Does lung adenocarcinoma subtype predict patient survival?: A clinicopathologic study based on the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary lung adenocarcinoma classification. J Thorac Oncol. 2011;6:1496-1504. [PMID: 21642859 DOI: 10.1097/jto.0b013e318221f701]

Predicting histopathological features of aggressiveness in lung cancer using CT radiomics: a systematic review

PURPOSE

To examine the accuracy of CT radiomics to predict histopathological features of aggressiveness in lung cancer using a systematic review of test accuracy studies.

METHODS

Data sources searched included Medline, Embase, Web of Science, and Cochrane Library from up to 3 November 2023. Included studies reported test accuracy of CT radiomics models to detect the presence of: spread through air spaces (STAS), predominant adenocarcinoma pattern, adenocarcinoma grade, lymphovascular invasion (LVI), tumour infiltrating lymphocytes (TIL) and tumour necrosis, in patients with lung cancer. The primary outcome was test accuracy. Two reviewers independently assessed articles for inclusion and assessed methodological quality using the QUality Assessment of Diagnostic Accuracy Studies-2 tool. A single reviewer extracted data, which was checked by a second reviewer. Narrative data synthesis was performed.

RESULTS

Eleven studies were included in the final analysis. 10/11 studies were in East Asian populations. 4/11 studies investigated STAS, 6/11 investigated adenocarcinoma invasiveness or growth pattern, and 1/11 investigated LVI. No studies investigating TIL or tumour necrosis met inclusion criteria. Studies were of generally mixed to poor methodological quality. Reported accuracies for radiomic models ranged from 0.67 to 0.94.

CONCLUSION

Due to the high risk of bias and concerns regarding applicability, the evidence is inconclusive as to whether radiomic features can accurately predict prognostically important histopathological features of cancer aggressiveness. Many studies were excluded due to lack of external validation. Rigorously conducted prospective studies with sufficient external validity will be required for radiomic models to play a role in improving lung cancer outcomes.

Peri- and postoperative morbidity and mortality in older patients with non-small cell lung cancer: a matched-pair study

BACKGROUND

Reports from case series suggest that operative outcomes are comparable amongst different age groups following surgery with curative intent for non-small cell lung cancer (NSCLC). The purpose of this study was to compare morbidity and mortality after NSCLC surgery in older patients (≥ 75 years) versus younger patients (< 75 years) and identify independent predictive risk factors.

METHODS

We identified 2015 patients with postoperative stages IA to IIIA according to AJCC/UICC 7th edition who had undergone NSCLC surgery with curative intent at a single specialized lung cancer center from January 2010 to December 2015. A matched-pair analysis was performed on 227 older patients and corresponding 227 younger patients. Short-term surgical outcomes were postoperative morbidity, length of hospital stay, 30-day and 90-day mortality. Long-term operative outcomes were disease-free and overall survival.

RESULTS

454 patients were included in the matched-pair analysis. 36% of younger patients developed postoperative complications versus 42% in older patients (p = 0.163). Age was not significantly associated with the occurrence of postoperative complications. Median length of hospital stay was 14 days in older patients and 13 days in younger patients (p = 0.185). 90-day mortality was 2.2% in younger patients compared to 4% in older patients (p = 0.424). In patients aged 75 and older impaired performance status (ECOG ≥ 1) was associated with decreased overall survival (HR = 2.15, CI 1.34-3.46), as were preoperative serum C-reactive protein / albumin ratio ≥ 0.3 (HR = 1.95, CI 1.23-3.11) and elevated preoperative serum creatinine levels ≥ 1.1 mg/dl (HR = 1.84, CI 1.15-2.95). In the younger cohort male sex (HR = 2.26, CI 1.17-4.36), postoperative stage III disease (HR 4.61, CI 2.23-9.54) and preoperative anemia (hemoglobin < 12 g/dl) (HR 2.09, CI 1.10-3.96) were associated with decreased overall survival.

CONCLUSIONS

Lung resection for NSCLC in older patients is associated with postoperative morbidity and mortality comparable to those of younger patients. In older patients, physical activity, comorbidities and nutritional status are related to survival and should influence the indication for surgery rather than age alone.

Spatial whole exome sequencing reveals the genetic features of highly-aggressive components in lung adenocarcinoma

In invasive lung adenocarcinoma (LUAD), patients with micropapillary (MIP) or solid (SOL) components had a significantly poorer prognosis than those with only lepidic (LEP), acinar (ACI) or papillary (PAP) components. It is interesting to explore the genetic features of different histologic subtypes, especially the highly aggressive components. Based on a cohort of 5,933 patients, this study observed that in different tumor size groups, LUAD with MIP/SOL components showed a different prevalence, and patients with ALK alteration or TP53 mutations had a higher probability of developing MIP/SOL components. To control individual differences, this research used spatial whole-exome sequencing (WES) via laser-capture microdissection of five patients harboring these five coexistent components and identified genetic features among different histologic components of the same tumor. In tracing the evolution of components, we found that titin (TTN) mutation might serve as a crucial intratumor potential driver for MIP/SOL components, which was validated by a cohort of 146 LUAD patients undergoing bulk WES. Functional analysis revealed that TTN mutations enriched the complement and coagulation cascades, which correlated with the pathway of cell adhesion, migration, and proliferation. Collectively, the histologic subtypes of invasive LUAD were genetically different, and certain trunk genotypes might synergize with branching TTN mutation to develop highly aggressive components.

Real-world applications of the new grading system in lung adenocarcinoma: A study of 907 patients focusing on revealing the relationship between pathologic grade and genetic status

BACKGROUND

The status of the lung adenocarcinoma (LUAD) grading system and the association between LUAD differentiation, driver genes, and clinicopathological features remain to be elucidated.

METHODS

We included patients with invasive non-mucinous LUAD, evaluated their differentiation, and collected available clinicopathological information, gene mutations, and analyzed clinical outcomes.

RESULTS

Among the 907 patients with invasive non-mucinous LUAD, 321 (35.4 %) were poorly differentiated, 422 (46.5 %) were moderately differentiated, and 164 (18.1 %) were well differentiated. EGFR mutation was more common in the LUADs accompanied without CGP (complex glandular pattern) than LUADs with CGP (p < 0.001). Correlation analysis between mutations and clinical characteristics showed that EGFR gene mutation (p < 0.001), KRAS gene mutation (p < 0.05), and ALK gene rearrangement (p < 0.001) were significantly related to the degree of tumor differentiation, and the KRAS and ALK gene mutation frequencies were higher in the low-differentiation group than in the high and medium differentiation groups. The EGFR mutation frequency was higher in the well/moderately differentiated adenocarcinoma group.

CONCLUSIONS

Our study adds to the evidence regarding the role of the grading system in prognosis. EGFR, KRAS, and ALK are related to the degree of tumor differentiation.

Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma

Objective

Invasive lung adenocarcinoma（ILA） with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution（SR） enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning.

Methods

Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC).

Result

A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility.

Conclusion

The study successfully constructed and evaluated a deep learning（DL）-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.

Establishment and characterization of novel high mucus-producing lung tumoroids derived from a patient with pulmonary solid adenocarcinoma

Among mucus-producing lung cancers, invasive mucinous adenocarcinoma of the lung is a rare and unique subtype of pulmonary adenocarcinoma. Notably, mucus production may also be observed in the five subtypes of adenocarcinoma grouped under the higher-level diagnosis of Invasive Non-mucinous Adenocarcinomas (NMA). Overlapping pathologic features in mucus-producing tumors can cause diagnostic confusion with significant clinical consequences. In this study, we established lung tumoroids, PDT-LUAD#99, from a patient with NMA and mucus production. The tumoroids were derived from the malignant pleural effusion of a patient with lung cancer and have been successfully developed for long-term culture (> 11 months). Karyotyping by fluorescence in situ hybridization using an alpha-satellite probe showed that tumoroids harbored aneuploid karyotypes. Subcutaneous inoculation of PDT-LUAD#99 lung tumoroids into immunodeficient mice resulted in tumor formation, suggesting that the tumoroids were derived from cancer. Xenografts from PDT-LUAD#99 lung tumoroids reproduced the solid adenocarcinoma with mucin production that was observed in the patient's metastatic lymph nodes. Immunoblot analysis showed MUC5AC secretion into the culture supernatant of PDT-LUAD#99 lung tumoroids, which in contradistinction was barely detected in the culture supernatants of NCI-A549 and NCI-H2122 pulmonary adenocarcinoma cells known for their mucin-producing abilities. Here, we established a novel high-mucus-producing lung tumoroids from a solid adenocarcinoma. This preclinical model may be useful for elucidating the pathogenesis of mucus-producing lung cancer.

Impact of Tumor Size and Differentiation Grade on Survival After Lobectomy and Segmentectomy for Patients with Early-Stage Lung Adenocarcinoma

BACKGROUND

The purpose of this study was to investigate the effect of tumor size and differentiation grade on long term survival in patients with early-stage lung adenocarcinoma (LUAD) after lobectomy and segmentectomy.

PATIENTS AND METHODS

Patients with stage T1-2N0M0 LUAD who underwent lobectomy and segmentectomy were identified from the Surveillance, Epidemiology, and End Results database. Patients were stratified as grade I (well differentiated), grade II (moderately differentiated), and grade III/IV (poorly differentiated/undifferentiated) carcinomas. The effect of tumor size on overall survival (OS) and lung cancer-specific survival (LCSS) was evaluated using the multivariate Cox regression model, including the interaction between tumor size, type of surgery, and tumor differentiation grade. The inverse probability of treatment weighting method was used to adjust for bias between the groups.

RESULTS

A total of 19,857 patients were identified, including 18,759 (94.4%) who underwent lobectomy and 1098 (5.5%) who underwent segmentectomy. A three-way interaction among tumor size, differentiation grade, and type of surgery was observed in the overall cohort. After stratifying by differentiation grade, plots of interaction revealed that lobectomy was associated with improved survival compared with segmentectomy when the tumor size exceeded 23 mm for grade I LUAD and 14 mm for grade II LUAD. No interaction was observed between the studied factors in grade III/IV carcinomas.

CONCLUSIONS

This study interpreted the interaction between tumor size and type of surgery on long-term survival in patients with early stage LUAD and established a tumor size threshold beyond which lobectomy provided survival benefits compared with segmentectomy.

The diagnostic potential role of thioredoxin reductase and TXNRD1 in early lung adenocarcinoma: A cohort study

Background

Lung adenocarcinoma (LUAD) is the primary form of lung cancer, yet the reliable biomarkers for early diagnosis remain insufficient. Thioredoxin reductase (TrxR) is strongly linked to the occurrence, development, and drug resistance of lung cancer, making it a potential biomarker. However, further research is required to assess its diagnostic value in LUAD.

Methods

A retrospective analysis was performed on patients who underwent pulmonary nodule resection at our center from 2018 to 2022. Clinical data, including preoperative TrxR levels, imaging, and laboratory characteristics, were identified as study variables. Two prediction models were constructed using multiple logistic regression, and their prediction performance was evaluated comprehensively. Besides, bioinformatics analyses of TrxR coding genes including differential expression, functional enrichment, immune infiltration, drug sensitivity, and single-cell landscape were performed based on TCGA database, which were subsequently validated by Human Protein Atlas.

Results

A total of 506 eligible patients (72 benign lesions, 77 AISs, 185 MIAs and 172 IACs) were identified in the clinical cohort. Two TrxR-based models were developed, which were able to distinguish between benign and malignant pulmonary nodules, as well as pathological subtypes of LUAD, respectively. The models exhibited good predictive ability with all AUC values ranging from 0.7 to 0.9. Based on calibration curves and clinical decision analysis, the nomogram models showed high reliability. Functional analysis indicated that TXNRD1 primarily participated in cell cycle and lipid metabolism. Immune infiltration analysis showed that TXNRD1 has a strong association with immune cells and could impact immunotherapy. Then, we identified small molecular compounds that inhibit TXNRD1 and confirmed TXNRD1 expression by single-cell landscape and immunohistochemistry.

Conclusion

This study validated the diagnostic value of TrxR and TXNRD1 in clinical cohorts and transcriptional data, respectively. TrxR and TXNRD1 could be used in the risk diagnosis of early LUAD and facilitate personalized treatment strategies.

Characteristics of mast cell infiltration in lung adenocarcinoma and its impact on prognosis

BACKGROUND

The role of mast cells in malignancies remains unclear, and there is no clear correlation between mast cells and tumor microvessels, tumor growth, or lung adenocarcinoma (LUAD) prognosis. This study aims to explore the association between mast cell density (MCD) and intratumoral microvessel density (MVD), clinicopathological parameters, and prognosis in LUAD, by evaluating mast cell infiltration characteristics and their prognostic significance.

METHODS

This retrospective investigation involved 238 patients with LUAD undergoing complete resection. Tumor and normal lung tissue sections outside the tumor were immunohistochemically stained for MCD in the intratumoral and outside regions, respectively. CD34 polyclonal antibody was used to measure intratumoral MVD.

RESULTS

Intratumoral regions of LUAD had a higher MCD (P < 0.001) than normal lung tissue. In the intratumoral region, MCD and CD34-MVD were positively correlated (r = 0.411, P < 0.001). Intratumoral MCD correlated with sex, smoking history, tumor differentiation, pathological subtype, and tumor size. Female sex (P = 0.012), no smoking history (P = 0.002), acinar predominant type (P = 0.012), and tumor size ≤ 3 cm (P = 0.009) were associated with a higher MCD, whereas poorly differentiated (P = 0.039) and solid/micropapillary predominant types (P = 0.001) were associated with a lower MCD. Higher intratumoral MCD exhibited a marginally improved overall survival, and individuals with higher MCD infiltration ratios (intratumoral MCD/outside the MCD) had higher disease-free and overall survival rates (log-rank P < 0.001). A high MCD infiltration ratio was associated with decreased risk of tumor progression and death following complete resection.

CONCLUSION

The tumor microenvironment controls mast cell infiltration in LUAD, and patients with increased intratumoral mast cell infiltration have better prognosis.

Morphological basis of the lung adenocarcinoma subtypes

Lung adenocarcinoma (LUAD), which accounts for a large proportion of lung cancers, is divided into five major subtypes based on histologic characteristics. The clinical characteristics, prognosis, and responses to treatments vary among subtypes. Here, we demonstrate that the variations of cell-cell contact energy result in the LUAD subtype-specific morphogenesis. We reproduced the morphologies of the papillary LUAD subtypes with the cellular Potts Model (CPM). Simulations and experimental validations revealed modifications of cell-cell contact energy changed the morphology from a papillary-like structure to micropapillary or solid subtype-like structures. Remarkably, differential gene expression analysis revealed subtype-specific expressions of genes relating to cell adhesion. Knockdown experiments of the micropapillary upregulated ITGA11 gene resulted in the morphological changes of the spheroids produced from an LUAD cell line PC9. This work shows the consequences of gene mutations and gene expressions on patient prognosis through differences in tissue composing physical forces among LUAD subtypes.

Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis

BACKGROUND

The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide images to investigate the relationship between the distribution of histological subtypes and clinicopathological factors.

METHODS

Using histological subtype images, which are typical for pathologists, we trained and validated an artificial intelligence model. Then, the model was applied to whole-slide images of resected lung adenocarcinoma specimens from 147 cases.

RESULT

The model achieved an accuracy of 99.7% in training sets and 90.4% in validation sets consisting of typical tiles of histological subtyping for pathologists. When the model was applied to whole-slide images, the predominant subtype according to the artificial intelligence model classification matched that determined by pathologists in 75.5% of cases. The predominant subtype and tumor grade (using the WHO fourth and fifth classifications) determined by the artificial intelligence model resulted in similar recurrence-free survival curves to those determined by pathologists. Furthermore, we stratified the recurrence-free survival curves for patients with different proportions of high-grade components (solid, micropapillary and cribriform) according to the physical distribution of the high-grade component. The results suggested that tumors with centrally located high-grade components had a higher malignant potential (P < 0.001 for 5-20% high-grade component).

CONCLUSION

The new artificial intelligence model for histological subtyping of lung adenocarcinoma achieved high accuracy, and subtype quantification and subtype distribution analyses could be achieved. Artificial intelligence model therefore has potential for clinical application for both quantification and spatial analysis.

Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm

Objective

In this study, we aimed to utilize computed tomography (CT)-derived radiomics and various machine learning approaches to differentiate between invasive mucinous adenocarcinoma (IMA) and invasive non-mucinous adenocarcinoma (INMA) preoperatively in solitary pulmonary nodules (SPN) ≤3 cm.

Methods

A total of 538 patients with SPNs measuring ≤3 cm were enrolled, categorized into either the IMA group (n = 50) or INMA group (n = 488) based on postoperative pathology. Radiomic features were extracted from non-contrast-enhanced CT scans and identified using the least absolute shrinkage and selection operator (LASSO) algorithm. In constructing radiomics-based models, logistic regression, support vector machines, classification and regression trees, and k-nearest neighbors were employed. Additionally, a clinical model was developed, focusing on CT radiological features. Subsequently, this clinical model was integrated with the most effective radiomic model to create a combined model. Performance assessments of these models were conducted, utilizing metrics such as the area under the receiver operating characteristic curve (AUC), DeLong's test, net reclassification index (NRI), and integrated discrimination improvement (IDI).

Results

The support vector machine approach showed superior predictive efficiency, with AUCs of 0.829 and 0.846 in the training and test cohorts, respectively. The clinical model had AUCs of 0.760 and 0.777 in the corresponding cohorts. The combined model had AUCs of 0.847 and 0.857 in the corresponding cohorts. Furthermore, compared to the radiomic model, the combined model significantly improved performance in both the training (DeLong test P = 0.045, NRI 0.206, IDI 0.024) and test cohorts (P = 0.029, NRI 0.125, IDI 0.032), as well as compared to the clinical model in both the training (P = 0.01, NRI 0.310, IDI 0.09) and test cohorts (P = 0.047, NRI 0.382, IDI 0.085).

Conclusion

the combined model exhibited excellent performance in distinguishing between IMA and INMA in SPNs ≤3 cm.

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study

OBJECTIVES

To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model.

METHODS

DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis.

RESULTS

In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13).

CONCLUSION

The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas.

CLINICAL RELEVANCE STATEMENT

Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management.

KEY POINTS

• A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).

Prognostic value of large amino acid transporter type 1 (LAT1) expression in pulmonary adenocarcinoma: A tissue microarray study

BACKGROUND

Large amino acid transporter type 1 (LAT1) provides cancer cells with essential amino acids for both protein synthesis and cell growth and may predict patient prognosis. Additionally, LAT1 inhibition can be a therapeutic target. This study aimed to examine the prognostic significance of LAT1 expression in lung cancer, paying special attention to adenocarcinoma subtypes.

METHODS

Tissue microarrays (TMA) of 1,560 total cores obtained from surgically resected lung cancer specimens between 1995 and 2008 at our hospital were used. Overall, 795 cases of adenocarcinoma were identified, and 717 underwent further evaluation. Immunohistochemical staining of whole slides and TMA cores were assessed to set H-score cutoff value.. Immunohistochemical expression of LAT1 was examined based on the subtypes of adenocarcinoma. Statistical analyses explored the prognostic significance of LAT1.

RESULTS

Adenocarcinoma accounted for 71.8% of all cases (n = 795), and 216 cases (27.1%) expressed LAT1. The 795 cases were categorized into five subtypes: lepidic (n = 29, 3.6%), papillary (n = 601, 75.6%), acinar (n = 58, 7.3%), and solid (n = 9, 1.1%); 717 of the 795 cases were further assessed according to the exclusion criteria. The LAT1-positive ratio increased as the architectural grade increased. Notably, in papillary adenocarcinoma, the LAT1-positive group had significantly lower overall survival compared to the negative group (10-year survival: 45.6% vs. 60.8%, p < 0.001).

CONCLUSION

LAT1 expression was higher in high-grade subtypes of pulmonary adenocarcinoma. Moreover, LAT1 expression is useful for predicting prognosis, particularly in papillary adenocarcinoma, facilitating prognostic stratification of papillary adenocarcinoma.

Lung adenocarcinoma: selection of surgical approaches in solid adenocarcinoma from the viewpoint of clinicopathologic features and tumor microenvironmental heterogeneity

Introduction

Solid adenocarcinoma represents a notably aggressive subtype of lung adenocarcinoma. Amidst the prevailing inclination towards conservative surgical interventions for diminutive lung cancer lesions, the critical evaluation of this subtype's malignancy and heterogeneity stands as imperative for the formulation of surgical approaches and the prognostication of long-term patient survival.

Methods

A retrospective dataset, encompassing 2406 instances of non-solid adenocarcinoma (comprising lepidic, acinar, and papillary adenocarcinoma) and 326 instances of solid adenocarcinoma, was analyzed to ascertain the risk factors concomitant with diverse histological variants of lung adenocarcinoma. Concurrently, RNA-sequencing data delineating explicit pathological subtypes were extracted from 261 cases in the TCGA database and 188 cases in the OncoSG database. This data served to illuminate the heterogeneity across lung adenocarcinoma (LUAD) specimens characterized by differential histological features.

Results

Solid adenocarcinoma is associated with an elevated incidence of pleural invasion, microscopic vessel invasion, and lymph node metastasis, relative to other subtypes of lung adenocarcinoma. Furthermore, the tumor microenvironment (TME) in solid pattern adenocarcinoma displayed suboptimal oxygenation and acidic conditions, concomitant with augmented tumor cell proliferation and invasion capacities. Energy and metabolic activities were significantly upregulated in tumor cells of the solid pattern subtype. This subtype manifested robust immune tolerance and capabilities for immune evasion.

Conclusion

This present investigation identifies multiple potential metrics for evaluating the invasive propensity, metastatic likelihood, and immune resistance of solid pattern adenocarcinoma. These insights may prove instrumental in devising surgical interventions that are tailored to patients diagnosed with disparate histological subtypes of LUAD, thereby offering valuable directional guidance.

Molecular heterogeneity in histomorphologic subtypes of lung adeno carcinoma represents a challenge for treatment decision

Lung cancer is the leading cause in cancer related death, with non-small cell lung cancer (NSCLC) being the most frequent subtype. The importance of NSCLC is reflected by the various targeted therapy options especially for NSCLC adenocarcinomas (lung adeno carcinoma (LUAD)) as well as a set of options for immune therapies. However, despite these therapy advances, the majority of patients do not show a long-term response to either targeted therapy or immune checkpoint inhibition. One reason for treatment failure appears to be the NSCLC tumor heterogeneity. NSCLC heterogeneity might lead to an insufficient molecular characterization of a given sample due to the limited tumor material used for pathological assessment as the majority of analyses is performed on small biopsies. To get a more detailed insight into the tumor heterogeneity of NSCLC LUAD, especially in the light of its different histomorphological growth patterns, we analysed isolated NSCLC growth pattern areas and the corresponding entire tumor samples of a cohort of 31 NSLCS LUAD patients and compared their mutational landscape and their expression profiles. While significant differences of complex biomarkers, like tumor mutational burden (TMB) or microsatellite instability (MSI), were not detected between the five growth patterns -lepidic, papillary, micropapillary, acinar, and solid- we observed various subclonal mutations and copy number variants. Moreover, RNASeq analysis revealed growth pattern specific expression profiles affecting cellular processes like apoptosis, metastasis and proliferation. Taken together, our data provide novel insights into the tumor heterogeneity of LUAD required to overcome tumor heterogeneity related therapy resistance.

Case of lung fine needle aspiration showing mucinous cells and extracellular mucin

Mucinous neoplasm with extracellular mucin can be challenging to interpret on fine needle aspiration and core biopsies. Determining the biologic origin of the mucin/mucinous cells, that is, benign/incidental versus neoplasm, invasive versus in situ, and primary versus metastatic tumors, requires a thorough multidisciplinary evaluation. The work up of these lesions includes morphologic analysis with ancillary immunohistochemical and/or molecular studies and correlation with clinical and imaging studies. This review outlines a practical approach to the diagnosis of mucinous lesions in the lung with comprehensive review of literature.

CT imaging features of lung ground-glass nodule patients with upgraded intraoperative frozen pathology

PURPOSE

Intraoperative frozen section pathology (FS) is widely used to guide surgical strategies while the accuracy is relatively low. Underestimating the pathological condition may result in inadequate surgical margins. This study aims to identify CT imaging features related to upgraded FS and develop a predictive model.

METHODS

Collected data from 860 patients who underwent lung surgery from January to December 2019. We analyzed the consistency rate of FS and categorized the patients into three groups: Group 1 (n = 360) had both FS and Formalin-fixed Paraffin-embedded section (FP) as non-invasive adenocarcinoma (IAC); Group 2 (n = 128) had FS as non-IAC but FP as IAC; Group 3 (n = 372) had both FS and FP as IAC. Clinical baseline characteristics were compared and propensity score adjustment was used to mitigate the effects of these characteristics. Univariate analyses identified imaging features with inter-group differences. A multivariate analysis was conducted to screen independent risk factors for FS upgrade, after which a logistic regression prediction model was established and a receiver operating characteristic (ROC) curve was plotted.

RESULTS

The consistency rate of FS with FP was 84.19%. 26.67% of the patients with non-IAC FS diagnosis were upgraded to IAC. The predictive model's Area Under Curve (AUC) is 0.785. Consolidation tumor ratio (CTR) ≤ 0.5 and smaller nodule diameter are associated with the underestimation of IAC in FS.

CONCLUSION

CT imaging has the capacity to effectively detect patients at risk of upstaging during FS.

Prognostic factors for invasive mucinous adenocarcinoma of the lung: systematic review and meta-analysis

BACKGROUND

Invasive mucinous adenocarcinoma of the lung (IMA) is a unique and rare subtype of lung adenocarcinoma with poorly defined prognostic factors and highly controversial studies. Hence, this study aimed to comprehensively identify and summarize the prognostic factors associated with IMA.

METHODS

A comprehensive search of relevant literature was conducted in the PubMed, Embase, Cochrane, and Web of Science databases from their inception until June 2023. The pooled hazard ratio (HR) and corresponding 95% confidence intervals (CI) of overall survival (OS) and/or disease-free survival (DFS) were obtained to evaluate potential prognostic factors.

RESULTS

A total of 1062 patients from 11 studies were included. In univariate analysis, we found that gender, age, TNM stage, smoking history, lymph node metastasis, pleural metastasis, spread through air spaces (STAS), tumor size, pathological grade, computed tomography (CT) findings of consolidative-type morphology, pneumonia type, and well-defined heterogeneous ground-glass opacity (GGO) were risk factors for IMA, and spiculated margin sign was a protective factor. In multivariate analysis, smoking history, lymph node metastasis, pathological grade, STAS, tumor size, and pneumonia type sign were found to be risk factors. There was not enough evidence that epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) mutations, CT signs of lobulated margin, and air bronchogram were related to the prognosis for IMA.

CONCLUSION

In this study, we comprehensively analyzed prognostic factors for invasive mucinous adenocarcinoma of the lung in univariate and multivariate analyses of OS and/or DFS. Finally, 12 risk factors and 1 protective factor were identified. These findings may help guide the clinical management of patients with invasive mucinous adenocarcinoma of the lung.

Survival benefit of adjuvant chemotherapy after resection of Stage I lung adenocarcinoma containing micropapillary components

BACKGROUND

The usefulness of postoperative adjuvant chemotherapy (ACT) for patients with stage I lung adenocarcinoma with micropapillary (MIP) components remains unclear. We analyzed whether postoperative ACT could reduce recurrence in patients with stage I lung adenocarcinoma with MIP components, thereby improving their overall survival (OS) and disease-free survival (DFS).

METHODS

Data for patients with pathologically confirmed stage I lung adenocarcinoma with MIP components from January 2012 to December 2018 were retrospectively analyzed. OS and DFS were analyzed in groups and subgroups.

RESULTS

Overall, 259 patients were enrolled. Patients who received ACT in stage IA showed significantly better survival than did those with no-adjuvant chemotherapy (NACT); (5-year OS 89.4% vs. 73.6%, p < 0.001; 5-year DFS 87.2% vs. 66.0%, p = 0.008). A difference was also observed for in-stage IB patients (5-year OS 82.0% vs. 51.8%, p = 0.001; 5-year DFS 76.0% vs. 41.11 %, p = 0.004). In subgroup analysis based on the proportion of MIP components, patients with 1%-5% MIP components had a significantly better prognosis in the ACT group than in the NACT group (5-year OS 82.4% vs. 66.0%, p = 0.005; 5-year DFS 76.5% vs. 49.1%, p = 0.032). A similar difference was observed for patients with MIP ≥5% (5-year OS 80.7% vs. 47.8%, p = 0.009; 5-year DFS 73.11% vs. 43.5%, p = 0.007).

CONCLUSION

Among patients with stage I lung adenocarcinoma with MIP components, those who received ACT showed significant survival benefits compared to those without ACT. Patients with lung adenocarcinoma with MIP components could benefit from ACT when the MIP was ≥1%.

The role of adenocarcinoma subtypes and immunohistochemistry in predicting lymph node metastasis in early invasive lung adenocarcinoma

BACKGROUND

Identifying lymph node metastasis areas during surgery for early invasive lung adenocarcinoma remains challenging. The aim of this study was to develop a nomogram mathematical model before the end of surgery for predicting lymph node metastasis in patients with early invasive lung adenocarcinoma.

METHODS

In this study, we included patients with invasive lung adenocarcinoma measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to January 2022. Preoperative biomarker results, clinical features, and computed tomography characteristics were collected. The enrolled patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. Recipient operating characteristic (ROC) curves were used to assess the discrimination ability of the model. Calibration capability was assessed using the Hosmer-Lemeshow test and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).

RESULTS

The overall incidence of lymph node metastasis was 13.23% (61/461). Six indicators were finally determined to be independently associated with lymph node metastasis. These six indicators were: age (P < 0.001), serum amyloid (SA) (P = 0.008); carcinoma antigen 125 (CA125) (P = 0. 042); mucus composition (P = 0.003); novel aspartic proteinase of the pepsin family A (Napsin A) (P = 0.007); and cytokeratin 5/6 (CK5/6) (P = 0.042). The area under the ROC curve (AUC) was 0.843 (95% CI: 0.779-0.908) in the training cohort and 0.838 (95% CI: 0.748-0.927) in the validation cohort. the P-value of the Hosmer-Lemeshow test was 0.0613 in the training cohort and 0.8628 in the validation cohort. the bias of the training cohort corrected C-index was 0.8444 and the bias-corrected C-index for the validation cohort was 0.8375. demonstrating that the prediction model has good discriminative power and good calibration.

CONCLUSIONS

The column line graphs created showed excellent discrimination and calibration to predict lymph node status in patients with ≤ 2 cm invasive lung adenocarcinoma. In addition, the predictive model has predictive potential before the end of surgery and can inform clinical decision making.

Clinicopathological significances of cribriform pattern in lung adenocarcinoma

The present study aimed to investigate the clinicopathological and prognostic implications of the cribriform pattern in lung adenocarcinoma through a meta-analysis. The estimated rates of cribriform pattern in lung adenocarcinomas were investigated. The correlations between cribriform pattern and clinicopathological characteristics, including genetic alterations and prognosis were evaluated. The estimated rate of cribriform pattern was 0.150 (95% confidence interval [CI], 0.101-0.218) in lung adenocarcinoma. The estimated rates of cribriform pattern in the 5% and 10% criteria were 0.230 (95% CI 0.125-0.386) and 0.130 (95% CI 0.062-0.252), respectively. The presence of cribriform pattern was significantly correlated with larger tumor size (> 30 mm), spread through air spaces, and lymph node metastasis (P < 0.001, P < 0.001, and P = 0.007, respectively, in the meta-regression test). There were no significant differences between cribriform pattern, smoking history, and vascular and lymphatic invasion. In lung adenocarcinoma with cribriform pattern, the estimated rates of ALK rearrangement, KRAS, and EGFR mutations were 0.407 (95% CI 0.165-0.704), 0.330 (95% CI 0.117-0.646), and 0.249 (95% CI 0.125-0.437), respectively. ALK rearrangement was significantly more frequent in lung adenocarcinomas with cribriform pattern than in those without. The overall survival rate was significantly worse in lung adenocarcinomas with a cribriform pattern than in those without (hazard ratio 2.051, 95% CI 1.369-3.075). In conclusion, the presence of a cribriform pattern can be a useful predictor of the clinicopathological characteristics and prognosis of patients with lung adenocarcinoma.

Radiomics and Clinical Characters Based Gaussian Naive Bayes (GNB) Model for Preoperative Differentiation of Pulmonary Pure Invasive Mucinous Adenocarcinoma From Mixed Mucinous Adenocarcinoma

Objective: To develop and validate predictive models based on clinical parameters, and radiomic features to distinguish pulmonary pure invasive mucinous adenocarcinoma (pIMA) from mixed mucinous adenocarcinoma (mIMA) before surgery. Method: From January 2017 to December 2022, 193 pIMA and 111 mIMA were retrospectively analyzed at our hospital in this retrospective study. From contrast-enhanced computed tomography, 1037 radiomic features were extracted. The patients were randomly divided into a training group and a test group (n = 213 and 91, respectively) in a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was used to select radiomic features. In this study, 9 machine learning radiomics prediction models were applied. The radiomics score was then calculated based on the best-performing machine learning model adopted. The clinical model was developed using the same machine learning model of radiomics. In the end, a combined model based on clinical factors and radiomics features was developed. The area under the receiver operating characteristic curve (AUC) value and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the prediction model. Results: The combined model established by the Gaussian Naive Bayes machine learning method exhibited the best performance. The AUC of the combined model, clinical model, and radiomics model were 0.81, 0.80, and 0.68 in the training group and 0.91, 0.80, and 0.81 in the test group, respectively. The Brier scores of the combined model were 0.171 and 0.112. The DCA curve also showed that the combined model was beneficial to clinical settings. Conclusion: The combined model integration of radiomics features and clinical parameters may have potential value for the preoperative differentiation of pIMA from mIMA.

A Shift in Paradigm: Selective Lymph Node Dissection for Minimizing Oversurgery in Early Stage Lung Cancer

Systematic lymph node dissection has been widely accepted and turned into a standard procedure for lung cancer surgery. In recent years, the concept of "minimal invasive surgery (MIS)" has greatly changed the surgical paradigm of lung cancer. Previous studies revealed that excessive dissection of lymph nodes without metastases had uncertain clinical benefit. Meanwhile, it leads to the elevated risk of postoperative complications including chylothorax and laryngeal nerve injury. In addition, dissection of nonmetastatic lymph nodes may disturb systematic immunity, resulting in the secondary effect on primary tumor or latent metastases. The past decades have witnessed the innovative strategies such as lobe-specific lymph node dissection and selective lymph node dissection. On the basis of evolution of lymph node dissection strategy, we discuss the negative effects of excessive nonmetastatic lymph node dissection and summarize the recent advances in the optimized dissection strategies, hoping to provide unique perspectives on the future directions.

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma

BACKGROUND

Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment.

OBJECTIVE

This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers.

DESIGN

A retrospective case control, diagnostic accuracy study.

METHODS

This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines.

RESULTS

The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively.

CONCLUSION

This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.

TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading

OBJECTIVES

Thyroid transcription factor 1 (TTF-1) is a well-established independent prognostic factor in lung adenocarcinoma (LUAD), irrespective of stage. This study aims to determine if TTF-1's prognostic impact is solely based on histomorphological differentiation (tumor grading) or if it independently relates to a biologically more aggressive phenotype. We analyzed a large bi-centric LUAD cohort to accurately assess TTF-1's prognostic value in relation to tumor grade.

PATIENTS AND METHODS

We studied 447 patients with resected LUAD from major German lung cancer centers (Berlin and Cologne), correlating TTF-1 status and grading with clinical, pathologic, and molecular data, alongside patient outcomes. TTF-1's impact was evaluated through univariate and multivariate Cox regression. Causal graph analysis was used to identify and account for potential confounders, improving the statistical estimation of TTF-1's predictive power for clinical outcomes.

RESULTS

Univariate analysis revealed TTF-1 positivity associated with significantly longer disease-free survival (DFS) (median log HR -0.83; p = 0.018). Higher tumor grade showed a non-significant association with shorter DFS (median log HR 0.30; p = 0,62 for G1 to G2 and 0.68; p = 0,34 for G2 to G3). In multivariate analysis, TTF-1 positivity resulted in a significantly longer DFS (median log HR -0.65; p = 0.05) independent of all other parameters, including grading. Adjusting for potential confounders as indicated by the causal graph confirmed the superiority of TTF-1 over tumor grading in prognostics power.

CONCLUSIONS

TTF-1 status predicts relapse and survival in LUAD independently of tumor grading. The prognostic power of tumor grading is limited to TTF-1-positive patients, and the effect size of TTF-1 surpasses that of tumor grading. We recommend including TTF1 status as a prognostic factor in the diagnostic guidelines of LUAD.

Artificial intelligence-based recurrence prediction outperforms classical histopathological methods in pulmonary adenocarcinoma biopsies

INTRODUCTION

Between 10 and 50% of early-stage lung adenocarcinoma patients experience local or distant recurrence. Histological parameters such as a solid or micropapillary growth pattern are well-described risk factors for recurrence. However, not every patient presenting with such a pattern will develop recurrence. Designing a model which can more accurately predict recurrence on small biopsy samples can aid the stratification of patients for surgery, (neo-)adjuvant therapy, and follow-up.

MATERIAL AND METHODS

In this study, a statistical model on biopsies fed with histological data from early and advanced-stage lung adenocarcinomas was developed to predict recurrence after surgical resection. Additionally, a convolutional neural network (CNN)-based artificial intelligence (AI) classification model, named AI-based Lung Adenocarcinoma Recurrence Predictor (AILARP), was trained to predict recurrence, with an ImageNet pre-trained EfficientNet that was fine-tuned on lung adenocarcinoma biopsies using transfer learning. Both models were validated using the same biopsy dataset to ensure that an accurate comparison was demonstrated.

RESULTS

The statistical model had an accuracy of 0.49 for all patients when using histology data only. The AI classification model yielded a test accuracy of 0.70 and 0.82 and an area under the curve (AUC) of 0.74 and 0.87 on patch-wise and patient-wise hematoxylin and eosin (H&E) stained whole slide images (WSIs), respectively.

CONCLUSION

AI classification outperformed the traditional clinical approach for recurrence prediction on biopsies by a fair margin. The AI classifier may stratify patients according to their recurrence risk, based only on small biopsies. This model warrants validation in a larger lung biopsy cohort.

Sublobar resection versus lobectomy for stage IA non-small-cell lung cancer ≤ 2 cm: a systematic review and patient-level meta-analysis

Despite lobectomy being the standard of care for early-stage non-small-cell lung cancer (NSCLC), sublobar resection (segmentectomy or wedge resection) has recently been suggested to achieve similar outcomes. An electronic literature search was conducted to retrieve randomized controlled trials (RCTs) or propensity score-matched studies (PSMs) comparing lobectomy to sublobar resection in stage IA NSCLC ≤ 2 cm in size, with provision of Kaplan-Meier curves for overall survival (OS) and disease-free survival (DFS). A graphical reconstructive algorithm was used to obtain OS and DFS of individual patients, which was then pooled under random-effects individual patient data meta-analysis using Cox models to determine hazard ratios (HRs). Sensitivity analyses for OS and DFS were also performed, restricting to results from RCTs only. Seven studies (2528 patients) were retrieved. There were no significant differences in OS (shared-frailty HR = 0.92, 95% CI = 0.77-1.11, p = 0.378) or DFS (shared-frailty HR = 1.06, 95% CI = 0.90-1.24, p = 0.476) between lobectomy and sublobar resection. This comparison remained non-significant even when restricted to RCTs only. Pooled Kaplan-Meier curves of OS appeared to diverge over time, in favor of sublobar resection. This was confirmed on analysis of restricted mean survival time curves. This patient-level meta-analysis of high-quality studies demonstrates that sublobar resection is equivalent to lobectomy in patients with small stage IA NSCLC. Sublobar resection offers greater down-the-road benefits in patients who experience recurrence or a second primary tumor since the lung-sparing index surgery allows patients to receive further treatment safely. This heralds sublobar resection as the new standard of care in carefully selected early-stage patients.Trial registration: PROSPERO CRD42023385358.

Long-Term Outcomes of Selected Patients with IIIA-N2 Non-small Cell Lung Cancer Receiving Upfront Surgical Resection

BACKGROUND

Stage IIIA non-small cell lung cancer (NSCLC) is a diverse group that requires multimodality treatment. The aim of this study was to report the long-term outcomes for patients with IIIA-N2 disease.

METHODS

We conducted a retrospective review of cases with IIIA-N2 (T1-2N2) NSCLC who underwent upfront surgery. Kaplan-Meier curves and Cox proportional hazard analyses were used to assess the impact of various variables on survival.

RESULTS

A total of 475 patients were ultimately included. With a median follow-up time of 108 months, the 5- and 10-year overall survival (OS) rates were 42.2% and 27.7%, respectively. R0 resection was found to be associated with improved progression-free survival (PFS) and OS compared with R1/R2 resection (p = 0.041 for PFS; p = 0.015 for OS). Patients with single-station N2 disease demonstrated significantly better PFS and OS than those with multiple-station N2 disease (p < 0.001 for PFS; p = 0.002 for OS). Following surgical resection, adjuvant therapy was significantly correlated with prolonged PFS and OS compared with those patients without any treatment. However, there was no significant difference in PFS and OS between chemotherapy and radiochemotherapy (p = 0.915 for PFS; p = 0.287 for OS). Patients with EGFR exon 19 deletion had significantly improved OS compared with those with L858R (p = 0.040).

CONCLUSIONS

Our study shows promising long-term outcomes for selected patients with stage IIIA-N2 NSCLC treated with upfront surgery followed by adjuvant therapy, especially those with R0 resection and single-station N2. This study sheds light on the potential management and treatment options for this challenging population.

Prognostic value of KRAS G12C mutation in lung adenocarcinoma stratified by stages and radiological features

OBJECTIVES

The role of KRAS G12C is of particular interest given the promising clinical activity of KRAS G12C-specific inhibitors. This study comprehensively investigated the clinicopathological characteristics and prognostic value of KRAS G12C mutation in patients with surgically resected lung adenocarcinoma.

METHODS

Data were collected on 3828 patients with completely resected primary lung adenocarcinomas who underwent KRAS mutation analysis between 2008 and 2020. The association between KRAS G12C and clinicopathologic characteristics, molecular profiles, recurrence patterns, and postoperative outcome were explored.

RESULTS

Two hundred seventy-five patients (7.2%) were confirmed to harbor a KRAS mutation, of whom 83 (30.2%) had the G12C subtype. KRAS G12C was more frequent in men, former/current smokers, radiologic solid nodules, invasive mucinous adenocarcinoma, and solid predominant tumors. KRAS G12C tumors had more lymphovascular invasion and higher programmed death-ligand 1 expression than KRAS wild-type tumors. TP53 (36.8%), STK11 (26.3%), and RET (18.4%) mutations were the 3 most frequent in the KRAS G12C group. Logistic regression analysis showed patients with KRAS G12C mutation were prone to experience early recurrence and locoregional recurrence. KRAS G12C mutation was found to be significantly associated with poor survival after propensity score matching. Stratified analysis showed that the KRAS G12C was an independent prognostic factor in stage I tumors and part-solid lesions, respectively.

CONCLUSIONS

The KRAS G12C mutation had a significant prognostic value in stage I lung adenocarcinomas as well as in part-solid tumors. Furthermore, it exhibited a potentially aggressive phenotype associated with early and locoregional recurrence. These findings might be relevant as better KRAS treatments are developed for clinical application.

Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma

Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients.

Programmed Death Ligand 1 Expression in Lung Adenocarcinoma: An Analysis of the Histomorphological Features

Background

Immunotherapy is now a vital target therapy in the advanced cases of lung adenocarcinoma. The outstanding result of therapies with medications that inhibit the interaction of programmed death ligand 1 with programmed death protein 1 has revolutionarized prognostic treatment regimes.

Aims

The study was undertaken with the objectives to analyze the detailed histomorphological features of adenocarcinoma lung with programmed death ligand-1 (PD-L1) expression in tumor cells and to compare the histomorphological features with PD-L1 negative group.

Materials and Methods

The present study is a retrospective case series with 100 cases of non-small cell lung cancer-adenocarcinoma phenotype in which testing for PD-L1 had been done using immunohistochemistry. Detailed histomorphological analysis and comparison was performed for both the PD-L1 positive and negative phenotype.

Results

Histomorphological features of 25 cases with positive PD-L1 positivity in the tumor cells and 75 cases that were negative for PD-L1 were analyzed. The most frequent pattern in the category that was PD-L1 positive was singly scattered cells or loose clusters present in 84% cases followed by solid nests that was identified in 60% cases. The presence of solid nests in the PD-L1 positive category was statistically significant (P = 0.018). Mucin was identified in 24% cases, and tumor necrosis was documented in 60% cases with PD-L1 positivity. In the cluster that was PD-L1 positive, 92% cases had moderate-to-severe nuclear pleomorphism.

Conclusion

The identification of histomorphological patterns and characteristics may aid in triaging cases that have the likelihood to harbor PD-L1-positive phenotype, which has predictive and prognostic outcome.

Clinical implication of the 2020 International Association for the Study of Lung Cancer histologic grading in surgically resected pathologic stage 1 lung adenocarcinomas: Prognostic value and association with computed tomography characteristics

OBJECTIVES

To investigate the incremental prognostic value of the 2020 International Association for the Study of Lung Cancer (IASLC) histologic grading system over traditional prognosticators in surgically resected pathologic stage 1 lung adenocarcinomas and to identify the clinical and radiologic characteristics of lung adenocarcinomas reclassified by the 2020 histologic grading system.

MATERIALS AND METHODS

We retrospectively enrolled 356 patients who underwent surgery for pathologic stage 1 adenocarcinoma between January 2016 and December 2017. The histologic grading was classified according to the predominant histologic subtype (conventional system) and the updated 2020 IASLC grading system. The clinical and computed tomography (CT) characteristics were compared according to the reclassification of the updated system. The performance of prognostic models for recurrence-free survival based on the combination of pathologic tumor size, histologic grade, and CT-based information was compared using the c-index.

RESULTS

Postoperative recurrence occurred in 6.7% of patients during the follow-up period (mean, 1589.2 ± 406.7 days). Fifty-nine of 244 (24.2%) tumors with intermediate grades in the conventional system were reclassified as grade 3 with the updated grading system. They showed significantly larger solid proportions and higher percentages of pure solid nodules on CT compared to tumors without reclassification (n = 185) (P < 0.05). Prognostic prediction models based on pathology tumor size and histologic grades had significantly higher c-indices (0.754-0.803) compared to the model based on pathologic tumor size only (c-index:0.723, P < 0.05).

CONCLUSION

The 2020 IASLC histologic grading system has significant incremental prognostic value over the pathologic stage in surgically resected pathologic stage 1 lung adenocarcinoma. Reclassified lung adenocarcinomas using the updated grading system have a larger solid proportion and a higher percentage of pure solid nodules on CT.

The colocatome as a spatial -omic reveals shared microenvironment features between tumour-stroma assembloids and human lung cancer

Computational frameworks to quantify and compare microenvironment spatial features of in-vitro patient-derived models and clinical specimens are needed. Here, we acquired and analysed multiplexed immunofluorescence images of human lung adenocarcinoma (LUAD) alongside tumour-stroma assembloids constructed with organoids and fibroblasts harvested from the leading edge (Tumour-Adjacent Fibroblasts;TAFs) or core (Tumour Core Fibroblasts;TCFs) of human LUAD. We introduce the concept of the "colocatome" as a spatial -omic dimension to catalogue all proximate and distant colocalisations between malignant and fibroblast subpopulations in both the assembloids and clinical specimens. The colocatome expands upon the colocalisation quotient (CLQ) through a nomalisation strategy that involves permutation analysis and thereby allows comparisons of CLQs under different conditions. Using colocatome analysis, we report that both TAFs and TCFs protected cancer cells from targeted oncogene treatment by uniquely reorganising the tumour-stroma cytoarchitecture, rather than by promoting cellular heterogeneity or selection. Moreover, we show that the assembloids' colocatome recapitulates the tumour-stroma cytoarchitecture defining the tumour microenvironment of LUAD clinical samples and thereby can serve as a functional spatial readout to guide translational discoveries.

Atezolizumab plus stereotactic ablative radiotherapy for medically inoperable patients with early-stage non-small cell lung cancer: a multi-institutional phase I trial

Stereotactic ablative radiotherapy (SABR) is a standard-of-care for medically-inoperable-early-stage non-small cell lung cancer (NSCLC). One third of patients progress and chemotherapy is rarely used in this population. We questioned if addition of the immune-checkpoint-inhibitor (ICI) atezolizumab to standard-of-care SABR can improve outcomes. We initiated a multi-institutional single-arm phase I study (NCT02599454) enrolling twenty patients with the primary endpoint of maximum tolerated dose (MTD); secondary endpoints of safety and efficacy; and exploratory mechanistic correlatives. Treatment is well tolerated and full dose atezolizumab (1200 mg) is the MTD. Efficacy signals include early responses (after 2 cycles of ICI, before initiation of SABR) in 17% of patients. Biomarkers of functional adaptive immunity, including T cell activation in the tumor and response to ex-vivo stimulation by circulating T cells, are highly predictive of benefit. These results require validation and are being tested in a phase III randomized trial.

Association of Molecular Profiles and Mutational Status With Distinct Histological Lung Adenocarcinoma Subtypes. An Analysis of the LACE-Bio Data

BACKGROUND

Adjuvant chemotherapy (AC) is indicated for stage II and stage III lung adenocarcinomas (ADC). Using the LACE Bio II database, we analyzed the distribution of various mutations across the subtypes of ADCs and studied the prognostic and predictive roles of PD-L1, TMB, and Tumor Infiltrating Lymphocytes (TILs).

MATERIALS AND METHODS

Clinical and genomic data from the LACE Bio II data were extracted. Patients were divided into ADC subtypes, in which the grouping was done based on their known clinical behavior (Lepidic [LEP], Acinar/Papillary [ACI or PAP], Micropapillary/Solid [MIP or SOL], Mucinous [MUC] and Others). Kaplan-Meier (KM) and log-rank test were used to compare survival based on PD-L1, TMB, TILs and combinations of TMB with PD-L1 and TILs. Adjusted Hazard Ratios (HR) were analyzed with Overall Survival (OS), Disease-Free Survival (DFS) and Lung Cancer-Specific Survival (LCSS) as endpoints.

RESULTS

A total of 375 ADC patients were identified. MIP/SOL was the subtype most commonly positive for various biomarkers. PD-L1 Negative/high TMB was associated with better outcomes in terms of OS (HR = 0.46 [0.23-0.89], P = .021) and DFS (HR = 0.52 [0.30-0.90], P = .02), relative to PD-L1 Negative/low TMB. High TMB predicted worse outcome with AC use in terms of OS (ratio of hazard ratio rHR = 2.75 [1.07-7.04], P = .035). Marked TILs had better outcome with AC for DFS (rHR = 0.22 [0.06-0.87], P = .031 and LCSS (rHR = 0.08 [0.01-0.66], P = .019) respectively. There was also a beneficial effect of AC among patients with Marked TILs/low TMB in terms of DFS (rHR = 0.06 [0.01-0.53], P = .011).

CONCLUSION

High TMB has a prognostic role in resectable lung ADC. The high TMB group had a poor outcome with AC, suggesting that this group may be better served with immune checkpoint therapy.

Papillary Adenocarcinoma: A Rare Subtype of Lung Adenocarcinoma

Papillary adenocarcinoma (PA) of the lung is a specific form of lung cancer characterized by papillary structures in tumor cells. This type of cancer is relatively rare and has distinct pathological and radiological features that differentiate it from other types of lung adenocarcinomas. Determining the specific subtype of adenocarcinoma is a crucial factor in the choice of chemotherapy treatment. Detecting PA is fundamental, as it has both prognostic and therapeutic implications for patients with lung carcinoma. In this paper, we discuss two cases of young patients diagnosed with PA of the lung. The cases we present are particularly intriguing due to the relatively young age of the patients.

Immune Infiltration in Tumor and Adjacent Non-Neoplastic Regions Codetermines Patient Clinical Outcomes in Early-Stage Lung Cancer

INTRODUCTION

In recent years, the proportion of patients with NSCLC diagnosed at an early stage has increased continuously.

METHODS

In this study, we analyzed samples and data collected from 119 samples from 67 early stage patients with NSCLC, including 52 pairs of tumor and adjacent non-neoplastic samples, and performed RNA-sequencing analysis with high sequencing depth.

RESULTS

We found that immune-related genes were highly enriched among the differentially expressed genes and observed significantly higher inferred immune infiltration levels in adjacent non-neoplastic samples than in tumor samples. In survival analysis, the infiltration of certain immune cell types in tumor, but not adjacent non-neoplastic, samples were associated with overall patient survival, and excitingly, the differential infiltration between paired samples (tumor minus non-neoplastic) was more prognostic than expression in either non-neoplastic or tumor tissues. We also performed B cell receptor (BCR) and T cell receptor (TCR) repertoire analysis and observed more BCR/TCR clonotypes and increased BCR clonality in tumor than in non-neoplastic samples. Finally, we carefully quantified the fraction of the five histologic subtypes in our adenocarcinoma samples and found that higher histologic pattern complexity was associated with higher immune infiltration and low TCR clonality in the tumor-proximal regions.

CONCLUSIONS

Our results indicated significantly differential immune characteristics between tumor and adjacent non-neoplastic samples and suggested that the two regions provided complementary prognostic values in early-stage NSCLCs.

Stromal micropapillary pattern and CD44s expression predict worse outcome in lung adenocarcinomas with micropapillary pattern

OBJECTIVES

This study aims to investigate the clinicopathologic characteristics of lung adenocarcinoma with micropapillary pattern (MPP) and the expression of CD44s and CD44v6 in MPP.

METHODS

A total of 202 patients diagnosed with primary lung adenocarcinoma with MPP were included. We estimated the proportion of MPP in each tumor tissue and divided MPP into aerogenous micropapillary pattern (AMP) and stromal micropapillary pattern (SMP). The expression of CD44s and CD44v6 was estimated by immunohistochemical staining. Clinicopathologic data were collected from the patients' medical records. We also collected patients' follow-up data and used PFS (progression-free survival) as a survival indicator.

RESULTS

Lung adenocarcinoma with MPP had a high risk of pleural invasion, lymph node metastasis, in advanced TNM stage, and a high rate of EGFR mutation. The presence of SMP indicated a higher rate of pleural invasion, lymphovascular invasion, lymph node metastasis, and a worse PFS compared with pure AMP. We found high expression of CD44s in micropapillary, especially in AMP, while the absence of CD44s expression indicated shorter survival, which was an independent unfavorable factor for PFS.

CONCLUSIONS

Lung adenocarcinoma with micropapillary pattern indicated an unfavorable prognosis, which had two different pattens, AMP and SMP. SMP indicated a worse survival than AMP, and was an independent unfavorable factor for PFS. So, AMP/SMP subclassification is necessary to evaluate patient's prognosis. Furthermore, the absent expression of CD44s in micropapillary indicated shorter survival, especially in patients with EGFR mutation. Herein, CD44s may be a biological marker for micropapillary lung adenocarcinoma.

Optimization of Indocyanine Green for Intraoperative Fluorescent Image-Guided Localization of Lung Cancer; Analysis Based on Solid Component of Lung Nodule

ICG fluorescence imaging has been used to detect lung cancer; however, there is no consensus regarding the optimization of the indocyanine green (ICG) injection method. The aim of this study was to determine the optimal dose and timing of ICG for lung cancer detection using animal models and to evaluate the feasibility of ICG fluorescence in lung cancer patients. In a preclinical study, twenty C57BL/6 mice with footpad cancer and thirty-three rabbits with VX2 lung cancer were used. These animals received an intravenous injection of ICG at 0.5, 1, 2, or 5 mg/kg, and the cancers were detected using a fluorescent imaging system after 3, 6, 12, and 24 h. In a clinical study, fifty-one patients diagnosed with lung cancer and scheduled to undergo surgery were included. Fluorescent images of lung cancer were obtained, and the fluorescent signal was quantified. Based on a preclinical study, the optimal injection method for lung cancer detection was 2 mg/kg ICG 12 h before surgery. Among the 51 patients, ICG successfully detected 37 of 39 cases with a consolidation-to-tumor (C/T) ratio of >50% (TNR: 3.3 ± 1.2), while it failed in 12 cases with a C/T ratio ≤ 50% and 2 cases with anthracosis. ICG injection at 2 mg/kg, 12 h before surgery was optimal for lung cancer detection. Lung cancers with the C/T ratio > 50% were successfully detected using ICG with a detection rate of 95%, but not with the C/T ratio ≤ 50%. Therefore, further research is needed to develop fluorescent agents targeting lung cancer.

Current and future role of double-lung transplantation for bilateral lung cancer

Technological advances have progressively enhanced the survival rate of lung transplant recipients and expanded its indications for various diseases, including the recent coronavirus disease 2019 (COVID-19). However, according to the International Society for Heart and Lung Transplantation, lung cancer constituted a mere 0.1% of the indications for lung transplantation over the past two decades. This statistic has remained stagnant, and numerous lung cancer patients continue to be excluded from lung transplantation candidacy. Contrary to the general exclusion of lung cancer patients from transplantation, the post-transplant survival rate for these patients is not inferior to that of patients with non-cancerous diseases. Furthermore, lung transplantation may offer curative treatment for patients with bilateral lung cancer whose respiratory insufficiency has advanced independently of cancer progression. This review aims to elucidate and examine the role of double lung transplantation (DLT) in bilateral lung cancer. We summarize the established indications for lung transplantation, appropriate histologic or molecular subtypes of lung cancer for transplantation, technical advances to minimize recurrence, post-DLT survival outcomes for lung cancer patients, and related translational research. We suggest that although DLT for bilateral lung cancer presents challenges, it may be considered a potential treatment option in select circumstances.

The Swin-Transformer network based on focal loss is used to identify images of pathological subtypes of lung adenocarcinoma with high similarity and class imbalance

PURPOSE

The classification of primary lung adenocarcinoma is complex and varied. Different subtypes of lung adenocarcinoma have different treatment methods and different prognosis. In this study, we collected 11 datasets comprising subtypes of lung cancer and proposed FL-STNet model to provide the assistance for improving clinical problems of pathologic classification in primary adenocarcinoma of lung.

METHODS

Samples were collected from 360 patients diagnosed with lung adenocarcinoma and other subtypes of lung diseases. In addition, an auxiliary diagnosis algorithm based on Swin-Transformer, which used Focal Loss for function in training, was developed. Meanwhile, the diagnostic accuracy of the Swin-Transformer was compared to pathologists.

RESULTS

The Swin-Transformer captures not only information in the overall tissue structure but also the local tissue details in the images of lung cancer pathology. Furthermore, training FL-STNet with the Focal Loss function can further balance the difference in the amount of data between different subtypes, improving recognition accuracy. The average classification accuracy, F1, and AUC of the proposed FL-STNet reached 85.71%, 86.57%, and 0.9903. The average accuracy of the FL-STNet was higher by 17% and 34%, respectively, than in the senior pathologist and junior pathologist group.

CONCLUSION

The first deep learning based on an 11-category classifier was developed for classifying lung adenocarcinoma subtypes based on WSI histopathology. Aiming at the deficiencies of the current CNN and Vit, FL-STNet model is proposed in this study by introducing Focal Loss and combining the advantages of Swin-Transformer model.

Heterogeneous expression of predictive biomarkers PD-L1 and TIGIT in non-mucinous lung adenocarcinoma and corresponding lymph node metastasis: A challenge for clinical biomarker testing

The use of immune checkpoint inhibitors (ICI) targeting the PD-L1:PD1 interaction revolutionized tumor treatment by re-activating the anti-tumoral capacity of the immune system. Assessment of tumor mutational burden, microsatellite instability, or expression of the surface marker PD-L1 have been used to predict individual response to ICI therapy. However, the predicted response does not always correspond to the actual therapy outcome. We hypothesize that tumor heterogeneity might be a major cause of this inconsistency. In this respect we recently demonstrated that PD-L1 shows heterogenous expression in the different growth patterns of non-small cell lung cancer (NSCLC) - lepidic, acinar, papillary, micropapillary and solid. Furthermore, additional inhibitory receptors, like T cell immunoglobulin and ITIM domain (TIGIT), appear to be heterogeneously expressed and affect the outcome of anti-PD-L1 treatment. Given this heterogeneity in the primary tumor, we set out to analyze the situation in corresponding lymph node metastases, since these are often used to obtain biopsy material for tumor diagnosis, staging and molecular analysis. Again, we observed heterogeneous expression of PD-1, PD-L1, TIGIT, Nectin-2 and PVR in relation to different regions and growth pattern distribution that varied between the primary tumor and their metastases. Together, our study underscores the complex situation regarding the heterogeneity of NSCLC samples and suggest that the analysis of a small biopsy from lymph node metastases may not be sufficient to ensure a reliable prediction of ICI therapy success.

Treatment Strategies for KRAS-Mutated Non-Small-Cell Lung Cancer

Activating mutations in KRAS are highly prevalent in solid tumours and are frequently found in 35% of lung, 45% of colorectal, and up to 90% of pancreatic cancers. Mutated KRAS is a prognostic factor for disease-free survival (DFS) and overall survival (OS) in NSCLC and is associated with a more aggressive clinical phenotype, highlighting the need for KRAS-targeted therapy. Once considered undruggable due to its smooth shallow surface, a breakthrough showed that the activated G12C-mutated KRAS isozyme can be directly inhibited via a newly identified switch II pocket. This discovery led to the development of a new class of selective small-molecule inhibitors against the KRAS G12C isoform. Sotorasib and adagrasib are approved in locally advanced or metastatic NSCLC patients who have received at least one prior systemic therapy. Currently, there are at least twelve KRAS G12C inhibitors being tested in clinical trials, either as a single agent or in combination. In this study, KRAS mutation prevalence, subtypes, rates of occurrence in treatment-resistant invasive mucinous adenocarcinomas (IMAs), and novel drug delivery options are reviewed. Additionally, the current status of KRAS inhibitors, multiple resistance mechanisms that limit efficacy, and their use in combination treatment strategies and novel multitargeted approaches in NSCLC are discussed.

Relationship Between the Diffusing Capacity of the Lung for Carbon Monoxide (DLCO) and Lung Adenocarcinoma Patterns: New Possible Insights

INTRODUCTION

This study aimed to evaluate a potential relationship between the diffusing capacity of the lung for carbon monoxide (DLCO) and the aggressiveness of lung adenocarcinoma (ADC).

METHODS

Patients who underwent radical surgery for lung ADC between 2001 and 2018 were retrospectively reviewed. DLCO values were dichotomized into DLCO_low (<80% of predicted) and DLCO_normal (≥80%). Relationships between DLCO and ADC histopathological features, clinical features, as well as with overall survival (OS), were evaluated.

RESULTS

Four-hundred and sixty patients were enrolled, of which 193 (42%) were included in the DLCO_low group. DLCO_low was associated with smoking status, low FEV₁, micropapillary and solid ADC, tumour grade 3, high tumour lymphoid infiltrate and presence of tumour desmoplasia. In addition, DLCO values were higher in low-grade ADC and progressively decreased in intermediate and high-grade ADC (p=0.024). After adjusting for clinical variables, at multivariable logistic regression analysis, DLCO_low still showed a significant correlation with high lymphoid infiltrate (p=0.017), presence of desmoplasia (p=0.065), tumour grade 3 (p=0.062), micropapillary and solid ADC subtypes (p=0.008). To exclude the association between non-smokers and well-differentiated ADC, the relationship between DLCO and histopathological ADC patterns was confirmed in the subset of 377 former and current smokers (p=0.021). At univariate analysis, gender, DLCO, FEV₁, ADC histotype, tumour grade, stage, pleural invasion, tumour necrosis, tumour desmoplasia, lymphatic and blood invasion were significantly related with OS. At multivariate analysis, only gender (p<0.001), tumour stage (p<0.001) and DLCO (p=0.050) were significantly related with the OS.

CONCLUSIONS

We found a relationship between DLCO and ADC patterns as well as with tumour grade, tumour lymphoid infiltrate and desmoplasia, suggesting that lung damage may be associated with tumour aggressiveness.

The prognostic value of histopathology in invasive lung adenocarcinoma: a comparative review of the main proposed grading systems

INTRODUCTION

An accurate histological evaluation of invasive lung adenocarcinoma is essential for a correct clinical and pathological definition of the tumour. Different grading systems have been proposed to predict the prognosis of invasive lung adenocarcinoma.

AREAS COVERED

Invasive non mucinous lung adenocarcinoma is often morphologically heterogeneous, consisting of complex combinations of architectural patterns with different proportions. Several grading systems for non-mucinous lung adenocarcinoma have been proposed, being the main based on architectural differentiation and the predominant growth pattern. Herein we perform a thorough review of the literature using PubMed, Scopus and Web of Science and we highlight the peculiarities and the differences between the main grading systems and compare the data about their prognostic value. In addition, we carried out an evaluation of the proposed grading systems for less common histological variants of lung adenocarcinoma, such as fetal adenocarcinoma and invasive mucinous adenocarcinoma.

EXPERT OPINION

The current IASLC grading system, based on the combined score of predominant growth pattern plus high-grade histological pattern, shows the stronger prognostic significance than the previous grading systems in invasive non mucinous lung adenocarcinoma.

Prognostic significance of location index in resected T1-sized early-stage non-small cell lung cancer

BACKGROUND

While the central location is a known adverse prognostic factor in lung cancer, a precise definition of central lung cancer has not yet emerged.

PURPOSE

To determine the prognostic significance of central lung cancer (defined by location index) in resected T1-sized early-stage non-small cell lung cancer (NSCLC).

MATERIAL AND METHODS

Patients with resected T1-sized early-stage NSCLC between 2010 and 2015 at a single tertiary cancer center were retrospectively reviewed. Central lung cancer was defined by a location index of the second tertile or less. Kaplan-Meier analysis with log-rank test and multivariable Cox regression analysis were performed to analyze the relationship between central lung cancer and the prognosis of relapse-free survival (RFS) and overall survival (OS). Inter-observer agreement was assessed using Cohen's kappa value and intraclass correlation coefficient (ICC).

RESULTS

Overall, 289 patients (169 men; median age 65 years; interquartile range 58-70 years) were evaluated. Central lung cancer (defined by location index) was adversely associated with RFS (P = 0.005) and OS (P = 0.01). Multivariable Cox regression analysis showed that central lung cancer was independently associated with poor RFS (adjusted hazard ratio 1.91; 95% confidence interval [CI] 1.12-3.24; P = 0.017) and OS (adjusted hazard ratio 1.69; 95% CI 1.04-2.74; P = 0.033). Location index demonstrated excellent inter-observer agreement (Cohen's kappa value 0.88; 95% CI 0.82-0.93) with a high ICC (0.98; 95% CI 0.97-0.98).

CONCLUSION

Central lung cancer defined by a location index of the second tertile or lower is an independent adverse prognostic factor in resected T1-sized early-stage NSCLC.

Invasive mucinous adenocarcinoma of the lung: clinicopathological features, 18F-FDG PET/CT findings, and survival outcomes

OBJECTIVE

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of lung adenocarcinoma. This study aimed to retrospectively evaluate the clinicopathological features, ¹⁸F-FDG PET/CT findings, and prognosis of IMA of the lung, as well as to investigate the associations among these variables, to improve the management of such patients.

METHODS

Clinicopathological and ¹⁸F-FDG PET/CT characteristics of 72 patients with pathologically confirmed IMA of the lung were retrospectively collected and investigated, and their predictive efficacy on progression-free survival (PFS) was evaluated.

RESULTS

The median age of the enrolled 72 patients was 61 years (range, 26-79 years), and the male-to-female ratio was 1:1.25. According to the radiological morphology of IMA, solidary nodule/mass type (n = 59, 81.9%) was the most common, followed by GGO type (n = 8, 11.1%) and pneumonia type (n = 5, 6.9%). Lobulated or spiculated margin and pleural traction were the most common radiological signs. The median SUVmax of IMA lesions was 3.0, ranging from 0.5 to 23.1. Higher SUVmax was observed in IMA with non-GGO type, clinical symptom, advanced stage, lobulated margin, pleural traction or spread through air spaces (STAS) (P < 0.05). Moreover, higher SUVmax was related to larger tumor size in non-pneumonia-type IMA (r = 0.708, P < 0.001). The median PFS was 21.3 months, and the 12-, 24- and 36-month PFS rates were 89.8%, 83.3% and 75.5%, respectively. A poorer PFS was significantly associated with SUVmax ≥ 3, advanced stage and STAS.

CONCLUSION

¹⁸F-FDG PET/CT combined with clinicopathological characteristics can aid the diagnosis and prognostic evaluation of lung IMA, which could provide guidance for the appropriate management of such patients.

Resection of a colloid adenocarcinoma of the lung: A case report

Colloid adenocarcinoma of the lung is a rare subtype of lung adenocarcinoma, accounting for only about 0.24% of lung cancers. Because of its rarity, long-term postoperative prognostic reports are limited. In this report, we describe a case of colloid adenocarcinoma of the lung with a 5-year recurrence-free follow-up. The patient is a 66-year-old woman. During postoperative follow-up for ovarian cancer, chest CT showed a 45 × 30 mm sized mass in the left lung with mixed low-absorption areas inside that were suspicious of cystic lesion. We suspected metastatic lung tumour, and performed lower lobectomy. Pathological examination revealed pale tumour cells forming a glandular lumen with internal mucus production. Based on the results of immunostaining we diagnosed colloid adenocarcinoma of the lung. She received postoperative adjuvant chemotherapy and is alive 4 years postoperatively without recurrence. Colloid adenocarcinoma of the lung, even if large, may have a good prognosis if completely resected.

An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening

OBJECTIVES

To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists.

METHODS

A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy.

RESULTS

The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847-0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882-0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737-0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779-0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793-0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673-0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05).

CONCLUSIONS

The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert.

KEY POINTS

• Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.