Improving the Diagnosis of Uterine Smooth Muscle Tumors

A recent study explored a new method that could better distinguish between benign and malignant uterine smooth muscle tumors. This study, which was published in the journal Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, demonstrated that Fourier transform infrared imaging (FTIRI) spectroscopy, when combined with histology and statistical analysis, can serve as a more effective alternative technique for this type of clinical analysis.¹

FTIRI is increasingly becoming more widely used, especially in clinical analysis. Numerous studies have applied this technique to study tumor samples in the oral cavity and lungs, for example.^2–4 Like with any other human organ or area of the body, uterine smooth muscle tumors require accurate classification. Because histological features can overlap, particularly in borderline cases such as smooth muscle tumors of uncertain malignant potential (STUMP), accurately classifying tumor samples is more difficult.¹ As a result, several studies explored this topic, testing how FTIRI can be effective in classifying tumors accurately.^5,6

In this study, the researchers used FTIRI spectroscopy to reproduce spectral profiles that correlated with tumor type. They accomplished this by applying micrometer-scale spatial resolution and analyzing the data with multivariate statistics.¹ For pathologists and clinical laboratories, this offers a potential route toward more objective, image-based diagnostics in gynecologic oncology, an area where misclassification can lead to overtreatment or undertreatment. Although preliminary, the work provides a framework for integrating label-free vibrational spectroscopy into routine tissue assessment workflows.¹

The team’s experimental procedure involved analyzing formalin-fixed uterine tissue sections from multiple tumor categories: five leiomyosarcomas (LMS), 13 leiomyomas spanning usual, cellular, apoplectic, and bizarre histotypes, and three samples of healthy myometrium.¹ For each case, adjacent serial sections were prepared (two on glass slides for histology and one on calcium fluoride windows for FT-IR imaging) to ensure spatial correspondence between morphological and spectroscopic data.¹

Once the spectral data sets were obtained, the researchers used multivariate approaches to evaluate them. In this study, they used principal component analysis (PCA) and hierarchical cluster analysis (HCA), as well as univariate metrics.¹ Using these methods helped uncover the clustering patterns aligned with tumor categories, which allowed them to highlight biochemical differences in extracellular matrix and cellular components.¹

What were the key findings from this study?

The researchers found that the major discriminators across tumor groups were the collagen-associated bands. Leiomyosarcomas showed altered collagen organization and relative abundance compared with benign leiomyomas and normal myometrium.¹ Additional markers associated with nucleic acids and glycosylated molecules further separated malignant from benign tissue and differentiated leiomyoma subtypes, including cellular and bizarre variants that can mimic malignancy histologically.¹

Importantly, the spectral signatures were consistent within groups despite known biological heterogeneity, suggesting robustness sufficient for further validation. The authors emphasize that FTIRI provided complementary information rather than replacing histology, supporting a combined diagnostic workflow.¹

For pathology laboratories exploring digital and molecular augmentation of microscopy, FTIRI offers several advantages: it is label-free, non-destructive, compatible with standard tissue preparation (aside from infrared-transparent substrates), and its ability to be configured to perform microscopic measurements.^1,7 The integration of chemometric analysis further enables automated pattern recognition, which could reduce inter-observer variability in challenging cases such as STUMP or atypical leiomyomas.¹

The approach presented in the study aligns with broader trends in spectral pathology and computational histology, where quantitative biochemical imaging is used to supplement morphology. If validated in larger cohorts, FTIRI-based markers could be incorporated into decision-support tools or adjunct assays, particularly in referral centers managing rare uterine sarcomas.¹

The authors caution that the study is preliminary, with a limited sample size and no external validation cohort.¹ Biological variability across patients and tumor subtypes could affect generalizability. Therefore, the next step in this research would be to expand case numbers, refine spectral markers, and test performance in independent data sets to establish diagnostic sensitivity and specificity.¹

“In this regard, further investigations will be carried out to increase the number of cases and to make the proposed spectral markers more robust and generalizable, also including an external validation cohort,” the authors wrote in their study.¹

References

1. Santoni, C.; Orilisi, G.; Greco, S. et al. Data Science Meets FT-IR Imaging: A Promising Probe to Improve the Diagnosis of Human Uterine Muscle Lesions. Spectrochimica Acta Part A: Mol. Biomol. Spectrosc. 2026, 353, 127572. DOI: 10.1016.j.saa.2026.127572
2. Togni, L.; Furlani, M.; Belloni, A. et al. Biomolecular Alterations Temporally Anticipate Microarchitectural Modifications of Collagen in Oral Tongue Squamous Cell Carcinoma. iScience 2024, 27, 110303. DOI: 10.1016/j.isci.2024.110303
3. Pallua, J. D.; Pezzei, C.; Zelger, B. et al. Fourier transform Infrared Imaging Analysis in Discrimination Studies of Squamous Cell Carcinoma. Analyst 2012, 137 (17), 3965. DOI: 10.1039/c2an35483
4. Großerueschkamp, F.; Kallenbach-Thieltges, A.; Behrens, T. et al. Marker-free Automated Histopathological Annotation of Lung Tumour Subtypes by FTIR Imaging. Analyst 2015, 140 (7), 2114–2120. DOI: 10.1039/C4AN01978D
5. Ali, S. M.; Bonnier, F.; Lambkin, H. et al. A Comparison of Raman, FTIR and ATR-FTIR Microspectroscopy for Imaging Human Skin Tissue Sections. Anal. Methods 2013, 5 (9), 2281. DOI: 10.1039/c3ay40185e
6. Salman, A.; Sebbag, G.; Argov, S. et al. Early Detection of Colorectal Cancer Relapse by Infrared Spectroscopy in “Normal” Anastomosis Tissue. J. Biomed. Opt. 2015, 20 (7), 075007. DOI: 10.1117/1.JBO.20.7.075007
7. Jasco, Principles of Infrared Spectroscopy (4) Advantages of FTIR Spectroscopy. Jasco Global. Available at: https://www.jasco-global.com/principle/principles-of-infrared-spectroscopy-4-advantages-of-ftir-spectroscopy/ (accessed 2026-02-23).