ArticleBiomedical optics express2019
Biophysical basis of skin cancer margin assessment using Raman spectroscopy.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 47 citations in OpenAlex.
- Raman Spectroscopy for Differentiating High- and Low-Grade Canine Cutaneous Mast Cell Tumours.Veterinary and comparative oncology · 2026Article
- Machine learning-guided bioelectrical impedance mapping for rapid adjunctive margin assessment in Mohs surgery.npj biomedical innovations · 2026Article
- Novel histological observation method using the scattered light of unstained collagen sections.Scientific reports · 2026Article
- AI-assisted identification of nonmelanoma skin cancer structures based on combined line-field confocal optical coherence tomography and confocal Raman microspectroscopy.Journal of biomedical optics · 2025Article
- Machine learning-based classification of spatially resolved diffuse reflectance and autofluorescence spectra acquired on human skin for actinic keratoses and skin carcinoma diagnostics aid.Journal of biomedical optics · 2025Article
- Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study.Physical and engineering sciences in medicine · 2024Article
- Portable System for In-Clinic Differentiation of Skin Cancers from Benign Skin Lesions and Inflammatory Dermatoses.JID innovations : skin science from molecules to population health · 2024Article
- From Vibrations to Visions: Raman Spectroscopy's Impact on Skin Cancer Diagnostics.Journal of clinical medicine · 2023Review
- Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review.Biosensors · 2023Review
- Diagnostic Biomarkers for Gestational Diabetes Mellitus Using Spectroscopy Techniques: A Systematic Review.Diseases (Basel, Switzerland) · 2023Review
- Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning.Nature communications · 2023Article
- Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.Journal of biomedical optics · 2022Article
- Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging.Biosensors · 2022Article
- Co-localized line-field confocal optical coherence tomography and confocal Raman microspectroscopy for three-dimensional high-resolution morphological and molecular characterization of skin tissuesBiomedical optics express · 2022Article
- Handheld macroscopic Raman spectroscopy imaging instrument for machine-learning-based molecular tissue margins characterization.Journal of biomedical optics · 2021Article
- Cutaneous Oncology: Strategies for Melanoma Prevention, Diagnosis, and Therapy.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Achieving adequate margins during tumor margin resection is critical to minimize the recurrence rate and maximize positive patient outcomes during skin cancer surgery. Although Mohs micrographic surgery is by far the most effective method to treat nonmelanoma skin cancer, it can be limited by its inherent required infrastructure, including time-consuming and expensive on-site histopathology. Previous studies have demonstrated that Raman spectroscopy can accurately detect basal cell carcinoma (BCC) from surrounding normal tissue; however, the biophysical basis of the detection remained unclear. Therefore, we aim to explore the relevant Raman biomarkers to guide BCC margin resection. Raman imaging was performed on skin tissue samples from 30 patients undergoing Mohs surgery. High correlations were found between the histopathology and Raman images for BCC and primary normal structures (including epidermis, dermis, inflamed dermis, hair follicle, hair shaft, sebaceous gland and fat). A previously developed model was used to extract the biochemical changes associated with malignancy. Our results showed that BCC had a significantly different concentration of nucleus, keratin, collagen, triolein and ceramide compared to normal structures. The nucleus accounted for most of the discriminant power (90% sensitivity, 92% specificity - balanced approach). Our findings suggest that Raman spectroscopy is a promising surgical guidance tool for identifying tumors in the resection margins.
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.