Stainless Steel vs Calcium Fluoride for biomedical Raman Spectroscopy
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- Evaluated articles (PREreview)
Abstract
Raman spectroscopy (RS) has been shown to differentiate various disease states in tissue samples, yet the impact of sample substrate on spectral quality and downstream machine-learning (ML) performance is under-explored. We collected 960 spectra from 36 formalin-fixed, paraffin-embedded colonic specimens mounted in parallel on calcium fluoride ( CaF 2 ) and mirrored stainless-steel ( SS ) slides. Raw SS spectra were more intense than CaF 2 , and routine dewaxing left prominent paraffin peaks on CaF 2 that were largely absent on SS . Extending CaF 2 xylene immersion to 48 h reduced key paraffin peaks by 11–23 %, aligning its biochemical profile with SS . Nested cross-validated classifiers were built to distinguish true disease (T1) from epithelial misplacement (EM). The highest accuracies were observed on SS (0.65–0.66), while CaF 2 improved from 0.41–0.57 to 0.65–0.69 after extended dewaxing, chiefly via gains in recall. Although optimal dewaxing renders CaF 2 diagnostically non-inferior, SS still offers superior accuracy, faster preparation, at approximately 4% of the cost, supporting its integration into scalable, AI-enabled RS pathology workflows.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23696751.
Stainless steel slides proved to be a game-changer for Raman spectroscopy, offering stronger signal intensity and high machine-learning accuracy at just four percent of the cost of traditional calcium fluoride slides. While calcium fluoride required long processing soaks to clear stubborn paraffin, stainless steel naturally avoided wax buildup and resisted breakage. By proving that cheap, rugged stainless steel can match or beat expensive specialty glass without sacrificing diagnostic power, this study removes a major practical bottleneck and paves the way for scalable, budget-friendly, artificial intelligence-driven digital pathology.
Major issues
The authors appropriately note the …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23696751.
Stainless steel slides proved to be a game-changer for Raman spectroscopy, offering stronger signal intensity and high machine-learning accuracy at just four percent of the cost of traditional calcium fluoride slides. While calcium fluoride required long processing soaks to clear stubborn paraffin, stainless steel naturally avoided wax buildup and resisted breakage. By proving that cheap, rugged stainless steel can match or beat expensive specialty glass without sacrificing diagnostic power, this study removes a major practical bottleneck and paves the way for scalable, budget-friendly, artificial intelligence-driven digital pathology.
Major issues
The authors appropriately note the limitation of the small cohort shown in the study. But it is worth emphasizing that validation on a larger, multi-center dataset will be necessary before broad clinical translation can be claimed.
Since confusing epithelial misplacement with true cancer could lead to unnecessary treatments, how often do these machine learning models make false positive errors? Reporting the specific false positive rates (or specificity) would help show whether stainless steel is truly safe for clinical decision-making.
If multiple slides came from the same patient, the model might accidentally learn from the same person twice, leading to data leakage. The authors should make sure their test splits are grouped strictly by patient.
The authors notes that SS substrates may "induce paraffin melting under laser exposure" in addition to facilitating better chemical dewaxing. Could localized laser heating cause paraffin to melt and spread or coat the tissue micro-environment during acquisition?
In this study, the tissue samples on the stainless steel and calcium fluoride slides were cut from different sections, minor changes in the tissue itself could affect the results. The authors should explain how they made sure these natural tissue differences didn't skew the comparison between the slides.
Minor issues
Section 1 (Introduction): "there remains several elements" should be corrected to "there remain several elements".
Section 3.3: "comparision" should be corrected to "comparison
Ensure terms like SNV (Standard Normal Variate) and EM (Epithelial Misplacement) are fully spelled out upon their first appearance in the main text body.
Ensure that all table and figure callouts in the text align cleanly with the final layout.
Competing interests
The author declares that they have no competing interests.
Use of Artificial Intelligence (AI)
The author declares that they did not use generative AI to come up with new ideas for their review.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23698341.
Stainless steel slides has proved to be a game-changer for Raman spectroscopy, offering stronger signal intensity and high machine-learning accuracy at just four percent of the cost of traditional calcium fluoride slides. While calcium fluoride required long processing soaks to clear stubborn paraffin, stainless steel naturally avoided wax buildup and resisted breakage. By proving that cheap, rugged stainless steel can match or beat expensive specialty glass without sacrificing diagnostic power, this study removes a major practical bottleneck and paves the way for scalable, budget-friendly, artificial intelligence-driven digital pathology.
Major issues
The authors appropriately note the …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23698341.
Stainless steel slides has proved to be a game-changer for Raman spectroscopy, offering stronger signal intensity and high machine-learning accuracy at just four percent of the cost of traditional calcium fluoride slides. While calcium fluoride required long processing soaks to clear stubborn paraffin, stainless steel naturally avoided wax buildup and resisted breakage. By proving that cheap, rugged stainless steel can match or beat expensive specialty glass without sacrificing diagnostic power, this study removes a major practical bottleneck and paves the way for scalable, budget-friendly, artificial intelligence-driven digital pathology.
Major issues
The authors appropriately note the limitation of the small cohort shown in the study. But it is worth emphasizing that validation on a larger, multi-center dataset will be necessary before broad clinical translation can be claimed.
Since confusing epithelial misplacement with true cancer could lead to unnecessary treatments, how often do these machine learning models make false positive errors? Reporting the specific false positive rates (or specificity) would help show whether stainless steel is truly safe for clinical decision-making.
If multiple slides came from the same patient, the model might accidentally learn from the same person twice, leading to data leakage. The authors should make sure their test splits are grouped strictly by patient.
The authors notes that SS substrates may "induce paraffin melting under laser exposure" in addition to facilitating better chemical dewaxing. Could localized laser heating cause paraffin to melt and spread or coat the tissue micro-environment during acquisition?
In this study, the tissue samples on the stainless steel and calcium fluoride slides were cut from different sections, minor changes in the tissue itself could affect the results. The authors should explain how they made sure these natural tissue differences didn't skew the comparison between the slides.
Minor issues
Section 1 (Introduction): "there remains several elements" should be corrected to "there remain several elements".
Section 3.3: "comparision" should be corrected to "comparison
Ensure terms like SNV (Standard Normal Variate) and EM (Epithelial Misplacement) are fully spelled out upon their first appearance in the main text body.
Ensure that all table and figure callouts in the text align cleanly with the final layout.
Competing interests
The author declares that they have no competing interests.
Use of Artificial Intelligence (AI)
The author declares that they did not use generative AI to come up with new ideas for their review.
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