ArticleRoyal Society open science2016
Automatic prediction of tumour malignancy in breast cancer with fractal dimension.
Article in Royal Society open science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Article
- Higher density of compact B cell clusters in invasive front may contribute to better prognosis in pancreatic ductal adenocarcinoma.Discover oncology · 2025Article
- Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals.Sensors (Basel, Switzerland) · 2024Article
- Article
- Breast Tumor Tissue Image Classification Using Single-Task Meta Learning with Auxiliary Network.Cancers · 2024Article
- Breast Tumor Tissue Image Classification Using DIU-Net.Sensors (Basel, Switzerland) · 2022Article
- Article
- Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy.iScience · 2022Article
- Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor.Laboratory investigation; a journal of technical methods and pathology · 2022Article
- Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection.BioMed research international · 2022Article
- Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy.PloS one · 2022Article
- A High-Precision Classification Method of Mammary Cancer Based on Improved DenseNet Driven by an Attention Mechanism.Computational and mathematical methods in medicine · 2022Article
- Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning.Computational intelligence and neuroscience · 2022Article
- Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis.Scientific reports · 2021Article
- Spatial distribution of B cells and lymphocyte clusters as a predictor of triple-negative breast cancer outcome.NPJ breast cancer · 2021Article
- Review
- Fractal dimension analysis as an easy computational approach to improve breast cancer histopathological diagnosis.Applied microscopy · 2021Article
- Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets.Computational intelligence and neuroscience · 2021Article
- Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images-a Comparative Insight.Journal of digital imaging · 2020Article
- Structured light imaging for breast-conserving surgery, part II: texture analysis and classification.Journal of biomedical optics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the most prevalent types of cancer today in women. The main avenue of diagnosis is through manual examination of histopathology tissue slides. Such a process is often subjective and error-ridden, suffering from both inter- and intraobserver variability. Our objective is to develop an automatic algorithm for analysing histopathology slides free of human subjectivity. Here, we calculate the fractal dimension of images of numerous breast cancer slides, at magnifications of 40×, 100×, 200× and 400×. Using machine learning, specifically, the support vector machine (SVM) method, the F1 score for classification accuracy of the 40× slides was found to be 0.979. Multiclass classification on the 40× slides yielded an accuracy of 0.556. A reduction of the size and scope of the SVM training set gave an average F1 score of 0.964. Taken together, these results show great promise in the use of fractal dimension to predict tumour malignancy.
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.