A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study

Abstract

Background Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of nonsmall cell lung carcinomas (NSCLCs). MethodsIn this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis.Findings For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1•48 [95% CI 1•06-2•08]; p=0•022 for The Cancer Genome Atlas [TCGA] LUSC group, 2•24 [1•04-4•80]; p=0•039 for the University of Bern LUSC group, and 1•62 [1•15-2•30]; p=0•0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways. Interpretation CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy.

Heterochronous Metastases of Lung Adenocarcinoma to Pancreas and Liver: A Case Report from Pathological Perspectives

“…17,18 On the contrary, localized lung adenocarcinoma, if treated promptly at an early stage, has a much better survival profile with a 5-year survival rate of around 50%. 19,20 In our case, EGFR exon 19 deletion, a mutation subtype associated with longer progressionfree survival, 21 was found, which might also contribute to the late progression.…”

Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review

“…Lu et al . ( 10 ) associated the cellular diversity features that derived from the non-small cell lung carcinomas with bulk gene data to investigate the underlying biological pathways of image features derived from the pathological image. Subramanian et al .…”

“…These include not only the nuclear architecture and graphical arrangement of a single histologic primitive, but also novel approaches that are focused on characterizing the spatial arrangement ( 5 - 7 ) of tumor infiltrated lymphocytes (TILs) and interplays between multiple different histological primitives simultaneously [e.g. interplay of lymphocytes and cancer cells ( 8 - 10 )], thus potentially providing a comprehensive portrait of tumor’s morphologic heterogeneity.…”

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

“…In contrast, Huang et al [ 39 ] used the XGBoost machine learning algorithm to build a model to predict the 1-year survival rate of NSCLC with bone metastases. Lu et al [ 40 ] used U-Net segmentation to process hematoxylin-eosin (H and E) stained histological images of NSCLC and computer extracted tumor cell diversity features from them to predict the overall 5-year survival in early-stage NSCLC in combination with a CPHM. Lai et al [ 41 ] developed a deep learning model combining gene biomarker expression and clinical data to predict the 5-year survival status of patients with NSCLC, showing high accuracy (AUC: 0.8163, accuracy: 75.44%).…”