ArticleThe Analyst2023
Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.
Article in The Analyst, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- The application of infrared thermography technology in flap: A perspective from bibliometric and visual analysis.International wound journal · 2023Pooled it
- Autoencoder-Based Detection of Nanoplastics in Biological Matrices via Infrared Hyperspectral Imaging.ACS measurement science au · 2026Article
- Biological and Biomedical Applications of Optical Photothermal Infrared Spectroscopy (O-PTIR).Applied spectroscopy · 2026Review
- Advancing early detection of chromophobe renal cell carcinoma: a Bayesian optimization approach to machine learning models.Scientific reports · 2026Article
- Probing 3D Molecular Orientation and Crystallization Behavior in PLLA/PEG Spherulites Using O-PTIR.Analytical chemistry · 2026Article
- An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images.Scientific reports · 2025Article
- Enabling Chemical Holography: A Comprehensive Framework and Implementation.IEEE photonics journal · 2025Article
- Rapid Hyperspectral Photothermal Mid-Infrared Spectroscopic Imaging from Sparse Data for Gynecologic Cancer Tissue Subtyping.Analytical chemistry · 2024Article
- Cervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging.Chemical & biomedical imaging · 2024Article
- A tutorial on optical photothermal infrared (O-PTIR) microscopy.APL photonics · 2024Article
- Deep fine-KNN classification of ovarian cancer subtypes using efficientNet-B0 extracted features: a comprehensive analysis.Journal of cancer research and clinical oncology · 2024Article
- An optical photothermal infrared investigation of lymph nodal metastases of oral squamous cell carcinoma.Scientific reports · 2024Article
- Surface plasmons-phonons for mid-infrared hyperspectral imaging.Science advances · 2024Article
- Photothermal Radiometry Data Analysis by Using Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming and subjective and requires extensive expertise. This paper presents the first label-free, quantitative, and automated histological recognition of ovarian tissue subtypes using a new MIRSI technique. This optical photothermal infrared (O-PTIR) imaging technique provides a 10× enhancement in spatial resolution relative to prior instruments. It enables sub-cellular spectroscopic investigation of tissue at biochemically important fingerprint wavelengths. We demonstrate that the enhanced resolution of sub-cellular features, combined with spectroscopic information, enables reliable classification of ovarian cell subtypes achieving a classification accuracy of 0.98. Moreover, we present a statistically robust analysis from 78 patient samples with over 60 million data points. We show that sub-cellular resolution from five wavenumbers is sufficient to outperform state-of-the-art diffraction-limited techniques with up to 235 wavenumbers. We also propose two quantitative biomarkers based on the relative quantities of epithelia and stroma that exhibit efficacy in early cancer diagnosis. This paper demonstrates that combining deep learning with intrinsic biochemical MIRSI measurements enables quantitative evaluation of cancerous tissue, improving the rigor and reproducibility of histopathology.
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.