# Improving the Generalizability of Deep Neural Networks by Teaching them Lung Cancer Pathophysiology

> **NIH NIH F99** · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · 2022 · $33,190

## Abstract

PROJECT SUMMARY
Diagnostic and treatment approaches for non-small cell lung cancer (NSCLC) have evolved over the last decade
from primarily empirical methodologies to objective strategies that rely on clinical characteristics of the patient
and morphological features of the nodule. Following recommendations by the United States Preventive Service
Task Force (USPSTF), high-risk individuals are screened yearly with low-dose computed tomography (LDCT)
as this provides high sensitivity with acceptable specificity for lung cancer. However, the introduction of LDCT
as the primary screening modality for lung cancer has increased detection rates of indeterminate pulmonary
nodules that then require invasive investigation. This decreases the quality of life for at-risk individuals through
repeated follow-ups and procedures, and greatly increases anxiety over what usually turns out to a benign
nodule. In this proposal, we aim to improve upon these outcomes by determining the features that convolutional
neural networks (CNNs) utilize when classifying lung nodules as either or benign. We will also determine if
providing CNNs with pre-specified histologic image features known to be associated with lung cancer improves
their ability to generalize to novel images outside the image set used to train them. The central hypothesis of
this proposal is that increasing the attention of a CNN on LDCT image features that are accepted as
being pathophysiologically relevant will improve its generalizability to novel images and thus its ability
to accurately distinguish between malignant versus benign nodules. In the F99 Aim of this proposal, we
will address this hypothesis by utilizing LDCT images from the National Lung Screening Trial (NLST) together
with concept activation vectors to determine which parenchymal and tumor-specific features are used by CNNs
to classify lung nodules. In the K00 aim, we will determine if endophenotypes extracted from the COPDgene
LDCT image set can be used to improve CNN generalizability. Completion of these aims will lead to an increased
understanding of the morphologic biomarkers of lung cancer inherent in LDCT images of the lung that are most
important for accurate diagnosis. This will have potential application to the improvement of CNN classification
performance in other medical domains. In addition, by adhering to the training program outlined in this proposal
I will gain high levels of expertise in image biomarkers, early cancer pathogenesis and detection, genetic
networks, and genomics. These will collectively serve as a solid foundation for my future career as an
independent biomedical investigator.

## Key facts

- **NIH application ID:** 10529498
- **Project number:** 1F99CA274713-01
- **Recipient organization:** UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
- **Principal Investigator:** Axel Herve Patrick Masquelin
- **Activity code:** F99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $33,190
- **Award type:** 1
- **Project period:** 2022-08-01 → 2023-08-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10529498

## Citation

> US National Institutes of Health, RePORTER application 10529498, Improving the Generalizability of Deep Neural Networks by Teaching them Lung Cancer Pathophysiology (1F99CA274713-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10529498. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*