# Spectral precision imaging for early diagnosis of colorectal lesions with CT colonography

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $385,444

## Abstract

Abstract
Colon cancer is the second leading cause of cancer deaths for men and women in the United States, even
though it could be prevented by early detection and removal of its precursor lesions. Computed tomographic
colonography (CTC) could substantially increase the capacity, safety, and patient compliance of colorectal
examinations. However, the current standard of cathartic bowel preparation for CTC and optical colonoscopy
(OC) is poorly tolerated by patients and has been recognized as a major barrier to colorectal examinations.
Our advanced non-cathartic multi-center computer-assisted CTC trial showed that non-cathartic CTC is easily
tolerated by patients and that radiologists who use computer-aided detection (CADe) can detect large polyps in
size in non-cathartic CTC with high sensitivity, comparable to that of OC. However, SF6-lesions (serrated
lesions, flat lesions <3 mm in height, and polyps 6 – 9 mm in size) were a significant source of false negatives
in the trial. The challenges of detection and visualization of these SF6-lesions in non-cathartic CTC are caused
largely by the inability of the current single-energy CTC technique to differentiate between soft tissues, fecal
tagging, and their partial volumes with lumen air. We propose to employ multi-spectral CTC precision imaging
and artificial intelligence (AI) to overcome these inherent limitations of non-cathartic CTC. Our goal in this
project is to develop a novel deep-learning AI (DEEP-AI) scheme for multi-spectral multi-material (MUSMA)
precision imaging, which will use deep super-learning of high-quality spectral CTC (spCTC) precision images
to boost the diagnostic performance of non-cathartic CTC. We hypothesize that (1) high-quality MUSMA
precision images can be reconstructed from ultra-low-dose (<1 mSv) spCTC scans, (2) DEEP-AI will yield a
detection sensitivity for ≥6 mm SF6-lesions comparable to that of OC, and that (3) the use of DEEP-AI as first
reader will significantly improve radiologists’ detection performance for SF6-lesions and reduce interpretation
time compared with unaided reading, and that it will yield a detection accuracy comparable to that of OC. Our
specific aims are (1) to establish a non-cathartic spCTC and MUSMA precision image database, (2) to develop
a DEEP-AI Interpretation System for visualization and detection of SF6-lesions, and (3) to evaluate the clinical
benefit of the DEEP-AI Interpretation System with non-cathartic spCTC cases. Successful development of the
proposed DEEP-AI Interpretation System will substantially improve human readers’ performance in the
detection of SF6-lesions from non-cathartic CTC examinations that address the problem of patient adherence
to colorectal screening guidelines. Such a scheme will make non-cathartic CTC a highly accurate and
acceptable screening option for large populations, leading to an increased colorectal screening rate, promoting
early diagnosis of colon cancer, and ultimately reducing mortality due to c...

## Key facts

- **NIH application ID:** 9828620
- **Project number:** 5R01CA212382-03
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** HIROYUKI YOSHIDA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $385,444
- **Award type:** 5
- **Project period:** 2017-12-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9828620

## Citation

> US National Institutes of Health, RePORTER application 9828620, Spectral precision imaging for early diagnosis of colorectal lesions with CT colonography (5R01CA212382-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9828620. Licensed CC0.

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