Clinicoradiographic Predictors of Aggressive Biology in Lung Cancer With Ground Glass Components

Quantification of Intratumoral Heterogeneity: Distinguishing Histological Subtypes in Clinical T1 Stage Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules on Computed Tomography

RATIONALE AND OBJECTIVES

To quantify intratumor heterogeneity (ITH) in clinical T1 stage lung adenocarcinoma presenting as pure ground-glass nodules (pGGN) on computed tomography, assessing its value in distinguishing histological subtypes.

MATERIALS AND METHODS

An ITH score was developed for quantitative measurement by integrating local radiomics features and global pixel distribution patterns. Diagnostic efficacy in distinguishing histological subtypes was evaluated using receiver operating characteristic curve analysis and area under the curve (AUC) values. The ITH score's performance was compared to those of conventional radiomics (C-radiomics), and radiological assessments conducted by experienced radiologists.

RESULTS

The ITH score demonstrated excellent performance in distinguishing lepidic-predominant adenocarcinoma (LPA) from other histological subtypes of clinical T1 stage lung adenocarcinoma presenting as pGGN. It outperformed both C-radiomics and radiological findings, exhibiting higher AUCs of 0.784 (95% confidence interval [CI]: 0.742-0.826) and 0.801 (95% CI: 0.739-0.863) in the training and validation cohorts, respectively. The AUCs of C-radiomics were 0.764 (95% CI: 0.718-0.810, DeLong test, p = 0.025) and 0.760 (95% CI: 0.692-0.829, p = 0.023) and those of radiological findings were 0.722 (95% CI: 0.673-0.771, p = 0.003) and 0.754 (95% CI: 0.684-0.823, p = 0.016) in the training and validation cohorts, respectively. Subgroup analysis revealed varying diagnostic efficacy across clinical T1 stages, with the highest efficacy in the T1a stage, followed by the T1b stage, and lowest in the T1c stage.

CONCLUSION

The ITH score presents a superior method for evaluating histological subtypes and distinguishing LPA from other subtypes in clinical T1 stage lung adenocarcinoma presenting as pGGN.

Peritumoral radiomics increases the efficiency of classification of pure ground-glass lung nodules: a multicenter study

PURPOSE

We aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC).

METHODS

A total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared. The diagnostic performance of the different models was evaluated using receiver operating characteristic curve analysis RESULTS: The pGGNs were classified into grade 1 (117), 2 (34), and 3 (0), according to the IASLC grading system. In all four radiomic models, pGGNs of grade 2 had significantly higher radiomic scores than those of grade 1 (P < 0.05). The AUC of the GTV and GTV + 5/10/15-mm PTV were 0.869, 0.910, 0.951, and 0.872 in the training cohort and 0.700, 0.715, 0.745, and 0.724 in the validation cohort, respectively.

CONCLUSIONS

The radiomic features we extracted from the GTV and PTV of pGGNs could effectively be used to differentiate grade-1 and grade-2 tumors. In particular, the radiomic features from the PTV increased the efficiency of the diagnostic model, with GTV + 10 mm PTV exhibiting the highest efficacy.

Enhancing the prediction of the invasiveness of pulmonary adenocarcinomas presenting as pure ground-glass nodules: Integrating intratumor heterogeneity score with clinical-radiological features via machine learning in a multicenter study

Objective

The invasiveness of lung adenocarcinoma significantly impacts clinical decision-making. However, assessing this invasiveness preoperatively, especially when it manifests as pure ground-glass nodules (pGGN) on CT scans, poses challenges. This study aims to quantify intratumor heterogeneity (ITH) and determine whether the ITH score can enhance the accuracy of invasiveness predictions.

Methods

A total of 524 patients with lung adenocarcinomas presenting as pGGN were enrolled in the study, with 177 (33.78%) receiving a pathologic diagnosis of invasiveness. Four diagnostic approaches were developed to predict the invasiveness of lung adenocarcinoma presenting as pGGN: (1) conventional lesion size, (2) ITH score, (3) clinical-radiological features (ClinRad), and (4) integration of the ITH score with ClinRad. ClinRad alone or in combination with the ITH score served as the input for 11 machine learning approaches. The trained models were evaluated in an independent validation cohort, and the area under the curve (AUC) was calculated to assess classification performance.

Results

The conventional lesion size showed the lowest performance, with an AUC of 0.826 (95% confidence interval [CI]: 0.758-0.894), while the ITH score outperformed it with an AUC of 0.846 (95% CI: 0.787-0.905). The CatBoost model performed best when the ITH score and ClinRad were both used as input features, leading to the development of an ITH-ClinRad-guided CatBoost classifier. CatBoost also excelled with ClinRad alone, resulting in a ClinRad-guided CatBoost classifier with an AUC of 0.830 (95% CI: 0.764-0.896), surpassed by the ITH-ClinRad-guided CatBoost classifier with an AUC of 0.871 (95% CI: 0.818-0.924).

Conclusion

The ITH-ClinRad-guided CatBoost classifier emerges as a promising tool with significant potential to revolutionize the management of lung adenocarcinomas presenting as pGGNs.

Lymph Node Metastases in Surgically Resected Solitary Ground-Glass Opacities: A Two-Center Retrospective Cohort Study and Pooled Literature Analysis

BACKGROUND

An increasing body of evidence supports the noninferiority of sublobar resection compared with lobectomy in terms of survival for patients with early-stage lung cancer with ground-glass opacities (GGOs). However, few studies have focused on the incidence of lymph node (LN) metastases in these patients. We aimed to analyze N1 and N2 lymph node involvement in patients with non-small cell lung cancer (NSCLC) with GGO components stratified with different consolidation tumor ratio (CTR).

PATIENTS AND METHODS

We performed two-center studies by retrospectively reviewing a total of 864 patients with NSCLC with semisolid or pure GGO manifestation (diameter ≤ 3 cm). Clinicopathologic features and outcomes were analyzed. We also reviewed 35 studies to characterize the patient with NSCLC population with the GGO manifestation.

RESULTS

In both cohorts, there was no LN involvement for pure GGO NSCLC, while solid predominant GGO exhibited a relatively high LN involvement rate. On the basis of a pooled literature analysis, the incidence of pathologic mediastinal LN was 0% and 3.8% for pure and semisolid GGOs, respectively. GGO NSCLCs with CTR ≤ 0.5 also had rare LN involvement (0.1%).

CONCLUSIONS

From two cohorts and pooled literature analysis, LN involvement was not observed in patients with pure GGO, and very few patients with semisolid GGO NSCLC with CTR ≤ 0.5 had LN involvement, revealing that it may be unnecessary to perform lymphadenectomy for pure GGOs, while mediastinal lymph node sampling (MLNS) is enough for semisolid GGOs with CTR ≤ 0.5. For the patients with GGO CTR > 0.5, mediastinal lymphadenectomy (MLD) or MLNS should be considered.

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.

Latest Clinical Evidence and Operative Strategy for Small-Sized Lung Cancers

Many thoracic surgeons revealed that consolidation tumor ratio or solid component size on thin-section computed tomography has been considered more prognostic than maximum tumor size in non-small cell lung cancer (NCSLC). According to the results, the 8th TNM classification drastically changed the staging system, i.e., clinical T category was determined based on the invasive or solid component size excluding a ground-glass opacity (GGO). However, several debates are arising over the application of radiological solid size for the clinical T staging. Meanwhile, recent several institutional reports have noticed a significantly simple fact that the presence of a GGO denotes an influence on the favorable prognosis of NSCLC. More important, radiologic pure-solid lung cancers without a GGO exhibit more malignant behaviors with regard to both the clinical and pathological aspects, and show several histologic types that have a poorer prognosis than radiologic part-solid lung cancer. In contrast, favorable prognostic impact of the presence of a GGO component was demonstrated, which was irrespective of the solid component size in cases in which the tumor showed a GGO component. Recently, this concept has been gradually noticed on a nationwide level. Obvious distinctions regarding the several baseline characteristics between the tumor with/without GGO component is a fundamental biological feature of early-stage lung cancer, which would result in a big difference in prognosis, modes of recurrence, overall behavior, and appropriate operative strategies. As a future perspective, the presence or absence of a GGO should be considered as an important parameter in the next clinical T classification.

Predicting malignancy: subsolid nodules detected on LDCT in a surgical cohort of East Asian patients

Background

Due to widespread use of low-dose computed tomography (LDCT) screening, increasing number of patients are found to have subsolid nodules (SSNs). The management of SSNs is a clinical challenge and primarily depends on CT imaging. We seek to identify risk factors that may help clinicians determine an optimal course of management.

Methods

We retrospectively reviewed the characteristics of 83 SSN lesions, including 48 pure ground-glass nodules and 35 part-solid nodules, collected from 83 patients who underwent surgical resection.

Results

Of the 83 SSNs, 16 (19.28%) were benign and 67 (80.72%) were malignant, including 23 adenocarcinomas in situ (AIS), 16 minimally invasive adenocarcinomas (MIA), and 28 invasive adenocarcinomas (IA). Malignant lesions were found to have significantly larger diameters (P<0.05) with an optimal cut-off point of 9.24 mm. Significant indicators of malignancy include female sex (P<0.05), air bronchograms (P<0.001), spiculation (P<0.05), pleural tail sign (P<0.05), and lobulation (P<0.05). When compared with AIS/MIA combined, IA lesions were found to be larger (P<0.05) with an optimal cut-off of 12 mm, and have a higher percentage of part-solid nodules (P<0.001), pleural tail sign (P<0.001), air bronchograms (P<0.05), and lobulation (P<0.05). Further multivariate analysis found that lesion size and spiculation were independent factors for malignancy while part-solid nodules were associated with IA histology.

Conclusions

East Asian females are at risk of presenting with a malignant lesion even without history of heavy smoking or old age. Nodule features associated with malignancy include larger size, air bronchograms, lobulation, pleural tail sign, spiculation, and solid components. A combination of patient characteristic and LDCT features can be effectively used to guide management of patients with SSNs.

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.

Prognostic impact of a ground-glass opacity component in clinical stage IA non-small cell lung cancer

OBJECTIVE

We performed a validation study to confirm the prognostic importance of the presence of a ground-glass opacity component based on data of the Japan Clinical Oncology Group study, JCOG0201, which was a prospective observational study to predict the pathological noninvasiveness of clinical stage IA lung cancer in Japan.

METHODS

Among the 811 patients registered in JCOG0201, 671 were confirmed eligible by study monitoring and a central review of computed tomography. Registered c-stage IA lung cancer was less than 30 mm in maximum tumor size, which was classified into a with ground-glass opacity group (pure ground-glass opacity and part-solid tumor) or solid group based on the status of a ground-glass opacity component. T staging was reassigned in accordance with the 8th edition of the TNM staging system. To validate the prognostic impact, overall survival was estimated.

RESULTS

Of the cases, 432 (64%) were in the with ground-glass opacity group and 239 (36%) were in the solid group with a median follow-up time of 10.1 years. The 5-year overall survival was significantly different between the with ground-glass opacity group and solid group (95.1% vs 81.1%). The 5-year overall survival was excellent regardless of the solid component size in the with ground-glass opacity group (c-T1a or less: 97.2%, c-T1b: 93.4%, c-T1c: 91.7%). In contrast, prognostic impact of the tumor size was definitive in the solid group (c-T1a: 87.5%, c-T1b: 85.9%, c-T1c: 73.7%).

CONCLUSIONS

Favorable prognostic impact of the presence of a ground-glass opacity component was demonstrated in JCOG0201. The presence or absence of a ground-glass opacity should be considered as an important parameter in the next clinical T classification.

Immune regulatory markers of lepidic-pattern adenocarcinomas presenting as ground glass opacities

Background

The tumor immune microenvironment of lepidic-pattern adenocarcinoma remains poorly understood. In this study, we characterized tumor infiltrating lymphocytes (TILs) and percent PD-L1 expression among adenocarcinoma presenting as either radiographic ground glass opacities (GGOs) or solid lesions.

Methods

Pathologic specimens of patients with clinical stage I lung adenocarcinoma were analyzed using tissue microarray sectioning. The invasive portion of the tumor was selected for the tissue core. Lepidic growth pattern was confirmed among the GGO lesions using whole section analysis. Progression was defined as pN+ or subsequent recurrence.

Results

A total of 181 patients were identified, among whom 13 (7%) represented GGOs without clinical progression, 113 (62%) represented radiographic solid lesions that never progressed, and 55 (30%) represented radiographic solid lesions that ultimately did progress. CD57+ cell density, a marker for antigen-specific, oligoclonal T cells and NK cells, differed among the three cohorts, with the highest cell density observed within radiographically solid lesions without progression, and lower cell density both in the radiographic solid lesions that progressed and GGOs. Other TIL phenotypes were not statistically different between cohorts. Of substantial clinical interest, median percent PD-L1 positive cells within GGOs was 14, whereas that of radiographic solid lesions without progression was 22, and radiographic solid lesions that subsequently progressed was 27 (P=0.07).

Conclusions

Lepidic pattern adenocarcinoma presenting as GGOs and radiographic solid lesions show differential immune regulation. Further studies to investigate whether GGOs representing adenocarcinoma have varying susceptibility to immune checkpoint inhibitor therapy are warranted.

New revisions and current issues in the eighth edition of the TNM classification for non-small cell lung cancer

In the eighth edition of the TNM classification of lung cancer, the prognostic impact of tumor size is emphasized as a descriptor of all T categories. Especially in lung cancer where tumor size is 5 cm or less, the 1-cm cutoff point significantly differentiated the survival outcome. In addition, the new staging categories were assigned, namely, Tis (adenocarcinoma in situ) and T1mi (minimally invasive adenocarcinoma). Furthermore, the measurement of a radiological solid component size excluding the ground glass opacity component or pathological invasive size without a lepidic component was proposed for deciding the cT/pT categories for lung adenocarcinoma. The N descriptors were kept the same as in the eventh edition on the whole, however, quantification of nodal disease had a prognostic impact based on the number of nodal stations involved in the eighth edition, i.e. N1a as a single N1 station, N1b as a multiple N1 station, N2a1 as a single N2 station without N1 (skip metastasis), N2a2 as a single N2 station with N1 disease, and N2b as a multiple N2 station. In the M descriptors, subclassification was performed based on the location or numbers of distantly metastatic lesions, i.e. M1a as any intrathoracic metastases, M1b as a single distant metastatic lesion in one organ, and M1c as multiple distant metastases in either a single organ or multiple organs. Survival analysis of the eighth edition of the TNM classification clearly separated the distinct groups, however, unsolved issues still remain that should be discussed and further revised for the forthcoming TNM staging system.

Ground Glass Lesions on Chest Imaging: Evaluation of Reported Incidence in Cancer Patients Using Natural Language Processing

BACKGROUND

Ground glass opacities (GGOs) on computed tomography (CT) have gained significant recent attention, with unclear incidence and epidemiologic patterns. Natural language processing (NLP) is a powerful computing tool that collects variables from unstructured data fields. Our objective was to characterize trends of GGO detection using NLP.

METHODS

Patients were identified at a large quaternary referral center who underwent chest CT from 2000 to 2016 via query of institutional databases. NLP was used to identify imaging reports with GGOs and to obtain additional demographic data. Incidence of reported GGOs was tracked over time. Multivariate regression was used to identify predictors of GGOs identified on chest CT.

RESULTS

A total of 244,391 chest CTs were included, with 35,386 (14.5%) revealing GGOs. There was a significant relationship between advancing year of chest CT and likelihood of reported GGOs (p < 0.001). GGOs were more likely to occur in older subjects (60.5 vs 58.5 years, p < 0.001), males (54.6% vs 51.5%, p < 0.001), and nonwhite races (21.2% Asian, 15.6% Hispanic, 14.4% black, 14.0% white; p < 0.001). Certain occupational histories predicted more frequent GGOs (p < 0.001), including transportation labor (47.4%), metal workers (42.3%), iron workers (33.3%), cabinetry (32.6%), and foremen (29.6%). Multivariate regression revealed age, sex, nonsmokers, increasing year of chest CT, and race as significant independent predictors of identifying GGOs.

CONCLUSIONS

NLP explored a large cohort of patients who underwent chest CT over the study period. Demographic features predicting reported GGOs include age, sex, race, and occupation. GGO recognition continues to increase with time, and further studies investigating etiology and prognostic implications are necessary.