Evidence map›Paper›PMID 41633187›Full record

ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2026

A deep learning-based automated pipeline for colorectal cancer detection in contrast-enhanced CT images.

Chenhui Qiu, Sarah Miller, Barathi Subramanian, Angela Ryu, Haiyu Zhang, George A Fisher, Nigam H Shah, John Mongan, Curtis Langlotz, Peter Poullos and 1 more

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In one paragraph

Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Chenhui QiuDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: chenhuiq@stanford.edu.
Sarah MillerDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Barathi SubramanianDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Angela RyuDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Haiyu ZhangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
George A FisherDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Nigam H ShahDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA; Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.
John MonganDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; Division of Clinical Informatics and Digital Transformation, Department of Medicine, University of California, San Francisco, CA, USA.
Curtis LanglotzDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA; Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.
Peter PoullosDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Jeanne ShenDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA. Electronic address: jeannes@stanford.edu.

Funding

Multi-modal machine learning to guide adjuvant therapy in surgically resectable colorectal cancerR01CA270437 · NCI · STANFORD UNIVERSITY · PI Jeanne Shen · 2023 to 2026
$2.5M
NCI NIH HHS R01 CA270437
6 · The paper itself

Abstract

Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide and a leading cause of cancer-related mortality. This study aims to investigate an automatic detection pipeline for identification and localization of the primary CRC in portal venous phase contrast-enhanced CT scans, which is a crucial first step for downstream CRC staging, prognostication, and treatment planning. We propose a deep learning-based automated detection pipeline using YOLOv11 as the baseline architecture. A ResNet50 module was incorporated into the YOLOv11 backbone to enhance image feature extraction. Additionally, a scale-adaptive loss function, which introduces an adaptive coefficient and a scaling factor to adaptively measure the Intersection over Union (IoU) and center point distance for improving box regression performance, was designed to further improve detection performance. The proposed pipeline achieved a recall of 0.8092, precision of 0.8187, and F-1 score of 0.8139 for CRC detection on our in-house dataset at the patient level (inter-patient evaluation) and a recall of 0.9949, precision of 0.9894, and F-1 score of 0.9921 at the slice level (intra-patient evaluation). Validation on an external public dataset demonstrated that our pipeline, when trained on a patient-level in-house dataset, obtained a recall of 0.8283, precision of 0.8414, and F-1 score of 0.8348 and, when trained on a slice-level in-house dataset, achieved a recall of 0.6897, precision of 0.7888, and F-1 score of 0.7358, outperforming existing representative detection methods. The superior CRC detection performance on the in-house CT dataset and state-of-the-art generalization performance on the public dataset (with a 31.97 %age point improvement in detection sensitivity (recall) over the next closest state-of-the-art method), highlight the potential translational value of our pipeline for CRC clinical decision support, conditional upon validation in larger cohorts.

Indexed as

Colorectal NeoplasmsContrast MediaDeep LearningRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedConvolutional Neural NetworksDetection AlgorithmsHumansContrast MediaArtificial intelligenceColorectal cancerCTDeep learningDetectionLocalization

Identifiers

PMID41633187
PMCPMC13035465

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.