ReviewDiagnostics (Basel, Switzerland)2022
Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis 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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Raman Spectroscopy and Machine Learning in the Diagnosis of Breast Cancer.Lasers in medical science · 2025Pooled it
- Rapid serum-based differentiation of ovarian tumors and assessment of post-treatment disease-free status using portable Raman spectroscopy and machine learning.Analytical and bioanalytical chemistry · 2026Article
- Machine learning reconstruction of digit bone Raman spectra enables noninvasive transcutaneous detection of systemic osteoporosis.Biomedical optics express · 2026Article
- Shining a light on brain tumors: a review of Raman spectroscopy in the operating room.Journal of biomedical optics · 2026Review
- Raman reporters for SERS/SERRS biosensing and biomedicine: design, applications, and challenges.Mikrochimica acta · 2026Review
- Potential of SERS and proteomics for biomarker detection in cancer cells.Analytical and bioanalytical chemistry · 2026Article
- Causal Responsibility Based Explainable AI for Vibrational Spectroscopy Applied to Oral FTIR and Oesophageal Raman Diagnostics.Research square · 2026Article
- Raman Spectroscopy for Probing Pathological Protein Aggregates: Potential and Perspectives for Advanced Diagnostic Applications.International journal of molecular sciences · 2026Review
- From Drops to Decisions: AI/ML-Driven Biofluidics for Clinical Diagnostics and Healthcare Intelligence.Analytical chemistry · 2026Review
- Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles.ACS nano · 2025Review
- Label-free estimation of regulatory T cell activation markers using Raman spectroscopy with machine learning.Scientific reports · 2025Article
- Explainable AI-Based Feature Selection Approaches for Raman Spectroscopy.Diagnostics (Basel, Switzerland) · 2025Article
- Fluorescence Guided Raman Spectroscopy enables the training of robust support vector machines for the detection of tumour marker proteins.Scientific reports · 2025Article
- An objective diagnosis of gout and calcium pyrophosphate deposition disease with machine learning of Raman spectra acquired in a point-of-care setting.Rheumatology (Oxford, England) · 2025Article
- Detection of Respiratory Disease Based on Surface-Enhanced Raman Scattering and Multivariate Analysis of Human Serum.Diagnostics (Basel, Switzerland) · 2025Article
- Random splicing assisted deep learning for breast cancer cell line classification via Raman spectroscopy.Computational and structural biotechnology journal · 2025Article
- Recent advances in applications of artificial intelligence-assisted Raman spectroscopy in diagnosis of cancers.Frontiers in molecular biosciences · 2025Review
- System transferability of Raman-based oesophageal tissue classification using modern machine learning to support multi-centre clinical diagnostics.BJC reports · 2024Article
- Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders.Biomedical optics express · 2024Article
- Power of Light: Raman Spectroscopy and Machine Learning for the Detection of Lung Cancer.ACS omega · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.
Indexed as
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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.