Clinical Significance of Pleural Attachment and Indentation of Subsolid Nodule Lung Cancer

Predictive value of spectral dual-detector computed tomography for PD-L1 expression in stage I lung adenocarcinoma: development and validation of a novel nomogram

Background

Programmed death ligand-1 (PD-L1) expression serves a predictive biomarker for the efficacy of immune checkpoint inhibitors (ICIs) in the treatment of patients with early-stage lung adenocarcinoma (LA). However, only a limited number of studies have explored the relationship between PD-L1 expression and spectral dual-layer detector-based computed tomography (SDCT) quantification, qualitative parameters, and clinical biomarkers. Therefore, this study was conducted to clarify this relationship in stage I LA and to develop a nomogram to assist in preoperative individualized identification of PD-L1-positive expression.

Methods

We analyzed SDCT parameters and PD-L1 expression in patients diagnosed with invasive nonmucinous LA through postoperative pathology. Patients were categorized into PD-L1-positive and PD-L1-negative expression groups based on a threshold of 1%. A retrospective set (N=356) was used to develop and internally validate the radiological and biomarker features collected from predictive models. Univariate analysis was employed to reduce dimensionality, and logistic regression was used to establish a nomogram for predicting PD-L1 expression. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, and external validation was performed in an independent set (N=80).

Results

The proportions of solid components and pleural indentations were higher in the PD-L1-positive group, as indicated by the computed tomography (CT) value, CT at 40 keV (CT40keV; a/v), electron density (ED; a/v), and thymidine kinase 1 (TK1) exhibiting a positive correlation with PD-L1 expression. In contrast, the effective atomic number (Zeff; a/v) showed a negative correlation with PD-L1 expression [r=-0.4266 (Zeff.a), -0.1131 (Zeff.v); P<0.05]. After univariate analysis, 18 parameters were found to be associated with PD-L1 expression. Multiple regression analysis was performed on significant parameters with an area under the curve (AUC) >0.6, and CT value [AUC =0.627; odds ratio (OR) =0.993; P=0.033], CT40keV.a (AUC =0.642; OR =1.006; P=0.025), arterial Zeff (Zeff.a) (AUC =0.756; OR =0.102; P<0.001), arterial ED (ED.a) (AUC =0.641; OR =1.158, P<0.001), venous ED (ED.v) (AUC =0.607; OR =0.864; P<0.001), TK1 (AUC =0.601; OR =1.245; P=0.026), and diameter of solid components (Dsolid) (AUC =0.632; OR =1.058; P=0.04) were found to be independent risk factors for PD-L1 expression in stage I LA. These seven predictive factors were integrated into the development of an SDCT parameter-clinical nomogram, which demonstrated satisfactory discrimination ability in the training set [AUC =0.853; 95% confidence interval (CI): 0.76-0.947], internal validation set (AUC =0.824; 95% CI: 0.775-0.874), and external validation set (AUC =0.825; 95% CI: 0.733-0.918). Decision curve analyses also revealed the highest net benefit for the nomogram across a broad threshold probability range (20-80%), with a clinical impact curve (CIC) indicating its clinical validity. Comparisons with other models demonstrated the superior discriminatory accuracy of the nomogram over any individual variable (all P values <0.05).

Conclusions

Quantitative parameters derived from SDCT demonstrated the ability to predict for PD-L1 expression in early-stage LA, with Zeff.a being notably effective. The nomogram established in combination with TK1 showed excellent predictive performance and good calibration. This approach may facilitate the improved noninvasive prediction of PD-L1 expression.

Predicting bone metastasis-free survival in non-small cell lung cancer from preoperative CT via deep learning

Accurate prediction of bone metastasis-free survival (BMFS) after complete surgical resection in patients with non-small cell lung cancer (NSCLC) may facilitate appropriate follow-up planning. The aim of this study was to establish and validate a preoperative CT-based deep learning (DL) signature to predict BMFS in NSCLC patients. We performed a retrospective analysis of 1547 NSCLC patients who underwent complete surgical resection, followed by at least 36 months of monitoring at two hospitals. We constructed a DL signature from multiparametric CT images using 3D convolutional neural networks, and we integrated this signature with clinical-imaging factors to establish a deep learning clinical-imaging signature (DLCS). We evaluated performance using Harrell's concordance index (C-index) and the time-dependent receiver operating characteristic. We also assessed the risk of bone metastasis (BM) in NSCLC patients at different clinical stages using DLCS. The DL signature successfully predicted BM, with C-indexes of 0.799 and 0.818 for the validation cohorts. DLCS outperformed the DL signature with corresponding C-indexes of 0.806 and 0.834. Ranges for area under the curve at 1, 2, and 3 years were 0.820-0.865 for internal and 0.860-0.884 for external validation cohorts. Furthermore, DLCS successfully stratified patients with different clinical stages of NSCLC as high- and low-risk groups for BM (p < 0.05). CT-based DL can predict BMFS in NSCLC patients undergoing complete surgical resection, and may assist in the assessment of BM risk for patients at different clinical stages.

Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules

Background

This study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs).

Method

We analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA).

Result

The final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817-0.871) and 0.812 (95% CI, 0.766-0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively.

Conclusion

Our predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options.

Analysis of postoperative recurrence-free survival in non-small cell lung cancer patients based on consensus clustering

AIMS

This study aims to assess whether consensus clustering, based on computed tomography (CT) radiomics from both intratumoral and peritumoral regions, can effectively stratify the risk of non-small cell lung cancer (NSCLC) patients and predict their postoperative recurrence-free survival (RFS).

MATERIALS AND METHODS

A retrospective analysis was conducted on the data of surgical patients diagnosed with NSCLC between December 2014 and April 2020. After preprocessing CT images, radiomic features were extracted from a 9-mm region encompassing both the tumor and its peritumoral area. Consensus clustering was utilized to analyze the radiomics features and categorize patients into distinct clusters. A comparison of the differences in clinical pathological characteristics was conducted among the clusters. Kaplan-Meier survival analysis was employed to investigate differences in survival among the clusters.

RESULTS

A total of 266 patients were included in this study, and consensus clustering identified three clusters (Cluster 1: n=111, Cluster 2: n=61, Cluster 3: n=94). Multiple clinical risk factors, including pathological TNM staging, programmed cell death ligand 1 (PD-L1), and epidermal growth factor receptor (EGFR) expression status exhibit significant differences among the three clusters. Kaplan-Meier survival analysis demonstrated significant variations in RFS across the clusters (P<0.001). The 3-year cumulative recurrence-free survival rates were 76.5% (95% CI: 68.6-84.4) for Cluster 1, 45.9% (95% CI: 33.4-58.4) for Cluster 2, and 41.5% (95% CI: 31.6-51.5) for Cluster 3.

CONCLUSIONS

Consensus clustering of CT radiomics based on intratumoral and peritumoral regions can stratify the risk of postoperative recurrence in patients with NSCLC.

Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma

BACKGROUND

Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma.

METHODS

A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model.

RESULTS

The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets.

CONCLUSIONS

The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer

The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.

Prognostic Analysis of Stage I Non-Small Cell Lung Cancer Abutting Adjacent Structures on Preoperative Computed Tomography

Background

Early non-small cell lung cancer (NSCLC) that abuts adjacent structures requires careful evaluation due to its potential impact on postoperative outcomes and prognosis. We examined stage I NSCLC with invasion into adjacent structures, focusing on the prognostic implications after curative surgical resection.

Methods

We retrospectively analyzed the records of 796 patients who underwent curative surgical resection for pathologic stage IA/IB NSCLC (i.e., visceral pleural invasion only) at a single center from 2008 to 2017. Patients were classified based on tumor abutment and then reclassified by the presence of visceral pleural invasion. Clinical characteristics, pathological features, and survival rates were compared.

Results

The study included 181 patients with abutting NSCLC (22.7% of all participants) and 615 with non-abutting tumors (77.3%). Those with tumor abutment exhibited higher rates of non-adenocarcinoma (26.5% vs. 9.9%, p<0.01) and visceral/lymphatic/vascular invasion (30.4%/33.1%/12.7% vs. 8.5%/22.4%/5.7%, respectively; p<0.01) compared to those without abutment. Multivariable analysis identified lymphatic invasion and male sex as risk factors for overall survival (OS) and disease-free survival (DFS) in stage I NSCLC measuring 3 cm or smaller. Age, smoking history, vascular invasion, and recurrence emerged as risk factors for OS, whereas the presence of non-pure ground-glass opacity was a risk factor for DFS.

Conclusion

NSCLC lesions 3 cm or smaller that abut adjacent structures present higher rates of various risk factors than non-abutting lesions, necessitating evaluation of tumor invasion into adjacent structures and lymph node metastasis. In isolation, however, the presence of tumor abutment without visceral pleural invasion does not constitute a risk factor.

Research progress in predicting visceral pleural invasion of lung cancer: a narrative review

Background and Objective

In lung cancer, visceral pleural invasion (VPI) affects the selection of surgical methods, the scope of lymph node dissection and the need for adjuvant chemotherapy. Preoperative or intraoperative prediction and diagnosis of VPI of lung cancer is helpful for choosing the best treatment plan and improving the prognosis of patients. This review aims to summarize the research progress of the clinical significance of VPI assessment, the intraoperative diagnosis technology of VPI, and various imaging methods for preoperative prediction of VPI. The diagnostic efficacy, advantages and disadvantages of various methods were summarized. The challenges and prospects for future research will also be discussed.

Methods

A comprehensive, non-systematic review of the latest literature was carried out in order to investigate the progress of predicting VPI. PubMed database was being examined and the last run was on 4 August 2022.

Key Content and Findings

The pathological diagnosis and clinical significance of VPI of lung cancer were discussed in this review. The research progress of prediction and diagnosis of VPI in recent years was summarized. The results showed that preoperative imaging examination and intraoperative freezing pathology were of great value.

Conclusions

VPI is one of the adverse prognostic factors in patients with lung cancer. Accurate prediction of VPI status before surgery can provide guidance and help for the selection of clinical operation and postoperative treatment. There are some advantages and limitations in predicting VPI based on traditional computed tomography (CT) signs, 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT and magnetic resonance imaging (MRI) techniques. As an emerging technology, radiomics and deep learning show great potential and represent the future research direction.

A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma

Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.

Risk factors for loss of pulmonary function after wedge resection for peripheral ground-glass opacity dominant lung cancer

OBJECTIVES

This study aimed to identify the risk factors for pulmonary functional deterioration after wedge resection for early-stage lung cancer with ground-glass opacity, which remain unclear, particularly in low-risk patients.

METHODS

We analysed 237 patients who underwent wedge resection for peripheral early-stage lung cancer in JCOG0804/WJOG4507L, a phase III, single-arm confirmatory trial. The changes in forced expiratory volume in 1 s were calculated pre- and postoperatively, and a cutoff value of -10%, the previously reported reduction rate after lobectomy, was used to divide the patients into 2 groups: the severely reduced group (≤-10%) and normal group (>-10%). These groups were compared to identify predictors for severe reduction.

RESULTS

Thirty-seven (16%) patients experienced severe reduction. Lesions with a total tumour size ≥1 cm were significantly more frequent in the severely reduced group than in the normal group (89.2% vs 71.5%; P = 0.024). A total tumour size of ≥1 cm [odds ratio (OR), 3.287; 95% confidence interval (CI), 1.114-9.699: P = 0.031] and pleural indentation (OR, 2.474; 95% CI, 1.039-5.890: P = 0.041) were significant predictive factors in the univariable analysis. In the multivariable analysis, pleural indentation (OR, 2.667; 95% CI, 1.082-6.574; P = 0.033) was an independent predictive factor, whereas smoking status and total tumour size were marginally significant.

CONCLUSIONS

Of the low-risk patients who underwent pulmonary wedge resection for early-stage lung cancer, 16% experienced severe reduction in pulmonary function. Pleural indentation may be a risk factor for severely reduced pulmonary function in pulmonary wedge resection.

Multivariate analysis based on the maximum standard unit value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography and computed tomography features for preoperative predicting of visceral pleural invasion in patients with subpleural clinical stage IA peripheral lung adenocarcinoma

PURPOSE

Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery.

METHODS

A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set.

RESULTS

The size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit.

CONCLUSION

The nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.

Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model

OBJECTIVE

To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma.

METHODS

A total of 612 patients with pathologically-confirmed lung adenocarcinoma were retrospectively enrolled and were randomly divided into training cohort (n = 489) and internal validation cohort (n = 123). Besides, 108 patients were enrolled and constituted an independent test cohort (n = 108). Patients' clinical characteristics and CT semantic features were collected. The radiomics features were derived from contrast-enhanced CT images. The clinical-semantic model and radiomics signature were built to predict LN metastasis. Furthermore, Swin Transformer was adopted to develop a DL signature predictive of LN metastasis. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. The comparisons of AUC were conducted by the DeLong test.

RESULTS

The proposed DL signature yielded an AUC of 0.948-0.961 across all three cohorts, significantly superior to both clinical-semantic model and radiomics signature (all p < 0.05). The calibration curves show that DL signature predicted probabilities fit well the actual observed probabilities of LN metastasis. DL signature gained a higher net benefit than both clinical-semantic model and radiomics signature. The incorporation of radiomics signature or clinical-semantic risk predictors failed to reveal an incremental value over the DL signature.

CONCLUSIONS

The proposed DL signature based on Swin Transformer achieved a promising performance in predicting LN metastasis and could confer important information in noninvasive mediastinal LN staging and individualized therapeutic options.

KEY POINTS

• Accurate prediction for lymph node metastasis is crucial to formulate individualized therapeutic options for patients with lung adenocarcinoma. • The deep learning signature yielded an AUC of 0.948-0.961 across all three cohorts in predicting lymph node metastasis, superior to both radiomics signature and clinical-semantic model. • The incorporation of radiomics signature or clinical-semantic risk predictors into deep learning signature failed to reveal an incremental value over deep learning signature.

Tumour-pleura relationship on CT is a risk factor for occult lymph node metastasis in peripheral clinical stage IA solid adenocarcinoma

OBJECTIVES

To investigate whether the tumour-pleura relationship on computed tomography (CT) is a risk factor for occult lymph node metastasis (OLNM) in peripheral clinical stage IA solid adenocarcinoma.

METHODS

A total of 232 patients were included in the study. The tumour-pleura relationship was divided into four types: type 1, the tumour was unrelated to the pleura; type 2, the tumour was not in contact with the pleura, and one or more linear or striated pleural tags were visible; type 3, the tumour was not in contact with the pleura, and one or more linear or striated pleural tags with soft tissue component at the pleural end were visible; and type 4, the tumour was in contact with the pleura. Univariate and multivariate logistic regression analyses were used to identify the predictive factors, including the tumour-pleura relationship, clinical factors, conventional CT findings, and pathology-reported visceral pleural invasion, for OLNM.

RESULTS

Type 3 and 4 tumour-pleura relationships were more likely to have visceral pleural invasion than type 1 and 2 tumour-pleura relationships (p < 0.001). Univariate and multivariate logistic regression analyses revealed that the type 3 or 4 tumour-pleura relationship (OR: 3.261, p = 0.026), carcinoembryonic antigen level (OR: 3.361, p = 0.006), cytokeratin 19 fragments level (OR: 2.539, p = 0.025), and mediastinal window tumour size (OR: 1.078, p = 0.020) were predictive factors for OLNM.

CONCLUSIONS

The type 3 or 4 tumour-pleura relationship is correlated with a greater risk of OLNM in peripheral clinical stage IA solid adenocarcinoma.

KEY POINTS

• The tumour-pleura relationship on CT is a risk factor for occult lymph node metastasis in peripheral clinical stage IA solid adenocarcinoma. • Other risk factors for OLNM include CEA level, CYFRA level, and mediastinal window tumour size. • Pathology-reported visceral pleural invasion is not a risk factor for OLNM.

Analysis of the relevance between computed tomography characterization and pathology of pulmonary ground-glass nodules with different pathology types

Background

In this study, we aimed to analyze the relevance between computed tomography characterization and pathology of pulmonary ground-glass nodules with different pathology types.

Methods

Between January 2017 and December 2018, a total of 657 patients (191 males, 466 females; mean age: 60.9±8.1 years; range, 34 to 80 years) with pathologically diagnosed ground-glass nodules were retrospectively analyzed. The clinicopathological characteristics and computed tomography characterizations of patients with ground-glass nodules who received surgical resection were analyzed. The clinical data including age, sex, smoking status and medical history were recorded. Computed tomography characterizations included the location and size of the tumor, the size of the consolidation components, density uniformity, shape, margin, tumor-lung interface, internal signs and surrounding signs.

Results

Based on the computed tomography imaging characteristics, a mean computed tomography value of ≥444.5 HU was more likely to indicate malignant lesions, while ≤444.5 HU indicated benign lesions. A malignant ground-glass nodules" maximum diameter of <6.78 mm, a diameter of the consolidation component of <3.88 mm, and a mean computed tomography value of <-536.5 HU were more likely to indicate atypical adenomatous hyperplasia and adenocarcinoma in situ. A maximum diameter of malignant ground-glass nodules of >11.52 mm, a diameter of the consolidation component of >6.20 mm, and a mean computed tomography value of ≥493.5 HU were more likely to indicate invasive adenocarcinomas. The focus between these parameters indicated minimally invasive adenocarcinomas.

Conclusion

Ill-defined tumor-lung interface, irregular in shape, and smooth nodule margins suggest benign lesions while round or oval, clear tumor-lung interface, spiculation signs, lobulation signs, bubble signs, air bronchograms, pleural indentations, and vessel convergences are helpful in the diagnosis of malignant lesions. A clear tumor-lung interface, the spiculation signs, lobulation signs, and bubble signs indicate the invasion of the lesions.

[Research Progress of Relationship between Pleural Deformation and Visceral Pleural Invasion in Lung Cancer Manifesting as Ground-glass Opacity]

Visceral pleural invasion (VPI) is one of the negative prognostic factors of non-small cell lung cancer (NSCLC). With the popularization of computed tomography (CT) screening for lung cancer, more and more ground-glass nodule (GGN) have been found. However, it remains unclear whether the relationship between the pleural deformation of lung cancer manifesting as ground-glass opacity (GGO) and VPI affects the effect of sub-lobectomy, which is reviewed in this paper.
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Pathological components and CT imaging analysis of the area adjacent pleura within the pure ground-glass nodules with pleural deformation in invasive lung adenocarcinoma

Background

Pleural deformation is associated with the invasiveness of lung adenocarcinoma(LAC). Our study focused on the pathological components of the area adjacent pleura in pulmonary pure ground-glass nodules(pGGNs) with pleural deformations(P-pGGNs) confirmed to be invasive LAC without visceral pleural invasion (VPI) pathologically.

Methods

Computed tomography(CT) imaging features of nodules and pathological components of the area adjacent pleura were analyzed and recorded. Statistical analysis was performed for subgroups of P-pGGNs.

Results

The 81 enrolled patients with 81 P-pGGNs were finally involved in the analysis. None of solid/micropapillary group and none of VPI was observed, 54 alveoli/lepidics and 27 acinar/papillarys were observed. In P-pGGN with acinar/papillary components of the area adjacent pleura, invasive adenocarcinoma (IAC) was more common compared to minimally invasive adenocarcinoma (MIA, 74.07% vs. 25.93%; p < 0.001). The distance in alveoli/lepidic group was significantly larger (1.50 mm vs. 0.00 mm; p < 0.001) and the depth was significantly smaller (2.00 mm vs. 6.00 mm; p < 0.001) than that in acinar/papillary group. The CT attenuation value, maximum diameter and maximum vertical diameter was valuable to distinguish acinar/papillary group form alveoli/lepidic group(p < 0.05). The type d pleural deformation was the common pleural deformation in IAC(p = 0.028).

Conclusions

The pathological components of the area adjacent pleura in P-pGGN without VPI confirmed to be invasive LAC could included alveoli/lepidics and acinar/papillarys. Some CT indicators that can identify the pathological invasive components of the area adjacent pleura in P-pGGNs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10043-2.

Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses

OBJECTIVES

To compare the performance of radiologists in characterizing and diagnosing pulmonary nodules/masses with and without deep learning (DL)-based computer-aided diagnosis (CAD).

METHODS

We studied a total of 101 nodules/masses detected on CT performed between January and March 2018 at Osaka University Hospital (malignancy: 55 cases). SYNAPSE SAI Viewer V1.4 was used to analyze the nodules/masses. In total, 15 independent radiologists were grouped (n = 5 each) according to their experience: L (< 3 years), M (3-5 years), and H (> 5 years). The likelihoods of 15 characteristics, such as cavitation and calcification, and the diagnosis (malignancy) were evaluated by each radiologist with and without CAD, and the assessment time was recorded. The AUCs compared with the reference standard set by two board-certified chest radiologists were analyzed following the multi-reader multi-case method. Furthermore, interobserver agreement was compared using intraclass correlation coefficients (ICCs).

RESULTS

The AUCs for ill-defined boundary, irregular margin, irregular shape, calcification, pleural contact, and malignancy in all 15 radiologists, irregular margin and irregular shape in L and ill-defined boundary and irregular margin in M improved significantly (p < 0.05); no significant improvements were found in H. L showed the greatest increase in the AUC for malignancy (not significant). The ICCs improved in all groups and for nearly all items. The median assessment time was not prolonged by CAD.

CONCLUSIONS

DL-based CAD helps radiologists, particularly those with < 5 years of experience, to accurately characterize and diagnose pulmonary nodules/masses, and improves the reproducibility of findings among radiologists.

KEY POINTS

• Deep learning-based computer-aided diagnosis improves the accuracy of characterizing nodules/masses and diagnosing malignancy, particularly by radiologists with < 5 years of experience. • Computer-aided diagnosis increases not only the accuracy but also the reproducibility of the findings across radiologists.

A Nomogram Combined Radiomics and Clinical Features as Imaging Biomarkers for Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma

Objectives

To develop and validate a nomogram model based on radiomics features for preoperative prediction of visceral pleural invasion (VPI) in patients with lung adenocarcinoma.

Methods

A total of 659 patients with surgically pathologically confirmed lung adenocarcinoma underwent CT examination. All cases were divided into a training cohort (n = 466) and a validation cohort (n = 193). CT features were analyzed by two chest radiologists. CT radiomics features were extracted from CT images. LASSO regression analysis was applied to determine the most useful radiomics features and construct radiomics score (radscore). A nomogram model was developed by combining the optimal clinical and CT features and the radscore. The model performance was evaluated using ROC analysis, calibration curve and decision curve analysis (DCA).

Results

A total of 1316 radiomics features were extracted. A radiomics signature model with a selection of the six optimal features was developed to identify patients with or without VPI. There was a significant difference in the radscore between the two groups of patients. Five clinical features were retained and contributed as clinical feature models. The nomogram combining clinical features and radiomics features showed improved accuracy, specificity, positive predictive value, and AUC for predicting VPI, compared to the radiomics model alone (specificity: training cohort: 0.89, validation cohort: 0.88, accuracy: training cohort: 0.84, validation cohort: 0.83, AUC: training cohort: 0.89, validation cohort: 0.89). The calibration curve and decision curve analyses suggested that the nomogram with clinical features is beyond the traditional clinical and radiomics features.

Conclusion

A nomogram model combining radiomics and clinical features is effective in non-invasively prediction of VPI in patients with lung adenocarcinoma.

A random forest algorithm predicting model combining intraoperative frozen section analysis and clinical features guides surgical strategy for peripheral solitary pulmonary nodules

Background

Intraoperative frozen section (FS) analysis has been used to guide the extent of resection in patients with solitary pulmonary nodules (SPNs), but its accuracy varies greatly among different hospitals. Artificial intelligence (AI) and multidimensional data technology are developing rapidly these years, meanwhile, surgeons need better methods to guide the surgical strategy of SPNs. We established predicting models combining FS results with multidimensional perioperative clinical features using logistic regression analysis and the random forest (RF) algorithm to get more accurate extent of SPN resection.

Methods

Patients with peripheral SPNs who underwent FS-guided surgical resection at the Shanghai Chest Hospital (January 2017-December 2018) were retrospectively examined (N=3,089). The accuracy of intraoperative FS-guided resection extent was analyzed and used as Model 1. The clinical features (sex, age, CT features, tumor markers, smoking history, lesion size and nodule location) of patients were collected, and Models 2 and 3 were established using logistic regression and RF algorithms to combine the FS with clinical features. We confirmed the performance of these models in an external validation cohort of 117 patients from Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital). We compared the effectiveness in classifying low/high-risk groups of SPN among them.

Results

The accuracy of FS analysis was 61.3%. Model 3 exhibited the best diagnostic accuracy and had an area under the curve of 0.903 in n the internal validation cohort and 0.919 in the external validation cohort. The calibration plots and net reclassification index (NRI) of Model 3 also exhibited significantly better performance than the other models. Improved diagnostic accuracy was observed in in both internal and external validation cohort.

Conclusions

Using an RF algorithm, clinical characteristics can be combined with intraoperative FS analysis to significantly improve intraoperative judgment accuracy for low- and high-risk tumors, and may serve as a reliable complementary method when FS evaluation is equivocal, improving the accuracy of the extent of surgical resection.

Volumetric analysis of intravoxel incoherent motion diffusion-weighted imaging in preoperative assessment of non-small cell lung cancer

PURPOSE

To evaluate the potential of intravoxel incoherent motion (IVIM) and apparent diffusion coefficient (ADC) in the prediction of tumor grade, lymph node metastasis and pleural invasion of non-small cell lung cancer (NSCLC) before surgery.

MATERIALS AND METHODS

65 patients diagnosed with NSCLC by surgery were enrolled. IVIM-DWI (10 b-values, 0-1000 s/mm²) was performed before surgery. The mean and minimum ADC (ADC_mean, ADC_min) and IVIM parameters D, D* and f were independently measured and calculated by 2 radiologists by drawing regions of interest (ROIs) including the solid component of the whole tumor. Intraclass correlation coefficients (ICCs) were analysed. Spearman analysis was used to determine the correlation between IVIM parameters and tumor differentiation. Independent sample t-tests (normal distribution) or Mann-Whitney U tests (non-normal distribution) were used to compare the differences between the parameters in moderately-well and poorly differentiated groups, with and without lymph node metastasis and pleural invasion groups. Receiver operating characteristic (ROC) curves were generated.

RESULTS

The ADC_mean, ADC_min, D and f values were negatively correlated with the pathological grades of tumor (P < 0.05). The ADC_mean and D values of patients with poor differentiation and lymph node metastasis were significantly lower than that of patients with moderately-well differentiation and without lymph node metastasis (P < 0.001-0.012). The D value was significantly lower and f value was significantly higher among patients with pleural invasion than those without (P =   0.033 and < 0.001). ROC analysis showed that the area under the ROC curve (AUC) was larger for D in predicting the degree of differentiation (0.832) and lymph node metastasis (0.806), and higher for f in predicting pleural invasion (0.832).

CONCLUSIONS

IVIM is useful for predicting the tumor differentiation, lymph node metastasis and pleural invasion in NSCLC patients before surgery.

The combination of computed tomography features and circulating tumor cells increases the surgical prediction of visceral pleural invasion in clinical T1N0M0 lung adenocarcinoma

Background

Visceral pleural invasion (VPI) is a clinical manifestation associated with a poor prognosis, and diagnosing it preoperatively is highly imperative for successful sublobar resection of these peripheral tumors. We evaluated the roles of computed tomography (CT) features and circulating tumor cells (CTCs) for improving VPI detection in patients with clinical T1N0M0 invasive lung adenocarcinoma.

Methods

Three hundred and ninety-one patients were reviewed retrospectively in this study, of which 234 presented with a pleural tag or pleural contact on CT images. CTCs positive for the foliate receptors were enriched and analyzed prior to surgery. Logistic regression analyses were performed to assess the association of CT features and CTCs with VPI, and the receiver operating characteristic (ROC) curve was generated to compare the predictive power of these variables.

Results

Patients mostly underwent either segmentectomies (18.9%) or lobectomies (79.0%). Only 49 of the 234 patients with pleural involvement on CT showed pathologically confirmed VPI. Multivariate logistic regression analysis revealed that CTC level ≥10.42 FU/3 mL was a significant VPI risk factor for invasive adenocarcinoma cases ≤30 mm [adjusted odds ratio (OR) =4.62, 95% confidence interval (CI): 2.05–10.44, P<0.001]. Based on CT features, subgroup analyses showed that the solid portion size was a statistically significant independent predictor of VPI for these peripheral nodules with pleural tag, while the solid portion length of the interface was an independent predictor of pleural contact. The receiver operating curve analyses showed that the combination of CTC and CT features were highly predictive of VPI [area under the curve (AUC) =0.921 for pleural contact and 0.862 for the pleural tag, respectively].

Conclusions

CTC, combined with CT features of pleural tag or pleural contact, could significantly improve VPI detection in invasive lung adenocarcinomas at clinical T1N0M0 stage prior to the patient’s surgery.

The value of CT radiomics features to predict visceral pleural invasion in ≤3 cm peripheral type early non-small cell lung cancer

OBJECTIVE

To investigate predictive value of CT-based radiomics features on visceral pleural invasion (VPI) in ≤3.0 cm peripheral type early non-small cell lung cancer (NSCLC).

METHODS

A total of 221 NSCLC cases were collected. Among them, 115 are VPI-positive and 106 are VPI-negative. Using a stratified random sampling method, 70% cases were assigned to training dataset (n = 155) and 30% cases (n = 66) were assigned to validation dataset. First, CT findings, imaging features, clinical data and pathological findings were retrospectively analyzed, the size, location and density characteristics of nodules and lymph node status, the relationship between lesions and pleura (RAP) were assessed, and their mean CT value and the shortest distance between lesions and pleura (DLP) were measured. Next, the minimum redundancy-maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) features were extracted from the imaging features. Then, CT imaging prediction model, texture feature prediction model and joint prediction model were built using multifactorial logistic regression analysis method, and the area under the ROC curve (AUC) was applied to evaluate model performance in predicting VPI.

RESULTS

Mean diameter, density, fractal relationship with pleura, and presence of lymph node metastasis were all independent predictors of VPI. When applying to the validation dataset, the CT imaging model, texture feature model, and joint prediction model yielded AUC = 0.882, 0.824 and 0.894, respectively, indicating that AUC of the joint prediction model was the highest (p < 0.05).

CONCLUSION

The study demonstrates that the joint prediction model containing CT morphological features and texture features enables to predict the presence of VPI in early NSCLC preoperatively at the highest level.

Clinicopathological and computed tomographic features associated with occult lymph node metastasis in patients with peripheral solid non-small cell lung cancer

OBJECTIVES

To investigate the value of combining clinicopathological characteristics with computed tomographic (CT) features of tumours for predicting occult lymph node metastasis (OLNM) in peripheral solid non-small cell lung cancer (PS-NSCLC).

METHODS

The study included 478 NSCLC clinically N0 (cN0) patients who underwent lobectomy and systemic lymph node dissection from January 2014 to August 2019. Patients were classified into OLNM and negative lymph node metastasis (NLNM) groups. The CT features of non-metastatic and metastatic lymph nodes with a largest short-diameter > 5 mm were compared in the OLNM group. Thereafter, the clinicopathological characteristics and CT morphological features of tumours were compared between both groups. Multivariable logistic regression analysis and receiver-operating characteristic curve were developed.

RESULTS

CT images detected 103 metastatic and 705 non-metastatic lymph nodes, and no significant differences in CT features of lymph nodes were found in all 161 OLNM patients (P > 0.05). For both groups, sex, carcinoembryonic antigen and pathological type differed significantly (all P < 0.05), while tumour size, necrosis, calcification, vascular convergence, pleural involvement, and the shortest interval of tumour-pleura differed significantly on CT images (all P < 0.05). Multivariable logistic regression analysis showed that carcinoembryonic antigen > 5.00 ng/ml, adenocarcinoma, absence of vascular convergence, and pleural involvement of Type II (one linear or cord-like pleural tag or tumour abut to the pleura with a broad base observed on both lung and mediastinal window images) were independent predicting factors of OLNM.

CONCLUSIONS

CT findings of lymph nodes can provide limited value and integrating clinicopathological characteristics with the CT morphological features of tumours is helpful in predicting OLNM in patients with PS-NSCLC.

Surgical Extent for Ground Glass Nodules

As diagnoses of small ground glass nodule (GGN)-type lung adenocarcinoma are increasing due to the increasing frequency of computed tomography (CT) screening, surgical treatment for GGN-type lung adenocarcinoma has rapidly become more common. However, the appropriate surgical extent for these lesions remains unclear; therefore, several retrospective studies have been published and prospectively randomized controlled trials are being undertaken. This article takes a closer look at each clinical study. Convincing evidence must be published on 2 issues for sublobar resection to be accepted as a standard surgical option for GGN lung adenocarcinoma. In the absence of such evidence, it is better to perform lobar resection as long as the patient has sufficient lung function. The first issue is the definition of a sufficient resection margin, and the second is whether lymph node metastasis is conclusively ruled out before surgery. An additional issue is the need for an accurate calculation of the total size and solid size on CT. Given the results of clinical studies so far, wedge resection or segmentectomy shows a good prognosis for GGNs with a total size of 2 cm or less. Therefore, sublobar resection will play a key role even in patients who can tolerate lobectomy.

Characterization of Newly Detected Costal Pleura-attached Noncalcified Nodules at Annual Low-Dose CT Screenings

Background Solid costal pleura-attached noncalcified nodules (CP-NCNs) less than 10.0 mm with lentiform, oval, or semicircular (LOS) or triangular shapes and smooth margins on baseline low-dose CT scans from the Mount Sinai Early Lung and Cardiac Action Program (MS-ELCAP) were reviewed, and it was determined that they can be followed up at the first annual screening rather than having a shorter-term work-up. Purpose To determine whether the same criteria could be used for solid CP-NCNs newly identified at annual screening examinations. Materials and Methods With use of the same MS-ELCAP database, all new solid CP-NCNs measuring 30.0 mm or less were identified at 4425 annual screening examinations between 2010 and 2019. In addition, to ensure that no malignant CP-NCNs met the criteria, all solid malignant CP-NCNs of 30.0 mm or less in the International Early Lung Cancer Action Program, or I-ELCAP, database of 111 102 annual screening examinations from the 76 participating institutions between 1992 and 2019 were identified; Mount Sinai is one of these institutions. All identified solid CP-NCNs were reviewed-with the radiologists blinded to diagnosis-for shape (triangular, LOS, polygonal, round, or irregular), margin (smooth or nonsmooth), pleural attachment (broad or narrow), and the presence of emphysema and/or fibrosis within 10.0 mm of each CP-NCN. Intra- and interreader readings were performed, and agreements were determined by using the B-statistic. Results Of the 76 new solid CP-NCNs, 21 were lung cancers. Benign CP-NCNs were smaller than malignant ones (median diameter, 4.2 mm vs 11 mm; P < .001), had a different shape distributions, more frequently had smooth margins (67% vs 14%; P < .001), and less frequently had emphysema (38% vs 81%; P = .003) or fibrosis (3.6% vs 19%; P = .045) within a 10.0 mm radius. All 22 solid CP-NCNs less than 10.0 mm in average diameter with triangular or LOS shapes and smooth margins were benign, and none of the 21 solid malignant CP-NCNs had these characteristics. Intra- and interobserver agreement for triangular or LOS-shaped CP-NCNs with smooth margins was almost perfect (0.77 and 0.69, respectively). Conclusion The same follow-up recommendation developed for baseline costal pleura-attached noncalcified nodules (CP-NCNs) can be used for CP-NCNs newly identified at annual screening rounds. © RSNA, 2021.

Predicting pathological lymph node status in clinical stage IA peripheral lung adenocarcinoma

OBJECTIVES

Even with current diagnostic technology, it is difficult to accurately predict pathological lymph node status (PLNS). This study aimed to develop a prediction model of PLNS in peripheral adenocarcinoma with a dominant solid component, based on clinical and radiological factors on thin-section computed tomography, to identify patients to whom wedge resection or other local therapies could be applied.

METHODS

Of 811 patients enrolled in a prospective multi-institutional study (JCOG0201), 420 patients with clinical stage IA peripheral lung adenocarcinoma having a dominant solid component were included. Multivariable logistic regression was performed to develop a model based on clinical and centrally reviewed radiological factors. Leave-one-out cross-validation and external validation analyses were performed, using independent data from 221 patients. Sensitivity, specificity and concordance statistics were calculated to evaluate diagnostic performance.

RESULTS

The formula for calculating the probability of pathological lymph node metastasis included the following variables: tumour diameter (including ground-glass opacity), consolidation-to-tumour ratio and density of solid component. The concordance statistic was 0.8041. When the cut-off value associated with the risk of incorrectly predicting negative pathological lymph node metastasis (pN-) was 4.9%, diagnostic sensitivity and specificity in predicting PLNS were 95.7% and 46.0%, respectively. The concordance statistic for the external validation set was 0.7972, and diagnostic sensitivity and specificity in predicting PLNS were 95.4% and 40.5%, respectively.

CONCLUSIONS

The proposed model is clinically useful and successfully predicts pN- in patients with clinical stage IA peripheral lung adenocarcinoma with a dominant solid component.

Association of postoperative recurrence with radiological and clinicopathological features in patients with stage IA-IIA lung adenocarcinoma

OBJECTIVES

To retrospectively investigate whether radiological and clinicopathological characteristics were associated with the presence of stage IA-IIA lung adenocarcinoma in patients at high risk for a postoperative recurrence.

MATERIALS AND METHODS

Three hundred twelve patients with biopsy-proven node-negative early-stage (IA-IIA) lung adenocarcinoma met the inclusion criteria for this study. Demographics data and histopathological findings were collected from medical records. Computed tomography (CT) performed approximately 1 month before surgery was manually scored using 23 CT descriptors. Univariate analyses were applied to demonstrate an association between clinicopathological and radiological features and 2-/5-year recurrences. Multivariate logistic regression was performed to assess the ability of radiological and clinicopathological features to discriminate low and high-risk factors for recurrence. A ROC curve was used to evaluate prediction performance.

RESULTS

Univariate analysis revealed that the 2-year recurrence was associated with six radiological features and two clinicopathological features, while 5-year recurrence was associated with five radiological features and two clinicopathological features. A multivariate logistic regression model of combined clinicopathological and radiological features showed that stage IIA (OR = 2.87), solid texture (solid part > 50 %: OR = 4.81; solid part = 100 %: OR = 3.61), pleural attachment (OR = 3.97) and bronchovascular bundle thickening (OR = 2.16) were associated with the independent predictors of 2-year recurrence, and stage IIA (OR = 3.52), solid texture (solid part > 50 %: OR = 3.56; solid part = 100 %: OR = 2.44) and pleural attachment (OR = 4.57) were associated with 5-year recurrence. Combined radiological and clinicopathological features could be significant indicators of 2- and 5-year recurrences (AUC = 0.784 and AUC = 0.815, respectively).

CONCLUSIONS

The combination of radiological and clinicopathological features has the potential to help predict postoperative recurrence in patients with stage IA-IIA lung adenocarcinomas and guide oncologists and patients whether to undergo additional treatment after surgery.

Discriminating Small-Sized (2 cm or Less), Noncalcified, Solitary Pulmonary Tuberculoma and Solid Lung Adenocarcinoma in Tuberculosis-Endemic Areas

Background. Pulmonary tuberculoma can mimic lung malignancy and thereby pose a diagnostic dilemma to clinicians. The purpose of this study was to establish an accurate, convenient, and clinically practical model for distinguishing small-sized, noncalcified, solitary pulmonary tuberculoma from solid lung adenocarcinoma. Methods. Thirty-one patients with noncalcified, solitary tuberculoma and 30 patients with solid adenocarcinoma were enrolled. Clinical characteristics and CT morphological features of lesions were compared between the two groups. Multivariate logistic regression analyses were applied to identify independent predictors of pulmonary tuberculoma and lung adenocarcinoma. Receiver operating characteristic (ROC) analysis was performed to investigate the discriminating efficacy. Results. The mean age of patients with tuberculoma and adenocarcinoma was 46.8 ± 12.3 years (range, 28–64) and 61.1 ± 9.9 years (range, 41–77), respectively. No significant differences were observed concerning smoking history and smoking index, underlying disease, or tumor markers between the two groups. Univariate and multivariate analyses showed age and lobulation combined with pleural indentation demonstrated excellent discrimination. The sensitivity, specificity, accuracy, and the area under the ROC curve were 87.1%, 93.3%, 90.2%, and 0.956 (95% confidence interval (CI), 0.901–1.000), respectively. Conclusion. The combination of clinical characteristics and CT morphological features can be used to distinguish noncalcified, solitary tuberculoma from solid adenocarcinoma with high diagnostic performance and has a clinical application value.

Lung-RADS Version 1.1: Challenges and a Look Ahead, From the AJR Special Series on Radiology Reporting and Data Systems

In 2014, the American College of Radiology (ACR) created Lung-RADS 1.0. The system was updated to Lung-RADS 1.1 in 2019, and further updates are anticipated as additional data become available. Lung-RADS provides a common lexicon and standardized nodule follow-up management paradigm for use when reporting lung cancer screening (LCS) low-dose CT (LDCT) chest examinations and serves as a quality assurance and outcome monitoring tool. The use of Lung-RADS is intended to improve LCS performance and lead to better patient outcomes. To date, the ACR's Lung Cancer Screening Registry is the only LCS registry approved by the Centers for Medicare & Medicaid Services and requires the use of Lung-RADS categories for reimbursement. Numerous challenges have emerged regarding the use of Lung-RADS in clinical practice, including the timing of return to LCS after planned follow-up diagnostic evaluation; potential substitution of interval diagnostic CT for future LDCT; role of volumetric analysis in assessing nodule size; assessment of nodule growth; assessment of cavitary, subpleural, and category 4X nodules; and variability in reporting of the S modifier. This article highlights the major updates between versions 1.0 and 1.1 of Lung-RADS, describes the system's ongoing challenges, and summarizes current evidence and recommendations.

Management of Nodules Attached to the Costal Pleura at Low-Dose CT Screening for Lung Cancer

Background Pulmonary nodule features have been used to differentiate benign from malignant nodules. Purpose To determine the frequency of solid noncalcified nodules attached to the costal pleura (CP-NCNs) at baseline low-dose CT and to identify key features of benignity. Materials and Methods A retrospective review was performed of baseline low-dose CT scans obtained in 8730 participants in the Mount Sinai Early Lung and Cardiac Action Program screening cohort between 1992 and 2019. Participants with one or more solid CP-NCNs between 3.0 mm and 30.0 mm in average diameter were included. For each CP-NCN, the size, location, shape (lentiform, oval, or semicircular [LOS]; triangular; polygonal; round; or irregular), margin (smooth or nonsmooth), and attachment to the costal pleura (broad or narrow) were documented. The manifestation of emphysema and fibrosis within a 10-mm radius of the CP-NCN was determined. Multivariable logistic regression analysis, with synthetic minority oversampling techniques, was used. Results The 569 eligible participants (average age, 62 years ± 9 [standard deviation]; 343 women) had 943 solid CP-NCNs, of which 934 (99.0%) were benign and nine (1.0%) were malignant. Multivariable analysis showed that five shapes could be consolidated into three (LOS and/or triangular, round and/or polygonal, and irregular shape); pleural attachment was not a significant independent predictor (odds ratio, 1.24; P = .70); and interaction terms of size with shape (odds ratio, 0.73; P = .005) and margin were significant (odds ratio, 0.80; P = .001). All 603 CP-NCNs less than 10.0 mm with LOS or triangular shapes and smooth margins were benign. Conclusion All baseline noncalcified solid nodules attached to the costal pleura less than 10.0 mm in average diameter with lentiform, oval, semicircular, or triangular shapes and smooth margins were benign; thus, for these nodules, an annual repeat scan in 1 year, rather than a more immediate work-up, is recommended. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Godoy in this issue.

CT-guided microcoil localization for pulmonary nodules before VATS: a retrospective evaluation of risk factors for pleural marking failure

OBJECTIVES

To summarize the experiences of CT-guided microcoil localization before video-assisted thoracoscopic surgery (VATS) and to investigate the risk factors associated with pleural marking failure.

METHODS

Totally, 249 consecutive patients with 279 pulmonary nodules who underwent CT-guided microcoil localization prior to VATS were enrolled in this study. According to intraoperative observation, all the nodules were divided into two groups. The clinical characteristics and microcoil localization procedure-related variables of the nodules were analyzed by univariate analysis and multivariate logistic regression analysis to screen the independent factors associated with procedure results.

RESULTS

Among the 279 nodules, 28 failed to observe the proximal end of the microcoil deployed on visceral pleura during VATS. The logistic regression revealed that needle-pleura angle (≤ 30°: OR = 39.022, p = 0.003), pleura-microcoil distance (≤ 10 mm: OR = 87.054, p < 0.001; 10~20 mm: OR = 10.088, p = 0.010), and presence of pleural indentation (OR = 21.623, p < 0.001) were independent risk factors for pleural marking failure.

CONCLUSIONS

CT-guided microcoil localization for pulmonary nodules is a safe and effective procedure. Small needle-pleura angle (≤ 30°), pleura-microcoil distance (≤ 20 mm), and the presence of pleural indentation during the procedure are significant risk factors contributing to microcoil pleura marking failure.

KEY POINTS

• CT-guided microcoil localization for pulmonary nodules was a safe and effective procedure. • CT-guided microcoil localization for pulmonary nodules yielded low complication rates. • Small needle-pleura angle, short pleura-microcoil distance, and the presence of pleural indentation were contributing to pleura marking failure.

The combined nomogram based on the CT features may be used as a complementary method of frozen sections to predict invasive lung adenocarcinoma manifesting as ground-glass nodules

Background

Frozen sections (FS) deferral sometimes occurs in the intraoperative pathological classification of early lung adenocarcinoma, which is not conducive to the decision-making of surgical treatment. Here, we compared the predictive performance of the combined nomogram based on the computer tomography (CT) features with FS to investigate whether the nomogram could be used as a complementary method for FS when FS deferral occurs to predict invasive adenocarcinoma (IAC) manifesting as ground-glass nodules (GGNs) during surgery.

Methods

In this study, 205 early lung adenocarcinomas manifesting as GGNs from 178 patients who had undergone surgical treatment were included and divided into a training set (n=123) and a validation set (n=82). The training set defined a hybrid nomogram incorporating CT features and intraoperative measured tumor size based on multivariate logistic regression to predict IAC, and the validation set was used to verified the predictive performance. We also collected the diagnostic results of FS and compared the predictive performance of the established nomogram with FS.

Results

The accuracy of combined nomogram in predicting IAC in the training and validation sets was 91.1% and 89.0%, respectively, and the predictive accuracy of FS in the training set and validation set was 87.0% and 86.6%, respectively. The predictive accuracy between the combined nomogram and FS have no significant difference.

Conclusions

Compared with FS, the performance of the combined nomogram in predicting the lung IAC manifesting as GGNs is satisfactory, which has the potential to be used as a complementary method for FS when FS deferrals during surgery.