Prognostic and predictive value of the newly proposed grading system of invasive pulmonary adenocarcinoma in Chinese patients: a retrospective multicohort study

Chen, Donglai; Zhang, Yu; Ma, Minjie; Chen, Yongbing; Chen, Chang; Chen, Chang; Jiang, Gening; Zhu, Yuming; Chen, Qiankun; Xie, Dong; She, Yunlang; Deng, Jiajun; Zhong, Yifan; Zhao, Mengmeng; Wu, Chunyan; Hou, Likun; Sun, Xuejun; Wang, Tingting; Jiang, Tao; Zhao, Guofang; Yang, Minglei; Jin, Feng; Zhang, Yunzeng; Yu, Bentong; Jiang, Lei; Song, Yimin; Chen, Cheng; Qu, Wendong; Zhao, Lilan; Pan, Xiaojie

doi:10.1038/s41379-021-00994-5

Cited by 36 publications

(37 citation statements)

References 22 publications

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“…This grading system classifies any tumor with ≥20% high‐grade patterns, including solid, micropapillary, and complex glandular patterns, as IASLC grade 3. Moreover, three further validation studies confirmed that this grading system provides significant prognostication 5–8 . However, IMA differs from invasive nonmucinous adenocarcinoma (INMA) because of major clinical, radiologic, pathologic, and genetic differences.…”

Section: Introductionmentioning

confidence: 77%

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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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 77%

“…Moreover, three further validation studies confirmed that this grading system provides significant prognostication. 5 , 6 , 7 , 8 However, IMA differs from invasive nonmucinous adenocarcinoma (INMA) because of major clinical, radiologic, pathologic, and genetic differences. Therefore, IMA was excluded from this grading system.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…This grading system better predicts patients' outcomes compared to systems using mitotic counts or nuclear grade [36]. Several validation studies found that the IASLC grading system had prognostic significance in patients with LADC, with a well-stratified recurrence-free survival or OS [41][42][43][44]. Therefore, we used this grading system to evaluate the performance of our trained models by comparing the DSS curves derived by the pathologists and the CNNs.…”

Section: Discussionmentioning

confidence: 99%

Deep learning for histopathological subtyping and grading of lung adenocarcinoma

Lami

Ota²,

Yamaoka³

et al. 2022

Preprint

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The histopathological distinction of lung adenocarcinoma (LADC) subtypes is subject to high inter-observer variability, which can compromise the optimal assessment of the patient prognosis. Therefore, this study developed convolutional neural networks (CNNs) capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the LADC tumour grades established recently by the International Association for the Study of Lung Cancer pathology committee. Consensus LADC ground truth histopathological images were obtained from seventeen expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained with EfficientNet b3 architecture to predict eight different LADC classes (lepidic, acinar, papillary, micropapillary, solid, invasive mucinous adenocarcinoma, other carcinoma types, and no carcinoma cells). 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. Moreover, the grading prediction of one of the trained models was more accurate than those of 14 out of 15 pulmonary pathologists involved in the study (p=0.0003). 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 CNNs improve the diagnostic accuracy of the pathologist, standardise LADC subtype recognition, 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.

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“…The model has consistently been found to correlate with prognosis and consists of: Grade 1: lepidic predominant tumor; Grade 2: acinar or papillary predominant tumor, both with no or less than 20% of high-grade patterns; and Grade 3: any tumor with 20% or more of high-grade patterns (solid, micropapillary and or complex gland). The established grading system is based on prognostic-related histological criteria and has utility and prognostic significance for IPA (Hou et al, 2022). Importantly, most lung cancer patients are initially diagnosed at an advanced stage of the disease, often with a poor prognosis.…”

Section: Introductionmentioning

confidence: 99%

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

Kong

Yan

et al. 2023

Front. Cell. Infect. Microbiol.

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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.

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Prognostic and predictive value of the newly proposed grading system of invasive pulmonary adenocarcinoma in Chinese patients: a retrospective multicohort study

Cited by 36 publications

References 22 publications

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

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

Deep learning for histopathological subtyping and grading of lung adenocarcinoma

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

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