Validation Study of the International Association for the Study of Lung Cancer Histologic Grading System of Invasive Lung Adenocarcinoma

Prognostic and clinicopathological significance of the new grading system for invasive pulmonary adenocarcinoma: A systematic review and meta-analysis

In 2020, the International Association for the Study of Lung Cancer (IASLC) introduced a new grading system for invasive pulmonary adenocarcinoma (IPA). This meta-analysis aimed to validate the prognostic utility of this grading system and identify relevant clinicopathological features. The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for relevant studies published between January 1, 2020 and March 5, 2024. Hazard ratios (HRs) with corresponding 95 % confidence intervals (CIs) were pooled to evaluate the effect of IASLC grading on prognosis. Odds ratios with corresponding 95 % CIs were pooled to assess relevant clinicopathological features. Twenty-two studies comprising 12,515 patients with IPA were included. Regarding overall survival, grade 3 adenocarcinomas had a worse prognosis compared with grades 1-2 (HR: 2.26, 95 % CI: 1.79-2.85, P<0.001), grade 1 (HR: 4.75, 95 % CI: 2.61-8.66, P<0.001), or grade 2 (HR: 1.71, 95 % CI: 1.28-2.29, P<0.001). Considering recurrence-free survival, grade 3 tumors had a higher recurrence risk than grades 1-2 (HR: 1.92, 95 % CI: 1.53-2.41, P<0.001), grade 1 (HR: 4.43, 95 % CI: 2.91-6.73, P<0.001), or grade 2 (HR: 1.67, 95 % CI: 1.33-2.10, P<0.001). In the subgroup analysis of stage I patients, grade 3 tumors exhibited a similarly poor prognosis. In addition, grade 3 adenocarcinomas were associated with aggressive clinicopathological features. This study demonstrated that the IASLC grading system is a robust predictor of prognostic stratification in patients with IPA, and warrants further promotion and worldwide implementation.

Genomic profiling of aggressive pathologic features in lung adenocarcinoma

INTRODUCTION

Pathologic features involving LVI (lympho-vascular invasion), PNI (perineural invasion), STAS (spread through air spaces), and Grade 3 pattern (from the International Association for the Study of Lung Cancer grading system) are related to having an aggressive phenotype and linked to poor prognosis. However, few studies have conducted in-depth analyses of these features simultaneously with genomic profiling.

METHODS

A total of 1559 sequencing of adenocarcinoma samples were included in the common driver mutations analysis, 1306 samples were brought into genomic mapping analysis. OncoSG's East Asian ancestry dataset was implemented for Tumor-Node-Metastasis-Biomarker (TNMB) classification and prognostic assessment.

RESULTS

EGFR was more significantly prevalent in LVI negativity (P = 0.021), STAS negativity (P = 0.002), and moderate grade (P < 0.001). ALK was significantly interrelated with LVI (P = 0.028), STAS (P < 0.001), and poor grade (P < 0.001); ROS1 and STAS positivity (P = 0.031), poor grade (P = 0.016) were significantly related. KRAS (P = 0.003) and BRAF-V600E (P = 0.002) were only significantly intertwined with poor grade. Apart from common driver mutations, TP53, CHEK2, KEAP1, PTEN, RB1, NF1 were significantly enriched in LVI samples (P < 0.05). TP53, PTEN, CTNNB1, HGF, NF1 were more prominent in STAS (P < 0.01). TP53, LRP1B, NF1 were significantly more prevalent in Grade 3 pattern (P < 0.001). The mixture of STK11, PTEN, and TOP2A generated by exclusive mutations may be a potential predictor of TNMB categorization towards survival. The HR of stage II compared I of TNMB was 2.28 (95 % CI 1.36-3.86, P < 0.001), while stage III compared II was 1.95 (95 % CI 1.04-3.21, P = 0.031).

CONCLUSIONS

This analysis demonstrated the correlation of pathologic features with common driver mutations, key mutations and canonical oncogenic signaling pathways. The data highlighted the similarities and differences among these features horizontally, and provide new insights in TNMB classification and prognostic assessment.

Establishing a threshold for maximum standardized uptake value on 18 F-fluorodeoxyglucose PET/CT to predict high-grade lung adenocarcinoma and its prognostic significance

OBJECTIVE

The objective of this study is to determine an optimal threshold for the maximum standardized uptake value (SUVmax) of 18 F-fluorodeoxyglucose PET/CT to predict the newly proposed high-grade tumor classification and assess its prognostic significance in invasive lung adenocarcinoma (LUAD).

METHODS

Surgical specimens from 185 patients with pathological stage I invasive LUAD in the training group, along with 90 patients in the validation group, were analyzed using the novel IASLC grading system. The receiver operating characteristic curve was used to determine the optimal SUVmax threshold and assess its predictive accuracy. Disease-free survival (DFS) and overall survival (OS) were analyzed using Kaplan-Meier survival curves and Cox regression analysis.

RESULTS

Linear correlation analysis demonstrated a significant positive association between SUVmax and the proportion of high-grade histological patterns ( R ² = 0.346, P  < 0.001). The optimal SUVmax cutoff for predicting grade 3 tumors was 3.8, with an area under the curve of 0.866 in the training dataset and 0.899 in the validation dataset. Multivariate logistic regression analysis identified an SUVmax >3.8 as an independent predictor of grade 3 tumors ( P  < 0.001). In Cox regression analysis, SUVmax >3.8 was independently associated with reduced DFS (HR = 4.009, 95% CI: 1.568-10.250, P  = 0.004) and OS (HR = 5.536, 95% CI: 1.175-26.075, P  = 0.030).

CONCLUSION

As a noninvasive preoperative parameter, SUVmax >3.8 is a significant indicator of high-grade tumors as classified by the IASLC grading system and is strongly associated with worse DFS and OS.

CT radiomics from intratumor and peritumor regions for predicting poorly differentiated invasive nonmucinous pulmonary adenocarcinoma

Per the 2021 World Health Organization (WHO) Classification of Thoracic Tumors, poorly differentiated invasive nonmucinous adenocarcinoma (INMA) demonstrates aggressive clinicopathological behavior characterized by lymphatic invasion, correlates with poor prognosis, and necessitates modifications to surgical planning even in early-stage adenocarcinoma. This study aimed to investigate the predictive value of radiomics features of intratumoral and peritumoral microenvironment in poorly differentiated INMA in the lung. A total of 451 patients with INMA were collected from three hospitals. They were divided into the train cohort (173 grade 1/2; 116 grade 3), internal test cohort (89 grade 1/2; 35 grade 3) and external test cohort (26 grade 1/2; 12 grade 3). The logistic regression analysis was used to establish the clinical and radiomic models. The receiver operating characteristic (ROC) curve and the decision curve analysis (DCA) were used to assess diagnostic performance of different models. The internal test dataset (124 patients) was used to evaluate the radiologists' performance without and with the assistance of optimal model. Nodule attenuation, consolidation size and consolidation-to-tumor ratio (CTR) meaning the ratio of maximum consolidation to tumor dimensions on axial imaging were independent predictors of poorly differentiated INMA (p < 0.05). In the internal test cohort, the area under the curve (AUC) values of the clinical model, the intratumor radiomic model, the combined 3 mm radiomic model, and the combined 5 mm radiomic model were 0.875(95%CI: 0.811-0.938), 0.882(95%CI: 0.807-0.957), 0.907(95%CI: 0.844-0.969) and 0.858(95%CI: 0.783-0.933), respectively. Corresponding results in the external test cohort showed moderate performance across all models: clinical model (AUC: 0.760, 95%CI: 0.603-0.916), intratumor radiomic model (AUC: 0.760, 95%CI: 0.580-0.939), and combined 3 mm/5 mm radiomic model(AUC: 0.772, 95%CI: 0.593-0.952; AUC: 0.766; 95%CIs: 0.580-0.953). Radiologists had higher diagnostic performance and confidence score with the aid of the combined 3 mm radiomic model. The combined 3 mm radiomic model of non-enhanced CT can identify poorly differentiated INMA with excellent performance and improve radiologists to achieve better diagnostic performance.

Pulmonary Adenocarcinoma Updates: Histology, Cytology, and Grading

CONTEXT.—

Adenocarcinomas are the most common histologic subtype of lung cancer, and exist within a widely divergent clinical, radiologic, molecular, and histologic spectrum. There is a strong association between histologic patterns and prognosis that served as the basis for a recently described grading system. As the study of molecular pathology rapidly evolves, all targetable mutations so far have been found in adenocarcinomas, thus requiring accurate diagnosis and classification for the triage of molecular alterations and adequate therapy.

OBJECTIVE.—

To discuss the rationale for adenocarcinoma classifications within the 2021 5th edition of the World Health Organization Classification, with a focus on nonmucinous tumors, including tumor grading and biopsy/cytology diagnosis.

DATA SOURCES.—

PubMed search.

CONCLUSIONS.—

A grading system for adenocarcinoma has improved prognostic impact of the classification of pulmonary adenocarcinoma. An accurate diagnosis of adenocarcinoma in small biopsy material is important for tissue triage for molecular studies and ultimately for patient management and treatment.

Evaluating the prognostic impact of EGFR mutation on adjuvant chemotherapy efficacy in grade 3 stage IB lung adenocarcinoma

OBJECTIVES

Adjuvant chemotherapy (ACT) for patients with stage IB lung adenocarcinoma (LUAD) is controversial, particularly in those with epidermal growth factor receptor (EGFR) mutations. This study aims to evaluate the efficacy of ACT in stage IB LUAD with Grade 3 and explore the prognostic impact of EGFR mutations status on chemotherapy effectiveness.

METHODS

We identified 707 high-risk (Grade 3) stage IB LUAD patients who underwent complete resection between 2014 and 2018. The Kaplan-Meier curves was used to assess recurrence-free survival (RFS) and overall survival (OS). Prognostic factors were evaluated using the Cox proportional hazards model, and propensity score matching was applied to reduce bias from confounding variables.

RESULTS

In the entire cohort, patients who received ACT showed significantly better 5-year RFS and OS compared to those who did not (P < 0.001 for both). Among 247 patients without EGFR mutations, 125 (50.6 %) received ACT and 122 (49.4 %) did not. In the propensity score-matched cohort of 84 pairs, those treated with ACT had significantly better 5-year RFS and OS (P < 0.01 for both). Among 460 patients with EGFR mutations, 237 (51.5 %) received ACT and 223 (48.5 %) did not. In the matched cohort of 184 pairs, ACT recipients had significantly better prognoses. Multivariable analysis confirmed ACT was an independent prognostic factor, while EGFR mutation status was not.

CONCLUSIONS

ACT significantly improves the prognosis of patients with Grade 3 stage IB LUAD, irrespective of EGFR mutation status. These findings support the clinical adoption of ACT for this patient subgroup.

Differentiating high-grade patterns and predominant subtypes for IASLC grading in invasive pulmonary adenocarcinoma using radiomics and clinical-semantic features

OBJECTIVES

The International Association for the Study of Lung Cancer (IASLC) grading system for invasive non-mucinous adenocarcinoma (ADC) incorporates high-grade patterns (HGP) and predominant subtypes (PS). Following the system, this study aimed to explore the feasibility of predicting HGP and PS for IASLC grading.

MATERIALS AND METHODS

A total of 529 ADCs from patients who underwent radical surgical resection were randomly divided into training and validation datasets in a 7:3 ratio. A two-step model consisting of two submodels was developed for IASLC grading. One submodel assessed whether the HGP exceeded 20% for ADCs, whereas the other distinguished between lepidic and acinar/papillary PS. The predictions from both submodels determined the final IASLC grades. Two variants of this model using either radiomic or clinical-semantic features were created. Additionally, one-step models that directly assessed IASLC grades using clinical-semantic or radiomic features were developed for comparison. The area under the curve (AUC) was used for model evaluation.

RESULTS

The two-step radiomic model achieved the highest AUC values of 0.95, 0.85, 0.96 for grades 1, 2, 3 among models. The two-step models outperformed the one-step models in predicting grades 2 and 3, with AUCs of 0.89 and 0.96 vs. 0.53 and 0.81 for radiomics, and 0.68 and 0.77 vs. 0.44 and 0.63 for clinical-semantics (p < 0.001). Radiomics models showed better AUCs than clinical-semantic models for grade 3 regardless of model steps.

CONCLUSIONS

Predicting HGP and PS using radiomics can achieve accurate IASLC grading in ADCs. Such a two-step radiomics model may provide precise preoperative diagnosis, thereby supporting treatment planning.

Solid Predominant Histology and High Podoplanin Expression in Cancer-Associated Fibroblast Predict Primary Resistance to Osimertinib in EGFR-Mutated Lung Adenocarcinoma

Introduction

Resistance to EGFR tyrosine kinase inhibitors is influenced by tumor-intrinsic and -extrinsic factors. We investigated the impact of tumor cell histology and tumor microenvironment on the efficacy of osimertinib.

Methods

We evaluated surgically resected adenocarcinoma from patients treated with first-line osimertinib at the National Cancer Center Hospital East (2016-2023), evaluating clinicopathologic characteristics, tumor cell histology, podoplanin expression in cancer-associated fibroblasts (CAFs) identified by immunohistochemistry, and outcomes. We also investigated HGF mRNA expression levels, using The Cancer Genome Atlas Program and Singapore Oncology Data Portal cohorts.

Results

The study included 93 patients. Solid (n = 19) versus non-solid predominant (n = 74) histology was not associated with worse disease-free survival after surgery (p = 0.12), but was significantly associated with worse progression-free survival (PFS) and overall survival following osimertinib treatment (p = 0.026, p = 0.004). Similarly, high-podoplanin (n = 31) versus low-podoplanin (n = 62) expression in CAFs was not associated with worse disease-free survival after surgery (p = 0.65), but was significantly associated with worse PFS and showed a trend towards worse overall survival following osimertinib treatment (p < 0.001, p = 0.11). In the multivariable analysis, solid predominant histology and high-podoplanin expression in CAFs were independently associated with worse PFS. In the cohorts of The Cancer Genome Atlas Program and Singapore Oncology Data Portal, EGFR-mutated lung adenocarcinoma with solid predominant histology or high-podoplanin expression exhibited significantly higher HGF expression.

Conclusions

Solid predominant histology and high-podoplanin expression in CAFs predicted osimertinib resistance, potentially guiding the selection of patients for more intensive treatments beyond osimertinib monotherapy.

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.

Application and progress of non-invasive imaging in predicting lung invasive non-mucinous adenocarcinoma under the new IASLC grading guidelines

Lung cancer is the leading cause of cancer-related deaths worldwide, with invasive non-mucinous adenocarcinoma (INMA) being the most common type and carrying a poor prognosis. In 2020, the International Association for the Study of Lung Cancer (IASLC) pathology committee proposed a new histological grading system, which offers more precise prognostic assessments by combining the proportions of major and high-grade histological patterns. Accurate identification of lung INMA grading is crucial for clinical diagnosis, treatment planning, and prognosis evaluation. Currently, non-invasive imaging methods (such as CT, PET/CT, and MRI) are increasingly being studied to predict the new grading of lung INMA, showing promising application prospects. This review outlines the establishment and prognostic efficiency of the new IASLC grading system, highlights the application and latest progress of non-invasive imaging techniques in predicting lung INMA grading, and discusses their role in personalized treatment of lung INMA and future research directions. CRITICAL RELEVANCE STATEMENT: The new IASLC grading system has important prognostic implications for patients with lung invasive non-mucinous adenocarcinoma (INMA), and non-invasive imaging methods can be used to predict it, thereby improving patient prognoses. KEY POINTS: The new IASLC grading system more accurately prognosticates for patients with lung INMA. Preoperative prediction of the new grading is challenging because of the complexity of INMA subtypes. It is feasible to apply non-invasive imaging methods to predict the new IASLC grading system.

Combination of grade and spread through air spaces (STAS) predicts recurrence in early stage lung adenocarcinoma: a retrospective cohort study

Adenocarcinomas, a common subtype of lung cancer, exhibit diverse histological patterns. In 2020, The International Association for the Study of Lung Cancer (IASLC) introduced a grading system emphasizing high-grade components, which has shown prognostic value. Spread through air spaces (STAS) is recognized as a prognostic feature increasing the risk of recurrence in lung cancer. This study evaluates the combination of STAS status and the IASLC-grading system in surgically resected Stage I lung adenocarcinomas. This study is a retrospective analysis of 123 patients with Stage I lung adenocarcinoma who underwent lobectomy between 2011 and 2019. Histological patterns were assessed according to the IASLC criteria, and STAS status was documented. Patients were categorized based on their IASLC Grade and STAS status. Statistical analyses included Kaplan-Meier survival estimates, Cox proportional hazards models, and comparisons using Chi-square and t-tests. The cohort comprised 43 females and 80 males with a mean age of 61.8 ± 7.6 years. STAS positivity was noted in 52.8% of patients. STAS positivity correlated significantly with Grade 3 tumors (p < 0.001). The 5-year recurrence-free survival was significantly lower in STAS-positive patients (70.7% vs. 88.7%, p = 0.026). Patients with Grade 3 and STAS positivity had significantly lower recurrence-free survival compared to other groups (p = 0.002). Grade 3 and STAS positivity were independent predictors of poor recurrence-free survival in multivariate analysis. IASLC Grade 3 tumors and STAS positivity are independent prognostic factors for poor recurrence-free survival in Stage I lung adenocarcinomas. Adjuvant treatment strategies should be considered for patients with these characteristics to improve outcomes.

Prognostic impact of the newly revised IASLC proposed grading system for invasive lung adenocarcinoma: a systematic review and meta-analysis

BACKGROUND

This study aimed to evaluate the prognostic value of the newly revised International Association for the Study of Lung Cancer (IASLC) grading system (2020) on the 5-year overall survival (OS) and recurrence-free survival (RFS) in patients with lung adenocarcinoma (LADC).

METHODS

Clinical studies that investigated the prognostic value of revised IASLC staging system in patients with LADC were retrieved from the PubMed, Web of Science, ScienceDirect, and Cochrane Library databases. This study was conducted in accordance to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklists.

RESULTS

Based on inclusion and exclusion criteria, we included 12 studies for analysis. The grade of LADC was assessed by revised IASLC system, which included three grades. Compared to Grade 3 LADC, grade 1 (total [95% CI]: 1.38 [1.19, 1.60]) and grade 2 (total [95% CI]: 1.29 [1.15, 1.44]) LADC had higher 5-year OS rates. Similarly, Grade 1 (total [95% CI]: 1.76 [1.42, 2.18]) and Grade 2 (total [95% CI]: 1.51 [1.28, 1.77]) had higher 5-year RFS rates Grade 3 LADC. However, 5-year OS and RFS had no significant difference between Grade 1 and Grade 2 patients.

CONCLUSION

This systematic review and meta-analysis provides evidence that the newly revised IASLC grading system is significantly associated with the prognosis of patients with LADC, where Grade 3 indicated unfavorable prognosis.

Clinical Utility of Tumor-Naïve Presurgical Circulating Tumor DNA Detection in Early-Stage NSCLC

OBJECTIVES

The use of tumor-informed circulating tumor DNA (ctDNA) testing in patients with early-stage disease before surgery is limited, mainly owing to restricted tissue access and extended turnaround times. This study aimed to evaluate the clinical value of a tumor-naïve, methylation-based cell-free DNA assay in a large cohort of patients with resected NSCLC.

METHOD

We analyzed presurgical plasma samples from 895 patients with EGFR and anaplastic lymphoma kinase-wild-type, clinical stage I or II NSCLC. The ctDNA status was evaluated for its prognostic significance in relation to tumor volume, metabolic activity, histologic diagnosis, histologic subtypes, and clinical-to-pathologic TNM upstaging.

RESULTS

Presurgical ctDNA detection was observed in 55 of 414 patients (13%) with clinical stage I lung adenocarcinoma (LUAD) and was associated with poor recurrence-free survival (2-year recurrence-free survival 69% versus 91%; log-rank p < 0.001), approaching that of clinical stage II LUAD. Presurgical ctDNA detection was not prognostic in patients with clinical stage II LUAD or non-LUAD. Within LUAD, tumor volume and positron emission tomography avidity interacted to predict presurgical ctDNA detection. Moreover, presurgical ctDNA detection was predictive of the postsurgical discovery of International Association for the Study of Lung Cancer grade 3 tumors (p < 0.001) and pathologic TNM upstaging (p < 0.001). Notably, presurgical ctDNA detection strongly correlated with higher programmed death-ligand 1 expression in tumors (positive rates 28% versus 55%, p < 0.001), identifying a subgroup likely to benefit from anti-programmed death-ligand 1 therapies.

CONCLUSION

These findings support the integration of ctDNA testing into routine diagnostic workflows in early-stage NSCLC without the need for tumor tissue profiling. Furthermore, it is clinically useful in identifying patients at high risk who might benefit from innovative treatments, including neoadjuvant immune checkpoint inhibitors.

The role of extensive lymph node dissection in the new grading system for lung adenocarcinoma

OBJECTIVES

This study evaluates the prognostic impact of the new grading system for lung adenocarcinoma, stratified by lymphadenectomy extent.

MATERIALS AND METHODS

We analyzed 1258 lung adenocarcinoma patients who underwent curative resections between 2006 and 2017. We analyzed overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS) across tumor grades and lymphadenectomy extent, categorized as IASLC-R0 (complete resection) or R(un) (uncertain resection).

RESULTS

The median age of cohort was 63 and 41.9 % were male. The majority had undergone lobectomy. The distribution of tumors was 274 grade 1, 558 grade 2, and 426 grade 3 cases. After a median follow-up time of 102 months, the 10-year OS/CSS/RFS rates worsened significantly across grade 1-3: 92.4/99.3/92.3 %, 77.8/87.5/71.7 %, and 63.6/70.2/52.0 %, respectively (p < 0.001). Multivariate Cox regression analysis identified grade 3, R(un) lymphadenectomy, higher Charlson Comorbidity Index, smoking history, thoracotomy, higher pathology stage, and angiolymphatic invasion as independent prognostic factors for lower OS, CSS, and RFS. Furthermore, grade 3 patients benefited significantly from IASLC-R0 lymphadenectomy, showing significantly better OS and RFS than those who underwent R(un) lymphadenectomy (p = 0.007 for OS, p = 0.001 for RFS, post-propensity score matching). Among grade 3 tumors underwent R0 or R(un) resections found the incidence rates of local, distant, and simultaneous local and distant recurrence were 8.5 % vs 13.7 %, 11.0 % vs 12.2 %, and 11.0 % vs 20.6 %, respectively.

CONCLUSION

Surgical outcomes for lung adenocarcinoma have declined across grades 1-3. IASLC-R(un) treatment worsens OS and RFS in grade 3. Intensive monitoring and adjuvant therapy should be considered when patients with grade 3 lung adenocarcinoma undergo R(un) lymphadenectomy.

Nomogram predicting overall and cancer specific prognosis for poorly differentiated lung adenocarcinoma after resection based on SEER cohort analysis

The prognosis of poorly differentiated lung adenocarcinoma (PDLA) is determined by many clinicopathological factors. The aim of this study is identifying prognostic factors and developing reliable nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with PDLA. Patient data from the Surveillance, Epidemiology and End Results (SEER) database was collected and analyzed. The SEER database was used to screen 1059 eligible patients as the study cohort. The whole cohort was randomly divided into a training cohort (n = 530) and a test cohort (n = 529). Cox proportional hazards analysis was used to identify variables and construct a nomogram based on the training cohort. C-index and calibration curves were performed to evaluate the performance of the model in the training cohort and test cohorts. For patients with PDLA, age at diagnosis, gender, tumor size were independent prognostic factors both for overall survival (OS) and cancer-specific survival (CSS), while race and number of nodes were specifically related to OS. The calibration curves presented excellent consistency between the actual and nomogram-predict survival probabilities in the training and test cohorts. The C-index values of the nomogram were 0.700 and 0.730 for OS and CSS, respectively. The novel nomogram provides new insights of the risk of each prognostic factor and can assist doctors in predicting the 1-year, 3-year and 5-year OS and CSS in patients with PDLA.

Predicting pathological grade of stage I pulmonary adenocarcinoma: a CT radiomics approach

Objectives

To investigate the value of CT radiomics combined with radiological features in predicting pathological grade of stage I invasive pulmonary adenocarcinoma (IPA) based on the International Association for the Study of Lung Cancer (IASLC) new grading system.

Methods

The preoperative CT images and clinical information of 294 patients with stage I IPA were retrospectively analyzed (159 training set; 69 validation set; 66 test set). Referring to the IASLC new grading system, patients were divided into a low/intermediate-grade group and a high-grade group. Radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO), the logistic regression (LR) classifier was used to establish radiomics model (RM), clinical-radiological features model (CRM) and combined rad-score with radiological features model (CRRM), and visualized CRRM by nomogram. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the performance and fitness of models.

Results

In the training set, RM, CRM, and CRRM achieved AUCs of 0.825 [95% CI (0.735-0.916)], 0.849 [95% CI (0.772-0.925)], and 0.888 [95% CI (0.819-0.957)], respectively. For the validation set, the AUCs were 0.879 [95% CI (0.734-1.000)], 0.888 [95% CI (0.794-0.982)], and 0.922 [95% CI (0.835-1.000)], and for the test set, the AUCs were 0.814 [95% CI (0.674-0.954)], 0.849 [95% CI (0.750-0.948)], and 0.860 [95% CI (0.755-0.964)] for RM, CRM, and CRRM, respectively.

Conclusion

All three models performed well in predicting pathological grade, especially the combined model, showing CT radiomics combined with radiological features had the potential to distinguish the pathological grade of early-stage IPA.

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.

Surgical prognosis of lung invasive mucinous and non-mucinous adenocarcinoma: propensity score matched analysis

OBJECTIVES

Invasive mucinous adenocarcinoma exhibits distinct prognostic outcomes compared to non-mucinous adenocarcinoma (ADC). This study investigated and compared the clinical outcomes and prognostic factors of invasive mucinous and non-mucinous ADC patients.

METHODS

This retrospective study included patients who underwent curative surgery for ADC between 2011 and 2021. Patient characteristics were balanced using propensity score matching. Cumulative incidence was analysed to evaluate cancer recurrence incidence, and the Kaplan-Meier method was used to calculate overall survival (OS) for each group.

RESULTS

A total of 6101 patients were included. After matching, the non-mucinous group and mucinous groups comprised 798 and 408 patients, respectively. The patients in the mucinous group had a lower recurrence incidence than those in the non-mucinous group (P = 0.014). The recurrence incidence in the mucinous group was between those of grades 1 (P = 0.011) and 2 (P = 0.012) and the OS rates were comparable to those of grades 2 (P = 0.6) and 3 (P = 0.2). Multivariable analysis revealed that the maximal standardized uptake value [hazard ratio (HR): 1.13, P = 0.11] and progressed pathological stages (pStage II, HR: 3.9, P = 0.028; pStage III, HR: 8.33, P = 0.038) served as adverse prognostic factors for the mucinous group.

CONCLUSIONS

Patients with mucinous ADC demonstrated lower recurrence incidence and similar OS rates compared to those with non-mucinous ADC. The recurrence incidence of mucinous ADC was between those of International Association for the Study of Lung Cancer grades 1 and 2, with the OS rates comparable to those of grades 2 and 3.

CLINICAL REGISTRATION NUMBER

None.

Integrating IASLC grading and radiomics for predicting postoperative outcomes in stage IA invasive lung adenocarcinoma

BACKGROUND

The International Association for the Study of Lung Cancer (IASLC) Pathology Committee introduced a histologic grading system for invasive lung adenocarcinoma (LUAD) in 2020. The IASLC grading system, hinging on the evaluation of predominant and high-grade histologic patterns, has proven to be practical and prognostic for invasive LUAD. However, there are still limitations in evaluating the prognosis of stage IA LUAD. Radiomics may serve as a valuable complement.

PURPOSE

To establish a model that integrates IASLC grading and radiomics, aimed at predicting the prognosis of stage IA LUAD.

METHODS

We conducted a retrospective analysis of 628 patients diagnosed with stage IA LUAD who underwent surgical resection between January 2015 and December 2018 at our institution. The patients were randomly divided into the training set (n = 439) and testing set (n = 189) at a ratio of 7:3. Overall survival (OS) and disease-free survival (DFS) were taken as the end points. Radiomics features were obtained by PyRadiomics. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). The prediction models for OS and DFS were developed using multivariate Cox regression analysis, and the models were visualized through nomogram plots. The model's performance was evaluated using area under the curves (AUC), concordance index (C-index), calibration curves, and survival decision curve analysis (DCA).

RESULTS

In total, nine radiomics features were selected for the OS prediction model, and 15 radiomics features were selected for the DFS prediction model. Patients with high radiomics scores were associated with a worse prognosis (p < 0.001). We built separate prediction models using radiomics or IASLC alone, as well as a combined prediction model. In the prediction of OS, we observed that the combined model (C-index: 0.812 ± 0.024, 3 years AUC: 0.692, 5 years AUC: 0.792) achieved superior predictive performance than the radiomics (C-index: 0.743 ± 0.038, 3 years AUC: 0.633, 5 years AUC: 0.768) and IASLC grading (C-index: 0.765 ± 0.042, 3 years AUC: 0.658, 5 years AUC: 0.743) models alone. Similar results were obtained in the models for DFS.

CONCLUSION

The combination of radiomics and IASLC pathological grading proves to be an effective approach for predicting the prognosis of stage IA LUAD. This has substantial clinical relevance in guiding treatment decisions for early-stage LUAD.

The Grading System for Lung Adenocarcinoma: Brief Review of its Prognostic Performance and Future Directions

Histologic grading of tumors is associated with prognosis in many organs. In the lung, the most recent grading system proposed by International association for the Study of Lung Cancer (IASLC) and adopted by the World Health Organization (WHO) incorporates the predominant histologic pattern, as well as the presence of high-grade architectural patterns (solid, micropapillary, and complex glandular pattern) in proportions >20% of the tumor surface. This system has shown improved prognostic ability when compared with the prior grading system based on the predominant pattern alone, across different patient populations. Interobserver agreement is moderate to excellent, depending on the study. IASLC/WHO grading system has been shown to correlate with molecular alterations and PD-L1 expression in tumor cells. Recent studies interrogating gene expression has shown correlation with tumor grade and molecular alterations in the tumor microenvironment that can further stratify risk of recurrence. The use of machine learning algorithms to grade nonmucinous adenocarcinoma under this system has shown accuracy comparable to that of expert pulmonary pathologists. Future directions include evaluation of tumor grade in the context of adjuvant and neoadjuvant therapies, as well as the development of better prognostic indicators for mucinous adenocarcinoma.

The Battle for Accuracy: Identifying the Most Effective Grading System for Lung Invasive Mucinous Adenocarcinoma

BACKGROUND

The prognostic analysis of lung invasive mucinous adenocarcinoma (IMA) is deficient due to the lack of a universally recommended histological grading system, leading to unregulated treatment approaches.

OBJECTIVE

We aimed to examine the clinical trajectory of IMA and assess the viability of utilizing the existing grading system for lung invasive non-mucinous adenocarcinoma in the context of IMA.

METHODS

We retrospectively collected clinicopathological data from 265 IMA patients. Each case re-evaluated the tumor grade using the following three classification systems: the 4th Edition of the World Health Organization classification system, the International Association for the Study of Lung Cancer (IASLC) grading system, and a two-tier grading system. We performed a comparative analysis of these grading systems and identified the most effective grading system for IMA.

RESULTS

The study comprised a total of 214 patients with pure IMA and 51 patients with mixed IMA. The 5-year overall survival (OS) rates for pure IMA and mixed IMA were 86.7% and 57.8%, respectively. All three grading systems proved to be effective prognostic classifiers for IMA. The value of area under the curve at 1-, 3-, and 5-year OS was highest for the IASLC grading system compared with the other grade systems and the clinical stage. The IASLC classification system was an independent prognostic predictor (p = 0.009, hazard ratio 2.243, 95% confidence interval 1.219-4.127).

CONCLUSION

Mixed IMA is more aggressive than pure IMA, with an OS rate on par with that of high-grade pure IMA. The IASLC grading system can better indicate prognosis and is recommended for lung IMA.

AI-driven Characterization of Solid Pulmonary Nodules on CT Imaging for Enhanced Malignancy Prediction in Small-sized Lung Adenocarcinoma

OBJECTIVES

Distinguishing solid nodules from nodules with ground-glass lesions in lung cancer is a critical diagnostic challenge, especially for tumors ≤2 cm. Human assessment of these nodules is associated with high inter-observer variability, which is why an objective and reliable diagnostic tool is necessary. This study focuses on artificial intelligence (AI) to automatically analyze such tumors and to develop prospective AI systems that can independently differentiate highly malignant nodules.

MATERIALS AND METHODS

Our retrospective study analyzed 246 patients who were diagnosed with negative clinical lymph node metastases (cN0) using positron emission tomography-computed tomography (PET/CT) imaging and underwent surgical resection for lung adenocarcinoma. AI detected tumor sizes ≤2 cm in these patients. By utilizing AI to classify these nodules as solid (AI_solid) or non-solid (non-AI_solid) based on confidence scores, we aim to correlate AI determinations with pathological findings, thereby advancing the precision of preoperative assessments.

RESULTS

Solid nodules identified by AI with a confidence score ≥0.87 showed significantly higher solid component volumes and proportions in patients with AI_solid than in those with non-AI_solid, with no differences in overall diameter or total volume of the tumors. Among patients with AI_solid, 16% demonstrated lymph node metastasis, and a significant 94% harbored invasive adenocarcinoma. Additionally, 44% were upstaging postoperatively. These AI_solid nodules represented high-grade malignancies.

CONCLUSION

In small-sized lung cancer diagnosed as cN0, AI automatically identifies tumors as solid nodules ≤2 cm and evaluates their malignancy preoperatively. The AI classification can inform lymph node assessment necessity in sublobar resections, reflecting metastatic potential.

Novel Insights Into the International Association for the Study of Lung Cancer Grading System for Lung Adenocarcinoma

The new grading system for lung adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) defines prognostic subgroups on the basis of histologic patterns observed on surgical specimens. This study sought to provide novel insights into the IASLC grading system, with particular focus on recurrence-specific survival (RSS) and lung cancer-specific survival among patients with stage I adenocarcinoma. Under the IASLC grading system, tumors were classified as grade 1 (lepidic predominant with <20% high-grade patterns [micropapillary, solid, and complex glandular]), grade 2 (acinar or papillary predominant with <20% high-grade patterns), or grade 3 (≥20% high-grade patterns). Kaplan-Meier survival estimates, pathologic features, and genomic profiles were investigated for patients whose disease was reclassified into a higher grade under the IASLC grading system on the basis of the hypothesis that they would strongly resemble patients with predominant high-grade tumors. Overall, 423 (29%) of 1443 patients with grade 1 or 2 tumors classified based on the predominant pattern-based grading system had their tumors upgraded to grade 3 based on the IASLC grading system. The RSS curves for patients with upgraded tumors were significantly different from those for patients with grade 1 or 2 tumors (log-rank P < .001) but not from those for patients with predominant high-grade patterns (P = .3). Patients with upgraded tumors had a similar incidence of visceral pleural invasion and spread of tumor through air spaces as patients with predominant high-grade patterns. In multivariable models, the IASLC grading system remained significantly associated with RSS and lung cancer-specific survival after adjustment for aggressive pathologic features such as visceral pleural invasion and spread of tumor through air spaces. The IASLC grading system outperforms the predominant pattern-based grading system and appropriately reclassifies tumors into higher grades with worse prognosis, even after other pathologic features of aggressiveness are considered.

Clinicopathological and molecular characterization of resected lung adenocarcinoma: Correlations with histopathological grading systems in Chinese patients

PURPOSE

Driver mutations inform lung adenocarcinoma (LUAD) targeted therapy. Association of histopathological attributes and molecular profiles facilitates clinically viable testing platforms. We assessed correlations between LUAD clinicopathological features, mutational landscapes, and two grading systems among Chinese cases.

METHODS

79 Chinese LUAD patients undergoing resection were subjected to targeted sequencing. 68 were invasive nonmucinous adenocarcinoma (INMA), graded via: predominant histologic pattern-based grading system (P-GS) or novel IASLC grading system (I-GS). Driver mutation distributions were appraised and correlated with clinical and pathological data.

RESULTS

Compared to INMA, non-INMA exhibited smaller, well-differentiated tumors with higher mucin content. INMA grade correlated with size, lymph invasion (P-GS), and driver/EGFR mutations. Mutational spectra varied markedly between grades, with EGFR p.L858R and exon 19 deletion mutations predominating in lower grades; while high-grade P-GS tumors often harbored EGFR copy number variants and complex alterations alongside wild-type cases. I-GS upgrade of P-GS grade 2 to grade 3 was underpinned by ≥20 % high-grade regions bearing p.L858R or ALK fusions. Both systems defined tumors of distinctive phenotypic attributes and molecular genotypes.

CONCLUSIONS

INMA represent larger, mucin-poor, molecularly heterogeneous LUAD with divergent grade-specific mutation profiles. Stronger predictor of clinicopathological attributes and driver mutations, P-GS stratification offers greater accuracy for molecular testing. A small panel encompassing EGFR and ALK captures the majority of P-GS grade 1/2 mutations whereas expanded panels are optimal for grade 3.

Prognostic impact of the IASLC grading system of lung adenocarcinoma: a systematic review and meta-analysis

AIMS

Tumour grading is an essential part of the pathologic assessment that promotes patient management. The International Association for the Study of Lung Cancer (IASLC) proposed a grading system for non-mucinous lung adenocarcinoma in 2020. We aimed to validate the prognostic impact of this novel grading system on overall survival (OS) and recurrence-free survival (RFS) based on literature data.

METHODS AND RESULTS

The review protocol was registered in PROSPERO (CRD42023396059). We aimed to identify randomized or non-randomized controlled trials published after 2020 comparing different IASLC grade categories in Medline, Embase, and CENTRAL. Hazard ratios (HRs) with 95% confidence intervals (CIs) of OS and RFS were pooled and the Quality In Prognosis Studies (QUIPS) tool was used to assess the risk of bias in the included studies. Ten articles were eligible for this review. Regarding OS estimates, grade 1 lung adenocarcinomas were better than grade 3 both in univariate and multivariate analyses (HROSuni = 0.19, 95% CI: 0.05-0.66, p = 0.009; HROSmulti = 0.21, 95% CI: 0.12-0.38, p < 0.001). Regarding RFS estimates, grade 3 adenocarcinomas had a worse prognosis than grade 1 in multivariate analysis (HRRFSmulti: 0.22, 95% CI: 0.14-0.35, p < 0.001).

CONCLUSION

The literature data and the result of our meta-analysis demonstrate the prognostic relevance of the IASLC grading system. This supports the inclusion of this prognostic parameter in daily routine worldwide.

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.

PD-L1 expression in resected lung adenocarcinoma: prevalence and prognostic significance in relation to the IASLC grading system

AIMS

Programmed death-ligand 1 (PD-L1) expression is a predictive biomarker for adjuvant immunotherapy and has been linked to poor differentiation in lung adenocarcinoma. However, its prevalence and prognostic role in the context of the novel histologic grade has not been evaluated.

METHODS

We analysed a cohort of 1233 patients with resected lung adenocarcinoma where PD-L1 immunohistochemistry (22C3 assay) was reflexively tested. Tumour PD-L1 expression was correlated with the new standardized International Association for the Study of Lung Cancer (IASLC) histologic grading system (G1, G2, and G3). Clinicopathologic features including patient outcome were analysed.

RESULTS

PD-L1 was positive (≥1%) in 7.0%, 23.5%, and 63.0% of G1, G2, and G3 tumours, respectively. PD-L1 positivity was significantly associated with male sex, smoking, and less sublobar resection among patients with G2 tumours, but this association was less pronounced in those with G3 tumours. PD-L1 was an independent risk factor for recurrence (adjusted hazard ratio [HR] = 3.25, 95% confidence intervals [CI] = 1.93-5.48, P < 0.001) and death (adjusted HR = 2.69, 95% CI = 1.13-6.40, P = 0.026) in the G2 group, but not in the G3 group (adjusted HR for recurrence = 0.94, 95% CI = 0.64-1.40, P = 0.778).

CONCLUSION

PD-L1 expression differs substantially across IASLC grades and identifies aggressive tumours within the G2 subgroup. This knowledge may be used for both prognostication and designing future studies on adjuvant immunotherapy.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection

BACKGROUND

Despite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy.

METHODS

We retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3.

FINDINGS

A total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001).

INTERPRETATION

IASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

18 F-FDG PET/CT characteristics of IASLC grade 3 invasive adenocarcinoma and the value of 18 F-FDG PET/CT for preoperative prediction: a new prognostication model

OBJECTIVE

This study is performed to investigate the imaging characteristics of the International Association for the Study of Lung Cancer grade 3 invasive adenocarcinoma (IAC) on PET/CT and the value of PET/CT for preoperative predicting this tumor.

MATERIALS AND METHODS

We retrospectively enrolled patients with IAC from August 2015 to September 2022. The clinical characteristics, serum tumor markers, and PET/CT features were analyzed. T test, Mann-Whitney U test, χ 2 test, Logistic regression analysis, and receiver operating characteristic analysis were used to predict grade 3 tumor and evaluate the prediction effectiveness.

RESULTS

Grade 3 tumors had a significantly higher maximum standardized uptake value (SUV max ) and consolidation-tumor-ratio (CTR) ( P  < 0.001), while Grade 1 - 2 tumors were prone to present with air bronchogram sign or vacuole sign ( P  < 0.001). A stepwise logistic regression analysis revealed that smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR were useful predictors for Grade 3 tumors. The established prediction model based on the above 5 parameters generated a high AUC (0.869) and negative predictive value (0.919), respectively.

CONCLUSION

Our study demonstrates that grade 3 IAC has a unique PET/CT imaging feature. The prognostication model established with smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR can effectively predict grade 3 tumors before the operation.

Proposed novel grading system for stage I invasive lung adenocarcinoma and a comparison with the 2020 IASLC grading system

BACKGROUND

Several studies have proposed grading systems for risk stratification of early-stage lung adenocarcinoma based on histological patterns. However, the reproducibility of these systems is poor in clinical practice, indicating the need to develop a new grading system which is easy to apply and has high accuracy in prognostic stratification of patients.

METHODS

Patients with stage I invasive nonmucinous lung adenocarcinoma were retrospectively collected from pathology archives between 2009 and 2016. The patients were divided into a training and validation set at a 6:4 ratio. Histological features associated with patient outcomes (overall survival [OS] and progression-free survival [PFS]) identified in the training set were used to construct a new grading system. The newly proposed system was validated using the validation set. Survival differences between subgroups were assessed using the log-rank test. The prognostic performance of the novel grading system was compared with two previously proposed systems using the concordance index.

RESULTS

A total of 539 patients were included in this study. Using a multioutcome decision tree model, four pathological factors, including the presence of tumor spread through air space (STAS) and the percentage of lepidic, micropapillary and solid subtype components, were selected for the proposed grading system. Patients were accordingly classified into three groups: low, medium, and high risk. The high-risk group showed a 5-year OS of 52.4% compared to 89.9% and 97.5% in the medium and low-risk groups, respectively. The 5-year PFS of patients in the high-risk group was 38.1% compared to 61.7% and 90.9% in the medium and low-risk groups, respectively. Similar results were observed in the subgroup analysis. Additionally, our proposed grading system provided superior prognostic stratification compared to the other two systems with a higher concordance index.

CONCLUSION

The newly proposed grading system based on four pathological factors (presence of STAS, and percentage of lepidic, micropapillary, and solid subtypes) exhibits high accuracy and good reproducibility in the prognostic stratification of stage I lung adenocarcinoma patients.

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study

Background

The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability.

Methods

We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance.

Results

The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05).

Conclusion

The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

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.

Radiomics Nomogram Based on Optimal Volume of Interest Derived from High-Resolution CT for Preoperative Prediction of IASLC Grading in Clinical IA Lung Adenocarcinomas: A Multi-Center, Large-Population Study

The novel grading system developed by the International Association for the Study of Lung Cancer (IASLC) for clinical stage IA lung adenocarcinomas has demonstrated remarkable prognostic capabilities. Notably, tumors classified as grade 3 have been associated with poor prognostic outcomes, thereby playing a crucial role in the formulation of personalized surgical strategies. The objective of this study is to develop a radiomics nomogram that utilizes the optimal volume of interest (VOI) derived from high-resolution CT (HRCT) scans to accurately predict the presence of grade 3 tumors in patients with clinical IA lung adenocarcinomas.In this multi-center, large-population study, clinical, pathological, and HRCT imaging data from 1418 patients who were pathologically diagnosed with lung adenocarcinomas were retrospectively collected. The data was obtained from four hospital databases between January 2018 and May 2022. From this patient cohort, 1206 individuals were screened from three databases and randomly divided into training and internal validation datasets in a 7:3 ratio. An additional dataset consisting of 212 individuals was used for external validation dataset. Radiomics features were extracted from HRCT images at various scales, including VOI-2mm, VOI entire, VOI +2mm, and VOI +4mm. To reduce dimensionality, select relevant features, and build radiomics signatures, the maximal redundancy minimal relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were utilized. Univariate and multivariate logistic regression analyses were conducted to identify independent clinic-radiological (Clin-Rad) predictors. Receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC) were used to evaluate the diagnostic efficiency. A nomogram predicting the risk of grade 3 in clinical stage IA lung adenocarcinoma was constructed based on multivariate logistic regression, combining independent predictors and the optimal radiomics signatures.Multivariate logistic regression revealed that males exhibited a higher prevalence of grade 3 tumors, and solid nodules were frequently observed through radiological assessments. The utilization of radiomics features extracted from the VOI entire resulted in significant improvements in predictive performance, as evidenced by AUC values of 0.900 (0.880-0.942), 0.885 (0.824-0.946), and 0.888 (0.782-0.993) for the training, internal validation, and external validation datasets, respectively. Furthermore, the nomogram that combined VOI entire -based radiomics signatures and Clin-Rad characteristics, exhibited remarkable predictive performance. This was indicated by AUC values of 0.910(0.873-0.942), 0.891 (0.845-0.937), and 0.905 (0.846-0.964) for the training, internal validation, and external validation datasets, respectively.The extraction of radiomics features from both the indented and peri-tumoral regions does not offer any additional benefits in predicting grade 3 tumors according to the IASLC system. However, when combining the VOI entire-based radiomics model with Clin-Rad characteristics, the resulting integrated nomogram exhibited remarkable predictive performance.

Clinical Impact of Genomic and Pathway Alterations in Stage I EGFR-Mutant Lung Adenocarcinoma

PURPOSE

We investigated the clinical impact of genomic and pathway alterations in stage I epidermal growth factor receptor (EGFR)-mutant lung adenocarcinomas, which have a high recurrence rate despite complete surgical resection.

MATERIALS AND METHODS

Out of the initial cohort of 257 patients with completely resected stage I EGFR-mutant lung adenocarcinoma, tumor samples from 105 patients were subjected to analysis using large-panel next-generation sequencing. We analyzed 11 canonical oncogenic pathways and determined the number of pathway alterations (NPA). Survival analyses were performed based on co-occurring alterations and NPA in three patient groups: all patients, patients with International Association for the Study of Lung Cancer (IASLC) pathology grade 2, and patients with recurrent tumors treated with EGFR-tyrosine kinase inhibitor (TKI).

RESULTS

In the univariate analysis, pathological stage, IASLC grade, TP53 mutation, NPA, phosphoinositide 3-kinase pathway, p53 pathway, and cell cycle pathway exhibited significant associations with worse recurrence-free survival (RFS). Moreover, RPS6KB1 or EGFR amplifications were linked to a poorer RFS. Multivariate analysis revealed that pathologic stage, IASLC grade, and cell cycle pathway alteration were independent poor prognostic factors for RFS (p=0.002, p < 0.001, and p=0.006, respectively). In the grade 2 subgroup, higher NPA was independently associated with worse RFS (p=0.003). Additionally, in patients with recurrence treated with EGFR-TKIs, co-occurring TP53 mutations were linked to shorter progression-free survival (p=0.025).

CONCLUSION

Genomic and pathway alterations, particularly cell cycle alterations, high NPA, and TP53 mutations, were associated with worse clinical outcomes in stage I EGFR-mutant lung adenocarcinoma. These findings may have implications for risk stratification and the development of new therapeutic strategies in early-stage EGFR-mutant lung cancer patients.

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.

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning

The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1 scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained convolutional neural networks improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

A pretreatment prediction model of grade 3 tumors classed by the IASLC grading system in lung adenocarcinoma

PURPOSE

The new grading system for invasive nonmucinous lung adenocarcinoma (LUAD) in the 2021 World Health Organization Classification of Thoracic Tumors was based on a combination of histologically predominant subtypes and high-grade components. In this study, a model for the pretreatment prediction of grade 3 tumors was established according to new grading standards.

METHODS

We retrospectively collected 399 cases of clinical stage I (cStage-I) LUAD surgically treated in Tianjin Chest Hospital from 2015 to 2018 as the training cohort. Besides, the validation cohort consists of 216 patients who were collected from 2019 to 2020. These patients were also diagnosed with clinical cStage-I LUAD and underwent surgical treatment at Tianjin Chest Hospital. Univariable and multivariable logistic regression analyses were used to select independent risk factors for grade 3 adenocarcinomas in the training cohort. The nomogram prediction model of grade 3 tumors was established by R software.

RESULTS

In the training cohort, there were 155 grade 3 tumors (38.85%), the recurrence-free survival of which in the lobectomy subgroup was better than that in the sublobectomy subgroup (P = 0.034). After univariable and multivariable analysis, four predictors including consolidation-to-tumor ratio, CEA level, lobulation, and smoking history were incorporated into the model. A nomogram was established and internally validated by bootstrapping. The Hosmer-Lemeshow test result was χ2 = 7.052 (P = 0.531). The C-index and area under the receiver operating characteristic curve were 0.708 (95% CI: 0.6563-0.7586) for the training cohort and 0.713 (95% CI: 0.6426-0.7839) for the external validation cohort.

CONCLUSIONS

The nomogram prediction model of grade 3 LUAD was well fitted and can be used to assist in surgical or adjuvant treatment decision-making.

Computed Tomography and 18F-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography Imaging Biomarkers of Lung Invasive Non-mucinous Adenocarcinoma: Prediction of Grade 3 Tumour Based on World Health Organization Grading System

AIMS

To evaluate computed tomography (CT) and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) findings of invasive non-mucinous adenocarcinoma (INMA) of the lung as a predictor of histological tumour grade according to 2021 World Health Organization (WHO) classification.

MATERIALS AND METHODS

This retrospective study included consecutive patients with surgically resected INMA who underwent both preoperative CT and 18F-FDG-PET/CT. A three-tiered tumour grade was performed based on the fifth edition of the WHO classification of lung tumours. CT imaging features and the maximum standardised uptake value (SUVmax) were compared among the three tumour grades.

RESULTS

In total, 214 patients with INMA (median age 70 years; interquartile range 65-76 years; 123 men) were histologically categorised: 36 (17%) as grade 1, 102 (48%) as grade 2 and 76 (35%) as grade 3. Pure solid appearance was more frequent in grade 3 (83%) than in grades 1 (0%) and 2 (26%) (P < 0.001). The SUVmax of the entire tumour was higher in grade 3 than in grades 1 and 2 (P < 0.001). Multivariable analysis revealed that pure solid appearance (odds ratio = 94.0; P < 0.001), round/oval shape (odds ratio = 4.01; P = 0.001), spiculation (odds ratio = 2.13; P = 0.04), air bronchogram (odds ratio = 0.40; P = 0.03) and SUVmax (odds ratio = 1.45; P < 0.001) were significant predictors for grade 3 INMAs.

CONCLUSION

Pure solid appearance, round/oval shape, spiculation, absence of air bronchogram and high SUVmax were associated with grade 3 INMAs. CT and 18F-FDG-PET/CT were potentially useful non-invasive imaging methods to predict the histological grade of INMAs.

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

OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Efficacy of Preoperative Biopsy in Predicting the Newly Proposed Histologic Grade of Resected Lung Adenocarcinoma

A novel histologic grading system for invasive lung adenocarcinomas (LUAD) has been newly proposed and adopted by the World Health Organization (WHO) classification. We aimed to evaluate the concordance of newly established grades between preoperative biopsy and surgically resected LUAD samples. Additionally, factors affecting the concordance rate and its prognostic impact were also analyzed. In this study, surgically resected specimens of 222 patients with invasive LUAD and their preoperative biopsies collected between January 2013 and December 2020 were used. We determined the histologic subtypes of preoperative biopsy and surgically resected specimens and classified them separately according to the novel WHO grading system. The overall concordance rate of the novel WHO grades between preoperative biopsy and surgically resected samples was 81.5%, which was higher than that of the predominant subtype. When stratified by grades, the concordance rate of grades 1 (well-differentiated, 84.2%) and 3 (poorly differentiated, 89.1%) was found to be superior compared to grade 2 (moderately differentiated, 66.2%). Overall, the concordance rate was not significantly different from biopsy characteristics, including the number of biopsy samples, biopsy sample size, and tumor area size. On the other hand, the concordance rate of grades 1 and 2 was significantly higher in tumors with smaller invasive diameters, and that of grade 3 was significantly higher in tumors with larger invasive diameters. Preoperative biopsy specimens can predict the novel WHO grades, especially grades 1 and 3 of surgically resected specimens, more accurately than the former grading system, regardless of preoperative biopsy or clinicopathologic characteristics.

A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma

RATIONALE AND OBJECTIVES

The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading.

MATERIALS AND METHODS

This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness.

RESULTS

Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram.

CONCLUSION

Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.

Effects of pre-operative biopsy on recurrence and survival in stage I lung adenocarcinoma patients in China

Background

Whether pre-operative biopsy affects post-operative recurrence and metastasis of lung cancer patients is still controversial.

Methods

In order to clarify these disputes, we collected relevant literature to conduct a meta-analysis. To validate the results of the meta-analysis, we retrospectively analysed 575 patients with stage I lung adenocarcinoma who underwent surgical resection at our centre from 2010 to 2018 using propensity score matching and competing risk models.

Results

5509 lung cancer patients from 11 articles were included in the meta-analysis. Summary analysis showed that the total recurrence rate of the biopsy group was higher than that of the nonbiopsy group (risk ratio 1.690, 95% CI 1.220-2.330; p=0.001). After propensity score matching, we found that there was no significant correlation between biopsy and total recurrence (risk ratio 1.070, 95% CI 0.540-2.120; p=0.850). In our cohort, of 575 stage I lung adenocarcinomas, 113 (19.7%) patients underwent pre-operative biopsy. During a median (interquartile range) follow-up of 71 (57-93) months, multivariable analyses showed pre-operative biopsy in the overall observation cohort (subdistribution hazard ratio (SHR) 1.522, 95% CI 0.997-2.320; p=0.051) and in the propensity score-matched cohort (SHR 1.134, 95% CI 0.709-1.810; p=0.600) was not significantly correlated with the risk of recurrence and metastasis. Moreover, the pre-operative biopsy did not affect disease-free survival (SHR 0.853, 95% CI 0.572-1.273; p=0.438) or overall survival (SHR 0.647, 95% CI 0.352-1.189; p=0.161).

Conclusion

Pre-operative biopsy might not increase the risk of recurrence and metastasis, suggesting that these procedures might be safe for patients with stage I lung adenocarcinoma whose diagnosis is difficult to determine before surgery.

A Simplified Version of the IASLC Grading System for Invasive Pulmonary Adenocarcinomas With Improved Prognosis Discrimination

Tumor grading enables better management of patients and treatment options. The International Association for the Study of Lung Cancer (IASLC) Pathology Committee has recently released a 3-tier grading system for invasive pulmonary adenocarcinoma consisting of predominant histologic patterns plus a cutoff of 20% of high-grade components including solid, micropapillary, and complex glandular patterns. The goal of this study was to validate the prognostic value of the new IASLC grading system and to compare its discriminatory performance to the predominant pattern-based grading system and a simplified version of the IASLC grading system without complex glandular patterns. This was a single-site retrospective study based on a 20-year data collection of patients that underwent lung cancer surgery. All invasive pulmonary adenocarcinomas confirmed by the histologic review were evaluated in a discovery cohort (n=676) and a validation cohort (n=717). The median duration of follow-up in the combined dataset (n=1393) was 7.5 years. The primary outcome was overall survival after surgery. The 3 grading systems had strong and relatively similar predictive performance, but the best parsimonious model was the simplified IASLC grading system (log-rank P =1.39E-13). The latter was strongly associated with survival in the validation set ( P =1.1E-18) and the combined set ( P =5.01E-35). We observed a large proportion of patients upgraded to the poor prognosis group using the IASLC grading system, which was attenuated when using the simplified IASLC grading system. In conclusion, we identified a histologic simpler classification for invasive pulmonary adenocarcinomas that outperformed the recently proposed IASLC grading system. A simplified grading system is clinically convenient and will facilitate widespread implementation.

Predictive scoring of high-grade histology among early-stage lung cancer patients: The MOSS score

BACKGROUND

Poor prognosis associated with adenocarcinoma of International Association for the Study of Lung Cancer (IASLC) grade 3 has been recognized. In this study we aimed to develop a scoring system for predicting IASLC grade 3 based before surgery.

METHODS

Two retrospective datasets with significant heterogeneity were used to develop and evaluate a scoring system. The development set was comprised of patients with pathological stage I nonmucinous adenocarcinoma and they were randomly divided into training (n = 375) and validation (n = 125) datasets. Using multivariate logistic regression, a scoring system was developed and internally validated. Later, this new score was further tested in the testing set which was comprised of patients with clinical stage 0-I non-small cell lung cancer (NSCLC) (n = 281).

RESULTS

Four factors that were related to IASLC grade 3 were used to develop the new scoring system the MOSS score; male (M, point 1), overweight (O, point 1), size>10 mm (S, point 1), and solid lesions (S, point 3). Predictability of IASLC grade 3 increased from 0.4% to 75.2% with scores from 0 to 6. The area under the curve (AUC) of the MOSS was 0.889 and 0.765 for the training and validation datasets, respectively. The MOSS score exhibited similar predictability in the testing set (AUC: 0.820).

CONCLUSION

The MOSS score, which combines preoperative variables, can be used to identify high-risk early-stage NSCLC patients with aggressive histological features. It can support clinicians in determining a treatment plan and surgical extent. Further refinement of this scoring system with prospective validation is needed.

Establishment and validation of nomograms for predicting survival of lung invasive adenocarcinoma based on the level of pathological differentiation: a SEER cohort-based analysis

BACKGROUND

The pathological differentiation of invasive adenocarcinoma (IAC) has been linked closely with epidemiological characteristics and clinical prognosis. However, the current models cannot accurately predict IAC outcomes and the role of pathological differentiation is confused. This study aimed to establish differentiation-specific nomograms to explore the effect of IAC pathological differentiation on overall survival (OS) and cancer-specific survival (CSS).

METHODS

The data of eligible IAC patients between 1975 and 2019 were collected from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided in a ratio of 7:3 into a training cohort and a validation cohort. The associations between pathological differentiation and other clinical characteristics were evaluated using chi-squared test. The OS and CSS analyses were performed using the Kaplan-Meier estimator, and the log-rank test was used for nonparametric group comparisons. Multivariate survival analysis was performed using a Cox proportional hazards regression model. The discrimination, calibration, and clinical performance of nomograms were assessed by area under receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS

A total of 4,418 IAC patients (1,001 high-differentiation, 1,866 moderate-differentiation, and 1,551 low-differentiation) were identified. Seven risk factors [age, sex, race, tumor-node-metastasis (TNM) stage, tumor size, marital status, and surgery] were screened to construct differentiation-specific nomograms. Subgroup analyses showed that disparate pathological differentiation played distinct roles in prognosis, especially in patients with older age, white race, and higher TNM stage. The AUC of nomograms for OS and CSS in the training cohort were 0.817 and 0.835, while in the validation cohort were 0.784 and 0.813. The calibration curves showed good conformity between the prediction of the nomograms and the actual observations. DCA results indicated that these nomogram models could be used as a supplement to the prediction of the TNM stage.

CONCLUSIONS

Pathological differentiation should be considered as an independent risk factor for OS and CSS of IAC. Differentiation-specific nomogram models with good discrimination and calibration capacity were developed in the study to predict the OS and CSS in 1-, 3- and 5-year, which could be used predict prognosis and select appropriate treatment options.

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.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Application of the Novel Grading System of Invasive Pulmonary Adenocarcinoma in a Real Diagnostic Scenario: A Brief Report of 9353 Cases

Introduction

The International Association for the Study of Lung Cancer proposed a novel grading system of invasive pulmonary adenocarcinoma (IPA), but the application of this grading system and its genotypic characterization in the real diagnostic scenario has never been reported.

Methods

We prospectively collected and analyzed the clinicopathological and genotypic features of a cohort of 9353 consecutive patients with resected IPA, including 7134 patients with detection of common driver mutation.

Results

In the entire cohort, 3 (0.3%) of lepidic, 1207 (19.0%) of acinar, and 126 (23.6%) of papillary predominant IPAs were diagnosed as grade 3. In chronological order, an evident downtrend of the proportion of grade 2 was observed in chronological order. Conversely, the diagnostic ratio of grade 1 (8.0%-14.5%) and grade 3 (27.9%-32.3%) experienced a gradual rise. EGFR mutation was more frequently detected in grade 2 (77.5%) and grade 1 (69.7%) IPA than grade 3 (53.7%, p < 0.001), whereas the mutation rates of KRAS, BRAF, ALK, and ROS1 were higher in grade 3 IPA. More importantly, the rate of EGFR mutation gradually fell as the proportion of high-grade components increased, to 24.3% in IPA with more than 90% high-grade components.

Conclusions

The grading system for IPA could be applied to stratify patients with different clinicopathological and genotypic features in a real diagnostic scenario.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KEY POINTS

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

Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma

PURPOSE

Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists.

MATERIALS AND METHODS

A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity.

RESULTS

No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05).

CONCLUSIONS

The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.