Prognostic value of the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society lung adenocarcinoma classification on death and recurrence in completely resected stage I lung adenocarcinoma

Prognostic implications, genomic and immune characteristics of lung adenocarcinoma with lepidic growth pattern

AIMS

Conflicting data were provided regarding the prognostic impact and genomic features of lung adenocarcinoma (LUAD) with lepidic growth pattern (LP+A). Delineation of the genomic and immune characteristics of LP+A could provide deeper insights into its prognostic implications and treatment determination.

METHODS

We conducted a search of articles in PubMed, EMBASE and the Cochrane Library from inception to January 2024. A domestic cohort consisting of 52 LUAD samples was subjected to whole-exome sequencing as internal validation. Data from The Cancer Genomic Atlas and the Gene Expression Omnibus datasets were obtained to characterise the genomic and immune profiles of LP+A. Pooled HRs and rates were calculated.

RESULTS

The pooled results indicated that lepidic growth pattern was either predominant (0.35, 95% CI 0.22 to 0.56, p<0.01) or minor (HR 0.50, 95% CI 0.36 to 0.70, p<0.01) histological subtype was associated with favourable disease-free survival. Pooled gene mutation rates suggested higher EGFR mutation (0.55, 95% CI 0.46 to 0.64, p<0.01) and lower KRAS mutation (0.14, 95% CI 0.02 to 0.25, p=0.02) in lepidic-predominant LUAD. Lepidic-predominant LUAD had lower tumour mutation burden and pooled positive rate of PD-L1 expression compared with other subtypes. LP+A was characterised by abundance in resting CD4+memory T cells, monocytes and γδ T cells, as well as scarcity of cancer-associated fibroblasts.

CONCLUSIONS

LP+A was a unique histological subtype with a higher EGFR mutation rate, lower tumour mutation burden and immune checkpoint expression levels. Our findings suggested potential benefits from targeted therapy over immunotherapy in LP+A.

The Critical Role of Minor Histological Patterns in Prognosis Prediction in Early-Stage Lung Adenocarcinomas

Lung adenocarcinomas often contain multiple histological components. This study discusses the role of minor components in the progression of the disease. A retrospective evaluation was conducted on 108 patients with lung adenocarcinoma who underwent surgery at our center between 2013 and 2018, with tumor sizes less than 3 cm. The patients were categorized into four groups based on the presence of lepidic (L) and micropapillary/solid (MP/S) patterns at a minimum threshold of 5% ("L+, MP/S-", "L+, MP/S+", "L-, MP/S-", "L-, MP/S+"). The groups were compared in terms of standard uptake value, pleural invasion, lymphovascular invasion, perineural invasion, spread through air spaces, N1-N2 station lymph node metastasis, recurrence, and survival. No tumors of perineural invasion, spread through air spaces, or lymph node metastasis was observed in the "L+, MP/S-" group, and lymphovascular invasion was found to be significantly lower compared to other groups (p = 0.040). The standard uptake value levels in groups containing the lepidic pattern were significantly lower than in other groups (p = 0.006). The time to recurrence in the "L+, MP/S-" group was 121.5 ± 10.9 months, with a median survival time of 110.9 ± 10.6 months, which was longer compared to the other groups (86.2 ± 5.9 and 77 ± 13.4 months). In lung adenocarcinomas, prognosis estimation should be based not only on the dominant component but also on the presence of histological components such as lepidic and micropapillary/solid, even if they are minor.

Impact of histopathological subtypes on invasive lung adenocarcinoma: from epidemiology to tumour microenvironment to therapeutic strategies

Lung adenocarcinoma is the most prevalent type of lung cancer, with invasive lung adenocarcinoma being the most common subtype. Screening and early treatment of high-risk individuals have improved survival; however, significant differences in prognosis still exist among patients at the same stage, especially in the early stages. Invasive lung adenocarcinoma has different histological morphologies and biological characteristics that can distinguish its prognosis. Notably, several studies have found that the pathological subtypes of invasive lung adenocarcinoma are closely associated with clinical treatment. This review summarised the distribution of various pathological subtypes of invasive lung adenocarcinoma in the population and their relationship with sex, smoking, imaging features, and other histological characteristics. We comprehensively analysed the genetic characteristics and biomarkers of the different pathological subtypes of invasive lung adenocarcinoma. Understanding the interaction between the pathological subtypes of invasive lung adenocarcinoma and the tumour microenvironment helps to reveal new therapeutic targets for lung adenocarcinoma. We also extensively reviewed the prognosis of various pathological subtypes and their effects on selecting surgical methods and adjuvant therapy and explored future treatment strategies.

Oncologic Outcomes of Thoracoscopic Segmentectomy in Patients with High-Grade Adenocarcinoma Pattern

BACKGROUND

Lung adenocarcinoma exhibits heterogeneity among different histological subtypes, with solid and micropapillary subgroups (classified as high-grade) associated with worse prognosis. The aim of this retrospective study was to investigate the impact of high-grade adenocarcinoma on survival in patients undergoing intentional thoracoscopic segmentectomy.

METHODS

Patients who underwent segmentectomy for clinical-stage IA non-small-cell lung cancer between 2016 and 2023 were reviewed. The adenocarcinoma population was divided and compared based on the presence of high-grade adenocarcinoma >20%, based on the 2021 WHO classification. Survival probabilities were estimated using the Kaplan-Meier method and log-rank test. The Cox proportional hazard regression model was used to test the association between survival and covariates.

RESULTS

The adenocarcinoma population included 216 patients, with high-grade adenocarcinoma >20% in 47 (21.7%). A consolidation-to-tumor ratio >0.8 was more frequent in the high-grade adenocarcinoma population. Survival analyses showed that overall (5-year OS rate 57% vs. 90%, p < 0.01), cancer-specific (5-year CSS rate 66% vs. 91%, p < 0.01) and disease-free survival (5-year DFS rate 53% vs. 75%, p < 0.01) were significantly worse in patients with high-grade adenocarcinoma. No significant differences in overall and disease-free survival were observed when compared to a contemporary cohort of lobectomy patients. Recurrence and high-grade pattern (HR 3.26, 95%CI 1.4-7.6, p < 0.01) were significant risk factors for reduced overall survival, whereas high-grade adenocarcinoma >20% (HR 2.43, 95%CI 1.25-4.71, p < 0.01) and a consolidation-to-tumor ratio >0.8 were risk factors for reduced disease-free survival.

CONCLUSIONS

The prognosis of high-grade adenocarcinoma is sub-optimal even in radically treated early-stage patients, and close monitoring and a complete bio-molecular assessment should be advisable in light of a multimodal adjuvant approach. However, the different subtypes of adenocarcinoma could be inserted as a staging parameter in future international staging systems.

Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma

BACKGROUND

This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

METHODS

In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method. Both intratumoral ecological diversity features (hereafter referred to as ITH) and conventional radiomics (hereafter referred to as C-radiomics) were extracted to generate ITH scores and C-radiomics scores. Next, univariate and multivariate logistic regression analyses were employed to identify clinical-radiological (Clin-Rad) features associated with the MP/S (+) group for constructing the Clin-Rad classification. Subsequently, a hybrid model which presented as a nomogram was developed, integrating the Clin-Rad classification and ITH score. The performance of models was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), accuracy, sensitivity, and specificity were determined.

RESULTS

The ITH score outperformed both C-radiomics scores and Clin-Rad classification, as evidenced by higher AUC values in the training set (0.820 versus 0.810 and 0.700, p = 0.049 and p = 0.031, respectively) and in the test set (0.805 versus 0.771 and 0.732, p = 0.041 and p = 0.025, respectively). Finally, the hybrid model consistently demonstrated robust predictive capabilities in identifying presence of MP/S components, achieving AUC of 0.830 in the training set and 0.849 in the test set (all p < 0.05).

CONCLUSION

The ITH derived from sub-region within the tumor has been shown to be a reliable predictor for MP/S (+) in clinical stage I solid LADC.

Deep Learning Model for Pathological Grading and Prognostic Assessment of Lung Cancer Using CT Imaging: A Study on NLST and External Validation Cohorts

RATIONALE AND OBJECTIVES

To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.

MATERIAL AND METHODS

This study utilized 572 cases from the National Lung Screening Trial cohort, dividing them randomly into training (461 cases) and internal validation (111 cases) sets in an 8:2 ratio. Additionally, 224 cases from four cohorts obtained from the Cancer Imaging Archive, all diagnosed with non-small cell lung cancer, were included for external validation. The deep learning model, built on the MobileNetV3 architecture, was assessed in both internal and external validation sets using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The model's prognostic value was further analyzed using Cox proportional hazards models.

RESULTS

The model achieved high accuracy, sensitivity, specificity, and AUC in the internal validation set (accuracy: 0.888, macro AUC: 0.968, macro sensitivity: 0.798, macro specificity: 0.956). External validation demonstrated comparable performance (accuracy: 0.807, macro AUC: 0.920, macro sensitivity: 0.799, macro specificity: 0.896). The model's predicted signatures correlated significantly with patient mortality and provided valuable insights for prognostic assessment (adjusted HR 2.016 [95% CI: 1.010, 4.022]).

CONCLUSIONS

This study successfully developed and validated a deep learning model for the preoperative grading of lung cancer pathology. The model's accurate predictions could serve as a useful adjunct in treatment planning for lung cancer patients, enabling more effective and customized interventions to improve patient outcomes.

Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer

PURPOSE

To explore the application value of a multimodal deep learning radiomics (MDLR) model in predicting the risk status of postoperative progression in solid stage I non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

A total of 459 patients with histologically confirmed solid stage I NSCLC who underwent surgical resection in our institution from January 2014 to September 2019 were reviewed retrospectively. At another medical center, 104 patients were reviewed as an external validation cohort according to the same criteria. A univariate analysis was conducted on the clinicopathological characteristics and subjective CT findings of the progression and non-progression groups. The clinicopathological characteristics and subjective CT findings that exhibited significant differences were used as input variables for the extreme learning machine (ELM) classifier to construct the clinical model. We used the transfer learning strategy to train the ResNet18 model, used the model to extract deep learning features from all CT images, and then used the ELM classifier to classify the deep learning features to obtain the deep learning signature (DLS). A MDLR model incorporating clinicopathological characteristics, subjective CT findings and DLS was constructed. The diagnostic efficiencies of the clinical model, DLS model and MDLR model were evaluated by the area under the curve (AUC).

RESULTS

Univariate analysis indicated that size (p = 0.004), neuron-specific enolase (NSE) (p = 0.03), carbohydrate antigen 19 - 9 (CA199) (p = 0.003), and pathological stage (p = 0.027) were significantly associated with the progression of solid stage I NSCLC after surgery. Therefore, these clinical characteristics were incorporated into the clinical model to predict the risk of progression in postoperative solid-stage NSCLC patients. A total of 294 deep learning features with nonzero coefficients were selected. The DLS in the progressive group was (0.721 ± 0.371), which was higher than that in the nonprogressive group (0.113 ± 0.350) (p < 0.001). The combination of size、NSE、CA199、pathological stage and DLS demonstrated the superior performance in differentiating postoperative progression status. The AUC of the MDLR model was 0.885 (95% confidence interval [CI]: 0.842-0.927), higher than that of the clinical model (0.675 (95% CI: 0.599-0.752)) and DLS model (0.882 (95% CI: 0.835-0.929)). The DeLong test and decision in curve analysis revealed that the MDLR model was the most predictive and clinically useful model.

CONCLUSION

MDLR model is effective in predicting the risk of postoperative progression of solid stage I NSCLC, and it is helpful for the treatment and follow-up of solid stage I NSCLC patients.

Dual-energy CT-based radiomics for predicting pathological grading of invasive lung adenocarcinoma

AIMS

The purpose of the study was to build a radiomics model using Dual-energy CT (DECT) to predict pathological grading of invasive lung adenocarcinoma.

MATERIALS AND METHODS

The retrospective study enrolled 107 patients (80 low-grade and 27 high-grade) with invasive lung adenocarcinoma before surgery. Clinical features, radiographic characteristics, and quantitative parameters were measured. Virtual monoenergetic images at 50kev and 150kev were reconstructed for extracting DECT radiomics features. To select features for constructing models, Pearson's correlation analysis, intraclass correlation coefficients, and least absolute shrinkage and selection operator penalized logistic regression were performed. Four models, including the DECT radiomics model, the clinical-DECT model, the conventional CT radiomics model, and the mixed model, were established. Area under the curve (AUC) and decision curve analysis were used to measure the performance and the clinical value of the models.

RESULTS

The radiomics model based on DECT exhibited outstanding performance in predicting tumor differentiation, with an AUC of 0.997 and 0.743 in the training and testing sets, respectively. Incorporating tumor density, lobulation, and effective atomic number at AP, the clinical-DECT model showed a comparable performance with an AUC of 0.836 in both the training and testing sets. In comparison to the conventional CT radiomics model (AUC of 0.998 in the training and 0.529 in the testing set) and the mixed model (AUC of 0.988 in the training and 0.707 in the testing set), the DECT radiomics model demonstrated a greater AUC value and provided patients with a more significant net benefit in the testing set.

CONCLUSIONS

In contrast to the conventional CT radiomics model, the DECT radiomics model produced greater predictive performance in pathological grading of invasive lung adenocarcinoma.

The impact of postoperative adjuvant therapy on EGFR-mutated stage IA lung adenocarcinoma with micropapillary pathological subtypes

BACKGROUND

Micropapillary (MPP) adenocarcinoma is considered one of the most aggressive pathological types of lung adenocarcinoma (LADC). This retrospective study aimed to evaluate the prognostic significance and benefit of postoperative adjuvant therapy (PAT) in stage IA LADC patients with different proportions of MPP components.

MATERIALS AND METHODS

We retrospectively examined clinical stage IA LADC patients who underwent surgical resection between August 2012 and December 2019. In terms of the proportion of MPP components (TPM), the tumors were reclassified into three categories: MPP patterns absent (TPMN); low proportions of MPP components (TPML); and high proportions of MPP components (TPMH). The dates of recurrence and metastasis were identified based on physical examinations and were confirmed by histopathological examination.

RESULTS

Overall, 505 (TPMN, n = 375; TPML, n = 92; TPMH, n = 38) patients harboring EGFR mutations were enrolled in the study. Male sex (P = 0.044), high pathological stage (P < 0.001), and MPP pathological subtype (P < 0.001) were more frequent in the TPM-positive (TPMP) group than in the TPM-negative (TPMN) group. Five-year disease-free survival (DFS) rates were significantly lower in the TPMP group than in the TPMN group (84.5% vs. 93.4%, P = 0.006). In addition, patients with high proportions (greater than 10%) of MPP components had worse overall survival (OS) (91.0% vs. 98.9%, P = 0.025) than those with low proportions (5%≤ TPM ≤ 10%). However, postoperative EGFR tyrosine kinase inhibitors (TKIs) or adjuvant chemotherapy (ACT) cannot improve DFS and OS between EGFR-mutated patients with different proportions of MPP components.

CONCLUSION

MPP was related to earlier recurrence and shortened survival time, even in stage IA. Further research needs a larger sample size to clarify that EGFR-mutated stage IA patients with MPP components obtain survival benefits from adjuvant therapy.

Identification and validation of an immune-derived multiple programmed cell death index for predicting clinical outcomes, molecular subtyping, and drug sensitivity in lung adenocarcinoma

OBJECTIVES

Comprehensive cross-interaction of multiple programmed cell death (PCD) patterns in the patients with lung adenocarcinoma (LUAD) have not yet been thoroughly investigated.

METHODS

Here, we collected 19 different PCD patterns, including 1911 PCD-related genes, and developed an immune-derived multiple programmed cell death index (MPCDI) based on machine learning methods.

RESULTS

Using the median MPCDI scores, we categorized the LUAD patients into two groups: low-MPCDI and high-MPCDI. Our analysis of the TCGA-LUAD training cohort and three external GEO cohorts (GSE37745, GSE30219, and GSE68465) revealed that patients with high-MPCDI experienced a more unfavorable prognosis, whereas those with low-MPCDI had a better prognosis. Furthermore, the results of both univariate and multivariate Cox regression analyses further confirmed that MPCDI serves as a novel independent risk factor. By combining clinical characteristics with the MPCDI, we constructed a nomogram that provides an accurate and reliable quantitative tool for personalized clinical management of LUAD patients. The findings obtained from the analysis of C-index and the decision curve revealed that the nomogram outperformed various clinical variables in terms of net clinical benefit. Encouragingly, the low-MPCDI patients are more sensitive to commonly used chemotherapy drugs, which suggests that MPCDI scores have a guiding role in chemotherapy for LUAD patients.

CONCLUSION

Therefore, MPCDI can be used as a novel clinical diagnostic classifier, providing valuable insights into the clinical management and clinical decision-making for LUAD patients.

High core 1β1,3-galactosyltransferase 1 expression is associated with poor prognosis and promotes cellular radioresistance in lung adenocarcinoma

PURPOSE

Core 1β1,3-galactosyltransferase 1 (C1GALT1) exhibits elevated expression in multiple cancers. The present study aimed to elucidate the clinical significance of C1GALT1 aberrant expression and its impact on radiosensitivity in lung adenocarcinoma (LUAD).

METHODS

The C1GALT1 expression and its clinical relevance were investigated through public databases and LUAD tissue microarray analyses. A549 and H1299 cells with either C1GALT1 knockdown or overexpression were further assessed through colony formation, gamma-H2A histone family member X immunofluorescence, 5-ethynyl-2'-deoxyuridine incorporation, and flow cytometry assays. Bioinformatics analysis was used to explore single cell sequencing data, revealing the influence of C1GALT1 on cancer-associated cellular states. Vimentin, N-cadherin, and E-cadherin protein levels were measured through western blotting.

RESULTS

The expression of C1GALT1 was significantly higher in LUAD tissues than in adjacent non-tumor tissues both at mRNA and protein level. High expression of C1GALT1 was correlated with lymph node metastasis, advanced T stage, and poor survival, and was an independent risk factor for overall survival. Radiation notably upregulated C1GALT1 expression in A549 and H1299 cells, while radiosensitivity was increased following C1GALT1 knockdown and decreased following overexpression. Experiment results showed that overexpression of C1GALT1 conferred radioresistance, promoting DNA repair, cell proliferation, and G2/M phase arrest, while inhibiting apoptosis and decreasing E-cadherin expression, alongside upregulating vimentin and N-cadherin in A549 and H1299 cells. Conversely, C1GALT1 knockdown had opposing effects.

CONCLUSION

Elevated C1GALT1 expression in LUAD is associated with an unfavorable prognosis and contributes to increased radioresistance potentially by affecting DNA repair, cell proliferation, cell cycle regulation, and epithelial-mesenchymal transition (EMT).

Factors influencing postoperative recurrence of early-stage non-small cell lung cancer

PURPOSE

This study aims to explain the factors that may influence recurrence after surgical resection for early non-small cell lung cancer (NSCLC).

METHODS

A retrospective analysis was made of 302 patients who underwent lung resection for stage I-IIA NSCLC in our clinic between January 2014 and August 2021.

RESULTS

The recurrence rate was higher in patients with squamous cell carcinoma (SCC) than in those with adenocarcinoma (AC) (p = 0.004). Disease-free survival (DFS) was shorter in SCC (p = 0.004). According to histopathological subtypes, the presence of lymphovascular invasion (LVI), vascular invasion (VI), visceral pleural invasion (VPI) and tumor spread through air spaces (STAS) caused an increased risk of recurrence ((p = 0.004), (p = 0.001), (p = 0.047), (p = < 0.001)) and shorter DFS ((p = 0.002), (p = < 0.001), (p = 0.038), (p = < 0.001)). LVI and VI was more common in patients with distant recurrence (p = 0.020, p = 0.002), while the STAS was more common with locoregional recurrence (p = 0.003).

CONCLUSION

The presence of LVI, VI, VPI, and STAS are negative risk factors for recurrence and DFS in all patients and in patients with AC. In patients with SCC, the diagnosis of SCC itself and the presence of STAS were risk factors for recurrence and DFS. Moreover, the risk of distant recurrence is higher in the presence of LVI or VI, and the risk of locoregional recurrence in the presence of STAS is higher.

Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model

BACKGROUND

In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combining radiomics and deep learning to predict OLNM preoperatively in SPILAC patients across multiple centers.

METHODS

In this study, 1325 cT1a-bN0M0 SPILAC patients from six hospitals were retrospectively analyzed and divided into pathological nodal positive (pN+) and negative (pN-) groups. Three predictive models for OLNM were developed: a radiomics model employing decision trees and support vector machines; a deep learning model using ResNet-18, ResNet-34, ResNet-50, DenseNet-121, and Swin Transformer, initialized randomly or pre-trained on large-scale medical data; and a fusion model integrating both approaches using addition and concatenation techniques. The model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

All patients were assigned to four groups: training set (n = 470), internal validation set (n = 202), and independent test set 1 (n = 227) and 2 (n = 426). Among the 1325 patients, 478 (36%) had OLNM (pN+). The fusion model, combining radiomics with pre-trained ResNet-18 features via concatenation, outperformed others with an average AUC (aAUC) of 0.754 across validation and test sets, compared to aAUCs of 0.715 for the radiomics model and 0.676 for the deep learning model.

CONCLUSION

The radiomics-deep learning fusion model showed promising ability to generalize in predicting OLNM from CT scans, potentially aiding personalized treatment for SPILAC patients across multiple centers.

Metastatic spread of primary lung adenocarcinoma to the small intestine: A case report

INTRODUCTION AND IMPORTANCE

Metastasis of primary lung cancer to the small intestine is rare, and the prognosis is poor. Early diagnosis of small intestinal metastasis is difficult because the incidence of clinically obvious symptoms is low.

CASE PRESENTATION

This report described a rare case of small intestine metastasis of lung adenocarcinoma. It is worth noting that the patient was diagnosed with lung adenocarcinoma (T2aN0M0, stage IB) over a year ago. However, he complained of fever, black stools, and abdominal pain for about a year after the surgery. Enhanced CT scans showed thickening of the intestinal wall and dilatation of the lumen in the right iliac area and adjacent pelvic cavity. Capsule endoscopy identified a space-occupying lesion with hemorrhaging in the ileum. A laparotomy was subsequently performed, and the histopathological confirmation revealed a metastatic lung adenocarcinoma and immunohistochemistry further showed positive results for TTF-1 and CK7.

CLINICAL DISCUSSION

When patients with a history of primary lung cancer experience gastrointestinal symptoms, the possibility of distant metastasis of lung cancer to the digestive tract should be considered.

CONCLUSION

Due to the rarity of primary lung cancer metastasis to the small intestine, we report the case of a 64-year-old male who presented with symptoms of gastrointestinal bleeding and was ultimately diagnosed with metastasis of primary lung cancer to the small intestine. When patients with lung cancer present with gastrointestinal symptoms, we cannot rule out the possibility of distant metastasis from primary lung cancer, although this possibility is unlikely.

Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier

INTRODUCTION

This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

METHODS

In this retrospective study, a total of 371 patients diagnosed with clinical stage I solid LADC were randomly categorized into training and validation sets in a 7:3 ratio. Uni- and multivariate logistic regression analyses were performed to examine the imaging findings that can be used to predict pathological high-grade patterns meticulously. Employing redundancy and the least absolute shrinkage and selection operator regression, a radiomics model was developed. Subsequently, radiomics refinement and deep learning features were employed using a machine learning algorithm to construct the RRDLC-Classifier, which aims to predict high-grade patterns in clinical stage I solid LADC. Evaluation metrics, such as receiver operating characteristic curves, areas under the curve (AUCs), accuracy, sensitivity, and specificity, were computed for assessment.

RESULTS

The RRDLC-Classifier attained the highest AUC of 0.838 (95% confidence interval [CI]: 0.766-0.911) in predicting high-grade patterns in clinical stage I solid LADC, followed by radiomics with an AUC of 0.779 (95% CI: 0.675-0.883), and imaging findings with an AUC of 0.6 (95% CI: 0.472-0.726).

CONCLUSIONS

This study introduces the RRDLC-Classifier, a novel diagnostic algorithm that amalgamates refined radiomics and deep learning features to predict high-grade patterns in clinical stage I solid LADC. This algorithm may exhibit excellent diagnostic performance, which can facilitate its application in precision medicine.

Micropapillary and solid components as high-grade patterns in IASLC grading system of lung adenocarcinoma: Clinical implications and management

OBJECTIVES

The grading system proposed by the International Association for the Study of Lung Cancer is based on a combination of predominant histologic subtypes and the proportion of high-grade components with a cutoff of 20%. We aimed to examine the clinical implications of the grading system beyond the discrimination of patient prognosis, while assessing the biological differences among high-grade subtypes.

METHODS

We retrospectively reviewed 648 consecutive patients with resected lung adenocarcinomas and examined their clinicopathologic, genotypic, and immunophenotypic features and treatment outcomes. Besides the differences among grades, the clinical impact of different high-grade components: micropapillary (MIP) and solid (SOL) patterns, was individually evaluated.

RESULTS

Survival outcomes were well-stratified according to the grading system. Grade 3 tumors exhibited aggressive clinicopathologic features, while being an independent prognostic factor in multivariable analysis. A small proportion (<20 %) of high-grade components in grade 2 had a negative prognostic impact. The prognostic difference bordering on the 20 % cutoff of the MIP proportion was validated; however, the proportion of SOL component did not affect prognosis. A survival benefit from adjuvant chemotherapy was observed in grade 3 tumors regardless of histologic subtype, but not in grade 1-2 tumors. The molecular and immunophenotypic features were different among grades, but still heterogeneous in grade 3, with MIP harboring frequent EGFR mutation and SOL exhibiting high PD-L1 expression. The treatment outcome after recurrence was worse in grade 3, but tumors with MIP pattern had an equivalent prognosis to that of grade 1-2 tumors, reflecting the high frequency of molecular targeted therapy.

CONCLUSIONS

In addition to stratifying patient prognosis, the current grading system could discriminate clinical course, therapeutic effects of adjuvant chemotherapy, and molecular and immunophenotypic features. Further stratification based on biological heterogeneity in grade 3 remains necessary to enhance the role of the grading system in guiding patient management.

Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma

OBJECTIVE

To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT).

METHODS

Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram.

RESULTS

The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P =  0.029 OR =  4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR =  1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model.

CONCLUSION

The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.

A Multi-institutional Analysis of the Combined Effect of Micropapillary Component and Consolidation-to-Tumor Ratio >0.5 on the Prognosis of Pathological, Stage IA3, Lung Adenocarcinoma

INTRODUCTION

The study investigated the synergistic effect of the micropapillary (MIP) component and consolidation-to-tumor ratio (CTR) on the recurrence and survival of patients with pathologic stage IA3 lung adenocarcinoma.

METHODS

We enrolled 419 patients confirmed pathological stage IA3 adenocarcinoma from four institutions. Kaplan-Meier analysis was performed to examine the value of the MIP component and CTR on relapse-free survival (RFS) and overall survival (OS). The cumulative recurrence between different stages was analyzed by using cumulative event curves.

RESULTS

RFS (P < 0.0001) and OS (P = 0.008) in the presence of the MIP group were significantly lower than those in the absence of the MIP group, and CTR > 5 only reduced RFS (P = 0.0004), but not OS (P = 0.063), in the patients. In addition, the prognosis of patients with both the MIP component and CTR > 5 was worse than that of those without the MIP component or CTR ≤ 5. Therefore, we established new subtypes of the stage IA3: IA3a, IA3b, and IA3c. RFS and OS for IA3c staging were significantly lower than those for IA3a and IA3b. For IA3c, the cumulative incidence of local recurrence (P < 0.001) and that of distant metastasis (P = 0.004) were significantly higher than those for IA3a and IA3b.

CONCLUSIONS

The MIP component combined with CTR > 0.5 can effectively predict the prognosis of patients with pathological stage IA3 lung adenocarcinoma and may offer more detailed recurrence and survival information according to the established subtype stage of IA3.

Frozen sections accurately predict the IASLC proposed grading system and prognosis in patients with invasive lung adenocarcinomas

INTRODUCTION

The International Association for the Study of Lung Cancer (IASLC) newly proposed grading system for lung adenocarcinomas (ADC) has been shown to be of prognostic significance. Hence, intraoperative consultation for the grading system was important regarding the surgical decision-making. Here, we evaluated the accuracy and interobserver agreement for IASLC grading system on frozen section (FS), and further investigated the prognostic performance.

METHODS

FS and final pathology (FP) slides were reviewed by three pathologists for tumor grading in 373 stage I lung ADC following surgical resection from January to June 2013 (retrospective cohort). A prospective multicenter cohort (January to June 2021, n = 212) were included to confirm the results.

RESULTS

The overall concordance rates between FS and FP were 79.1% (κ = 0.650) and 89.6% (κ = 0.729) with substantial agreement in retrospective and prospective cohorts, respectively. Presence of complex gland was the only independent predictor of discrepancy between FS and FP (presence versus. absence: odds ratio, 2.193; P = 0.015). The interobserver agreement for IASLC grading system on FS among three pathologists were satisfactory (κ = 0.672 for retrospective cohort; κ = 0.752 for prospective cohort). Moreover, the IASLC grading system by FS diagnosis could well predict recurrence-free survival and overall survival for patients with stage I invasive lung ADC.

CONCLUSIONS

Our results suggest that FS had high diagnostic accuracy and satisfactory interobserver agreement for IASLC grading system. Future prospective studies are merited to validate the feasibility of using FS to match patients into appropriate surgical type.

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.

Matrix Metallopeptidase-Gene Signature Predicts Stage I Lung Adenocarcinoma Survival Outcomes

Tumor recurrence poses a significant challenge to the clinical management of stage I lung adenocarcinoma after curative surgical resection. Matrix metalloproteinases (MMPs) increase expression and correlate with recurrence and metastasis in surgically resected non-small cell lung cancer. However, the impact of MMPs on survival outcome varies, and their roles in patients with stage I lung adenocarcinoma remain unclear. In two discovery cohorts, we first analyzed 226 stage I-II lung adenocarcinoma cases in the GSE31210 cohort using a clustering-based method and identified a 150-gene MMP cluster with increased expression in tumors associated with worse survival outcomes. A similar analysis was performed on 517 lung adenocarcinoma cases in the Cancer Genome Atlas cohort. A 185-gene MMP cluster was identified, which also showed increased expression in tumors and correlated with poor survival outcomes. We further streamlined from the discovery cohorts a 36-gene MMP signature significantly associated with recurrence and worse overall survival in patients with stage I lung adenocarcinoma after surgical resection. After adjusting for covariates, the high MMP-gene signature expression remained an independent risk factor. In addition, the MMP-gene signature showed enrichment in epidermal growth factor receptor wild-type lung tumors, especially for those with Kirsten rat sarcoma virus mutations. Using an independent validation cohort, we further validated the MMP-gene signature in 70 stage I lung adenocarcinoma cases. In conclusion, MMP-gene signature is a potential predictive and prognostic biomarker to stratify patients with stage I lung adenocarcinoma into subgroups based on their risk of recurrence for aiding physicians in deciding the personalized adjuvant therapeutics.

Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study

OBJECTIVES

This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features.

METHODS

The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results.

RESULTS

The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035-1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911-18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438-5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583-0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806-0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808-0.896) (sensitivity = 74.3%, specificity = 85.8%).

CONCLUSION

The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC.

KEY POINTS

• The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583-0.699), 0.851 (95% CI 0.806-0.896), and 0.852 (95% CI 0.808-0.896). • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.

Genomic and clinicopathological features of lung adenocarcinomas with micropapillary component

BACKGROUND

Lung adenocarcinoma (LA) with a micropapillary component (LAMPC) is a histological subtype of lung cancer that has received increasing attention due to its correlation with poor prognosis, and its tendency to recur and metastasize. At present, comprehensive genomic profiles and clinicopathological features for LAMPC remain unclear and require further investigation.

METHODS

From September 2009 to October 2020, a total of 465 LAMPC patients were recruited and divided into four groups according to MPC proportions, and the correlations between varying proportions of MPCs and clinicopathological characteristics were analyzed. Twenty-nine (29) LAMPC patients and 89 LA patients without MPC (non-MPC) that had undergone NGS testing were selected for further study The comprehensively analyze genomic variations and the difference between LAMPC and MPC were determined. In addition, Gene alterations of LAMPC between Chinese and Western populations were also compared using cBioPortal data.

RESULTS

A higher proportion of MPCs, associated with higher tumor stage, pleural invasion, and vascular tumor thrombus formation, was determined in LA patients. Compared to non-MPC patients, LAMPC patients were determined to have a lower frequency of single nucleotide variants and a higher frequency of insertion-deletion mutations. Mutations in TP53, CTNNB1, and SMAD4, and ALK rearrangements/fusions were significantly more frequent in LAMPC patients. ERBB2 mutations were only detected in non-MPC patients. Gene mutations in the Wnt pathway were significantly more common in LAMPC patients as compared to non-MPC patients. ALK fusions were more prevalent in younger patients. Patients with KRAS or LBP1B mutations had significantly larger tumor diameters than patients with wild-type KRAS or LBP1B. Patients with KRAS mutations were more likely to develop vascular tumor thrombus. Using the cBioPortal public database, we determined that mutations in EGFR were significantly higher in Chinese patients than in a Memorial Sloan Kettering Cancer Center (MSKCC) Western cohort. ALK fusions were exclusively detected in the Chinese cohort, while mutations in KEAP1 and NOTCH4 were only detected in the MSKCC cohort. Our analysis of signaling pathways revealed that Wnt pathway gene mutations were significantly higher in the Chinese cohort.

CONCLUSION

LA patients with higher proportions of MPCs were determined to have a higher tumor stage, pleural invasion, and vascular tumor thrombosis formation. We comprehensively analyzed the genomic mutation characteristics of LAMPC patients and identified multiple, novel MPC-related gene alterations and pathway changes. Our data provide further understanding of the nature of the LAMPC and potential drug-targeted gene alterations, which may lead to new therapeutic strategies.

Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm

Objective

We aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance.

Methods

The clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test.

Results

The R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model.

Conclusion

In this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.

Immunogenomic profiling of lung adenocarcinoma reveals poorly differentiated tumors are associated with an immunogenic tumor microenvironment

OBJECTIVES

Pathologists have routinely observed distinct histologic patterns of growth in early-stage lung adenocarcinoma (LUAD), which have been suggested to be associated with prognosis. Herein, we investigated the relationship between LUAD patterns of growth, as defined by the updated international association for the study of lung cancer (IASLC) grading criteria, and differences in the tumor immune microenvironment to identify predictors of response to immunotherapy.

METHODS

174 resected stage I-III LUAD tumors were classified by histologic pattern of growth (i.e. solid, micropapillary, acinar, papillary, and lepidic) and then grouped as well differentiated, moderately differentiated, and poorly differentiated. Comprehensive multiplatform analysis including whole exome sequencing, gene expression profiling, immunohistochemistry, CIBERSORT, and T-cell receptor sequencing was performed and groups were compared for differences in genomic drivers, immune cell infiltrate, clonality, and survival. Finally, multivariate analysis was performed adjusting for pathologic stage and smoking status.

RESULTS

Poorly differentiated tumors demonstrated a strong association with smoking relative to moderately differentiated or well differentiated tumors. However, unlike in prior reports, poorly differentiated tumors were not associated with a worse survival after curative-intent resection. Genomic analysis revealed that poorly differentiated tumors are associated with high tumor mutation burden but showed no association with oncogenic drivers. Immune analyses revealed that poorly differentiated tumors are associated with increased T-cell clonality, expression of PD-L1, and infiltration by cytotoxic CD8 T-cells, activated CD4 T-cells, and pro-inflammatory (M1) macrophages. Finally, multivariate analysis controlling for stage and smoking status confirmed independence of immune differences between IASLC grade groups.

CONCLUSIONS

Poorly differentiated tumors, as defined by the updated IASLC grading criteria, are associated with a distinct immunogenic tumor microenvironment that predicts for therapeutic response to immune agents, including checkpoint inhibitors, and should be included in the clinical trial design of immunotherapy studies in early-stage lung adenocarcinoma.

Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography

BACKGROUND

High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with those treated with lobectomy. We investigated the potential of incorporating solid attenuation component masks with deep learning in the prediction of high-grade components to optimize surgical strategy preoperatively.

METHODS

A total of 502 patients with pathologically confirmed high-grade adenocarcinomas were retrospectively enrolled between 2016 and 2020. The SACs attention DL model was developed to apply solid-attenuation-component-like subregion masks (tumor area ≥ - 190 HU) to guide the DL model for predicting high-grade subtypes. The SACA-DL was assessed using 5-fold cross-validation and external validation in the training and testing sets, respectively. The performance, which was evaluated using the area under the curve (AUC), was compared between SACA-DL and the DL model without SACs attention (DL_woSACs), the prior radiomics model, or the model based on the consolidation/tumor (C/T) diameter ratio.

RESULTS

We classified 313 and 189 patients into training and testing cohorts, respectively. The SACA-DL achieved an AUC of 0.91 for the cross-validation, which was significantly superior to those of the DL_woSACs (AUC = 0.88; P = 0.02), prior radiomics model (AUC = 0.85; P = 0.004), and C/T ratio (AUC = 0.84; P = 0.002). An AUC of 0.93 was achieved for external validation in the SACA-DL and was significantly better than those of the DL_woSACs (AUC = 0.89; P = 0.04), prior radiomics model (AUC = 0.85; P < 0.001), and C/T ratio (AUC = 0.85; P < 0.001).

CONCLUSIONS

The combination of solid-attenuation-component-like subregion masks with the DL model is a promising approach for the preoperative prediction of high-grade adenocarcinoma subtypes.

Novel Genetic Prognostic Signature for Lung Adenocarcinoma Identified by Differences in Gene Expression Profiles of Low- and High-Grade Histological Subtypes

The 2021 WHO classification proposed a pattern-based grading system for early-stage invasive non-mucinous lung adenocarcinoma. Lung adenocarcinomas with high-grade patterns have poorer outcomes than those with lepidic-predominant patterns. This study aimed to establish genetic prognostic signatures by comparing differences in gene expression profiles between low- and high-grade adenocarcinomas. Twenty-six (9 low- and 17 high-grade adenocarcinomas) patients with histologically “near-pure” patterns (predominant pattern comprising >70% of tumor areas) were selected retrospectively. Using RNA sequencing, gene expression profiles between the low- and high-grade groups were analyzed, and genes with significantly different expression levels between these two groups were selected for genetic prognostic signatures. In total, 196 significant candidate genes (164 upregulated and 32 upregulated in the high- and low-grade groups, respectively) were identified. After intersection with The Cancer Genome Atlas–Lung Adenocarcinoma prognostic genes, three genes, exonuclease 1 (EXO1), family with sequence similarity 83, member A (FAM83A), and disks large-associated protein 5 (DLGAP5), were identified as prognostic gene signatures. Two independent cohorts were used for validation, and the areas under the time-dependent receiver operating characteristic were 0.784 and 0.703 in the GSE31210 and GSE30219 cohorts, respectively. Our result showed the feasibility and accuracy of this novel three-gene prognostic signature for predicting the clinical outcomes of lung adenocarcinoma.

Correlation of Epidermal Growth Factor Receptor Mutation With Major Histologic Subtype of Lung Adenocarcinoma According to IASLC/ATS/ERS Classification

OBJECTIVE

Our prospective study aims to define the correlation of EGFR(epidermal growth factor receptor) mutations with major histological subtypes of lung adenocarcinoma from resected and non-resected specimens, according to the WHO 2015 classification, in Moroccan North East Population.

METHODS

Epidermal growth factor receptor mutations of 150 primary lung adenocarcinoma were performed using Real-Time PCR or SANGER sequencing. SPSS 21 was used to assess the relationship between histological subtypes of lung adenocarcinoma and EGFR mutation status.

RESULTS

25 mutations were detected in the series of 150 lung adenocarcinomas, most of which were found in cases with papillary, acinar, patterns than without these patterns and more frequently occurred in the cases without solid pattern than with this pattern. A significant correlation was observed between EGFR mutation and acinar (P = 0,024), papillary pattern (P = 0,003) and, negative association with a solid pattern (P < 0,001). In females, EGFR mutations were significantly correlated with the acinar pattern (P = 0,02), whereas in males with the papillary pattern (P = 0,01). Association between the histologic component and exon 19 deletions and exon 21 mutations were also evaluated and, we found a significant correlation between the papillary major pattern with exon 19 mutations (P = 0,004) and, ex21 with the acinar component (P = 0,03).

CONCLUSION

An analysis of resected and non-resected lung ADC specimens in 150 Moroccan Northeast patients, revealed that acinar and papillary patterns may predict the presence of a mutation in the EGFR gene. While the solid major pattern may indicate a low mutation rate of the EGFR gene.

Prognostic implication of tumor spread through air spaces in patients with pathologic N0 lung adenocarcinoma

OBJECTIVES

Tumor spread through air spaces (STAS) has been identified as an invasive pattern in lung adenocarcinoma (ADC), but the prognostic implication of STAS has not been well studied in patients with pathologic N0 lung ADC. The purpose of this study was to evaluate the prognostic implication of STAS in pathologic N0 lung ADC patients after radical surgery.

MATERIALS AND METHODS

Between January 2017 and December 2018, 796 patients with completely resected pathologic N0 lung ADC were reviewed. Pearson's chi-square test or Fisher exact test was used for comparing the relationship between STAS and clinicopathological features. The log-rank test and multivariate Cox regression models were used to explore prognostic factors.

RESULTS

Among the 796 patients, STAS was positive in 201 patients (25.3%). The presence of STAS was significantly associated with patients with solid nodules (P < 0.001), micropapillary pattern-predominant adenocarcinoma/solid pattern-predominant adenocarcinoma (P < 0.001), larger tumor size (P < 0.001), visceral pleural invasion (P < 0.001) and lymphovascular invasion (P < 0.001). Multivariable analysis showed that STAS was an independent prognostic factor for recurrence-free survival (RFS) in pathologic N0 lung ADC patients (P = 0.014). For patients with acinar pattern-predominant adenocarcinoma (APA) / papillary pattern-predominant adenocarcinoma (PPA) / invasive mucinous adenocarcinoma (IMA) and patients who underwent lobectomy, STAS was an independent prognostic factor for RFS (P = 0.015, P = 0.011; respectively) and overall survival (OS) (P = 0.038, P = 0.020; respectively).

CONCLUSION

In this study, STAS was an independent prognostic factor for RFS in pathologic N0 lung adenocarcinomas, and it was also an independent prognostic factor for RFS and OS in patients with APA/PPA/IMA and those who received lobectomy.

[Pathological and Molecular Features of Lung Micropapillary Adenocarcinoma]

肺微乳头腺癌作为高级别肺腺癌，具频发转移、淋巴结浸润、复发率高和总体生存率低的临床特征。该亚型肿瘤中存在特征致癌因子通路的激活和肿瘤免疫微环境的建立。本文拟对近年来微乳头腺癌的病理学表现及分子学特征研究进展作一综述，旨在加深对微乳头型病变的认识，进而为制定特异性治疗策略奠定基础。

The prediction of spread through air spaces with preoperative 18F-FDG PET/CT in cases with primary lung adenocarcinoma, its effect on the decision for an adjuvant treatment and its prognostic role

PURPOSE

In lung adenocarcinoma cases, 'spread through air spaces' (STAS) is a new indicator of invasion and directly related to disease survival. The aim of our study is to establish whether a preoperatively performed 18F-Fluorodeoxyglucose (FDG) PET/computed tomography (CT) imaging data can predict the presence of STAS in cases with lung adenocarcinoma and thus predict the decision for the type of surgery and adjuvant chemotherapy.

MATERIALS AND METHODS

Between 2000 and 2019, we retrospectively analyzed 63 patients with lung adenocarcinoma cases that had undergone lobectomy or pneumonectomy. Semiquantitative parameters were calculated and metabolic tumor volume (MTV)/CT volume (CTV) ratio was recorded from FDG PET/CT data. The pathological samples from these patients were evaluated for STAS. All these values were evaluated for their correlation with the alveolar spread.

RESULTS

There was no statistically significant correlation to be found between CTV, MTV, total lesion glycolysis (TLG), standardized uptake value (SUV)max, SUVmean and STAS (P > 0.05). However, MTV/CTV ratio above 1 had statistically more alveolar spread. In the group with an MTV ratio above 1, STAS positivity was 27 (75%), and 9 (25%) did not have STAS, whereas these were 6 (22.2%) patients who had STAS, and 21 (77.8%) did not have STAS in the group with below 1 (P < 0.001).

CONCLUSIONS

In the preoperative PET study inoperable lung adenocarcinoma cases, MTV/CTV ratio higher than 1 was found to predict STAS positivity. As a result, it was found that it provided significant clinical additional information regarding the need for a surgical approach (lobar resection instead of sublobar) and adjuvant chemotherapy.

Subsolid Lung Adenocarcinomas: Radiological, Clinical and Pathological Features and Outcomes

Lung adenocarcinomas manifesting as subsolid nodules usually have a favorable prognosis. This study aimed to have a comprehensive investigation of the radiological and clinicopathologic features and oncological outcomes of subsolid nodules. Between March 2010 and December 2015, 865 patients with surgically resected clinical IA subsolid lung adenocarcinoma were retrospectively reviewed. Patients were classified into the pure ground-glass nodules (GGN) (pGGN [n = 358], without solid component on lung and mediastinal windows), heterogeneous GGN (hGGN [n = 65], only with solid components on lung window), and real part-solid nodule (rPSN [n = 442], with solid component on both lung and mediastinal windows) groups. The clinicopathological features and survival time of the three groups were compared between groups. There was a significant increase in median tumor size (P < 0.001), solid component size measured at lung window (LW-SCS) (P < 0.001), and the proportion of invasive adenocarcinoma subtypes (P < 0.001) from pGGNs to hGGNs to rPSNs. After adjustment for LW-SCS, adenocarcinomas with predominant lepidic patterns were still more common in hGGNs than in rPSNs (P = 0.009). Patients with rPSNs had a significantly worse recurrence-free survival (RFS) than those with pGGNs and hGGNs (5-year: 91.9% versus 100% versus 100%, P < 0.001). Multivariate Cox analyses revealed that gender (both P < 0.05) and clinical T category (based on lung window [LW-cT] [P = 0.002] or mediastinal window [MW-cT] [P < 0.001]) were independent prognostic factors of RFS in the rPSN group. HGGNs represented as an intermediate subtype between pGGNs and rPSNs. Both pGGNs and hGGNs had excellent outcomes, while rPSNs exhibited a worse prognosis than them. Clinical T category and gender had prognostic implications for rPSNs.

Clinicopathological predictors of survival in resected primary lung adenocarcinoma

Aims

Primary lung adenocarcinoma consists of a spectrum of clinical and pathological subtypes that may impact on overall survival (OS). Our study aims to evaluate the impact of adenocarcinoma subtype and intra-alveolar spread on survival after anatomical lung resection and identify different prognostic factors based on stage and histological subtype.

Methods

Newly diagnosed patients undergoing anatomical lung resections without induction therapy, for pT1-3, N0-2 lung adenocarcinoma from April 2011 to March 2013, were included. The effect of clinical–pathological factors on survival was retrospectively assessed.

Results

Two hundred and sixty-two patients were enrolled. The 1-year, 3-year and 5-year OS were 88.8%, 64.3% and 51.1%, respectively. Univariate analysis showed lymphovascular, parietal pleural and chest wall invasion to confer a worse 1-year and 5-year prognosis (all p<0.0001). Solid predominant adenocarcinomas exhibited a significantly worse OS (p=0.014). Multivariate analysis did not identify solid subtype as an independent prognostic factor; however, identified stage >IIa, lymphovascular invasion (p=0.002) and intra-alveolar spread (p=0.009) as significant independent predictors of worse OS. Co-presence of intra-alveolar spread and solid predominance significantly reduced OS. Disease-free survival (DFS) was reduced with parietal pleural (p=0.0007) and chest wall invasion (p<0.0001), however, adenocarcinoma subtype had no significant impact on DFS.

Conclusions

Our study demonstrates that solid predominant adenocarcinoma, intra-alveolar spread and lymphovascular invasion confer a worse prognosis and should be used as a prognostic tool to determine appropriate adjuvant treatment.

A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics

BACKGROUND

Micropapillary/solid (MP/S) growth patterns of lung adenocarcinoma are vital for making clinical decisions regarding surgical intervention. This study aimed to predict the presence of a MP/S component in lung adenocarcinoma using radiomics analysis.

METHODS

Between January 2011 and December 2013, patients undergoing curative invasive lung adenocarcinoma resection were included. Using the "PyRadiomics" package, we extracted 90 radiomics features from the preoperative computed tomography (CT) images. Subsequently, four prediction models were built by utilizing conventional machine learning approaches fitting into radiomics analysis: a generalized linear model (GLM), Naïve Bayes, support vector machine (SVM), and random forest classifiers. The models' accuracy was assessed using a receiver operating curve (ROC) analysis, and the models' stability was validated both internally and externally.

RESULTS

A total of 268 patients were included as a primary cohort, and 36.6% (98/268) of them had lung adenocarcinoma with an MP/S component. Patients with an MP/S component had a higher rate of lymph node metastasis (18.4% versus 5.3%) and worse recurrence-free and overall survival. Five radiomics features were selected for model building, and in the internal validation, the four models achieved comparable performance of MP/S prediction in terms of area under the curve (AUC): GLM, 0.74 [95% confidence interval (CI): 0.65-0.83]; Naïve Bayes, 0.75 (95% CI: 0.65-0.85); SVM, 0.73 (95% CI: 0.61-0.83); and random forest, 0.72 (95% CI: 0.63-0.81). External validation was performed using a test cohort with 193 patients, and the AUC values were 0.70, 0.72, 0.73, and 0.69 for Naïve Bayes, SVM, random forest, and GLM, respectively.

CONCLUSIONS

Radiomics-based machine learning approach is a very strong tool for preoperatively predicting the presence of MP/S growth patterns in lung adenocarcinoma, and can help customize treatment and surveillance strategies.

Prognostic impact of lung adenocarcinoma second predominant pattern from a large European database

BACKGROUND AND OBJECTIVES

Adenocarcinoma patterns could be grouped based on clinical behaviors: low- (lepidic), intermediate- (papillary or acinar), and high-grade (micropapillary and solid). We analyzed the impact of the second predominant pattern (SPP) on disease-free survival (DFS).

METHODS

We retrospectively collected data of surgically resected stage I and II adenocarcinoma.

SELECTION CRITERIA

anatomical resection with lymphadenectomy and pathological N0. Pure adenocarcinomas and mucinous subtypes were excluded. Recurrence rate and factors affecting DFS were analyzed according to the SPP focusing on intermediate-grade predominant pattern adenocarcinomas.

RESULTS

Among 270 patients, 55% were male. The mean age was 68.3 years. SPP pattern appeared as follows: lepidic 43.0%, papillary 23.0%, solid 14.4%, acinar 11.9%, and micropapillary 7.8%. The recurrence rate was 21.5% and 5-year DFS was 71.1%. No difference in DFS was found according to SPP (p = .522). In patients with high-grade SPP, the percentage of SPP, age, and tumor size significantly influenced DFS (p = .016). In patients with lepidic SPP, size, male gender, and lymph-node sampling (p = .005; p = .014; p = .038, respectively) significantly influenced DFS.

CONCLUSIONS

The impact of SPP on DFS is not homogeneous in a subset of patients with the intermediate-grade predominant patterns. The influence of high-grade SPP on DFS is related to its proportion in the tumor.

New International Association for the Study of Lung Cancer (IASLC) Pathology Committee Grading System for the Prognostic Outcome of Advanced Lung Adenocarcinoma

Simple Summary

This study investigated the association between survival outcome and the new grading system among advanced stage lung adenocarcinoma (LADC) (stages IIIA, IIIB and IV) patients who were diagnosed as LADC with a pathologic report according to a new grading system by the International Association for the Study of Lung Cancer (IASLC) pathology committee. The results indicate that the poorly differentiated group had a poorer prognosis in PFS, as did patients with wild-type EGFR who were treated with chemotherapy. No survival difference could be found among EGFR mutation patients. Older age and a lower body mass index also led to worse survival. Patients with poorly differentiated adenocarcinoma likewise had worse survival, especially compared to those with moderately differentiated adenocarcinoma. Our findings highlight that the therapeutic regimen should be adjusted for wild-type EGFR patients with poorly differentiated adenocarcinoma treated with chemotherapy to provide better outcomes. No survival difference could be seen among EGFR mutation patients.

Abstract

The impact of the new International Association for the Study of Lung Cancer pathology committee grading system for advanced lung adenocarcinoma (LADC) on survival is unclear, especially in Asian populations. In this study, we reviewed the prognostic outcomes of patients with late-stage disease according to the new grading system. We reviewed 136 LADC cases who underwent a small biopsy from 2007 to 2018. Tumors were classified according to the new grading system for LADC. Baseline characteristics (age, sex, smoking status, body mass index, and driver gene mutations) were analyzed. Kaplan–Meier and Cox regression analyses were used to determine correlations with the new grading system and prognosis. Patients with poorly differentiated adenocarcinoma were significantly correlated with a poor progression-free survival (PFS) (p = 0.013) but not overall survival (OS) (p = 0.154). Subgroup analysis showed that wild-type EGFR patients with poorly differentiated adenocarcinoma treated with chemotherapy had significantly worse PFS (p = 0.011). There was no significant difference in survival among the patients with epidermal growth factor receptor mutations who were treated with tyrosine kinase inhibitors. Patients aged >70 years and those with a BMI ≤ 25 kg/m² and wild-type patients had significantly worse OS in both univariate (HR = 1.822, p = 0.006; HR = 2.250, p = 0.004; HR = 1.537, p = 0.046, respectively) and multivariate analyses (HR = 1.984, p = 0.002; HR = 2.383, p = 0.002; HR = 1.632, p = 0.028, respectively). Despite therapy, patients with poorly differentiated tumors still fared worse than those with better differentiated tumors. No differences were found among the EGFR mutations treated with TKI. Our findings highlight that the therapeutic regimen should be adjusted for EGFR Wild-type patients with poorly differentiated adenocarcinoma treated with chemotherapy to provide better outcomes.

Nomograms for Predicting Overall and Recurrence-free Survival From Pathologic Stage IA and IB Lung Cancer After Lobectomy

BACKGROUND

Stage I non-small-cell lung cancer (NSCLC) is potentially curable with surgical resection. Significant proportions of patients may still experience recurrence and death despite undergoing curative surgery. This study describes predictive nomograms for recurrence-free (RFS) and overall survival (OS) after lobectomy.

PATIENTS AND METHODS

A total of 301 patients with the American Joint Committee on Cancer pathologic stage IA and IB NSCLC who underwent open, thoracoscopic, or robotic lobectomy from January 2011 to April 2017 were analyzed. Multivariate Cox proportional hazards regression models were used to create nomograms for OS and RFS. Kaplan-Meier survival curves were calculated for OS and RFS comparing high-risk and low-risk cohorts based on nomogram scores.

RESULTS

Histology (hazard ratio [HR], 0.24; 95% confidence interval [CI], 0.10-0.56; P = .002), lymphovascular invasion (HR, 0.46; 95% CI, 0.29-0.74; P = .001), smoking status (HR, 3.46; 95% CI, 1.25-9.55: P = .02), and total lymph nodes removed (HR, 1.05; 95% CI, 1.01-1.10; P = .021) were significant predictors for OS in a multivariate model. Lymphovascular invasion (HR, 0.55; 95% CI, 0.36-0.83; P = .0040), smoking status (HR, 2.56; 95% CI, 1.16-5.62; P = .02), total lymph nodes removed (HR, 1.04; 95% CI, 1.00-1.08; P = .029), and tumor size (HR, 1.30; 95% CI, 1.30-1.68; P = .047) were significant predictors of RFS in a multivariate model.

CONCLUSION

Nomograms can predict OS and RFS for pathologic stage IA and IB NSCLC after lobectomy regardless of operative approach. The risk for death and recurrence after stratification by the nomogram scores may provide guidance regarding adjuvant therapy and surveillance.

A comprehensive study on the oncogenic mutation and molecular pathology in Chinese lung adenocarcinoma patients

BACKGROUND

Several genetic driver alterations have been identified in micropapillary lung adenocarcinoma (MPA). However, the frequency of co-alteration of ROS1, EGFR, and EML4-ALK is yet unclear. Herein, we investigated the relationship between clinicopathologic characteristics and well-identified driver mutations of MPA compared with non-micropapillary lung adenocarcinoma (LA).

METHODS

Formalin-fixed paraffin-embedded (FFPE) sections derived from lung adenocarcinoma patients who never received adjuvant chemotherapy or radiation therapy prior to surgical resection were collected from October 2016 to June 2019. EGFR mutations, ROS1 rearrangements, and EML4-ALK fusion were identified in a set of 131 MPA and LA cases by using the amplification refractory mutation system (ARMS). The response rate and duration of response were assessed using Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1).

RESULTS

EGFR mutations had occurred in 42 (76.4%) MPA patients and 42 (55.3%) LA patients. Interestingly, ROS1 rearrangements were highly enriched only in the MPA cases (6/55, 10.9%) but rarely in the LA cases (1/76, 1.3%). Furthermore, 7.3% (4/55) MPA samples had double gene mutations, while only 1.3% (1/76) LA cases had double gene alterations. Of 5 patients with harboring two driver oncogene mutations, four patients (80%) obtained partial response, and one patient (20%) suffered recurrence.

CONCLUSIONS

A higher prevalence of ROS1 rearrangement or combined mutations of ROS1, EGFR, and EML4-ALK may play a critical role in the tumorigenesis of MPA. These findings provide a novel therapeutic strategy for patients with malignant MPA through combining TKIs than one TKI.

Procedure-specific prognostic impact of micropapillary subtype may guide resection strategy in small-sized lung adenocarcinomas: a multicenter study

Background:

Limited resection has gradually become an acceptable treatment for lung adenocarcinomas (ADCs) presenting as ground-glass nodules (GGNs). However, its role in lung ADCs presenting as pure solid nodules (PSN) remains unclear. In this study, we aimed to identify potential candidates for limited resection in lung ADCs presenting as PSN.

Methods:

We retrospectively reviewed 772 patients from seven hospitals with lung ADCs ⩽2 cm, presenting as PSN on computed tomography scans, who had undergone surgery between 2009 and 2013. Histological subtypes were listed in 5% increments. To investigate the value of histological subtypes in surgical decision making, five pathologists prospectively evaluated the feasibility of identifying histological subtypes using frozen section (FS) in two cohorts.

Results:

The percentage of micropapillary (MIP) subtype had a striking impact on recurrence-free survival (RFS) and overall survival (OS) for lung ADCs ⩽2 cm presenting as PSNs. In multivariable Cox analysis, segmentectomy was significantly associated with worse RFS and OS in patients with MIP >5% than lobectomy, but not in those with MIP ⩽5%. With wedge resection, worse RFS and OS were observed in patients with MIP >5% and those with MIP ⩽5% than lobectomy. The sensitivity and specificity for detecting MIP by FS were 74.2% and 85.6%, respectively, with substantial inter-rater agreement.

Conclusion:

Segmentectomy and lobectomy had similar oncological outcomes in patients with lung ADCs ⩽2 cm presenting as PSN with MIP ⩽5%. Randomized trials are necessary to validate the feasibility of intraoperative FS to choose candidates for segmentectomy.

Gene expression profiling by targeted RNA sequencing in pathological stage I lung adenocarcinoma with a solid component

OBJECTIVES

Solid predominant adenocarcinoma is considered an independent predictor of an unfavorable prognosis in patients with stage I lung adenocarcinoma (LUAD). Furthermore, solid minor components are related to poor prognosis in patients with stage I LUAD. Therefore, it is imperative to elucidate the molecular determinants of the malignant potential of solid components (SC). Several studies reported the gene expression profiling specific for lepidic predominant adenocarcinoma or solid predominant adenocarcinoma, however; there is no report identifying the differentially expressed genes (DEGs) between SC and acinar component (AC) within the same tumor tissue in pathological (p)-stage I LUAD patients.

MATERIALS AND METHODS

LUAD tissue samples containing both SC and AC were obtained from 8 patients with p-stage I LUAD and each component was microdissected. Targeted RNA sequencing was performed by a high-throughput chip-based approach.

RESULTS

In total, 1272 DEGs were identified, including 677 upregulated genes and 595 downregulated genes in SC compared with AC. The most highly upregulated gene was TATA binding protein associated factor 7 (TAF7) and the most highly downregulated gene was homeobox B3 (HOXB3), which acts as a metastasis suppressor. A protein-protein interaction (PPI) network analysis of upregulated genes in SC identified ribosomal protein S27a (RPS27a) as a hub gene with the highest degree. First neighbors of RPS27a included PSMA6, which is a highly promising target for lung cancer. The subnetwork of PD-L1 had 10 first neighbors, including CMTM6, which enhances the ability of PD-L1-expressing tumor cells to inhibit T cells. The staining score for PD-L1 in SC was significantly higher than that in AC by immunohistochemistry (p = 0.001).

CONCLUSION

Our results revealed several new DEGs and key PPI network in SC compared to AC, contributing to understanding the biological features of SC and providing therapeutic targets for early-stage LUAD with SC in the future.

Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans

PURPOSE

Adenocarcinoma (ADC) is the most common histological subtype of lung cancers in non-small cell lung cancer (NSCLC) in which ground glass opacifications (GGOs) found on computed tomography (CT) scans are the most common lesions. However, the presence of a micropapillary or a solid component is identified as an independent predictor of prognosis, suggesting a more extensive resection. The purpose of our study is to explore imaging phenotyping using a method combining radiomics with deep learning (RDL) to predict high-grade patterns within lung ADC.

METHODS

Included in this study were 111 patients differentiated as having GGOs and pathologically confirmed ADC. Four different groups of methods were compared to classify the GGOs for the prediction of the pathological subtypes of high-grade lung ADCs in definitive hematoxylin and eosin stain, including radiomics with gray-level features, radiomics with textural features, deep learning method, and the RDL.

RESULTS

We evaluated the performance of different models on 111 NSCLC patients using 4-fold cross-validation. The proposed RDL has achieved an overall accuracy of 0.913, which significantly outperforms the other methods (p <  0.01, analysis of variation, ANOVA). In addition, we also verified the generality and practical effectiveness of these models on an independent validation dataset of 28 patients. The results showed that our RDL framework with an accuracy of 0.966 significantly surpassed other methods.

CONCLUSION

High-grade lung ADC based on histologic pattern spectrum in GGO lesions might be predicted by the framework combining radiomics with deep learning, which reveals advantage over radiomics alone.

2D and 3D texture analysis to predict lymphovascular invasion in lung adenocarcinoma

PURPOSE

Lymphovascular invasion (LVI) impairs surgical outcomes in lung adenocarcinoma (LAC) patients. Preoperative prediction of LVI is challenging by using traditional clinical and imaging factors. The purpose of this study was to evaluate the value of two-dimensional (2D) and three-dimensional (3D) CT texture analysis (CTTA) in predicting LVI in LAC.

METHODS

A total of 149 LAC patients (50 LVI-present LACs and 99 LVI-absent LACs) were retrospectively enrolled. Clinical data and CT findings were analyzed to select independent clinical predictors. Texture features were extracted from 2D and 3D regions of interest (ROI) in 1.25 mm slice CT images. The 2D and 3D CTTA signatures were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The optimized CTTA signature was selected by comparing the predicting efficacy and clinical usefulness of 2D and 3D CTTA signatures. A CTTA nomogram was developed by integrating the optimized CTTA signature and clinical predictors, and its calibration, discrimination and clinical usefulness were evaluated.

RESULTS

Maximum diametre and spiculation were independent clinical predictors. 1125 texture features were extracted from 2D and 3D ROIs and reduced to 11 features to build 2D and 3D CTTA signatures. There was significant difference (P < 0.001) in AUC (area under the curve) between 2D signature (AUC, 0.938) and 3D signature (AUC, 0.753) in the training set. There was no significant difference (P = 0.056) in AUC between 2D signature (AUC, 0.856) and 3D signature (AUC, 0.701) in the test set. Decision curve analysis showed the 2D signature outperformed the 3D signature in terms of clinical usefulness. The 2D CTTA nomogram (AUC, 0.938 and 0.861, in the training and test sets), which incorporated the 2D signature and clinical predictors, showed a similar discrimination capability (P = 1.000 and 0.430, in the training and test sets) and clinical usefulness as the 2D signature, and outperformed the clinical model (AUC, 0.678 and 0.776, in the training and test sets).

CONCLUSIONS

2D CTTA signature performs better than 3D CTTA signature. The 2D CTTA nomogram with the 2D signature and clinical predictors incorporated provides the similar performance as the 2D signature for individual LVI prediction in LAC.

Clinicopathological features, survival outcomes, and appropriate surgical approaches for stage I acinar and papillary predominant lung adenocarcinoma

Background

Whether prognosis differs between lung acinar predominant adenocarcinoma (ACN) and papillary predominant adenocarcinoma (PAP) patients remains controversial. Furthermore, the appropriate surgical plan for each subtype is undetermined.

Methods

Data of stage I ACN or PAP patients from 2004 to 2015 were retrospectively reviewed by SEER*Stat 8.3.5. The primary outcome was overall survival (OS) and lung cancer specific survival (LCSS).

Results

1531 patients (PAP, 484; ACN, 1047) were included. ACN patients had better OS (P = .001) and LCSS (P = .003) than PAP patients. Among stage I ACN patients, lobectomy with mediastinal lymph node dissection (Lob) (P = .001) or segmentectomy (Seg) (P = .003) provided a better OS than wedge resection (Wed). And ACN patients who received Lob had a equivalent LCSS, compared to those who received Seg (P = .895). For patients with PAP in stage I, those who received Lob tended to have a better prognosis than that received Seg (HR of OS, 0.605, 95% CI: 0.263‐1.393; HR of LCSS, 0.541, 95% CI: 0.194‐1.504) or Wed (HR of OS, 0.735, 95% CI: 0.481‐1.123; HR of LCSS, 0.688, 95% CI: 0.402‐1.180).

Conclusions

Among patients with lung adenocarcinoma in stage I, those with ACN have a better OS and LCSS than that with PAP. For patients with stage I ACN, Seg and Lob, rather than Wed, seem to be an equivalent treatment choice; however, Seg is the prior option because it could preserve more lung function than Lob. For patients with PAP, Lob tends to be a better choice than Wed and Seg, although the prognostic difference between them is nonsignificant.

Skip N2 Metastasis in Pulmonary Adenocarcinoma: Good Prognosis Similar to N1 Disease

INTRODUCTION

The prognostic effect and mechanism of skip N2 lung cancer remain unclear. Our study aimed to elucidate the influence of skip N2 on overall survival (OS) and disease-free survival (DFS) compared with N1 and non-skip N2 in patients with lung adenocarcinoma.

PATIENTS AND METHODS

Patients with lung adenocarcinoma and lymph node involvement between May 2011 and December 2015 were retrospectively analyzed. The outcomes of skip N2 patients were compared with N1 and non-skip N2 patients. Prognosis was further investigated according to the N status in different adenocarcinoma subtypes. Univariate and multivariate analyses were carried out to define independent risk factors for OS and DFS.

RESULTS

A total of 456 patients with lung adenocarcinoma, 169 with N1 disease, 81 with skip N2 disease, and 206 with non-skip N2 disease, were enrolled in this study. All tumors were invasive adenocarcinoma, and the predominant subtypes were acinar in 252, papillary in 42, solid in 119, micropapillary in 20, and invasive mucinous adenocarcinoma in 23 patients. The DFS and OS of N1 and skip N2 diseases were similar and significantly better than those of patients with non-skip N2 disease. The prognosis according to lymph node status was significantly different in acinar-predominant subtypes in terms of both OS and DFS.

CONCLUSIONS

Skip N2 disease has a similar prognosis to N1 disease and is significantly better than that of non-skip N2 disease in relation to OS and DFS. Skip N2 has a prognostic advantage in patients with the acinar-predominant subtype.

Clinicopathological variables influencing overall survival, recurrence and post-recurrence survival in resected stage I non-small-cell lung cancer

BACKGROUND

To investigate clinicopathological variables influencing overall survival, overall recurrence, and post-recurrence survival (PRS) in patients who experienced curative-intent surgical resection of stage I non-small-cell lung cancer (NSCLC).

METHODS

We investigated a series of 1387 patients with stage I NSCLC who underwent surgical resection from 2008 to 2015. The effect clinicopathological factors on death, recurrence, and PRS were evaluated by Kaplan-Meier estimates and cox regression analysis.

RESULTS

Among the 1387 stage I patients, 301 (21.7%) experienced recurrence. The 5-year cumulative incidence of recurrence (CIR) for all patients was 20.2% and median PRS was 25.5 months. The older age (P = 0.036), p-stage IB (P = 0.001), sublobar resection(P<0.001), histology subtype (P<0.001), and lymphovascular invasion (LVI) (P = 0.042) were significantly associated with worse overall survival. Among 301 recurrent patients, univariable analysis indicated that p-stage IB (versus IA) (P<0.001), LVI (P<0.001) and visceral pleural invasion (VPI) (P<0.001) were remarkably correlated with the higher incidence of recurrence. Taking the effect of clinicopathological variables on PRS into consideration, smoking history (P = 0.043), non-adenocarcinoma (P = 0.013), high architectural grade of LUAD (P = 0.019), EGFR wild status (P = 0.002), bone metastasis (P =0.040) and brain metastasis (P = 0.042) were substantially related with poorer PRS. Multivariate analysis demonstrated that high architectural grade of LUAD (P = 0.008), brain metastasis (P = 0.010) and bone metastasis (P = 0.043) were independently associated with PRS.

CONCLUSION

In patients with resected stage I NSCLC, the older age, p-stage IB (versus IA), sublobar resection, histology subtype, and LVI were significantly associated with worse overall survival. P-stage IB (versus IA), LVI, and VPI were significantly correlated with the higher incidence of recurrence. High architectural grade of LUAD, brain metastasis and bone metastasis were independent risk factors with PRS.

Micropapillary pattern is associated with the development of brain metastases and the reduction of survival time in EGFR-mutation lung adenocarcinoma patients with surgery

OBJECTIVE

The role of micropapillary pattern (MIP) in EGFR-mutated NSCLC patients with brain metastases (BM) after complete surgical resection still remains unclear. Therefore, a retrospective study was conducted to evaluate the role of MIP in those patients.

METHODS

This study included 332 stage I-III patients with EGFR-mutant lung adenocarcinoma and complete resection. Patients were classified in four groups: the MIP-positive patients without BM development, the MIP-negative patients without BM development, the MIP-positive patients with BM development and the MIP-negative patients with BM development. Intracranial disease-free survival (iDFS), systemic disease-free survival (DFS) and overall survival (OS) were evaluated.

RESULTS

The median OS in the whole group was 70 months. The patients with MIP show inferior DFS (13 months vs. 22 months; P < 0.001) and OS (56 months vs. 74 months; P < 0.001). Furthermore, BM development was more likely to be found in patients with MIP (P = 0.001). In addition, the MIP-positive patients showed a significantly shorter iDFS compared with MIP-negative patients (14.5 months vs. 26 months; P < 0.001). Furthermore, the MIP-positive patients had significantly inferior iDFS in both BM as first line development groups (13 months vs. 19 months; P < 0.001) and BM as non-first line development groups (18 months vs. 33 months; P = 0.007).

CONCLUSIONS

MIP was related to the earlier recurrence and shortened survival time. In addition, MIP was an independent poor prognostic factor for the increase of BM rate and the shortened time of BM development after surgery.

Micropapillary Predominant Lung Adenocarcinoma in Stage IA Benefits from Adjuvant Chemotherapy

PURPOSE

The benefit of adjuvant chemotherapy remains unknown for patients with stage IA micropapillary predominant (MPP) lung adenocarcinoma (ADC). This study investigated the effect of adjuvant chemotherapy in ADC and MPP patients in stage IA.

METHODS

A total of 5220 stage IA lung ADC patients from SEER database and 152 MPP subtype patients from Qilu Hospital of Shandong University were retrospectively analyzed. Propensity score matching analysis was used to adjust the confounding factors. The benefits of improved overall survival (OS) or progression-free survival (PFS) from adjuvant chemotherapy in patients with resected stage IA ADC or MPP patients were investigated.

RESULTS

Based on SEER database, for ADC patients in stage IA, chemotherapy (no vs. yes: hazard ratio [HR]: 0.674, 95% confidence interval [CI] 0.474-0.958, P = 0.030), together with radiotherapy (no vs. yes: HR: 0.519, 95% CI 0.358-0.751, P = 0.001), race, gender, age, and T stage were all statistically significant independent factors for OS. However, in propensity model, there was no significant difference in OS between patients who received adjuvant chemotherapy and those who did not. Only age was a significant prognostic predictor for OS. For patients with MPP subtype in stage IA, multivariate analysis revealed that chemotherapy (no vs. yes: HR: 2.054, 95% CI 1.085-3.886, P = 0.027) as well as T stage were prognostic predictors for OS. Chemotherapy (no vs. yes: HR: 2.205, 95% CI 1.118-4.349, P = 0.022) and T stage also were significant predictors for PFS.

CONCLUSIONS

Adjuvant chemotherapy is a favorable prognostic factor for MPP patients in stage IA but not for lung ADC patients. MPP subtype could benefit from adjuvant chemotherapy.

Development and External Validation of a Nomogram for Predicting Survival in Patients With Stage IA Non-small Cell Lung Cancer ≤2 cm Undergoing Sublobectomy

Background: Postoperative prognosis of early stage non-small cell lung cancer (NSCLC) undergoing sublobectomy is heterogeneous. Therefore, we sought to construct a novel survival prediction model for stage IA NSCLC ≤2 cm undergoing sublobectomy.

Methods: Based on the data from the Surveillance, Epidemiology, and End Results (SEER) program, we successfully determined and incorporated independent prognostic markers to construct the nomogram. Internal validation of the constructed nomogram was conducted through 1,000 bootstrap resamples. The constructed nomogram was further subjected to external validation with an independent cohort of patients from two Chinese institutions. The performance of the survival prediction model was assessed by concordance index, calibration plots, and risk subgroup classification.

Results: A total of 3,238 patients from SEER registries (development cohort), as well as 769 patients from two Chinese institutions (validation cohort) was included. Gender, age, size, histologic type, grade, and examined lymph nodes count were identified as significant prognostic parameters. A novel nomogram was developed and externally validated. Concordance index of constructed nomogram was significantly better than that of the current TNM staging system. Calibration plots demonstrated an optimal consistency between the nomogram predicted and actual observed probability of survival. Survival curves of different risk subgroups within respective TNM stage demonstrated significant distinctions.

Conclusion: We developed and externally validated a survival prediction model for patients with stage IA NSCLC ≤2 cm undergoing sublobectomy. This novel nomogram outperforms the conventional TNM staging system and could help clinicians in postoperative surveillance and future clinical trial design.

Combined Evaluation of Tumor-Infiltrating CD8 + and FoxP3 + Lymphocytes Provides Accurate Prognosis in Stage IA Lung Adenocarcinoma

BACKGROUND

Immunotherapy has become a standard treatment option for non-small cell lung cancer (NSCLC), with the tumor microenvironment attracting significant attention. CD8 + and forkhead box protein P3 + (FoxP3 +) tumor-infiltrating lymphocytes (TILs) influence the tumor microenvironment, but the clinical significance of CD8 + and FoxP3 + TILs in stage IA lung adenocarcinoma (LAD) is poorly understood.

METHODS

We analyzed 203 patients with stage IA primary LAD who had undergone surgery at Kyushu University from January 2003 to December 2012. We evaluated CD8 + and FoxP3 + TILs by immunohistochemistry. We set the cutoff values at 50 cells/0.04 mm² for CD8 + TILs and 20 cells/0.04 mm² for FoxP3 + TILs, respectively. We divided the patients into four groups: CD8-Low/FoxP3-Low; CD8-High/FoxP3-Low; CD8-Low/FoxP3-High; and CD8-High/FoxP3-High. We compared clinical outcomes among them. Programmed cell death ligand-1 (PD-L1) expression by tumor cells was also evaluated as previously reported.

RESULTS

Respectively, 104 (51.2%), 46 (22.7%), 22 (10.8%), and 31 (15.3%) patients were classified as CD8-Low/FoxP3-Low, CD8-High/FoxP3-Low, CD8-Low/FoxP3-High, and CD8-High/FoxP3-High. Both disease-free survival (DFS) and overall survival (OS) were significantly worse in the CD8-Low/FoxP3-High group than the other groups (5-year DFS: 66.3% vs. 90.5%; P = 0.0007, 5-year OS: 90.9% vs. 97.0%; P = 0.0077). In the multivariate analysis, CD8-Low/FoxP3-High and PD-L1 expression were independent prognostic factors of DFS, and lymphatic invasion, surgical procedure, and PD-L1 expression were independent prognostic factors of OS.

CONCLUSIONS

CD8-Low/FoxP3-High was an independent prognostic factor of DFS (hazard ratio: 3.22; 95% confidence interval: 1.321-7.179; P = 0.0121) in stage IA LAD. Immunosuppressive conditions were associated with poor prognosis in stage IA LAD.