Defining Morphologic Features of Invasion in Pulmonary Nonmucinous Adenocarcinoma With Lepidic Growth: A Proposal by the International Association for the Study of Lung Cancer Pathology Committee

Tenascin-C in the early lung cancer tumor microenvironment promotes progression through integrin αvβ1 and FAK

Pre-cancerous lung lesions are commonly initiated by activating mutations in the RAS pathway, but do not transition to lung adenocarcinomas (LUAD) without additional oncogenic signals. Here, we show that expression of the extracellular matrix protein Tenascin-C (TNC) is increased in and promotes the earliest stages of LUAD development in oncogenic KRAS-driven lung cancer mouse models and in human LUAD. TNC is initially expressed by fibroblasts and its expression extends to tumor cells as the tumor becomes invasive. Genetic deletion of TNC in the mouse models reduces early tumor burden and high-grade pathology and diminishes tumor cell proliferation, invasion, and focal adhesion kinase (FAK) activity. TNC stimulates cultured LUAD tumor cell proliferation and migration through engagement of αv-containing integrins and subsequent FAK activation. Intringuingly, lung injury causes sustained TNC accumulation in mouse lungs, suggesting injury can induce additional TNC signaling for early tumor cell transition to invasive LUAD. Biospecimens from patients with stage I/II LUAD show TNC in regions of FAK activation and an association of TNC with tumor recurrence after primary tumor resection. These results suggest that exogenous insults that elevate TNC in the lung parenchyma interact with tumor-initiating mutations to drive early LUAD progression and local recurrence.

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

BACKGROUND

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

METHODS

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

RESULT

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

CONCLUSION

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

Morphologic Features of Invasion in Lung Adenocarcinoma: Diagnostic Pitfalls

Reproducibility of pulmonary invasive adenocarcinoma diagnosis is poor when applying the World Health Organization (WHO) classification. In this article, we aimed first to explain by 3-dimensional morphology why simple pattern recognition induces pitfalls for the assessment of invasion as applied in the current WHO classification of pulmonary adenocarcinomas. The underlying iatrogenic-induced morphologic alterations in collapsed adenocarcinoma in situ overlap with criteria for invasive adenocarcinoma. Pitfalls in seemingly acinar and papillary carcinoma are addressed with additional cytokeratin 7 and elastin stains. In addition, we provide more stringent criteria for a better reproducible and likely generalizable classification.

Reproducibility of Assessment of Lepidic (Noninvasive) Patterns in Lung Adenocarcinoma With Cytokeratin Immunostain Compared With Hematoxylin and Eosin and the Proposed New International Association for the Study of Lung Cancer (IASLC) Algorithm

Introduction

Lepidic growth is considered noninvasive in lung nonmucinous adenocarcinoma, whereas other patterns are invasive. Considerable interobserver variability in assessing "invasion" has been reported. We assessed the utility of cytokeratin 7 (CK7) stain and recently proposed International Association for the Study of Lung Cancer criteria to improve assessment of noninvasion in lung adenocarcinoma.

Methods

Four pathologists (two staff, two trainees) assessed 158 hematoxylin and eosin (HE)- and CK7-stained slides of 108 pT1N0-2 nonmucinous lung adenocarcinoma cases. Scoring took place in four rounds. First, sections were independently scored for percentage of noninvasive or probable noninvasive and invasive or probable invasive patterns. Second, after a consensus scoring algorithm for CK7 was formulated, the slides were rescored. Subsequent third-round scoring was conducted only on HE slides using the 2023 International Association for the Study of Lung Cancer proposed criteria, and fourth-round scoring on both HE and CK7 slides simultaneously. Intraclass correlation coefficient (ICC) was calculated for each round. Recurrence-free survival was assessed using Cox proportional hazards regression methods.

Results

In the first two rounds, interobserver concordance was consistently higher with CK7 (ICC range = 0.44-0.6) than HE (range = 0.24-0.49) scores. The IASLC proposed algorithm improved ICC of HE scores to 0.60 (95% confidence interval: 0.52-0.67), and round 4 HE and CK7 combined improved ICC to 0.75 (95% confidence interval: 0.70-0.80). Continuous measures of averaged noninvasive and probable noninvasive scores on HE were associated with improved recurrence-free survival (hazard ratio: 0.83-0.86).

Conclusions

CK7 staining consistently increased interobserver concordance in assessment of invasive versus noninvasive patterns than HE. Combining CK7 with the 2023 IASLC criteria for morphologic features of invasion may further improve the interobservers' concordance for the recognition of lepidic growth in nonmucinous lung adenocarcinoma.

Diagnostic Potential of Supplemental Static and Dynamic 68Ga-FAPI-46 PET for Primary 18F-FDG-Negative Pulmonary Lesions

PET using 68Ga-labeled fibroblast activation protein (FAP) inhibitors (FAPIs) holds high potential for diagnostic imaging of various malignancies, including lung cancer (LC). However, 18F-FDG PET is still the clinical gold standard for LC imaging. Several subtypes of LC, especially lepidic LC, are frequently 18F-FDG PET-negative, which markedly hampers the assessment of single pulmonary lesions suggestive of LC. Here, we evaluated the diagnostic potential of static and dynamic 68Ga-FAPI-46 PET in the 18F-FDG-negative pulmonary lesions of 19 patients who underwent surgery or biopsy for histologic diagnosis after PET imaging. For target validation, FAP expression in lepidic LC was confirmed by FAP immunohistochemistry. Methods: Hematoxylin and eosin staining and FAP immunohistochemistry of 24 tissue sections of lepidic LC from the local tissue bank were performed and analyzed visually. Clinically, 19 patients underwent static and dynamic 68Ga-FAPI-46 PET in addition to 18F-FDG PET based on individual clinical indications. Static PET data of both examinations were analyzed by determining SUVmax, SUVmean, and tumor-to-background ratio (TBR) against the blood pool, as well as relative parameters (68Ga-FAPI-46 in relation to18F-FDG), of histologically confirmed LC and benign lesions. Time-activity curves and dynamic parameters (time to peak, slope, k 1, k 2, k 3, and k 4) were extracted from dynamic 68Ga-FAPI-46 PET data. The sensitivity and specificity of all parameters were analyzed by calculating receiver-operating-characteristic curves. Results: FAP immunohistochemistry confirmed the presence of strongly FAP-positive cancer-associated fibroblasts in lepidic LC. LC showed markedly elevated 68Ga-FAPI-46 uptake, higher TBRs, and higher 68Ga-FAPI-46-to-18F-FDG ratios for all parameters than did benign pulmonary lesions. Dynamic imaging analysis revealed differential time-activity curves for LC and benign pulmonary lesions: initially increasing time-activity curves with a decent slope were typical of LC, and steadily decreasing time-activity curve indicated benign pulmonary lesions, as was reflected by a significantly increased time to peak and significantly smaller absolute values of the slope for LC. Relative 68Ga-FAPI-46-to-18F-FDG ratios regarding SUVmax and TBR showed the highest sensitivity and specificity for the discrimination of LC from benign pulmonary lesions. Conclusion: 68Ga-FAPI-46 PET is a powerful new tool for the assessment of single 18F-FDG-negative pulmonary lesions and may optimize patient stratification in this clinical setting.

Invasion and Grading of Pulmonary Non-Mucinous Adenocarcinoma

Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.

Clinical Significance of the Proposed Pathologic Criteria for Invasion by the International Association for the Study of Lung Cancer in Resected Nonmucinous Lung Adenocarcinoma

INTRODUCTION

Accurate diagnostic criteria for tumor invasion are essential for precise pathologic tumor (pT) staging. Recently, the International Association for the Study of Lung Cancer (IASLC) Pathology Committee suggested a new set of criteria for assessing tumor invasion, but the clinical usefulness of the proposed criteria has not been evaluated.

METHODS

The study included 1295 patients with resected part-solid lung adenocarcinoma from January 2017 to December 2019 at the Samsung Medical Center, Seoul, Korea. The revised pT stage was determined by the extent of the newly measured invasive component using the IASLC criteria. The primary outcome was to compare the performance of the revised pT stage with the original pT stage in predicting recurrence-free survival and proof of invasion status (i.e., recurrence or lymph node metastasis). The secondary outcome was the correlation with radiologic surrogates of tumor invasiveness (consolidation-to-tumor ratio and maximum standardized uptake value) and pathologic risk factors.

RESULTS

The re-evaluation resulted in a 22% downstaging and 2.5% upstaging of pT, which improved the correlation with radiologic (consolidation-to-tumor ratio and maximum standardized uptake value) and pathologic risk factors. The revised pT staging allowed for more accurate discrimination of recurrence-free survival than the original pT staging (c-index = 0.794 versus 0.717). Moreover, the revised pT staging significantly improved the prediction of recurrence or lymph node metastasis (area under the curve = 0.818 versus 0.741, p < 0.001).

CONCLUSIONS

To our knowledge, this is the first study evaluating the clinical significance of the IASLC-proposed criteria for invasion. The proposed IASLC criteria offered better alignment with clinicopathologic risk factors and improved prognostication. Further studies are warranted to assess the impact of the IASLC criteria on treatment decisions and patient outcomes.

The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma.

Pulmonary Low Malignant Potential Adenocarcinoma: A Validation of the Proposed Criteria for This Novel Subtype

Adenocarcinoma (ADC) is the most common histologic type of lung cancer, including in situ (lepidic), minimally invasive, and invasive forms. While the former 2 types are associated with a favorable outcome, the latter includes tumors with variable behavior, often tumor stage-related. A recent study proposed strict morphologic criteria defining a new subgroup of resected stage I invasive ADC (16% of cases) with favorable outcomes (100% disease-specific survival), named "ADC of low malignant potential (LMP-ADC)." The following criteria were met: ≤3 cm size, nonmucinous histotype, ≥15% lepidic growth, and the absence of the following: high-grade patterns, >1 mitosis/2 mm 2 , necrosis, and vascular/pleural invasion. The aim of the present study was to validate the performance of such criteria to identify LMP-ADC in a series of 274 stage IA resected lung ADCs from a single institution. Thirty-four tumors (12.4%) met the proposed criteria for LMP-ADC, as confirmed by additional stains for mitotic figures, Ki67 index, and elastic fibers (helpful to assess alveolar wall invasion). Minor differences between the lepidic and invasive components were observed regarding cell atypia and proliferation. p53 was normally expressed by invasive tumor cells. Mutations occurred in known lung cancer genes (mostly KRAS and EGFR). Five patients (14.7%) developed disease progression and 2 of them (5.9%) died of the disease. In our series, the disease-specific survival was 94.1%. In conclusion, in resected invasive lung ADC, a subgroup presenting low-grade morphologic features and associated with favorable prognosis does exist. Morphologic criteria for LMP-ADC supported by ancillary techniques represent a valid tool to better define this novel subgroup and to refine the stratification of invasive lung ADC, possibly suggesting modified follow-up protocols, based on the observed indolent behavior in most cases.

Updates on lung adenocarcinoma: invasive size, grading and STAS

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

Advances in diagnosis and prediction for aggression of pure solid T1 lung cancer

A growing number of early-stage lung cancers presenting as malignant pulmonary nodules have been diagnosed because of the increased adoption of low-dose spiral computed tomography. But pure solid T1 lung cancer with ≤3 cm in the greatest dimension is not always at an early stage, despite its small size. This type of cancer can be highly aggressive and is associated with pathological involvement, metastasis, postoperative relapse, and even death. However, it is easily misdiagnosed or delay diagnosed in clinics and thus poses a serious threat to human health. The percentage of nodal or extrathoracic metastases has been reported to be >20% in T1 lung cancer. As such, understanding and identifying the aggressive characteristics of pure solid T1 lung cancer is crucial for prevention, diagnosis, and therapeutic strategies, and beneficial to improving the prognosis. With the widespread of lung cancer screening, these highly invasive pure solid T1 lung cancer will become the main advanced lung cancer in future. However, there is limited information regarding precision medicine on how to identify these "early-stage" aggressive lung cancers. To provide clinicians with new insights into early recognition and intervention of the highly invasive pure solid T1 lung cancer, this review summarizes its clinical characteristics, imaging, pathology, gene alterations, immune microenvironment, multi-omics, and current techniques for diagnosis and prediction.

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation

OBJECTIVES

The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manual evaluation, invasive non-mucinous lung adenocarcinoma (ADC) is difficult to diagnose. We aim to offer a deep learning solution that automatically classifies invasive non-mucinous lung ADC histological subtypes.

DESIGN

For this investigation, 523 whole-slide images (WSIs) were obtained. We divided 376 of the WSIs at random for model training. According to WHO diagnostic criteria, six histological components of invasive non-mucinous lung ADC, comprising lepidic, papillary, acinar, solid, micropapillary and cribriform arrangements, were annotated at the pixel level and employed as the predicting target. We constructed the deep learning model using DeepLab v3, and used 27 WSIs for model validation and the remaining 120 WSIs for testing. The predictions were analysed by senior pathologists.

RESULTS

The model could accurately predict the predominant subtype and the majority of minor subtypes and has achieved good performance. Except for acinar, the area under the curve of the model was larger than 0.8 for all the subtypes. Meanwhile, the model was able to generate pathological reports. The NDCG scores were greater than 75%. Through the analysis of feature maps and incidents of model misdiagnosis, we discovered that the deep learning model was consistent with the thought process of pathologists and revealed better performance in recognising minor lesions.

CONCLUSIONS

The findings of the deep learning model for predicting the major and minor subtypes of invasive non-mucinous lung ADC are favourable. Its appearance and sensitivity to tiny lesions can be of great assistance to pathologists.