ArticleScientific reports2017
Automated Classification of Benign and Malignant Proliferative Breast Lesions.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed.
- Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.Journal of pathology informatics · 2024Review
- Artificial Intelligence in Histologic Diagnosis of Ductal Carcinoma In Situ.Mayo Clinic proceedings. Digital health · 2023Review
- Artificial intelligence applied to breast pathology.Virchows Archiv : an international journal of pathology · 2022Review
- Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.Journal of medical systems · 2021Review
- Intraprocedural Artificial Intelligence for Colorectal Cancer Detection and Characterisation in Endoscopy and Laparoscopy.Surgical innovation · 2021Article
- Improving DCIS diagnosis and predictive outcome by applying artificial intelligence.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions.BioMed research international · 2020Review
- Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.JAMA network open · 2019Article
- Artificial intelligence in gastrointestinal endoscopy: The future is almost here.World journal of gastrointestinal endoscopy · 2018Review
- A METHOD FOR QUANTIFICATION OF CALPONIN EXPRESSION IN MYOEPITHELIAL CELLS IN IMMUNOHISTOCHEMICAL IMAGES OF DUCTAL CARCINOMA IN SITU.Proceedings. IEEE International Symposium on Biomedical Imaging · 2018Article
- Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology.Journal of pathology informatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Misclassification of breast lesions can result in either cancer progression or unnecessary chemotherapy. Automated classification tools are seen as promising second opinion providers in reducing such errors. We have developed predictive algorithms that automate the categorization of breast lesions as either benign usual ductal hyperplasia (UDH) or malignant ductal carcinoma in situ (DCIS). From diagnosed breast biopsy images from two hospitals, we obtained 392 biomarkers using Dong et al.'s (2014) computational tools for nuclei identification and feature extraction. We implemented six machine learning models and enhanced them by reducing prediction variance, extracting active features, and combining multiple algorithms. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for performance evaluation. Our top-performing model, a Combined model with Active Feature Extraction (CAFE) consisting of two logistic regression algorithms, obtained an AUC of 0.918 when trained on data from one hospital and tested on samples of the other, a statistically significant improvement over Dong et al.'s AUC of 0.858. Pathologists can substantially improve their diagnoses by using it as an unbiased validator. In the future, our work can also serve as a valuable methodology for differentiating between low-grade and high-grade DCIS.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.