LUNG_CANCER

Spatial Distribution and Molecular Characterization of Lung Cancer in a Northern Israeli Cohort

A Hospital-Based Epidemiological Study

Nik Danilov, MD, MPH

School of Public Health, University of Haifa, Israel

2025

View the final report on GitHub Pages

Overview

This repository contains a fully reproducible epidemiological analysis of 94 lung cancer (LC) cases managed at the Lady Davis Carmel Medical Center, Haifa, Israel (2024–2025). The dataset was compiled through systematic review of anonymized electronic medical records extracted from the Ofek intra/interhospital medical informatics platform.

The analysis pursues two complementary objectives:

To characterize the spatial distribution of LC cases across the Haifa District and Northern District of Israel and evaluate whether observed geographic patterns are explained by established confounders — particularly smoking prevalence and population size.
To describe the molecular and histopathological profile of the cohort and examine how biomarker distributions align with internationally reported data.

The report is authored in Quarto and rendered as an interactive HTML document, combining spatial epidemiology with clinical-molecular characterization in a single reproducible workflow.

Tech Stack

Component	Tool
Reproducible report	Quarto (.qmd) → HTML
Data wrangling	R (`readxl`, `dplyr`)
Tables	R (`knitr`, `kableExtra`)
Static visualization	R (`ggplot2`, `patchwork`)
Interactive maps	R (`leaflet`)
Spatial statistics	R (`spdep`)

Dataset

Source: Anonymized electronic medical records, Lady Davis Carmel Medical Center, Haifa
Platform: Ofek intra/interhospital medical informatics system
Sample: 94 consecutive LC cases, 2024–2025
Variables (24): Demographics (age, sex, ethnic group, locality), smoking status, histopathological diagnosis, immunohistochemistry (PD-L1), next-generation sequencing results (EGFR, KRAS, BRAF, MET, ALK, ROS1, ERBB2, RET, NTRK), mismatch repair status (MSI), tumor mutational burden (TMB)

The full data dictionary is available in the rendered report (Table 1).

Key Findings

Spatial analysis

Global Moran’s I = −0.008 (permutation p = 0.373) — no significant spatial autocorrelation
LISA identified three Low–High outliers (Beitegen, Fassuta, Kiryat Shmona) — no true hotspots
Stratified Moran’s I (smokers only: p = 0.725; non-smokers only: p = 0.626) confirmed smoking as the primary spatial confounder
Population adjustment substantially redistributed apparent risk from large coastal cities to smaller inland localities

Molecular profiling

Adenocarcinoma NOS was the dominant subtype (67.0%)
EGFR, BRAF, and ROS1 alterations occurred exclusively in adenocarcinomas
Mean TMB ranged from 6.7 mut/Mb (ACA) to 16.1 mut/Mb (NEC)
Fisher’s exact test: no significant difference in smoking prevalence between Jewish and Arab cases (p = 0.815)

Repository Structure

LUNG_CANCER/
├── data/
│   └── CarmelMC_LungCancer.xlsx       # Clinical-laboratory dataset (94 cases)
├── EnvEpi_LungCancer_files/           # Quarto HTML supporting files
├── EnvEpi_LungCancer.qmd              # Source document (Quarto/R)
├── EnvEpi_LungCancer.html             # Rendered interactive report
├── EnvEpi_LungCancer.Rproj            # RStudio project file
├── .gitattributes                     # Marks R as a primary tool 
├── .gitignore
└── README.md

Reproducing the Analysis

Clone the repository
Open EnvEpi_LungCancer.Rproj in RStudio

Install required R packages:

install.packages(c("readxl", "dplyr", "knitr", "kableExtra",
                "ggplot2", "patchwork", "tidyr",
                "leaflet", "spdep"))

Render the report:

quarto::quarto_render("EnvEpi_LungCancer.qmd")

Methodological Note

The analysis demonstrates a key principle of spatial epidemiology: apparent geographic clusters of disease cannot be interpreted at face value. Sequential adjustment for population denominators and behavioral confounders (smoking) overturned an initially significant clustering signal, illustrating how rigorous analysis can prevent misleading conclusions from crude case maps.