AIMC Topic: Carcinoma, Intraductal, Noninfiltrating
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Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features.
EBioMedicine
May 28, 2025
BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if untreated. Currently, DCIS that might progress to IBC cannot reliably be identified. Therefore, most ...
Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.
Scientific reports
Mar 3, 2025
Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current dia...
Precision HER2: a comprehensive AI system for accurate and consistent evaluation of HER2 expression in invasive breast Cancer.
BMC cancer
Sep 30, 2024
BACKGROUND: With the development of novel anti-HER2 targeted drugs, such as ADCs, it has become increasingly important to accurately interpret HER2 expression in breast cancer. Previous studies have demonstrated high intra-observer and inter-observer...
Histopathologic Differential Diagnosis and Estrogen Receptor/Progesterone Receptor Immunohistochemical Evaluation of Breast Carcinoma Using a Deep Learning-Based Artificial Intelligence Architecture.
The American journal of pathology
Sep 4, 2024
In breast carcinoma, invasive ductal carcinoma (IDC) is the most common histopathologic subtype, and ductal carcinoma in situ (DCIS) is a precursor of IDC. These two often occur concomitantly. The immunohistochemical staining of estrogen receptor (ER...
Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study.
Breast cancer research : BCR
May 24, 2024
BACKGROUND: Patients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70-80% of the biopsies are negative/benign. In this study, we developed a deep learning classification algorithm...
Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.
Cancer imaging : the official publication of the International Cancer Imaging Society
Apr 5, 2024
BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal ca...
Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients.
Science bulletin
Mar 29, 2024
An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specime...
Deep-learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy.
Histopathology
Jan 30, 2024
AIMS: Risk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low...
Potential of AI-assisted analysis of mammograms for extensive intraductal component in invasive breast cancer.
European radiology
Nov 16, 2023
Performance of artificial intelligence in 7533 consecutive prevalent screening mammograms from the BreastScreen Australia program.
European radiology
Nov 13, 2023
OBJECTIVES: To assess the performance of an artificial intelligence (AI) algorithm in the Australian mammography screening program which routinely uses two independent readers with arbitration of discordant results.
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