ArticleLaboratory investigation; a journal of technical methods and pathology2021
Deep learning-based grading of ductal carcinoma in situ in breast histopathology images.
Article in Laboratory investigation; a journal of technical methods and pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Mechanosensitive TRPV4 immunohistochemistry improves deep learning-based classification of ductal carcinoma in situ beyond H&E morphology.The journal of pathology. Clinical research · 2026Article
- Enabling DCIS subtyping: leveraging foundation models for robust grading and molecular biomarker scoring.NPJ breast cancer · 2026Article
- A novel multimodal framework integrating pathomics, deep learning, and machine learning for breast cancer histological grades classification.Diagnostic pathology · 2026Article
- AI-Based Detection of Dental Features on CBCT: Dual-Layer Reliability Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features.EBioMedicine · 2025Article
- Integrating deep learning features from mammography with SHAP values for a machine learning model predicting over 5-year recurrence of breast ductal carcinomaFrontiers in immunology · 2025Article
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
- Invasive recurrence after breast conserving treatment of ductal carcinoma in situ of the breast in the Netherlands: time trends and the association with tumour grade.British journal of cancer · 2024Article
- An algorithm-based technique for counting mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections.Journal of cancer research and clinical oncology · 2024Article
- Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.The Journal of pathology · 2023Review
- Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists' Assessment Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.Scientific reports · 2022Article
- A deep learning model for breast ductal carcinoma in situ classification in whole slide images.Virchows Archiv : an international journal of pathology · 2022Article
- Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence applied to breast pathology.Virchows Archiv : an international journal of pathology · 2022Review
- Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning.Cancers · 2021Article
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8 authors.
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Abstract
Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κ
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