# Artificial Intelligence for Lung Cancer Characterization in HIV affected populations in Uganda and Tanzania

> **NIH NIH U54** · CASE WESTERN RESERVE UNIVERSITY · 2021 · $187,000

## Abstract

ABSTRACT – Project 3
The age standardized rates (ASRs) show a steady rise in the incidence of lung cancer in Uganda and Tanzania
compared to other cancers. Unfortunately, there is no established lung cancer screening program in either of
Tanzania or Uganda. The cases of lung cancer recorded have mostly been found incidentally on chest computed
tomography (CT) scans done to establish the cause of patients' respiratory symptomatology. This problem of
diagnostic specificity is exacerbated in Tanzania and Uganda on account of the high incidence of tuberculosis
(TB) which can cause a chronic granulomatous reaction in the lungs manifesting as benign pulmonary nodules
on CT and X-rays. Skilled personnel to acquire good quality chest x-ray and CT images and to interpret them is
lacking in most tertiary health centers in Uganda and Tanzania. Additionally, the number of people living with
HIV AIDS continues to rise, and in 2014, it was reported that Tanzania had 1,411,829 people living with HIV
AIDS. However, very little is known about lung cancer and HIV in Africa. With the currently observed increasing
incidence rates of lung cancer, there is an urgent need to study the link between lung cancer and HIV in Uganda
and Tanzania. An additional intriguing question is whether the same radiographic criteria for lung cancer
screening should be uniformly applied across both HIV+ and HIV- patients.
 Our group has been developing new classes of radiomic (computerized feature analysis of radiographic
scans) features for improved discrimination of malignant from benign lung nodules. For instance, we have shown
that the tortuosity of nodule vasculature is substantially different between benign and malignant nodules.
Additionally, we have shown that radiomic features of the peri-nodular surface (immediately outside the lung
nodule on CT and X-rays) were associated with degree of immune response on biopsy tissue specimens. Given
that HIV patients tend to have a low immune cell population, a reasonable conjecture is that the radiomic
signature on radiographic scans will reflect the absence of an immune signature.
 In this project we will develop a radiomics based machine classifier called LunIRiS (Lung Image Risk
Score) for predicting risk of malignancy for a nodule on a chest CT or X-ray scan. We hypothesize that the new
radiomic biomarkers can enable improved non-invasive lung diagnosis in Uganda and Tanzania which has a
higher prevalence of TB and hence TB induced granulomas. Additionally, we will seek to employ these tools to
identify possibly differences in the radiographic phenotype on CT and chest X-rays between HIV+ and HIV- lung
cancer patients and to employ these differences to develop HIV status specific lung cancer screening models.
Finally, the fourth objective will be to create a web-based deployment of LunIRiS to enable decision support and
teleradiology based services between Cleveland and Uganda and Tanzania for improving lung nodule diagnosis
on screeni...

## Key facts

- **NIH application ID:** 10267200
- **Project number:** 5U54CA254566-02
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Anant Madabhushi
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $187,000
- **Award type:** 5
- **Project period:** 2020-09-21 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10267200

## Citation

> US National Institutes of Health, RePORTER application 10267200, Artificial Intelligence for Lung Cancer Characterization in HIV affected populations in Uganda and Tanzania (5U54CA254566-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10267200. Licensed CC0.

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