ReviewBioengineering (Basel, Switzerland)2024
Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.
Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 18F-FDG PET/CT Semiquantitative and Radiomic Features for Assessing Pathologic Axillary Lymph Node Status in Clinical Stage I-III Breast Cancer Patients: A Systematic Review.Current oncology (Toronto, Ont.) · 2025Pooled it
- Artificial intelligence in breast ultrasound: a systematic review of research advances.Frontiers in oncology · 2025Pooled it
- AI-Assisted Scoring Improves Interobserver Agreement in Breast Cancer Biomarker Evaluation.Cancers · 2026Article
- Optical Imaging for Biomedical Applications: From Rich Optical Contrast to Reliable Clinical Information.Bioengineering (Basel, Switzerland) · 2026Article
- Radioguided Surgery and Axillary Management in Breast Cancer: From Molecular Imaging to 3D Navigation Toward Personalized Treatment.Life (Basel, Switzerland) · 2026Review
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Risk of Radiation-Associated Contralateral Breast Cancer in Germline Mutation Carriers: A Meta-Analysis and Systematic Review.Cancers · 2026Review
- Exploring the clinical value of quantitative ultrasound scoring method in optimizing the classification of breast BI-RADS category 4a nodules in the Tibetan Plateau region.Frontiers in oncology · 2026Article
- Dendritic cell-based immunotherapy modulates the systemic inflammatory profile in a 4T1 breast cancer model.Exploration of targeted anti-tumor therapy · 2026Article
- Response-adaptive breast cancer care in the grey zones: integrating ER and HER2 targeted PET, FDG PET/CT under immunotherapy, and ctDNA kinetics.Exploration of targeted anti-tumor therapy · 2026Review
- Multi-colorized tint map for distinguishing triple-negative breast cancers from cysts and fibroadenomas based on the tumor margin.Frontiers in oncology · 2026Article
- Emerging roles of haemostatic proteins as markers of disease progression and prognosis in breast cancer.Exploration of targeted anti-tumor therapy · 2026Review
- Ultrasound-based artificial intelligence for breast lesion classification.Frontiers in oncology · 2026Review
- Survival prediction in triple-negative breast cancer: a Cox model with fairness assessment using ISO/IEC TR 24027:2021 in a MENA cohort.Exploration of targeted anti-tumor therapy · 2026Article
- Exploring the recurrence and metastasis of breast invasive ductal carcinoma based on machine learning and survival analysis.Frontiers in oncology · 2026Article
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- Intra- and peritumoral radiomics for predicting equivocal HER2 status of breast cancer on contrast-enhanced mammography.Frontiers in oncology · 2026Article
- Ultrasound-based radiomics and habitat analysis for noninvasive assessment of Ki-67 overexpression in breast cancer.Frontiers in oncology · 2026Article
- Latent class analysis of conventional ultrasound features: a novel approach to predicting non-response to neoadjuvant chemotherapy in breast cancer.Frontiers in oncology · 2026Article
- Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The most commonly occurring cancer in the world is breast cancer with more than 500,000 cases across the world. The detection mechanism for breast cancer is endoscopist-dependent and necessitates a skilled pathologist. However, in recent years many computer-aided diagnoses (CADs) have been used to diagnose and classify breast cancer using traditional RGB images that analyze the images only in three-color channels. Nevertheless, hyperspectral imaging (HSI) is a pioneering non-destructive testing (NDT) image-processing technique that can overcome the disadvantages of traditional image processing which analyzes the images in a wide-spectrum band. Eight studies were selected for systematic diagnostic test accuracy (DTA) analysis based on the results of the Quadas-2 tool. Each of these studies' techniques is categorized according to the ethnicity of the data, the methodology employed, the wavelength that was used, the type of cancer diagnosed, and the year of publication. A Deeks' funnel chart, forest charts, and accuracy plots were created. The results were statistically insignificant, and there was no heterogeneity among these studies. The methods and wavelength bands that were used with HSI technology to detect breast cancer provided high sensitivity, specificity, and accuracy. The meta-analysis of eight studies on breast cancer diagnosis using HSI methods reported average sensitivity, specificity, and accuracy of 78%, 89%, and 87%, respectively. The highest sensitivity and accuracy were achieved with SVM (95%), while CNN methods were the most commonly used but had lower sensitivity (65.43%). Statistical analyses, including meta-regression and Deeks' funnel plots, showed no heterogeneity among the studies and highlighted the evolving performance of HSI techniques, especially after 2019.
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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.