Non-invasive detection of choroidal melanoma via tear-derived protein corona on gold nanoparticles: a machine learning approach Article Swipe
Hakimeh Rakhshandeh
,
Ahmad Nasiraei
,
Hamid Riazi‐Esfahani
,
Babak Masoomian
,
Fariba Ghassemi
,
Mojtaba Arjmand
,
Saeed Heidari Keshel
,
Fatemeh Atyabi
,
Rassoul Dinarvand
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-17835-z
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-17835-z
This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approach for the detection of choroidal melanoma. The aim is to assess whether protein-nanoparticle interactions can support early and reliable identification of this ocular condition. Tear samples were collected using Schirmer strips from six healthy individuals and six patients diagnosed with choroidal melanoma, with subsequent augmentation to 18 samples per group. Gold nanoparticles (AuNPs, ~ 20 nm) were synthesized via citrate reduction and incubated with tear samples to form protein coronas, which were analyzed using ESI-MS. Eight statistical and entropy-based features (mean, variance, skewness, kurtosis, Shannon entropy, approximate entropy, sample entropy, and permutation entropy) were extracted from spectral data. Additionally, Continuous Wavelet Transform (CWT) with Mexican hat wavelet was applied to convert mass spectrometry data into 128 × 128 RGB images for deep learning analysis. Classification was performed using traditional machine learning models (Random Forest, Support Vector Machine, Decision Tree, Deep Neural Network) and transfer learning with pre-trained CNNs (VGG16, ResNet50, Xception), evaluated through 5-fold cross-validation. Significant differences in spectral intensity parameters were observed between healthy individuals and choroidal melanoma patients (p < 0.001), with notably lower Mean_Intensity values in cancer patients (56.41 ± 46.06 vs. 111.02 ± 10.01, Cohen's d = 1.64). While m/z parameters showed moderate differences that didn't reach statistical significance (p = 0.082), entropy-based features demonstrated strong discriminative power. Among traditional machine learning models, Random Forest achieved the highest accuracy (0.959 ± 0.003) and ROC AUC (0.993 ± 0.000) with remarkable computational efficiency (3.90 s per fold). For deep learning approaches using CWT-generated images, VGG16 demonstrated superior performance (Accuracy: 0.976 ± 0.008, ROC AUC: 0.997 ± 0.002) despite requiring significantly higher computational resources (1349.52 s per fold). This study demonstrates that tear sample analysis using protein corona formation on gold nanoparticles with ESI-MS and advanced machine learning techniques offers a promising non-invasive approach for choroidal melanoma detection with performance metrics that compare favorably to existing methods. The significant differences in spectral intensity parameters between groups suggest distinctive proteomic signatures that can be leveraged for diagnostic purposes. While both traditional machine learning and deep learning approaches achieved exceptional performance, each offers distinct advantages in terms of computational efficiency and feature extraction capabilities.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-17835-z
- https://www.nature.com/articles/s41598-025-17835-z.pdf
- OA Status
- gold
- References
- 67
- OpenAlex ID
- https://openalex.org/W4414493192
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414493192Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-17835-zDigital Object Identifier
- Title
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Non-invasive detection of choroidal melanoma via tear-derived protein corona on gold nanoparticles: a machine learning approachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
- Publication date
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2025-09-25Full publication date if available
- Authors
-
Hakimeh Rakhshandeh, Ahmad Nasiraei, Hamid Riazi‐Esfahani, Babak Masoomian, Fariba Ghassemi, Mojtaba Arjmand, Saeed Heidari Keshel, Fatemeh Atyabi, Rassoul DinarvandList of authors in order
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https://doi.org/10.1038/s41598-025-17835-zPublisher landing page
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https://www.nature.com/articles/s41598-025-17835-z.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-17835-z.pdfDirect OA link when available
- Cited by
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0Total citation count in OpenAlex
- References (count)
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67Number of works referenced by this work
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| abstract_inverted_index.Continuous | 132 |
| abstract_inverted_index.Xception), | 183 |
| abstract_inverted_index.advantages | 377 |
| abstract_inverted_index.approaches | 276, 370 |
| abstract_inverted_index.condition. | 56 |
| abstract_inverted_index.diagnostic | 360 |
| abstract_inverted_index.efficiency | 268, 382 |
| abstract_inverted_index.extraction | 385 |
| abstract_inverted_index.ionization | 21 |
| abstract_inverted_index.parameters | 193, 227, 348 |
| abstract_inverted_index.remarkable | 266 |
| abstract_inverted_index.signatures | 354 |
| abstract_inverted_index.subsequent | 76 |
| abstract_inverted_index.techniques | 323 |
| abstract_inverted_index.Significant | 188 |
| abstract_inverted_index.approximate | 119 |
| abstract_inverted_index.differences | 189, 230, 344 |
| abstract_inverted_index.distinctive | 352 |
| abstract_inverted_index.exceptional | 372 |
| abstract_inverted_index.feasibility | 4 |
| abstract_inverted_index.individuals | 67, 198 |
| abstract_inverted_index.performance | 283, 334 |
| abstract_inverted_index.permutation | 124 |
| abstract_inverted_index.pre-trained | 179 |
| abstract_inverted_index.significant | 343 |
| abstract_inverted_index.statistical | 109, 234 |
| abstract_inverted_index.synthesized | 90 |
| abstract_inverted_index.techniques, | 28 |
| abstract_inverted_index.traditional | 161, 246, 364 |
| abstract_inverted_index.augmentation | 77 |
| abstract_inverted_index.demonstrated | 241, 281 |
| abstract_inverted_index.demonstrates | 305 |
| abstract_inverted_index.electrospray | 20 |
| abstract_inverted_index.interactions | 46 |
| abstract_inverted_index.investigates | 2 |
| abstract_inverted_index.non-invasive | 31, 327 |
| abstract_inverted_index.performance, | 373 |
| abstract_inverted_index.significance | 235 |
| abstract_inverted_index.spectrometry | 23, 145 |
| abstract_inverted_index.Additionally, | 131 |
| abstract_inverted_index.CWT-generated | 278 |
| abstract_inverted_index.capabilities. | 386 |
| abstract_inverted_index.computational | 267, 297, 381 |
| abstract_inverted_index.entropy-based | 111, 239 |
| abstract_inverted_index.nanoparticles | 17, 84, 316 |
| abstract_inverted_index.significantly | 295 |
| abstract_inverted_index.Classification | 157 |
| abstract_inverted_index.Mean_Intensity | 209 |
| abstract_inverted_index.discriminative | 243 |
| abstract_inverted_index.identification | 52 |
| abstract_inverted_index.cross-validation. | 187 |
| abstract_inverted_index.protein-nanoparticle | 45 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.60794538 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |