Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images

Abstract

Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation, texture, shape, and tumor architecture to predict disease recurrence in early stage NSCLC from digitized H&E tissue microarray (TMA) slides. Using a retrospective cohort of early stage NSCLC patients (Cohort #1, n = 70), we constructed a supervised classification model involving the most predictive features associated with disease recurrence. This model was then validated on two independent sets of early stage NSCLC patients, Cohort #2 (n = 119) and Cohort #3 (n = 116). The model yielded an accuracy of 81% for prediction of recurrence in the training Cohort #1, 82% and 75% in the validation Cohorts #2 and #3 respectively. A multivariable Cox proportional hazard model of Cohort #2, incorporating gender and traditional prognostic variables such as nodal status and stage indicated that the computer extracted histomorphometric score was an independent prognostic factor (hazard ratio = 20.81, 95% CI: 6.42–67.52, P < 0.001).

Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer

“…Such studies appear to suggest that the spatial organization of lymphocytic infiltration in the context of nearby cancer cells is an important prognostic hallmark of certain types of tumors. This suggests that the study of the immune response with respect to patient outcome should take into account not only the quantity of immune cells, but also the spatial arrangement of the cancerous and surrounding immune cells (20,21,23,35). Previous related studies have found a strong association between the spatial location of nuclei and surrounding cytoplasmic features with OS (20,21) and RFS (4,23) in patients with early-stage NSCLC.…”

“…Given the recent evidence that the colocalization of immune and cancer nuclei is prognostic of disease outcome (20)(21)(22)(23), this work aims to develop and evaluate new computer-extracted spatial TIL (SpaTIL) features relating to (i) the spatial architecture of TIL clusters, (ii) colocalization of clusters of both TILs and cancer nuclei, and (iii) variation in density of TIL clusters across the tissue slide image. Specifically, our goal was to evaluate the association between disease recurrence and the SpaTIL features on patients with stage I and II of NSCLC.…”

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

“…The results of this study suggest that pathomics-based analysis using deep learning architecture may provide information about breast cancer prognosis. Such prognostic pathomic markers have been studied for oropharyngeal cancer [25] and lung cancer [26][27][28].…”

Topological Tumor Graphs: A Graph-Based Spatial Model to Infer Stromal Recruitment for Immunosuppression in Melanoma Histology

“…In this study, we present TTGs: a new approach to study the spatial architecture of the tumor microenvironment in primary and metastatic melanoma. Although cell-cell network analysis has previously been used for tasks such as aiding nucleus identification (27), it has not been explicitly used to study tumor-host interactions. To construct TTGs, the melanoma tumor microenvironment learned from wholetumor pathologic section images were transformed into a network of cells using computational pathology.…”