Evidence map›Paper›PMID 42117201›Full record

ArticleTurk patoloji dergisi2026

Artificial Intelligence-Assisted Invasion Risk Scoring in Tru-Cut Biopsies of Ductal Carcinoma in Situ; Predictive Role of Estrogen Receptor and HER2 Expression.

Evren Uzun, Kubra Bulut

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Article in Turk patoloji dergisi, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Evren UzunDepartment of Pathology, Gaziantep University Hospital, GAZİANTEP, TÜRKİYE.
Kubra BulutDepartment of Pathology, Şanl�±urfa Education and Research Hospital, ŞANLIURFA, TÜRKİYE.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveInvasion can be detected in up to 50% of excision specimens in cases diagnosed with ductal carcinoma in-situ (DCIS) in tru-cut biopsies. The predicting of invasion is critical for managing the treatment. The objective of this study was to ascertain the predictive role of histological characteristics and estrogen receptor (ER) and HER2 expression of DCIS with respect to invasion and to create an artificial intelligence (AI)-assisted invasion risk scoring. MATERIAL AND

methodsA total of 140 tru-cut biopsies with DCIS were included. We evaluated the predominant structural pattern, nuclear grade, presence of comedonecrosis, and ER/HER2 expressions and their relationship with invasion upstage. Based on the results, an invasion risk scoring system was created using OpenAI-ChatGPT 5.2.

resultsOf the 140 cases, 70 (50%) were high-grade (DCIS-3), 49 (35%) were intermediate-grade (DCIS-2), and 21 (15%) were low-grade (DCIS-1). DCIS-1 and DCIS-2 showed significant association with a cribriform pattern, and DCIS-3 showed with a solid pattern. When the results were compared with those of the final specimens, invasion upstaging occurred in 9.5% of DCIS-1, 38.7% of DCIS-2, and 52.8% of DCIS-3 cases. A solid pattern, high nuclear grade, comedonecrosis, and ER-/HER2- profile showed high correlation with invasion. In AI-assisted risk scoring, DCIS cases were divided into three groups: low, medium, and high risk. The invasion upstaging rates in these groups were 11.44%, 28.97%, and 64.19%, respectively.

conclusionDCIS with high nuclear grade, comedonecrosis, a solid pattern, and the ER-/HER2+ or ER-/HER2- immunoprofile should be considered high risk for invasion. They should be approached as invasive tumors and evaluated in terms of lymph node staging and neoadjuvant treatment.

Indexed as

Biomarkers, TumorBreast NeoplasmsCarcinoma, Intraductal, NoninfiltratingGenerative Artificial IntelligencePredictive Learning ModelsAdultAgedAged, 80 and overBiopsy, NeedleBreastErb-b2 Receptor Tyrosine KinasesFemaleHumansImmunohistochemistryMiddle AgedNeoplasm GradingBiomarkers, TumorERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesReceptors, Estrogen

Identifiers

PMID42117201
PMCPMC13600665

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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.