# Risk stratifying indeterminate pulmonary nodules with jointly learned features from longitudinal radiologic and clinical big data

> **NIH NIH F30** · VANDERBILT UNIVERSITY · 2024 · $53,351

## Abstract

PROJECT SUMMARY
Indeterminate pulmonary nodules (IPNs) are highly prevalent radiologic findings that represent a substantial
burden to patients and the national health care system because of the diagnostic challenge they present.
There is a dire need to accurately stratify IPNs into low and high malignancy risk subgroups which are
associated with clinical management pathways that are standardized and well validated. Clinical prediction
models have the potential to do so in a scalable, cost-efficient, automated, and noninvasive manner, but
advances in predictive accuracy must be made before they can make a substantial impact in medical practice.
An unexplored direction in this area is integrating repeated measures of computed tomography (CT) studies
and clinically-collected information within the same prediction model. This joint learning strategy has advantage
of potentially modeling how dynamic radiologic changes like nodule growth rate vary with the trajectory of
clinical variables such as smoking patterns and laboratory abnormalities. This perspective motivates the
hypothesis that integrating information from longitudinal imaging and longitudinal clinical records will
improve personalized IPN risk stratification and lung cancer subclassification From a clinician’s lens,
this finding would not be surprising given the many time-varying modalities that are involved in diagnosis and
decision making. This project leverages artificial intelligence (AI) and radiomic methods to analyze three
retrospective cohorts with the possible addition of a large prospective cohort. The proposed work in Aim 1 will
extend upon existing deep learning techniques to train a joint learning model on longitudinal images and
clinical records to estimate the malignancy probability across time in patients with IPNs in a combined cohort
exceeding 2000 subjects. This novel strategy will be evaluated against single-modality models and convention
models that are used in practice. The evaluation will compare the models’ performance in stratifying IPNs into
the low and high risk subgroups as a measure of clinical utility. Aim 2 asks if longitudinal change in radiomic
features can distinguish between indolent and aggressive lung adenocarcinoma, other lung cancer subtypes,
and pulmonary metastases. The proposed study will be the first to comprehensively characterize longitudinal
radiomics across lung cancer subtypes and has the potential to identify novel longitudinal radiomic features
that will aid early IPN evaluation and noninvasive lung cancer subclassification in patients with repeated
imaging. In summary, the proposed research asks if clever integration of longitudinal information across
different modalities can be leveraged to advance IPN risk stratification and lung cancer subclassification. This
fellowship will be conducted at Vanderbilt University in a highly collaborative training environment with mentors
in medical imaging AI, pulmonary oncology, biomedical informatics,...

## Key facts

- **NIH application ID:** 10871696
- **Project number:** 5F30CA275020-02
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Thomas Zhihe Li
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $53,351
- **Award type:** 5
- **Project period:** 2023-07-01 → 2026-09-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10871696

## Citation

> US National Institutes of Health, RePORTER application 10871696, Risk stratifying indeterminate pulmonary nodules with jointly learned features from longitudinal radiologic and clinical big data (5F30CA275020-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10871696. Licensed CC0.

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