Validation of the Novel International Association for the Study of Lung Cancer Grading System for Invasive Pulmonary Adenocarcinoma and Association With Common Driver Mutations

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.

Prognostic value of the international association for the study of lung cancer grading system and its association with the tumor microenvironment in stage I EGFR-muted lung adenocarcinoma

INTRODUCTION

The International Association for the Study of Lung Cancer (IASLC) grading system predicts early lung adenocarcinoma outcomes.

METHODS

The purpose of this study is to examine prognostic value of the IASLC grading system and its association with the tumor microenvironment (TME) in Stage I EGFR-muted lung adenocarcinoma. Based on the IASLC grading system, we compared the clinicopathological characteristics of EGFR-mutated lung adenocarcinoma (n = 296). In addition, we examined the expression level of E-cadherin in tumor cells and counted the number of tumor-infiltrating lymphocytes (TILs; CD8, CD20, CD138, and Foxp3), tumor-associated macrophages (TAMs; CD204), and cancer-associated fibroblasts (CAFs; podoplanin) using semi-automatic digital pathology image analysis.

RESULTS

Recurrence-free survival (RFS) curve showed that survival of grade 3 was significantly shorter than that of grade 1 (P < 0.01) and grade 2 (P = 0.03). Multivariate analysis of RFS revealed the invasive size, lymphatic permeation, and grade 3 (P < 0.01) as independent poor prognostic factors. The number of CD204 +TAMs and PDPN+CAFs was significantly higher in grade 3 than in grade 1 or 2 (all P < 0.01). Among the intermediate grade by the predominant subtype based classification, cases classified as grade 3 by the new classification had higher number of CD204 +TAMs (P < 0.01) and PDPN+CAFs (P = 0.02) than those classified as grade 2.

CONCLUSION

The IASLC grading system correlated with the outcomes of EGFR-mutated lung adenocarcinoma. Grade 3 was found to have the TME that most contributes to tumor progression, which probably explained their poor prognosis.

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

AIMS

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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.

Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas

PURPOSE

The purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA.

MATERIALS AND METHODS

Sixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29-78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models.

RESULTS

The 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73-0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66-0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74-0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74-0.93) (P = 0.60).

CONCLUSION

Both whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.

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.

Exploring the relationship between intestinal microbiota and immune checkpoint inhibitors in the treatment of non-small cell lung cancer: insights from the "lung and large intestine stand in exterior-interior relationship" theory

Objective

This study is aim to discern the Traditional Chinese Medicine (TCM) syndrome classifications relevant to immunotherapy sensitive in non-small cell lung cancer (NSCLC) patients, and to delineate intestinal microbiota biomarkers and impact that wield influence over the efficacy of NSCLC immunotherapy, grounded in the TCM theory of "lung and large intestine stand in exterior-interior relationship."

Methods

The study cohort consisted of patients with advanced NSCLC who received treatment at the Oncology Department of Chengdu Fifth People's Hospital. These patients were categorized into distinct TCM syndrome types and subsequently administered immune checkpoint inhibitors (ICIs), specifically PD-1 inhibitors. Stool specimens were collected from patients both prior to and following treatment. To scrutinize the differences in microbial gene sequences and species of the intestinal microbiota, 16S rRNA amplicon sequencing technology was employed. Additionally, peripheral blood samples were collected, and the analysis encompassed the assessment of T lymphocyte subsets and myeloid suppressor cell subsets via flow cytometry. Subsequently, alterations in the immune microenvironment pre- and post-treatment were thoroughly analyzed.

Results

The predominant clinical manifestations of advanced NSCLC patients encompassed spleen-lung Qi deficiency syndrome and Qi-Yin deficiency syndrome. Notably, the latter exhibited enhanced responsiveness to ICIs with a discernible amelioration of the immune microenvironment. Following ICIs treatment, significant variations in microbial abundance were identified among the three strains: Clostridia, Lachnospiraceae, and Lachnospirales, with a mutual dependency relationship. In the subset of patients manifesting positive PD-L1 expression and enduring therapeutic benefits, the study recorded marked increases in the ratios of CD3+%, CD4+%, and CD4+/CD8+ within the T lymphocyte subsets. Conversely, reductions were observed in the ratios of CD8%, Treg/CD4+, M-MDSC/MDSC, and G-MDSC/MDSC.

Conclusion

The strains Clostridia, Lachnospiraceae, and Lachnospirales emerge as potential biomarkers denoting the composition of the intestinal microbiota in the NSCLC therapy. The immunotherapy efficacy of ICIs markedly accentuates in patients displaying durable treatment benefits and those expressing positive PD-L1.

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

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

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.

Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort

The nodule diameter was commonly used to predict the invasiveness of pulmonary adenocarcinomas in pure ground-glass nodules (pGGNs). However, the diagnostic performance and optimal cut-off values were inconsistent. We conducted a meta-analysis to evaluate the diagnostic performance of the nodule diameter for predicting the invasiveness of pulmonary adenocarcinomas in pGGNs and validated the cut-off value of the diameter in an independent cohort. Relevant studies were searched through PubMed, MEDLINE, Embase, and the Cochrane Library, from inception until December 2022. The inclusion criteria comprised studies that evaluated the diagnostic accuracy of the nodule diameter to differentiate invasive adenocarcinomas (IAs) from non-invasive adenocarcinomas (non-IAs) in pGGNs. A bivariate mixed-effects regression model was used to obtain the diagnostic performance. Meta-regression analysis was performed to explore the heterogeneity. An independent sample of 220 pGGNs (82 IAs and 128 non-IAs) was enrolled as the validation cohort to evaluate the performance of the cut-off values. This meta-analysis finally included 16 studies and 2564 pGGNs (761 IAs and 1803 non-IAs). The pooled area under the curve, the sensitivity, and the specificity were 0.85 (95% confidence interval (CI), 0.82-0.88), 0.82 (95% CI, 0.78-0.86), and 0.73 (95% CI, 0.67-0.78). The diagnostic performance was affected by the measure of the diameter, the reconstruction matrix, and patient selection bias. Using the prespecified cut-off value of 10.4 mm for the mean diameter and 13.2 mm for the maximal diameter, the mean diameter showed higher sensitivity than the maximal diameter in the validation cohort (0.85 vs. 0.72, p < 0.01), while there was no significant difference in specificity (0.83 vs. 0.86, p = 0.13). The nodule diameter had adequate diagnostic performance in differentiating IAs from non-IAs in pGGNs and could be replicated in a validation cohort. The mean diameter with a cut-off value of 10.4 mm was recommended.

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.

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study

PURPOSE

No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction.

METHODS

A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups.

RESULTS

The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS).

CONCLUSION

The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.

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.

Oncological characteristics of epidermal growth factor receptor-mutated clinical stage IA lung adenocarcinoma with radiologically pure-solid appearance

OBJECTIVES

We evaluated the clinicopathological and oncological characteristics of epidermal growth factor receptor-mutated clinical stage IA radiological pure-solid lung adenocarcinoma and compared them with those of a ground-glass opacity component.

METHODS

Between 2008 and 2020, data from 1014 surgically resected clinical stage 0-IA epidermal growth factor receptor-mutated lung adenocarcinomas were evaluated. Oncological outcomes were assessed using multivariable analysis. Overall survival was estimated using Kaplan-Meier analysis and the log-rank test. The cumulative incidence of recurrence was estimated using the Gray's test.

RESULTS

Of these, 233 (23%) were radiologically pure-solid tumors, which demonstrated a higher proportion of nodal metastasis, micropapillary component, spread through alveolar space, and Ex19 subtype compared with those of tumors with ground-glass opacity (P < .001). Multivariable analysis revealed that the presence of ground-glass opacity was an independently significant factor for overall survival (P = .037) and cumulative incidence of recurrence (P < .001). In cases where the oncological outcomes were stratified by the presence of ground-glass opacity component, the 5-year overall survival was excellent at more than 90% in tumors with ground-glass opacity despite clinical-T categories (P = .2044); however, tumor size significantly affected survival only in pure-solid tumors (T1a, 100%; T1b, 77.7%; T1c, 68.5%; P = .0056). Furthermore, the cumulative incidence of recurrence was low in tumors with ground-glass opacity despite the clinical-T categories, whereas tumor size significantly affected the cumulative incidence of recurrence only in pure-solid tumors (5-year cumulative incidence of recurrence: T1a-b, 18.9%; T1c, 41.3%; P < .001).

CONCLUSIONS

Oncologic behavior and prognosis of radiologically pure-solid tumors were significantly poorer than those of tumors with ground-glass opacity among patients with epidermal growth factor receptor-mutated early-stage lung adenocarcinoma. These findings imply distinct tumorigenesis based on the presence of ground-glass opacity, even in tumors with epidermal growth factor receptor mutations.

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.

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.

Tumor-derived Vimentin as a novel biomarker for distinct subtypes predicting adjuvant chemotherapy resistance and T-cell-inflamed phenotype in small cell lung cancer

Despite recent progress in subtype classification for small cell lung carcinoma (SCLC), little is known about the biomarker for triple-negative (ASCL1, NEUROD1, and POU2F3 negative) tumors. The long-term survival, adjuvant chemotherapy (ACT) response, and immune milieu in different SCLC subtypes have also not been well established. Here, we retrospectively collected a large cohort of 192 primary SCLC tumors and reported that ASCL1-, NEUROD1- and POU2F3-dominant subtypes counted for 61.38%, 19.31%, and 6.21%, respectively. Subtype intra-tumoral heterogeneity and co-expression at the single-cell level existed substantially. The expression of tumor-derived Vimentin (VIM) was nearly restricted to triple-negative SCLC tumors (15/19, 78.9%) while YAP1 expression was distributed widely in other subtypes. The SCLC subtyping model was independently prognostic of OS and RFS (p <  0.001 and p = 0.043). In particular, patients with ASCL1-positive SCLC tumors can benefit more from ACT, and VIM-positive tumors did the opposite. Compared with other subtypes, the VIM-dominant SCLC subtype was associated with abundant but functionally impaired CD4+ and CD8+ T-cells, which highly expressed inhibitory checkpoints and potentially benefit from PD-L1 blockade therapy. Our study showed that tumor-derived SCLC-V subtype could independently predict ACT response. The distinct immune landscape between subtypes may help inform personalized immune therapeutic approaches.

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.

Differences of molecular events driving pathological and radiological progression of lung adenocarcinoma

BACKGROUND

Ground-glass opacity (GGO)-like lung adenocarcinoma (LUAD) has been detected increasingly in the clinic and its inert property and superior survival indicate unique biological characteristics. However, we do not know much about them, which hampers identification of key reasons for the inert property of GGO-like LUAD.

METHODS

Using whole-exome sequencing and RNA sequencing, taking into account both radiological and pathological classifications of the same 197 patients concomitantly, we systematically interrogate genes driving the progression from GGO to solid nodule and potential reasons for the inertia of GGO. Using flow cytometry and IHC, we validated the abundance of immune cells and activity of cell proliferation.

FINDINGS

Identifying the differences between GGO and solid nodule, we found adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) and GGO-like LUAD exhibited lower TP53 mutation frequency and less active cell proliferation-related pathways than solid nodule in LUAD. Identifying the differences in GGO between AIS/MIA and LUAD, we noticed that EGFR mutation frequency and CNV load were significantly higher in LUAD than in AIS/MIA. Regulatory T cell was also higher in LUAD, while CD8+ T cell decreased from AIS/MIA to LUAD. Finally, we constructed a transcriptomic signature to quantify the development from GGO to solid nodule, which was an independent predictor of patients' prognosis in 11 external LUAD datasets.

INTERPRETATION

Our results provide deeper insights into the indolent nature of GGO and provide a molecular basis for the treatment of GGO-like LUAD.

FUNDING

This study was supported in part by the National Natural Science Foundation of China (32170657), the National Natural Science Foundation of China (82203037), and Shanghai Sailing Program (22YF1408900).

The solid component within part-solid nodules: 3-dimensional quantification, correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas, and comparisons with 2-dimentional measures and semantic features in low-dose computed tomography

BACKGROUND

There is no consensus on 3-dimensional (3D) quantification method for solid component within part-solid nodules (PSNs). This study aimed to find the optimal attenuation threshold for the 3D solid component proportion in low-dose computed tomography (LDCT), namely the consolidation/tumor ratio of volume (CTRV), basing on its correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas (PAs) according to the 5th edition of World Health Organization classification. Then we tested the ability of CTRV to predict high-risk nonmucinous PAs in PSNs, and compare its performance with 2-dimensional (2D) measures and semantic features.

METHODS

A total of 313 consecutive patients with 326 PSNs, who underwent LDCT within one month before surgery and were pathologically diagnosed with nonmucinous PAs, were retrospectively enrolled and were divided into training and testing cohorts according to scanners. The CTRV were automatically generated by setting a series of attenuation thresholds from - 400 to 50 HU with an interval of 50 HU. The Spearman's correlation was used to evaluate the correlation between the malignant grade of nonmucinous PAs and semantic, 2D, and 3D features in the training cohort. The semantic, 2D, and 3D models to predict high-risk nonmucinous PAs were constructed using multivariable logistic regression and validated in the testing cohort. The diagnostic performance of these models was evaluated by the area under curve (AUC) of receiver operating characteristic curve.

RESULTS

The CTRV at attenuation threshold of -250 HU (CTRV_- 250HU) showed the highest correlation coefficient among all attenuation thresholds (r = 0.655, P < 0.001), which was significantly higher than semantic, 2D, and other 3D features (all P < 0.001). The AUCs of CTRV_- 250HU to predict high-risk nonmucinous PAs were 0.890 (0.843-0.927) in the training cohort and 0.832 (0.737-0.904) in the testing cohort, which outperformed 2D and semantic models (all P < 0.05).

CONCLUSIONS

The optimal attenuation threshold was - 250 HU for solid component volumetry in LDCT, and the derived CTRV_- 250HU might be valuable for the risk stratification and management of PSNs in lung cancer screening.

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.

Association of CT findings with invasive subtypes and the new grading system of lung adenocarcinoma

AIM

To predict the differentiation between invasive growth patterns and new grades of lung adenocarcinoma (LAC) using computed tomography (CT).

MATERIALS AND METHODS

The CT features of 180 surgically treated LAC patients were compared retrospectively to pathological invasive subtypes and tumour grades as defined by the new grading system published in 2021 by the World Health Organization. Two radiologists reviewed the images semi-quantitatively and independently. Univariable and multivariable regression models were built from the statistical means of their assessments to predict invasive subtypes and grades. The area under the curve (AUC) calculation was used to select the best models. The Youden index was applied to determine the cut-off values for radiological parameters.

RESULTS

The acinar/papillary patterns were associated with ill-defined margins, lower consolidation/tumour ratio and air bronchogram. The solid growth pattern was associated with a well-defined margin and hypodensity, and the micropapillary (MP) subtype with spiculation. From Grades 1 to 3, the amount of air bronchogram decreased and the consolidation/tumour ratio increased. In the sub-analyses, the best model for differentiating Grade 2 from Grade 1 had the following CT features: solid/subsolid type, consolidation/tumour ratio, well-defined margin, and air bronchogram (AUC = 0.783) and Grade 3 from Grade 2: size of the consolidation part/whole tumour ratio, size of the consolidation part, and well-defined margin (AUC = 0.759). The interobserver agreements between the two radiologists varied between 0.67 and 0.98.

CONCLUSIONS

Air bronchogram, consolidation/tumour ratio, and well-defined margin are among the best imaging findings to discriminate between both invasive subtypes and the new grades in LAC.

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.

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.

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.

Clinicopathological Features and Significance of Epidermal Growth Factor Receptor Mutation in Surgically Resected Early-Stage Lung Adenocarcinoma

The clinicopathological presentation of early-stage lung adenocarcinoma patients with epidermal growth factor receptor (EGFR) mutations has been seldom studied. Our study enrolled patients with stage I and II lung adenocarcinoma between January 2014 and December 2017 at the National Taiwan University Hospital. Clinicopathological features and prognosis were retrospectively reviewed and analyzed depending on EGFR mutation status. EGFR mutations were detected in 622 (60%) out of 1034 patients. Compared to the group without EGFR mutations, the group with EGFR mutations had more patients above 65 years of age (p < 0.001), more non-lepidic histological subtypes (p < 0.001), higher CEA levels (p = 0.044), higher grade of pleural (p = 0.02) and lymphovascular (p = 0.001) invasion, higher histological grade (p < 0.001), and a more advanced pathological stage (p = 0.022). In multivariate analysis, there was no significant difference in PFS or OS between the EGFR mutant and wild-type groups. In subtype analysis, the tumors with an L858R mutation had a more lepidic predominant histological type (p = 0.019) and less lymphovascular invasion (p = 0.011). No significant differences in PFS or OS were detected between the exon 19 deletion and L858R mutation groups. In early-stage lung adenocarcinoma, EGFR mutation may be considered as a treatment response predictor for tyrosine kinase inhibitors, instead of a predictor of clinical prognosis.

Combined analysis of gut microbiome and serum metabolomics reveals novel biomarkers in patients with early-stage non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the predominant form of lung cancer and is one of the most fatal cancers worldwide. Recently, the International Association for the Study of Lung Cancer (IASLC) proposed a novel grading system based on the predominant and high-grade histological patterns for invasive pulmonary adenocarcinoma (IPA). To improve outcomes for NSCLC patients, we combined serum metabolomics and fecal microbiology to screen biomarkers in patients with early-stage NSCLC and identified characteristic microbial profiles in patients with different grades of IPA. 26 genera and 123 metabolites were significantly altered in the early-stage NSCLC patients. Agathobacter, Blautia, Clostridium, and Muribaculacea were more abundant in the early-stage NSCLC patients compared with healthy controls. For the different grades of IPA, the characteristic microorganisms are as follows: Blautia and Marinobacter in IPA grade type 1; Dorea in IPA grade type 2; and Agathobacter in IPA grade type 3. In the metabolome results, the early-stage NSCLC group mainly included higher levels of sphingolipids (D-erythro-sphingosine 1-phosphate, palmitoyl sphingomyelin), fatty acyl (Avocadyne 1-acetate, 12(S)-HETE, 20-Carboxy-Leukotriene B4, Thromboxane B3, 6-Keto-prostaglandin f1alpha, Sebacic acid, Tetradecanedioic acid) and glycerophospholipids (LPC 20:2, LPC 18:0, LPC 18:4, LPE 20:2, LPC 20:1, LPC 16:1, LPC 20:0, LPA 18:2, LPC 17:1, LPC 17:2, LPC 19:0). Dysregulation of pathways, such as sphingolipid metabolism and sphingolipid signaling pathway may become an emerging therapeutic strategy for early-NSCLC. Correlation analysis showed that gut microbiota and serum metabolic profiles were closely related, while Muribaculacea and Clostridium were the core genera. These findings provide new biomarkers for the diagnosis of early-stage NSCLC and the precise grading assessment of prognostic-related IPAs, which are of clinical importance and warrant further investigation of the underlying molecular mechanisms.

The IASLC grading system for invasive pulmonary adenocarcinoma: a potential prognosticator for patients receiving neoadjuvant therapy

Background

Grading system for resected invasive pulmonary adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) was validated as a strong prognostic indicator. Nonetheless, the efficacy of utilizing such grading system in prognostic assessment of patients receiving neoadjuvant therapy still needs elucidating.

Methods

A retrospective study was conducted including patients with resected adenocarcinoma following neoadjuvant chemotherapy or targeted therapy from August 2012 to December 2020 in Shanghai Pulmonary Hospital. All the surgical specimens were re-evaluated and graded. The prognostic value of the grading system was further validated.

Results

Ultimately, a total of 198 patients were enrolled in this study, and subdivided into three cohorts according to the grading system. There were 13 (6.6%), 37 (18.7%), and 148 (74.7%) patients belonging to Grades 1, 2, and 3, respectively. IASLC grading system demonstrated significant power in prognosis differentiation of the entire cohort [recurrence-free survival (RFS), p < 0.001; overall survival (OS), p < 0.001] and the neoadjuvant chemotherapy and targeted therapy cohorts separately, and was further verified as a significant prognostic indicator for RFS and OS in multivariable Cox analysis. Since the majority of the patients (84.8%) did not achieve major pathologic response (MPR), representing a wide spectrum of survival, the prognostic value of grading system in non-MPR cohort was further evaluated. Similar results were also obtained that IASLC grading system was assessed significant in univariable analysis of RFS (p < 0.001) and univariable analysis of OS (p = 0.001).

Conclusions

The prognostic efficacy of pathological evaluation of the residual proportion of pulmonary adenocarcinoma post-neoadjuvant therapy using IASLC grading system was preliminarily verified. Such grading system might assist prognostic evaluation of neoadjuvant cohort other than traditional pathological parameters.

Proposal of a revised International Association for the Study of Lung Cancer grading system in pulmonary non-mucinous adenocarcinoma: The importance of the lepidic proportion

OBJECTIVES

We aimed to measure the validity of the International Association for the Study of Lung Cancer (IASLC) grading system in Korean patients and propose a modification for an increase of its predictability, especially in grade 2 patients.

MATERIALS AND METHODS

From 2012 to 2017, histopathologic characteristics of 1358 patients with invasive pulmonary adenocarcinoma (stage I-III) from two institutions were retrospectively reviewed and re-classified according to the IASLC grading system. Considering the amount of the lepidic proportion, the validity of the revised model (Lepidic-10), derived from the training cohort (hospital A), was measured using the validation cohort (hospital B). Its predictability was compared to that of the IASLC system.

RESULTS

Of the 1358 patients, 259 had a recurrence, and 189 died during follow-up. The Harrell's concordance index and area under the curve of the IASLC system were 0.685 and 0.699 for recurrence-free survival (RFS) and 0.669 and 0.679 for death, respectively. From the training cohort, the IASLC grade 2 patients were divided into grades 2a and 2b (Lepidic-10 model) with a 10 % lepidic pattern. This new model further distinguished patients in both institutions that had better performance than the IASLC grading (Hospital A, p < 0.001 for RFS and death; Hospital B, p = 0.0215 for RFS, p = 0.0429 for death).

CONCLUSION

The IASLC grading system was easily applicable; its clinical use in predicting the prognosis of Korean patients with pulmonary adenocarcinoma was validated. Furthermore, the introduction of the lepidic proportion as an additional criterion to differentiate grade 2 patients improved its predictability.

Validation of the novel International Association for the Study of Lung Cancer grading system and prognostic value of filigree micropapillary and discohesive growth pattern in invasive pulmonary adenocarcinoma

INTRODUCTION

The Pathology Committee of the International Association for the Study of Lung Cancer (IASLC) proposed a new histological grading system based on the combination of predominant and high-grade patterns in 2020.

MATERIALS AND METHODS

Pathological sections from 631 patients with stage I-III invasive lung adenocarcinoma were reviewed. We then determined the histological grade according to the new grading system and confirmed the pathological features that included the filigree micropapillary and discohesive growth pattern. Applying of the novel IASLC grading system in prognosis stratification was verified and the clinical significance of the pathological characteristics was explored.

RESULTS

Cox multivariable analysis revealed that in the stage I-III invasive lung adenocarcinoma, the IASLC grading system was significantly associated with disease-free survival (DFS) [hazard ratio (HR) = 1.419; 95 % confidence interval (CI): 1.040-1.937; P = 0.027] and overall survival (OS) (HR = 1.899; 95 % CI: 1.168-3.087; P = 0.010). In patients with IASLC Grades 1 and 2, the simultaneous presence of filigree micropapillary and discohesive growth pattern was significantly correlated with DFS (HR = 1.899; 95 % CI:1.168-3.087; P = 0.010). However, the filigree micropapillary and discohesive growth pattern did not affect the OS (HR = 2.786; P = 0.317). The competitive risk model revealed that in the stage I cohort, the simultaneous presence of filigree micropapillary and discohesive growth pattern was a risk factor for recurrence and metastasis [sub- distribution HR (SHR) = 1.987; 95 %CI: 1.122-3.518; P = 0.019].

CONCLUSION

Our study verified that the new prognostic stratification system was an effective stratification tool. Filigree micropapillary and discohesive growth pattern may also be risk factors for DFS, postoperative recurrence and metastasis.

Prognosis of resected invasive mucinous adenocarcinoma compared with the IASLC histologic grading system for invasive nonmucinous adenocarcinoma: Surgical database study in the TKIs era in Korea

BACKGROUND

The prognosis of invasive mucinous adenocarcinoma (IMA) remains controversial and should be clarified by comparison with the International Association for the Study of Lung Cancer (IASLC) histologic grading system for invasive nonmucinous adenocarcinoma (INMA).

METHODS

This study included patients with IMA who underwent curative resection. Their clinicopathological outcomes were compared with those of patients with INMA. Propensity score matching was performed to compare the prognosis of IMA with IASLC grade 2 or 3. Kaplan-Meier survival curves and log-rank tests were used to analyze recurrence-free survival (RFS) and overall survival (OS).

RESULTS

The prognoses of IMA and IASLC grade 2 were similar in terms of RFS and OS. Although patients with IMA had better RFS than patients with IASLC grade 3, the OS was not significantly different. After propensity score matching, IMA demonstrated similar RFS to IASLC grade 2 but superior to IASLC grade 3; there was no difference in the OS compared with grades 2/3. Multivariate analysis revealed that tumor size (hazard ratio [HR] = 1.20, p = 0.028), lymphovascular invasion (HR = 127.5, p = 0.003), and maximum standardized uptake value (HR = 1.24, p = 0.005) were poor prognostic predictors for RFS. Patients with IMA demonstrated RFS similar to and significantly better than that of patients with IASLC grades 2 and 3, respectively. For OS, IMA prognosis was between that of IASLC grades 2 and 3.

CONCLUSIONS

Since the prognosis of IMA among lung adenocarcinomas appears to be relatively worse, further clinical studies investigating IMA-specific treatment and follow-up plans are necessary to draw more inferences.

Validation Study of New IASLC Histology Grading System in Stage I Non-Mucinous Adenocarcinoma Comparing With Minimally Invasive Adenocarcinoma

BACKGROUND

A new histologic grading system for pulmonary non-mucinous invasive adenocarcinoma was proposed by the International Association for the Study of Lung Cancer (IASLC). We evaluated its clinical impact on prognosis in stage I patients, including minimally invasive adenocarcinoma (MIA).

PATIENTS AND METHODS

919 patients underwent surgery for lung adenocarcinoma between 2012 and 2019. Stage I patients (n = 500) were retrospectively reviewed. They were divided into 4 categories: MIA and 3 new IASLC grades (grades 1-3). Cox proportional hazards analysis was performed to identify risk factors associated with recurrence and mortality. Furthermore, we compared the predictability of the IASLC grading system with different models that are based on the clinicopathologic characteristics (baseline model), TNM staging, and predominant histologic pattern. The area under the receiver operating characteristic curve (AUC) was calculated for comparison.

RESULTS

Recurrence-free survival (RFS) and overall survival (OS) were significantly stratified by the IASLC grading system in patients with stage I adenocarcinoma (P < .001 and P = .003, respectively). In multivariate analyses, IASLC grade 3 was a significant factor for RFS (hazard ratio [HR] 3.18, P < .001) and OS (HR 2.31, P = .013). The AUCs of the new IASLC model were 0.781 for recurrence and 0.770 for mortality, compared with those of the predominant pattern (0.769 for recurrence, 0.747 for death) and TNM staging (0.762 for recurrence, 0.747 for death).

CONCLUSION

The IASLC grading system effectively predicted the prognosis of early-stage adenocarcinoma compared with previous models. The IASLC classification appears to improve the current system; therefore, precise pathologic examination for early-stage adenocarcinoma is warranted.

The new IASLC grading system for invasive non-mucinous lung adenocarcinoma is a more useful indicator of patient survival compared with previous grading systems

BACKGROUND

The International Association for the Study of Lung Cancer (IASLC) Pathology Committee recently proposed a new histological grading system for invasive lung adenocarcinoma (ADC). This study evaluated the usefulness of this grading system.

METHODS

A total of 395 patients with ADC were examined. ADCs were reclassified based on comprehensive histological subtyping according to the IASLC grading system. We evaluated the following histological grading systems for invasive ADC: the architectural (Arch), Sica's grading, and IASLC grading systems. Multivariate analyses of overall and recurrence-free survival (RFS) based on these three grading systems were performed using Cox proportional hazards models.

RESULTS

Multivariate analysis showed that all three grading systems were useful for predicting the outcomes of patients at all stages. However, the IASLC grading system was superior to the Arch and Sica's grading systems in differentiating grade 3 from grade 1 ADCs in terms of both overall survivals (IASLC vs. Arch vs. Sica's grading systems: hazard ratio [HR] = 3.77 vs. 3.03 vs. 2.63) and RFS (HR = 4.25 vs. 2.69 vs. 2.4).

CONCLUSION

The newly proposed IASLC grading system was useful for predicting patient outcomes and was superior to the other grading systems in detecting high-grade malignancy.

Adjuvant chemotherapy can benefit the survival of stage I lung adenocarcinoma patients with tumour spread through air spaces after resection: Propensity-score matched analysis

Background

It is still unclear whether stage I lung adenocarcinoma patients with tumour spread through air spaces (STAS) can benefit from postoperative adjuvant chemotherapy (ACT) after lobectomy. This study investigated the effect of ACT on the postoperative survival of patients with stage I (STAS⁺) lung adenocarcinoma.

Methods

We retrospectively analysed the clinical data of stage I (STAS⁺) invasive lung adenocarcinoma patients who underwent lobectomy in the Department of Thoracic Surgery of our hospital from January 1, 2013 to January 1, 2016. Propensity score matching (PSM) was performed to group patients to investigate whether ACT could lead to better prognosis of patients.

Results

A total of 593 patients with stage I (STAS⁺) lung adenocarcinoma were enrolled. The study after PSM included 406 patients. Kaplan–Meier survival analysis showed the experimental group had a better 3-year recurrence-free survival (RFS) rate (p = 0.037) and the 5-year RFS rate (p = 0.022) than the control group. It also had higher 5-year overall survival (p = 0.017). The multivariate analysis by Cox proportional hazard regression model showed that stage I STAS⁺ lung adenocarcinoma patients with lymphatic vessel invasion (HR: 1.711, 95% CI: 1.052-2.784; p = 0.045), vascular invasion (HR: 5.014, 95% CI: 3.154-7.969; p < 0.001), and visceral pleural invasion (HR: 2.086, 95% CI: 1.162-3.743; p = 0.014), and without ACT (HR: 1.675, 95% CI: 1.043-2.689; p = 0.033) had a significant survival disadvantage.

Conclusion

ACT can boost the postoperative survival of patients with stage I (STAS⁺) lung adenocarcinoma.

Clinicopathologic and genomic features of high-grade pattern and their subclasses in lung adenocarcinoma

INTRODUCTION

Recent lung adenocarcinoma (LUAD) grading system proposed by the International Association for the Study of Lung Cancer (IASLC) has emphasized the proportion of high-grade patterns (HGPs). We aimed to evaluate the clinicopathologic and genomic characteristics associated with HGP which has not yet been fully investigated.

METHODS

Tissue samples from 174 patients who underwent surgical resection of LUAD from January to December 2015 were histologically evaluated. Proportions of HGPs, including solid, micropapillary, cribriform, and complex glandular patterns, were individually quantified. Prognostic implications of HGP proportion, both as a continuous variable and as subclasses divided by cutoffs of 20%, 50%, and 90% (low-intermediate grade [LIG], HGP <20%; high grade 1 [HG1], 20-<50%, HG2, 50-<90%; HG3, ≥90%) were evaluated. Different clinicopathologic factors and genomic alterations according to the HGP subclasses were assessed.

RESULTS

Relative hazards of the HGP gradually elevated as its proportion increased over 20%, the cut-off value established by the IASLC grading system, and the cancer-specific overall survival (OS) of HG1 subclass was not significantly decreased compared to the LIG subclass on univariate analysis. However, further subgrouping showed significantly increased frequencies of male, advanced stage tumors, lymphovascular invasion, and spread through alveolar space in higher HGP subclasses. Also, common LUAD driver mutations, particularly EGFR mutations, were less frequent, whereas alterations in TP53 and cell cycle pathway-related genes were more frequent. Higher HGP subclasses and TP53 gene alteration were associated with shorter cancer-specific OS and RFS in multivariate survival analysis.

CONCLUSIONS

HGP subclasses of LUAD displayed distinct clinicopathological characteristics and genomic alterations, including TP53 and cell cycle pathway, emphasizing the clinical value of these subclasses in LUAD. Higher HGP subclass and alteration in TP53 may be markers of poor post-operative survival.

A Novel Radiopathological Grading System to Tailor Recurrence Risk for Pathologic Stage IA Lung Adenocarcinoma

To validate the efficiency of pathologic grading system in pathologic stage IA lung adenocarcinoma (LUAD), and explore whether integrating preoperative radiological features would enhance the performance of recurrence discrimination. We retrospectively collected 510 patients with resected stage IA LUAD between January 2012 and December 2019 from Guangdong Provincial People's Hospital (GDPH). Pathologic grade classification of each case was based on the International Association for the Study of Lung Cancer (IASLC) pathologic staging system. Kaplan-Meier curves was used to assess the power of recurrence stratification. Concordance index (C-Index) and receiver operating characteristic curves (ROC) were used for evaluating the clinical utility of different grading systems for recurrence discrimination. Patients of lower IASLC grade showed improved recurrence-free survival (RFS) (P < 0.0001) where numerically difference was found between grade II and grade III (P = 0.119). By integrating the IASLC grading system and radiological feature, we found the RFS rate decreased as the novel radiopathological (RP) grading system increased (P < 0.0001). The difference of RFS curves between any 2 groups as per the RP grading system was statisticallysignificant (RP grade I vs RP grade II, p = 0.007; RP grade I vs RP grade III, P < 0.0001; RP grade II vs RP grade III, P = 0.0003). Compared with the IASLC grading system, the RP grading system remarkably improved recurrence survival discrimination (C-index: 0.822; area under the curve, 0.845). Integrating imaging features into pathologic grading system enhanced the efficiency of recurrence discrimination for resected stage IA LUAD and might help conduct subsequent management.

Cytomorphometric and flow cytometric analyses using liquid-based cytology materials in subtypes of lung adenocarcinoma

BACKGROUND

The histological classifications of invasive lung adenocarcinoma subtypes are considered to predict patient prognosis after surgical treatment. The objectives of this study were to evaluate cytomorphological characteristics and proliferative activities among the histological predominant patterns by performing cytomorphometric and flow cytometric analyses using liquid-based cytology materials.

METHODS

Cytological samples fixed by liquid-based cytology preservatives from 53 surgically-resected lung adenocarcinoma specimens were obtained between August 2018 and November 2019. The Papanicolaou-stained and paired Ki-67-stained slides were analyzed for calculating nuclear morphology (nuclear area, nuclear perimeter and nuclear circularity) and Ki-67 labeling index using software. The cell proliferation index (CPIx) was calculated and cellular information including cell cycle stage of tumor cells was obtained by flow cytometry.

RESULTS

The 53 cases included papillary (n = 29), acinar (n = 8), lepidic (n = 5), and solid (n = 4) subtypes, and invasive mucinous adenocarcinoma (n = 7) were also included. In the lepidic pattern, nuclear area (79.6 ± 28.8 μm² ) and perimeter (34.1 ± 6.1 μm) were relatively larger and longer than those of the other predominant patterns. The Ki-67 labeling index of the solid pattern (27.9 ± 12.5%) was highest compared with those of other predominant patterns. There were statistically significant differences in the lepidic versus solid patterns and the papillary versus solid patterns (p = .013 and p = .039, respectively). The calculated mean CPIx of the lepidic and the acinar patterns were approximately two-fold higher than those of the other predominant patterns.

CONCLUSION

By revealing the differences of cytomorphological characteristics, these methodologies might be used for diagnosing cytopathological materials using digital cytopathology.

Daily Practice Assessment of KRAS Status in NSCLC Patients: A New Challenge for the Thoracic Pathologist Is Right around the Corner

Simple Summary

RAS mutation is the most frequent oncogenic alteration in human cancers and KRAS is the most frequently mutated, notably in non-small cell lung carcinomas (NSCLC). Various attempts to inhibit KRAS in the past were unsuccessful in these latter tumors. However, recently, several small molecules (AMG510, MRTX849, JNJ-74699157, and LY3499446) have been developed to specifically target KRAS G12C-mutated tumors, which seems promising for patient treatment and should soon be administered in daily practice for non-squamous (NS)-NSCLC. In this context, it will be mandatory to systematically assess the KRAS status in routine clinical practice, at least in advanced NS-NSCLC, leading to new challenges for thoracic oncologists.

Abstract

KRAS mutations are among the most frequent genomic alterations identified in non-squamous non-small cell lung carcinomas (NS-NSCLC), notably in lung adenocarcinomas. In most cases, these mutations are mutually exclusive, with different genomic alterations currently known to be sensitive to therapies targeting EGFR, ALK, BRAF, ROS1, and NTRK. Recently, several promising clinical trials targeting KRAS mutations, particularly for KRAS G12C-mutated NSCLC, have established new hope for better treatment of patients. In parallel, other studies have shown that NSCLC harboring co-mutations in KRAS and STK11 or KEAP1 have demonstrated primary resistance to immune checkpoint inhibitors. Thus, the assessment of the KRAS status in advanced-stage NS-NSCLC has become essential to setting up an optimal therapeutic strategy in these patients. This stimulated the development of new algorithms for the management of NSCLC samples in pathology laboratories and conditioned reorganization of optimal health care of lung cancer patients by the thoracic pathologists. This review addresses the recent data concerning the detection of KRAS mutations in NSCLC and focuses on the new challenges facing pathologists in daily practice for KRAS status assessment.