and Lung-RADS classifications compared with low-dose CT

AIM

To evaluate the accuracy of ultra-low-dose computed tomography (ULDCT) with ASiR-V using a noise index (SmartmA) for pulmonary nodule detection and Lung CT Screening Reporting And Data System (Lung-RADS) classifications compared with low-dose CT (LDCT).

MATERIALS AND METHODS

Two-hundred and ten patients referred for lung cancer screening underwent conventional chest LDCT (0.80 ± 0.28 mSv) followed immediately by ULDCT (0.16 ± 0.03 mSv). ULDCT was scanned using 120 kV/SmartmA with a noise index of 28 HU and reconstructed with ASiR-V70%. The types and diameters of all nodules were recorded. The attenuation of pure ground-glass nodules (pGGNs) was measured on LDCT. All nodules were further classified using Lung-RADS. Sensitivities of nodule detection on ULDCT were analysed using LDCT as the reference standard. Logistic regression was used to establish a prediction model for the sensitivity of nodules.

RESULTS

LDCT revealed 362 nodules and the overall sensitivity on ULDCT was 90.1%. The sensitivity for solid nodules (SNs) of ≥1 mm diameter was 96.6% (228/236) and 100% (26/26) for SNs of ≥6 mm diameter. For pGGNs of ≥6 mm, the overall sensitivity was 93% (40/43) and 100% (29/29) for nodules with a attenuation value -700 HU or more. The agreement of Lung-RADS classification between two scans was good. On logistic regression, diameter was the only independent predictor for sensitivity of SNs (p<0.05). Diameter and attenuation value were predictors for pGGNs (p<0.05).

CONCLUSION

ULDCT with ASiR-V using SmartmA is suitable for lung-cancer screening in people with a BMI ≤35 kg/m² as it has a low radiation dose of 0.16 mSv, high sensitivity for nodule detection and good performance of Lung-RADS classifications.

Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation

OBJECTIVE

To explore the natural history of pulmonary subsolid nodules (SSNs) with different pathological types by deep learning-assisted nodule segmentation.

METHODS

Between June 2012 and June 2019, 95 resected SSNs with preoperative long-term follow-up were enrolled in this retrospective study. SSN detection and segmentation were performed on preoperative follow-up CTs using the deep learning-based Dr. Wise system. SSNs were categorized into invasive adenocarcinoma (IAC, n = 47) and non-IAC (n = 48) groups; according to the interval change during the preoperative follow-up, SSNs were divided into growth (n = 68), nongrowth (n = 22), and new emergence (n = 5) groups. We analyzed the cumulative percentages and pattern of SSN growth and identified significant factors for IAC diagnosis and SSN growth.

RESULTS

The mean preoperative follow-up was 42.1 ± 17.0 months. More SSNs showed growth or new emergence in the IAC than in the non-IAC group (89.4% vs. 64.6%, p = 0.009). Volume doubling time was non-significantly shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days, p = 0.077). Median mass doubling time was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001). Lobulated sign (p = 0.002) and SSN mass (p = 0.004) were significant factors for differentiating IACs. IACs showed significantly higher cumulative growth percentages than non-IACs in the first 70 months of follow-up. The growth pattern of SSNs may conform to the exponential model. The initial volume (p = 0.042) was a predictor for SSN growth.

CONCLUSIONS

IACs appearing as SSNs showed an indolent course. The mean growth rate was larger for IACs than for non-IACs. SSNs with larger initial volume are more likely to grow.

KEY POINTS

• Invasive adenocarcinomas (IACs) appearing as subsolid nodules (SSNs), with a mean volume doubling time (VDT) of 1436.0 ± 1188.2 days and median mass doubling time (MDT) of 821.7 days, showed an indolent course. • The VDT was shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days), but the difference was not significant (p = 0.077). The median MDT was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001). • SSNs with lobulated sign and larger mass (> 390.5 mg) may very likely be IACs. SSNs with larger initial volume are more likely to grow.

Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma

Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).

Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA.

Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model.

Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.

Factors distinguishing invasive from pre-invasive adenocarcinoma presenting as pure ground glass pulmonary nodules

Background

To investigate predictors of pathological invasiveness and prognosis of lung adenocarcinoma in patients with pure ground-glass nodules (pGGNs).

Methods

Clinical and computed tomography (CT) features of invasive adenocarcinomas (IACs) and pre-IACs were retrospectively compared in 641 consecutive patients with pGGNs and confirmed lung adenocarcinomas who had undergone postoperative CT follow-up. Potential predictors of prognosis were investigated in these patients.

Results

Of 659 pGGNs in 641 patients, 258 (39.1%) were adenocarcinomas in situ, 265 (40.2%) were minimally invasive adenocarcinomas, and 136 (20.6%) were IACs. Respective optimal cutoffs for age, serum carcinoembryonic antigen concentration, maximal diameter, mean diameter, and CT density for distinguishing pre-IACs from IACs were 53 years, 2.19 ng/mL, 10.78 mm, 10.09 mm, and − 582.28 Hounsfield units (HU). Univariable analysis indicated that sex, age, maximal diameter, mean diameter, CT density, and spiculation were significant predictors of lung IAC. In multivariable analysis age, maximal diameter, and CT density were significant predictors of lung IAC. During a median follow-up of 41 months no pGGN IACs recurred.

Conclusions

pGGNs may be lung IACs, especially in patients aged > 55 years with lesions that are > 1 cm in diameter and exhibit CT density > − 600 HU. pGGN IACs of < 3 cm in diameter have good post-resection prognoses.

Computed tomography density is not associated with pathological tumor invasion for pure ground-glass nodules

OBJECTIVE

Pure ground-glass nodules are considered to be radiologically noninvasive in lung adenocarcinoma. However, some pure ground-glass nodules are found to be invasive adenocarcinoma pathologically. This study aims to identify the computed tomography parameters distinguishing invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma.

METHODS

From May 2011 to December 2015, patients with completely resected adenocarcinoma appearing as pure ground-glass nodules were reviewed. To evaluate the association between computed tomography features and the invasiveness of pure ground-glass nodules, logistic regression analyses were conducted.

RESULTS

Among 432 enrolled patients, 118 (27.3%) were classified as adenocarcinoma in situ, 213 (49.3%) were classified as minimally invasive adenocarcinoma, 101 (23.4%) were classified as invasive adenocarcinoma. There was no postoperative recurrence for patients with pure ground-glass nodules. Logistic regression analyses demonstrated that computed tomography size was the only independent radiographic factor associated with adenocarcinoma in situ (odds ratio, 47.165; 95% confidence interval, 19.279-115.390; P < .001), whereas computed tomography density was not (odds ratio, 1.002; 95% confidence interval, 0.999-1.005; P = .127). Further analyses revealed that there was no distributional difference in computed tomography density among 3 groups (P = .173). Even after propensity score matching for adenocarcinoma in situ/minimally invasive adenocarcinoma and invasive adenocarcinoma, no significant difference in computed tomography density was observed (P = .741). The subanalyses for pure ground-glass nodules with 1 cm or more in size also indicated similar results.

CONCLUSIONS

In patients with pure ground-glass nodules, computed tomography size was the only radiographic parameter associated with tumor invasion. Measuring computed tomography density provided no advantage in differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma.

CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features

Objective: To evaluate whether radiomic features extracted from intra and peri-nodular lesions can enhance the ability to differentiate between invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), and adenocarcinoma in situ (AIS) manifesting as ground-glass nodule (GGN). Materials and Methods: This retrospective study enrolled 120 patients with a total of 121 pathologically confirmed lung adenocarcinomas (85 IA and 36 AIS/MIA) from January 2015 to May 2019. The recruited patients were randomly divided into training (84 nodules) and validation sets (37 nodules), with a ratio of 7:3. The minority group in the training set was balanced by the synthetic minority over-sampling (SMOTE) method. The intra-, peri-nodular, and gross region of interests (ROI) were delineated with manual annotation. Image features were quantitatively extracted from each ROI on CT images. The minimum redundancy maximum relevance (mRMR) feature ranking method and the least absolute shrinkage and selection operator (LASSO) classifier were used to eliminate unnecessary features. The intra- and peri-nodular radiomic features were combined to produce the gross radiomic signature. A combined clinical-radiomic model was constructed by multivariable logistic regression analysis. The predicted performances of different models were evaluated using receiver operating curve (ROC) and calibration curve. Results: The gross radiomic signature (AUC: training set = 0.896; validation set = 0.876) showed a good ability to discriminate the invasiveness of adenocarcinoma, comparing to intra-nodular (AUC: training set = 0.862; validation set = 0.852) or peri-nodular radiomic signature (AUC: training set = 0.825; validation set = 0.820). The AUC of the combined clinical-radiomic model was 0.917 for the training and 0.876 for the validation cohort, respectively. Conclusions: The gross radiomic signature of intra- and peri-nodular regions improved the prediction ability and aided predicting the invasiveness of lung adenocarcinoma appearing as GGN.

CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules

OBJECTIVE. The objective of our study was to investigate the differences in the CT features of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN) with the aim of determining parameters predictive of invasiveness. MATERIALS AND METHODS. A total of 161 patients with 172 pGGNs (14 AAHs, 59 AISs, 68 MIAs, and 31 IAs) were retrospectively enrolled. The following CT features of each histopathologic subtype of nodule were analyzed and compared: lesion location, diameter, area, shape, attenuation, uniformity of density, margin, nodule-lung interface, and internal and surrounding changes. RESULTS. ROC curves revealed that nodule diameter and area (cutoff value, 10.5 mm and 86.5 mm²; sensitivity, 87.1% and 87.1%; specificity, 70.9% and 65.2%) were significantly larger in IAs than in AAHs, AISs, and MIAs (p < 0.001), whereas the latter three were similar in size (p > 0.050). CT attenuation higher than -632 HU in pGGNs indicated invasiveness (sensitivity, 78.8%; specificity, 59.8%). As opposed to noninvasive pGGNs (AAHs and AISs), invasive pGGNs (MIAs and IAs) usually had heterogeneous density, irregular shape, coarse margin, lobulation, spiculation, pleural indentation, and dilated or distorted vessels (each, p < 0.050). Multivariate analysis showed that mean CT attenuation and presence of lobulation were predictors for invasive pGGNs (p ≤ 0.001). CONCLUSION. The likelihood of invasiveness is greater in pGGNs with larger size (> 10.5 mm or > 86.5 mm²), higher attenuation (> -632 HU), heterogeneous density, irregular shape, coarse margin, spiculation, lobulation, pleural indentation, and dilated or distorted vessels.

Utility of Maximum CT Value in Predicting the Invasiveness of Pure Ground-Glass Nodules

PURPOSE

To predict the histologic invasiveness of pure GGNs using the maximum CT value.

PATIENTS AND METHODS

One hundred eighty patients underwent a resection of pure GGNs. On preoperative CT imaging studies, we selected the axial section that showed the densest component of each GGN. The CT value was measured using a DICOM (Digital Imaging and Communication in Medicine) viewer, excluding portions of vessels and bronchi. The correlation between the CT value and GGN histologic diagnosis was analyzed.

RESULTS

The numbers of patients with atypical adenomatous hyperplasia, adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) were 9, 108, 56, and 7, respectively. One of the IAC tumors exhibited lymphatic invasion, and there were no cases of vascular invasion. In comparison to preinvasive lesions (atypical adenomatous hyperplasia and AIS), invasive lesions (MIA and IAC) were correlated with a higher maximum CT value (-404 ± 113 Hounsfield units [HU] vs. -216 ± 125 HU, P < .01). The cutoff point of maximum CT value was determined at -300 HU using receiver operating characteristic curve analysis, and exhibited sensitivity and specificity of 83% and 88%, respectively. Multivariate analysis revealed that maximum CT value was an independent predictor of histologic invasiveness (odds ratio 39, P < .01). The interobserver reliability was satisfactory (intraclass correlation coefficient, 0.738; unweighted kappa-values, 0.722).

CONCLUSION

IAC and MIA accounted for 4% and 31% of the pure GGN lesions, respectively. Higher maximum CT value (≥ -300 HU) was a useful predictor of histologic invasiveness.

CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction

Background

The value of the CT features and quantitative analysis of lung subsolid nodules (SSNs) in the prediction of the pathological grading of lung adenocarcinoma is discussed.

Methods

Clinical data and CT images of 207 cases (216 lesions) with CT manifestations of an SSNs lung adenocarcinoma confirmed by surgery pathology were retrospectively analysed. The pathological results were divided into three groups, including atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). Then, the quantitative and qualitative data of these nodules were compared and analysed.

Results

The mean size, maximum diameter, mean CT value and maximum CT value of the nodules were significantly different among the three groups of AAH/AIS, MIA and IAC and were different between the paired groups (AAH/AIS and MIA or MIA and IAC) (P < 0.05). The critical values of the above indicators between AAH/AIS and MIA were 10.05 mm, 11.16 mm, − 548.00 HU and − 419.74 HU. The critical values of the above indicators between MIA and IAC were 14.42 mm, 16.48 mm, − 364.59 HU and − 16.98 HU. The binary logistic regression analysis of the features with the statistical significance showed that the regression model between AAH/AIS and MIA is logit(p) = − 0.93 + 0.216X¹ + 0.004X⁴. The regression model between MIA and IAC is logit(p) = − 1.242–1.428X⁵(1) − 1.458X⁶(1) + 1.146X⁷(1) + 0.272X² + 0.005X³. The areas under the curve (AUC) obtained by plotting the receiver operating characteristic curve (ROC) using the regression probabilities of regression models I and II were 0.815 and 0.931.

Conclusions

Preoperative prediction of pathological classification of CT image features has important guiding value for clinical management. Correct diagnosis results can effectively improve the patient survival rate. Through comprehensive analysis of the CT features and qualitative data of SSNs, the diagnostic accuracy of SSNs can be effectively improved. The logistic regression model established in this study can better predict the pathological classification of SSNs lung adenocarcinoma on CT, and the predictive value is significantly higher than the independent use of each quantitative factor.

Qualitative and quantitative imaging features of pulmonary subsolid nodules: differentiating invasive adenocarcinoma from minimally invasive adenocarcinoma and preinvasive lesions

Background

To explore the role of qualitative and quantitative imaging features of pulmonary subsolid nodules (SSNs) in differentiating invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) and preinvasive lesions.

Methods

We reviewed the clinical records of our institute from October 2010 to December 2015 and included 316 resected SSNs from 287 patients: 260 pure ground-glass nodules, 47 part-solid nodules with solid components ≤5 mm, and 9 ground-glass nodules (GGNs) with cystic airspaces. According to the pathologic review results, 307 SSNs in addition to nine GGNs with cystic airspaces were divided into two groups: A, including atypical adenomatous hyperplasia (AAH) (n=15), adenocarcinoma in situ (AIS) (n=56), and MIA (n=41); B, including 195 IACs. Univariate and binary logistic regression analyses were conducted to identify independent risk factors for IAC.

Results

Univariate analysis showed significant differences between groups regarding patient age, mean diameter, mean and relative computed tomography (CT) values, volume, mass (all P<0.001), and morphological features including lobulated sign (P<0.001), spiculated sign (P=0.028), vacuole sign/air bronchogram (P<0.001), and pleural retraction (P=0.017). Binary logistic regression and receiver operating characteristic analysis indicated the SSN mass as the only independent risk factor of IAC (odds ratio, 1.007; P<0.001), with an optimal cutoff value of 283.2 mg [area under curve (AUC): 0.859; sensitivity: 68.7%; specificity: 92.9%]. Among lepidic, acinar, and papillary adenocarcinomas, we found significant differences for the vacuole sign/air bronchogram (P=0.032) and mean and relative CT values (P<0.001). All nine GGNs with cystic airspaces were IACs.

Conclusions

The SSN mass with an optimal cutoff value of 283.2 mg may be reliable for differentiating IAC from MIA and preinvasive lesions.

Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma

Purpose

We aimed to assess the classification performance of a computed tomography (CT)-based radiomic signature for discriminating invasive and non-invasive lung adenocarcinoma.

Patients and Methods

A total of 192 patients (training cohort, n=116; validation cohort, n=76) with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in the present study. Radiomic features were extracted from preoperative unenhanced chest CT images to build a radiomic signature. Predictive performance of the radiomic signature were evaluated using an intra-cross validation cohort. Diagnostic performance of the radiomic signature was assessed via receiver operating characteristic (ROC) analysis.

Results

The radiomic signature consisted of 14 selected features and demonstrated good discrimination performance between invasive and non-invasive adenocarcinoma. The area under the ROC curve (AUC) for the training cohort was 0.83 (sensitivity, 0.84 ; specificity, 0.78; accuracy, 0.82), while that for the validation cohort was 0.77 (sensitivity, 0.94; specificity, 0.52 ; accuracy, 0.82).

Conclusion

The CT-based radiomic signature exhibited good classification performance for discriminating invasive and non-invasive lung adenocarcinoma, and may represent a valuable biomarker for determining therapeutic strategies in this patient population.

[Diagnosis and Treatment of Pulmonary Ground-glass Nodules]

Recent widespread use of high resolution computed tomography (HRCT) for the screening of lung cancer have led to an increase in the detection rate of very faint and smaller lesions known as ground-glass nodule (GGN). However, it had been proved that GGN was well associated with lung cancer in previous studies. Therefore, the classification, imaging characteristics, pathological type, follow-up, suggested managements and other clinical concerns of GGN were reviewed in this paper.
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Changes in quantitative parameters of pulmonary nonsolid nodule induced by lung inflation according to paired inspiratory and expiratory computed tomography imaging

OBJECTIVE

To evaluate quantitative parameters of nonsolid nodules on paired inspiratory and expiratory computed tomography (CT) and to examine whether these parameters are sensitive to lung inflation reflected by lung volume.

METHODS

Thirty-three patients with 41 nonsolid nodules were included in this prospective study. Paired inspiratory and low-dose respiratory plain chest CT were performed. The volume and density of nonsolid nodule(s), both lungs, the right and left lung, and five lobes, were analyzed in inspiratory and expiratory CT scans. The ratio of expiratory to inspiratory parameters was calculated and labeled as parameter_(E-I)/I. To standardize the changes in nonsolid nodule quantitative parameters, the ratio of nonsolid nodule parameter to lung parameter was also calculated. Quantitative parameters were compared between inspiratory and expiratory CT.

RESULTS

Nonsolid nodule volumes on expiratory CT were reduced by 19.8% ± 12.9%, while the density was increased by 11.4% ± 8.8%. The volume of nonsolid nodules was significantly greater on inspiratory compared with expiratory CT (p < 0.001). The density of nonsolid nodules was significantly greater on expiratory than inspiratory CT (p < 0.001). The volume_(E-I)/I was significantly greater than density_(E-I)/I both in nonsolid nodules and lung. The volume_(E-I)/I and density_(E-I)/I of nonsolid nodules were independent of size. The density_(E-I)/I of nonsolid nodule was greater in the lower lobe than that in the upper lobe (p = 0.002).

CONCLUSION

Volume changes in nonsolid nodules were more sensitive than density changes in expiratory phase. The density of lower lobe nodules was more susceptible to respiration. Expiratory scanning is not recommended for quantification of nonsolid nodules and/or follow-up.

KEY POINTS

• The nonsolid nodule volume on expiratory CT was reduced by 19.8% ± 12.9%. • The nonsolid nodule density on expiratory CT was increased by 11.4% ± 8.8%. • The volume _(E-I)/I and density _(E-I)/I of nonsolid nodules were independent of size.

Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule

OBJECTIVES

To identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules.

METHODS

This retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared.

RESULTS

355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value.

CONCLUSIONS

The radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma.

KEY POINTS

• The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma. • The radiomics signature outweighed CT morphological and quantitative indices. • A three-centre study showed that radiomics signature had good predictive performance.

Is a 5-mm diameter an appropriate cut-off value for the diagnosis of atypical adenomatous hyperplasia and adenocarcinoma in situ on chest computed tomography and pathological examination?

Background

Preinvasive lesions, such as atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), usually appear as pure ground-glass nodules (pGGNs) on thin-section computed tomography (TSCT). AAH is usually less than 5 mm wide on imaging and pathological examinations. We aimed to determine whether a 5-mm cut-off value was appropriate for the diagnosis of AAH and AIS.

Methods

We retrospectively analyzed the performance of TSCT in evaluating 80 pathologically confirmed preinvasive lesions (33 AAH lesions in 31 patients and 47 AIS lesions in 45 patients). We compared the following characteristics between the AAH and AIS groups: lesion diameter, density, rim, lobulation, spiculation, vacuole sign, aerated bronchus sign, pleural indentation sign, and pathological findings.

Results

All 80 lesions appeared as pGGNs. On TSCT, the average diameter of AAH lesions (6.0±1.64 mm) was significantly smaller than that of AIS lesions (8.7±3.16 mm; P<0.001). The area under the curve (AUC) for diameter was 0.792, and the best diagnostic cut-off value was 6.99 mm. On gross pathological examination, the average diameter of AAH lesions (4.6±1.99 mm) was significantly smaller that of AIS lesions (6.8±2.06 mm; P<0.001). The AUC was 0.794, and the best diagnostic cut-off value was 4.5 mm. The vacuole sign was common in AIS (P=0.021). AAH did not significantly differ from AIS (P>0.05) in terms of average CT value, uniformity of density, morphology, rim, lobulation, spiculation, pleural indentation sign, and aerated bronchus sign.

Conclusions

Lesion size and the vacuole sign were beneficial in the diagnosis of AAH and AIS. The vacuole sign was common in AIS. The best diagnostic cut-off value of nodular diameter for differentiating between AAH and AIS was 6.99 mm on TSCT and 4.5 mm on gross pathology.