Evidence map›Paper›PMID 41062885›Full record

ArticleVirchows Archiv : an international journal of pathology2026

Utility of quantitative pathologic analysis of pT1 colorectal carcinomas to improve prediction of lymph node metastasis.

Priya Nayak, Heidi Kosiorek, Reetesh K Pai, Sameer Shivji, Catherine E Hagen, Rondell P Graham, Daniel D Buchanan, Mark A Jenkins, Amanda I Phipps, Loic Le Marchand and 10 more

Abstract readMulticenter Study
In one paragraph

Article in Virchows Archiv : an international journal of pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

20 authors.

Priya NayakDepartment of Pathology and Laboratory Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA.
Heidi KosiorekDepartment of Quantitative Health Sciences, Mayo Clinic, Phoenix, AZ, USA.
Reetesh K PaiDepartment of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Sameer ShivjiDepartment of Pathology and Laboratory Medicine, Mount Sinai Hospital, 600 University Ave, Toronto, ON, Canada.
Catherine E HagenDepartment of Pathology and Laboratory Medicine, Mayo Clinic, Rochester, MN, USA.
Rondell P GrahamDepartment of Pathology and Laboratory Medicine, Mayo Clinic, Rochester, MN, USA.
Daniel D BuchananColorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia.
Mark A JenkinsUniversity of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia.
Amanda I PhippsPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Loic Le MarchandPopulation Sciences in the Pacific Program (Epidemiology), University of Hawaii, Honolulu, HI, USA.
Christina WuDivision of Medical Oncology, Department of Medicine, Mayo Clinic, Phoenix, AZ, USA.
Niloy J SamadderDivision of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Phoenix, AZ, USA.
Carol J SwallowSinai Health System, Lunenfeld-Tanenbaum Research Institute, Toronto, ON, Canada.
Steven J GallingerSinai Health System, Lunenfeld-Tanenbaum Research Institute, Toronto, ON, Canada.
Robert C GrantDivision of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Thomas Westerling-BuiAiforia Inc., Cambridge, MA, USA.
James ConnerDepartment of Pathology and Laboratory Medicine, Mount Sinai Hospital, 600 University Ave, Toronto, ON, Canada.
David P CyrSinai Health System, Lunenfeld-Tanenbaum Research Institute, Toronto, ON, Canada.
Richard KirschDepartment of Pathology and Laboratory Medicine, Mount Sinai Hospital, 600 University Ave, Toronto, ON, Canada.
Rish K PaiDepartment of Pathology and Laboratory Medicine, Mount Sinai Hospital, 600 University Ave, Toronto, ON, Canada. rish.pai@sinaihealth.ca.ORCID http://orcid.org/0000-0002-2692-221X

Funding

Division of Cancer Epidemiology and Genetics, National Cancer Institute U01CA167551
6 · The paper itself

Abstract

According to the National Comprehensive Cancer Network (NCCN), submucosally invasive (pT1) colorectal carcinomas (CRCs) should be evaluated for tumor grade, lymphatic invasion, and tumor budding to determine the risk of lymph node metastasis. The presence of any one of these high-risk features is an indication for surgery in endoscopically removed pT1 CRCs. In this study, we determined if quantitative pathologic analysis with the QuantCRC algorithm can augment NCCN risk stratification in a multi-institutional cohort of 512 surgically resected pT1 CRC. LASSO regression identified %high-grade, %inflammatory stroma (stromal area), and %tumor budding/poorly differentiated clusters (%TB/PDC) as important QuantCRC features and were used in subsequent logistic regression analysis. Five logistic regression models were built using NCCN and QuantCRC variables, with the combined NCCN + QuantCRC model providing the highest Area Under the Curve (AUC) of 0.74 (95% CI 0.68-0.81). A predicted probability cutoff of 0.092 provided a sensitivity of 78.3% and specificity of 62.1% in the NCCN + QuantCRC model with a 24.3% rate of lymph node positivity for high-risk (HR) tumors compared to 5.2% for low-risk (LR) CRCs. Fifteen pT1 CRCs were reclassified from NCCN LR to NCCN + QuantCRC HR and 3/15 (20%) demonstrated lymph node positivity. The median predicted probability of lymph node metastasis in the NCCN + QuantCRC model was used to define two HR groups (HR1: 0.092-0.218 and HR2: > 0.218). HR2 CRCs had a rate of lymph node positivity of 31.5% compared to 17.1% for HR1 CRCs (P = 0.02). Lastly, the NCCN + QuantCRC model was validated in a cohort of 29 endoscopically resected pT1 CRCs followed by surgical resection. In the NCCN + QuantCRC model, the 8 pN + CRCs in this cohort had a higher median predicted probability of lymph node metastasis compared to 21 pN0 CRCs (0.219 vs. 0.080, P = 0.04). In summary, the addition of variables from QuantCRC can improve risk stratification of pT1 CRCs over NCCN criteria alone.

Indexed as

Colorectal NeoplasmsLymphatic MetastasisLymph NodesAgedFemaleHumansMaleMiddle AgedNeoplasm StagingPredictive Value of TestsRisk AssessmentColorectal cancerLymph node metastasisMalignant polypQuantitative digital pathology

Identifiers

PMID41062885
PMCPMC13264598

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.