Application of SEM-EDX for the Identification of Malignant Cells in Bronchial Brush Cytology: A Prospective Study

Application of SEM-EDX for the Identification of Malignant Cells in Bronchial Brush Cytology: A Prospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Application of SEM-EDX for the Identification of Malignant Cells in Bronchial Brush Cytology: A Prospective Study Tatsuya Imabayashi, Akiko Hisada, Yuji Matsumoto, Hideaki Furuse, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8234993/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Scanning electron microscopy (SEM) provides ultra-high-resolution imaging, and when combined with energy-dispersive X-ray spectroscopy (EDX), it enables quantitative elemental analysis. We aimed to evaluate whether SEM-EDX provides quantitative criteria for differentiating between malignant and benign cells in cytology specimens. In this prospective observational study, 49 cytology specimens obtained via bronchial brushing of peripheral pulmonary lesions between April 2021 and March 2022 were analyzed using a tabletop SEM (TM4000PlusII, Hitachi High-Tech). For each specimen, the phosphorus characteristic X-ray signal counts (P-counts) and nuclear areas (N-areas) were quantified in malignant and normal cells. Cases were classified into four distribution patterns based on statistically significant differences in P-counts and N-areas: (I) increased P-counts only, (II) increased P-counts and N-areas, (III) increased N-areas only, or (IV) no significant differences. All 47 malignant cases demonstrated significantly higher P-counts and/or N-areas than normal cells. Patterns I, II, and III were observed in 3 (6.4%), 37 (78.7%), and 7 (14.9%) cases, respectively; no cases met the Pattern IV criteria. The two benign cases showed distributions overlapping those of normal cells. SEM-EDX distinguished between malignant and non-malignant cells by quantifying the nuclear phosphorus content and area, supporting its potential role as an adjunct diagnostic tool in cancer cytopathology. Biological sciences/Cancer Health sciences/Diseases Health sciences/Medical research Health sciences/Oncology bronchoscopy cytology energy-dispersive X-ray spectroscopy lung cancer peripheral pulmonary lesions scanning electron microscope Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lung cancer is the leading cause of cancer-related mortality worldwide [ 1 ]. The growing adoption of low-dose computed tomography has markedly improved the early detection of lung cancer [ 2 , 3 ]. Consequently, the number of peripheral pulmonary lesions (PPLs) requiring pathological evaluation has increased [ 4 ]. Bronchoscopy is widely used for the diagnosis of PPLs [ 5 ]. However, pathological diagnosis is challenging in early-stage lung cancer, particularly those with ground-glass opacity, owing to limited cellularity or subtle cytological atypia. Although rapid on-site evaluation (ROSE) may improve diagnostic yield [ 6 , 7 ], it is unavailable in some institutions and subject to false positives and negatives, even among experienced cytopathologists. To overcome these diagnostic limitations, we considered scanning electron microscopy (SEM). In contrast to conventional optical microscopy, which relies on visible light, SEM utilizes an electron beam with a significantly shorter wavelength. This enables the ultra-high-resolution imaging of subcellular structures [ 8 ]. SEM, when combined with energy-dispersive X-ray spectroscopy (EDX), enables morphological observations and quantitative elemental analysis within cells [ 9 , 10 ]. This technique provides objective and numerical criteria for cytological diagnosis and may potentially serve as a complementary tool to ROSE in clinical practice. Recent advances have prompted the development of compact benchtop low-vacuum SEMs that overcome the operational barriers of conventional electron microscopes (Fig. 1 a). In particular, plate-transmission electron microscopy technique enables internal cellular imaging using cytology specimens mounted on scintillator plates [ 11 – 13 ]. This method allows for the imaging of conventionally prepared specimens without ultra-thin sectioning, and the straightforward sample preparation process does not require specialized training. When combined with EDX, localized elemental analyses, such as phosphorus quantification, are feasible, even for air-dried or stained samples (Fig. 1 b). These capabilities provide morphological and compositional information essential for distinguishing between malignant and benign cells. We hypothesized that malignant cells with a higher nuclear deoxyribonucleic acid density than that of non-malignant cells would exhibit an increased phosphorus mass concentration, and that this difference could be detected using SEM-EDX (Fig. 1 c). Based on this hypothesis, we aimed to prospectively investigate whether malignant cells in brush cytology specimens could be identified through the quantitative analysis of phosphorus characteristic X-ray signal counts (P-counts) and nuclear areas (N-areas) using SEM-EDX. Methods Study design and participants This single-center, prospective observational study was conducted at the National Cancer Center Hospital, Tokyo, Japan. We included 49 patients who underwent bronchoscopy for the diagnosis of PPLs, from whom residual cytology specimens were obtained via bronchial brushing between April 2021 and March 2022. The study was approved by the institutional review board (No. 2020 − 291), and written informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. Bronchoscopic procedures All bronchoscopic procedures were performed under moderate sedation using intravenous sedatives (midazolam) and opioids (fentanyl or pethidine). First, a flexible bronchoscope (BF-P260F or BF-P290; Olympus, Tokyo, Japan) was inserted into the bronchial tree, and bronchial brushing was performed at a bronchoscopically normal site. The bronchoscope was advanced to the target lesion, and a 1.4-mm radial endobronchial ultrasound probe (UM-S20-17S, Olympus) was used to confirm lesion localization. Subsequently, the bronchial brushing of the lesions was conducted under fluoroscopic guidance. Additional sampling, including repeated bronchial brushing, forceps biopsy, and transbronchial needle aspiration (TBNA), was performed at the discretion of the bronchoscopist. Sample preparation Brush cytology specimens obtained from the bronchoscopically normal sites and target lesions were smeared onto two glass slides each. The slides were air-dried, fixed in alcohol, and subsequently stained with Diff-QUIK® (Sysmex, Kobe, Japan). One slide was routinely evaluated at the pathology department, whereas the other was retained as a residual specimen and sent to Hitachi for analysis. Bright-field images of cell-rich regions, excluding red blood cells, were acquired using an optical microscope (BX53, Olympus) equipped with a 50× coverglass-free objective lens (MplanApo N, 50×, Olympus) and a digital camera (DP74, Olympus). A total of 20 to 100 cells per specimen were clearly identified as normal (from the normal site) or malignant (from the target lesion) cells by two interventional pulmonologists (one with board certification in cytopathology from the Japanese Society of Clinical Cytology, and the other with over 5 years of extensive experience in ROSE), with all cell classifications confirmed through double-checking. SEM-EDX and data collection Brush cytology specimens were examined using a tabletop SEM (TM4000PlusII, Hitachi High-Tech, Tokyo, Japan; Fig. 1 a) with a 15-kV accelerating voltage and a 10-mm working distance. Using the corresponding bright-field images as references, the nuclear regions of the selected cells were identified at 400× magnification. Elemental analysis was performed using an EDX detector (Oxford Instruments, Abingdon, UK), and the P-counts were acquired by scanning each nucleus for 20 s. The analyzed N-area was manually traced based on contrast differences in the backscattered electron images. This scan duration was selected to ensure sufficient signal intensity for reliable quantification while maintaining practical throughput. The P-counts for each nucleus reflected the differences in phosphorus density per nucleus. For each specimen, 20–100 clearly identified cells were analyzed. The N-area was measured from the same regions used for EDX acquisition using the ImageJ software (NIH, Bethesda, MD, USA). The enclosed area was calculated based on the image pixel size of 248.0469 nm and quantified in square micrometers (µm²), representing the two-dimensional projected area of each nucleus. This measurement was a reference for correlating P-counts with the nuclear size. In addition, the distribution of normal cells was visualized in a histogram format based on intervals of 50 P counts per nucleus. To establish a reference distribution for normal cells, a scatter plot was constructed based on values representing the phosphorus density per nucleus and plotted on a scatter plot against the corresponding N-area values from all 49 cases. Subsequently, the distribution of malignant cells was plotted on the same axes to assess the deviation from this reference. Statistical comparisons between the distribution of measurements from normal cells across all specimens and malignant cells in each specimen were performed using the Wilcoxon rank-sum test. This non-parametric test was selected to evaluate the differences without assuming normality in the data distributions. Based on the presence of significant differences ( p < 0.05), the cases were classified into four distribution patterns: (I) significantly increased P-counts only, (II) significantly increased P-counts and N-areas, (III) significantly increased N-areas only, or (IV) no significant differences (Fig. 1 c). All statistical analyses were performed using JMP (SAS Institute, Cary, NC, USA) version 18. Statistical significance was set at a two-sided p -value of < 0.05. Results Residual cytology specimens were obtained for all 49 cases, and the cytological and final diagnoses are summarized in Table 1 and Supplementary Table S1 . Of these patients, 46 were definitively diagnosed with malignancy based on cytology. One additional specimen, which was not diagnosed via brushing cytology owing to mild cytological atypia, was confirmed as a well-differentiated adenocarcinoma through forceps biopsy, prompting its inclusion in the malignant group (n = 47). The remaining two specimens were classified as benign: one was a typical carcinoid tumor diagnosed via TBNA after malignant cells could not be obtained through brushing, and the other showed granulomatous inflammation. Table 1 Pathological diagnosis of the test specimens Cytological diagnosis Final diagnosis Total (N = 49) Positive for malignancy (N = 46) Negative for malignancy (N = 3) Adenocarcinoma 30 29 1 Squamous cell carcinoma 9 9 0 Non-small cell carcinoma 3 3 0 Small cell carcinoma 3 3 0 High-grade neuroendocrine carcinoma 1 1 0 Metastatic carcinoma (colon) 1 1 0 Carcinoid 1 0 1 Granuloma 1 0 1 Distribution of P-counts and N-areas in normal cells To establish reference values for normal cells, SEM-EDX was performed on brush cytology specimens collected from bronchoscopically normal sites in all 49 cases. The distributions of P-counts and N-areas are shown as histograms in Figs. 2 a and b , respectively. Figure 2 c shows representative images of the EDX analysis regions indicated in the SEM image and the corresponding optical microscope image. Comparison of P-counts between malignant and normal cells The P-counts measured within the nuclear regions of malignant and normal cells are shown in Fig. 3 . The normal cell data of all 49 cases were combined to define the reference distribution. Malignant cells from each case were arranged in ascending order of median P-counts. Of the 47 malignant cases, 40 (85.1%) showed a statistically significant increase in P-counts compared with those of normal cells ( p < 0.05), whereas seven cases (14.9%) showed no significant differences. Distribution patterns based on P-counts and N-areas All malignant cases (n = 47) were classified into four patterns (Table 2 ). Patterns I, II, and III were observed in 3 (6.4%), 37 (78.7%), and 7 cases (14.9%), respectively; no cases met the criteria for Pattern IV. Figure 4 displays representative cases for Patterns I–III, showing characteristic scatter plot distributions of P-counts versus N-areas alongside the corresponding cytology images. Figure 5 depicts the scatter plots and corresponding cytology images of the two analyzed benign cases. In these cases, the cell distributions largely overlapped with the reference range of normal cells, with no marked increase in the P-count or N-area values. Table 2 Distribution patterns based on phosphorus characteristic X-ray signal counts and nuclear areas Distribution pattern † Phosphorus characteristic X-ray signal counts ( p < 0.05) Nucleus areas ( p < 0.05) Number of cases I, Proliferation * - 3 II, Proliferation and enlargement * * 37 III, Enlargement - * 7 IV, False negative - - 0 † I, II, III, IV refer to Fig. 1 ; An asterisk (*) indicates that a significant difference was found in the median value in the test, and a minus sign (-) indicates that no significant difference was found. Discussion We investigated the diagnostic utility of SEM-EDX for cytology specimens obtained via the bronchial brushing of PPLs. By quantifying P-counts and N-areas, we demonstrated that malignant cells exhibited significantly higher P-count and/or N-area values than did normal cells, enabling their stratification into distinct distribution patterns. These findings suggest that SEM-EDX complements conventional cytology in differentiating malignant and non-malignant cells, particularly in challenging cases where standard cytology may be inconclusive. Previous applications of SEM and EDX in pathology have primarily focused on morphological or compositional analyses, rather than direct cytological diagnosis. For example, SEM has been used to visualize microbial organisms, including fungal hyphae and viral particles, in pathological tissues [ 14 , 15 ], whereas SEM-EDX has proven valuable in detecting metal contaminants and asbestos fibers in lung cancer [ 16 , 17 ], as well as microcalcifications in breast cancer [ 18 ]. These applications illustrate the versatility of SEM-EDX in identifying exogenous and endogenous deposits; however, no study has been designed to directly apply this technique to cytology specimens from lung cancer to distinguish between malignant and benign cells. Thus, our investigation represents a novel extension of SEM-EDX to diagnostic cytopathology and provides an initial step toward evaluating its feasibility in clinical practice. When analyzing the P-count/N-area distribution patterns, we found that malignant cells (n = 47) were characterized by a significant increase in P-counts and/or N-areas compared with those in normal cells. Distinct distribution patterns emerged, with malignant cases predominantly classified as Pattern II (concurrent increases in P-counts and N-areas) or III (increase in the N-area only). Notably, small-cell lung cancer was expected to align predominantly with Pattern I (increase in P-counts only) owing to its small nuclear area; however, only one of the three cases conformed to this prediction. This discrepancy underscores the heterogeneity of nuclear morphology across tumor subtypes and highlights the need for a larger case series to refine the interpretive framework. Furthermore, one case of cytology-negative adenocarcinoma demonstrated Pattern II, suggesting that SEM-EDX reveals malignant features, even in instances where cytological assessment fails to detect malignancy. This suggests that SEM-EDX could serve as an adjunctive diagnostic modality in equivocal cases, providing independent confirmation of malignancy. Furthermore, two benign cases were analyzed, and their distributions did not align with the predominant malignant patterns. Although both cases tended to exhibit low P-counts and relatively small N-areas, the minimal benign specimens precluded definitive conclusions. Benign and reactive conditions may encompass a broader spectrum of P-count/N-area distributions than those observed in this study. Therefore, further investigations incorporating a wider variety of inflammatory and non-neoplastic pulmonary lesions are essential to determine whether SEM-EDX reliably distinguishes between benign and malignant cells in routine cytology practice. Expanding the scope to include conditions such as granulomatous inflammation or reactive atypia is crucial to ensuring clinical applicability and minimizing the risk of false-positive interpretations. This study had some limitations. First, the number of cases was limited and derived from a single center; hence, there was a biased distribution of tumor histology. Although the observed P-count/N-area distribution patterns were consistent across most malignant cases, validation in a larger multicenter cohort that includes a broader range of benign and malignant diseases is essential. Second, the current analyses were performed offline; that is, specimen acquisition, image acquisition, and data processing were conducted sequentially after the bronchoscopic procedure. However, this approach does not reflect real-time conditions; therefore, the feasibility of integrating SEM-EDX into ROSE workflows should be validated. Applying SEM-EDX intraprocedurally requires addressing two challenges: the automation of cell selection and the acceleration of the analysis. Manual cell selection is labor-intensive and operator-dependent, whereas artificial intelligence (AI)-based methods are emerging as powerful tools for automating ROSE and can be adapted for SEM-EDX. In parallel, improvements in instrument design and analytical protocols are required to shorten the processing time and enable real-time decision-making during bronchoscopy. Recent studies demonstrating the reliability of remote and AI-assisted cytological evaluations support the feasibility of this approach and highlight the potential of integrating SEM-EDX into future workflows. Conclusions This study provides preliminary evidence that using SEM-EDX on the cytology specimens obtained via bronchial brushing differentiates between malignant and benign cells based on P-counts and N-areas. Although the number of cases was limited, the results suggest that distinct P-count/N-area distribution patterns reflect underlying biological differences across tumor subtypes and that SEM-EDX enhances diagnosis in cases where conventional cytology is inconclusive. Further multicenter validation, the inclusion of benign and reactive conditions, and technical innovations aimed at real-time automation are critical for determining the ultimate clinical utility of SEM-EDX in cytopathological practice. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of the National Cancer Center Hospital, Tokyo, Japan (No. 2020 − 291), and written informed consent was obtained from all participants. Consent for publication Not applicable Competing Interests The Hitachi High-Tech Corporation has the right to obtain patents (PCT/JP2018/032823).A.H. is employed by Hitachi, Ltd., and Y.O. is employed by Hitachi High-Tech Corporation.This study was supported by collaborative research funding from Hitachi Ltd. and the Hitachi High-Tech Corporation (Project No. C2020-031, C2021-177). Funding This study was supported by collaborative research funding from Hitachi Ltd. and the Hitachi High-Tech Corporation (Project No. C2020-031, C2021-177). Author Contribution T.I. and Y. M. designed the study, coordinated clinical sample collection, and wrote the manuscript. A.H. performed the SEM-EDX imaging and elemental analysis. H.F. and T.T. assisted with bronchoscopic procedures and clinical evaluation. Y.O. supervised the instrumentation and provided technical guidance for SEM operation. T.T. contributed to the study design and manuscript review. All authors reviewed and approved the final version of the manuscript. Acknowledgement The authors thank Dr. Erino Matsumoto, Dr. Toshihide Agemura, and Dr. Mami Konomi for their technical expertise in SEM-EDX image acquisition and analysis. We also extend our appreciation to Kimie Mase for the technical assistance. Data Availability The original contributions presented in this study are included in the article and supplementary files. Further inquiries can be directed to the corresponding author. References Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71 , 209–249. https://doi.org/10.3322/caac.21660 (2021). Aberle, D. R. et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl. J. Med. 365 , 395–409. https://doi.org/10.1056/NEJMoa1102873 (2011). Jonas, D. E. et al. Screening for lung cancer with low-dose computed tomography: Updated evidence report and systematic review for the US preventive services task force. JAMA 325 , 971–987. https://doi.org/10.1001/jama.2021.0377 (2021). Gould, M. K. et al. Recent trends in the identification of incidental pulmonary nodules. Am. J. Respir Crit. Care Med. 192 , 1208–1214. 10.1164/rccm.201505-0990OC (2015). 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Microcalcifications in breast cancer: an active phenomenon mediated by epithelial cells with mesenchymal characteristics. BMC Cancer . 14 , 286. 10.1186/1471-2407-14-286 (2014). Tondo, P. et al. Reliability of rapid on-site evaluation achieved by remote sharing systems (E-ROSE) and AI algorithms (AI-ROSE) compared with the gold standard in the diagnosis of lung cancer. Respirology https://doi.org/10.1111/resp.70104 (2025). Additional Declarations Competing interest reported. The Hitachi High-Tech Corporation has the right to obtain patents (PCT/JP2018/032823). A.H. is employed by Hitachi, Ltd., and Y.O. is employed by Hitachi High-Tech Corporation. This study was supported by collaborative research funding from Hitachi Ltd. and the Hitachi High-Tech Corporation (Project No. C2020-031, C2021-177). Supplementary Files Additionalfile1.docx List of additional files Additional file 1.docx Title of data: Table S1. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8234993","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":561835570,"identity":"daf20207-bd6b-4337-b82d-83e6b271c97a","order_by":0,"name":"Tatsuya 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12:30:27","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46304,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/d2c121b3825b10366792bdfc.png"},{"id":98779476,"identity":"6bf52a05-d40f-49d9-84a6-02fcf9855b15","added_by":"auto","created_at":"2025-12-22 12:30:23","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151181,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/581f1aab591b60357463406c.png"},{"id":98779550,"identity":"5b62327a-c10a-45aa-a5cb-b7c6ee10f29e","added_by":"auto","created_at":"2025-12-22 12:30:27","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33786,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/6f43bd923942846a0db90224.png"},{"id":98763532,"identity":"08e6bc19-af28-483e-bd22-dc7555b0009e","added_by":"auto","created_at":"2025-12-22 10:04:32","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":395240,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/7c6b9de73185f2918f5bc95d.png"},{"id":98778593,"identity":"17b44988-7512-43b9-9bf5-f0620b3ef72f","added_by":"auto","created_at":"2025-12-22 12:29:27","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206079,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/6d96a88435995b25e74cf599.png"},{"id":98763530,"identity":"a095c73f-a548-45f1-b11d-4cc2f864fd1c","added_by":"auto","created_at":"2025-12-22 10:04:32","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70294,"visible":true,"origin":"","legend":"","description":"","filename":"8f39fda9cdd54ac1844068b9976aad1d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/ddeb7955d1c3cd59640a1e07.xml"},{"id":98763527,"identity":"caad6f73-264c-4557-aaf6-2ea2261a1e0e","added_by":"auto","created_at":"2025-12-22 10:04:32","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":81400,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/97fd51d148e655a0ace8046c.html"},{"id":98763509,"identity":"f9a3a4ab-66e9-4e1a-9ae9-5543999d669f","added_by":"auto","created_at":"2025-12-22 10:04:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":935143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework for malignant cell identification using scanning electron microscopy-energy-dispersive X-ray spectroscopy (SEM-EDX)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) A tabletop SEM (TM4000PlusII, Hitachi High-Tech, Tokyo, Japan) (Source: https://www.hitachi-hightech.com/global/en/products/microscopes/sem-tem-stem/tabletop-microscopes/tm4000ii.html). (\u003cstrong\u003eb\u003c/strong\u003e) Smear cytology specimens prepared via bronchial brushing were analyzed using SEM-EDX. Nuclear regions were identified on backscattered electron images, and elemental analysis was performed by evaluating characteristic X-rays. (\u003cstrong\u003ec\u003c/strong\u003e) In proliferative malignant cells, differences in the nuclear deoxyribonucleic acid (DNA) content during the cell cycle (G0/G1, S, and G2/M phases) may lead to variations in nuclear phosphorus density, as phosphorus is a major DNA component. We hypothesized that these differences could be detected via SEM-EDX and used to profile malignant cells based on phosphorus characteristic X-ray signal counts (P-counts) and nuclear area (N-area). In the schematic diagram, the blue intensity represents the phosphorus density, and the circle size indicates nuclear enlargement. Malignant cells were distributed in Patterns I–III, depending on the case, whereas cases without significant differences from normal cells were classified as exhibiting Pattern IV.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/a8b367c00456607e3bdd112b.png"},{"id":98763524,"identity":"169c85b7-827d-41b9-93e6-bd018e898e36","added_by":"auto","created_at":"2025-12-22 10:04:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2975574,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of P-counts and N-area in normal cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHistograms showing the distribution of (\u003cstrong\u003ea\u003c/strong\u003e) P-counts and (\u003cstrong\u003eb\u003c/strong\u003e) N-areas in normal cells obtained from bronchoscopically normal sites in 49 patients. (\u003cstrong\u003ec\u003c/strong\u003e) EDX analysis regions indicated in the SEM image of a representative case (left; white circles) and the corresponding Diff-QUIK-stained optical microscopy image (right). Scale bar=10 μm.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/42bcbbc6353dfcd54de9e983.png"},{"id":98779413,"identity":"f00d5c0e-b6a5-4af5-a468-b2982017147e","added_by":"auto","created_at":"2025-12-22 12:30:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":621335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of malignant cases based on the distribution of P-counts measured via SEM-EDX\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBox plots illustrating the distribution of P-counts within the nuclear regions of the cells. Red indicates the distribution of normal cells in the 49 cases. Malignant cells from each case are arranged in ascending order of median P values; blue and white (cytology-negative adenocarcinoma) denote cases with a statistically significant increase compared with that of normal cells (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05), and yellow denotes no significant difference. The horizontal line within each box represents the median, and the cross mark indicates the mean. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, *\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/5c515cb65af59fb009d60560.png"},{"id":98763517,"identity":"9aa65f4a-e62f-4889-a7b3-9dda571392b9","added_by":"auto","created_at":"2025-12-22 10:04:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11521279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution patterns of malignant cells according to P-counts and N-areas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScatter plots and corresponding cytology images illustrating malignant cell distribution patterns based on P-counts and N-areas compared with those of normal cells. Cases were classified as (a, b, c, d) significant increases in P-counts and N-areas, (e, f) significant increase in P-counts only, and (g, h) significant increase in N-areas only (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). In the scatter plots (a, c, e, g), the dark blue-green dots represent malignant cells from each case, and the orange dots represent normal cells from all cases. Cytology images (b, d, f, and h) showing representative Diff-Quik-stained optical microscopy images corresponding to the EDX analysis areas. Scale bars=10 μm.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/540376b5cd1c47d0b0567990.png"},{"id":98763515,"identity":"78d74147-4c93-470b-b1b9-8484553e8619","added_by":"auto","created_at":"2025-12-22 10:04:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6673952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution patterns of benign cells according to P-counts and N-areas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e,\u003cstrong\u003eb\u003c/strong\u003e) Benign cells from a case of carcinoid tumor; (\u003cstrong\u003ec\u003c/strong\u003e, \u003cstrong\u003ed)\u003c/strong\u003e benign cells from a case of granuloma. (\u003cstrong\u003ea\u003c/strong\u003e, \u003cstrong\u003ec\u003c/strong\u003e) Scatter plots of P-counts versus N-areas, with the dark blue-green dots indicating cells from each benign case, and the orange dots indicating normal cells from the 49 cases. (\u003cstrong\u003eb\u003c/strong\u003e, \u003cstrong\u003ed\u003c/strong\u003e) Corresponding Diff-Quik–stained cytology images illustrating morphological features consistent with benign cytology. Scale bars=10 μm.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/d38766e1605d2ea7a4e59a32.png"},{"id":105754911,"identity":"6d7653f4-63a7-45d8-9812-63f9917d279b","added_by":"auto","created_at":"2026-03-30 16:22:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21836168,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/3840a73a-6ad4-436c-8a1d-84f3a4991680.pdf"},{"id":98778508,"identity":"1d885b85-2b2c-49b2-8a91-7ba745591352","added_by":"auto","created_at":"2025-12-22 12:29:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38359,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eList of additional files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file 1.docx\u003c/p\u003e\n\u003cp\u003eTitle of data: \u003cstrong\u003eTable S1.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8234993/v1/9552b601115d681a4bd3e37b.docx"}],"financialInterests":"Competing interest reported. The Hitachi High-Tech Corporation has the right to obtain patents (PCT/JP2018/032823).\nA.H. is employed by Hitachi, Ltd., and Y.O. is employed by Hitachi High-Tech Corporation.\nThis study was supported by collaborative research funding from Hitachi Ltd. and the Hitachi High-Tech Corporation (Project No. C2020-031, C2021-177).","formattedTitle":"Application of SEM-EDX for the Identification of Malignant Cells in Bronchial Brush Cytology: A Prospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the leading cause of cancer-related mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The growing adoption of low-dose computed tomography has markedly improved the early detection of lung cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, the number of peripheral pulmonary lesions (PPLs) requiring pathological evaluation has increased [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Bronchoscopy is widely used for the diagnosis of PPLs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, pathological diagnosis is challenging in early-stage lung cancer, particularly those with ground-glass opacity, owing to limited cellularity or subtle cytological atypia. Although rapid on-site evaluation (ROSE) may improve diagnostic yield [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], it is unavailable in some institutions and subject to false positives and negatives, even among experienced cytopathologists.\u003c/p\u003e \u003cp\u003eTo overcome these diagnostic limitations, we considered scanning electron microscopy (SEM). In contrast to conventional optical microscopy, which relies on visible light, SEM utilizes an electron beam with a significantly shorter wavelength. This enables the ultra-high-resolution imaging of subcellular structures [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. SEM, when combined with energy-dispersive X-ray spectroscopy (EDX), enables morphological observations and quantitative elemental analysis within cells [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This technique provides objective and numerical criteria for cytological diagnosis and may potentially serve as a complementary tool to ROSE in clinical practice.\u003c/p\u003e \u003cp\u003eRecent advances have prompted the development of compact benchtop low-vacuum SEMs that overcome the operational barriers of conventional electron microscopes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In particular, plate-transmission electron microscopy technique enables internal cellular imaging using cytology specimens mounted on scintillator plates [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This method allows for the imaging of conventionally prepared specimens without ultra-thin sectioning, and the straightforward sample preparation process does not require specialized training. When combined with EDX, localized elemental analyses, such as phosphorus quantification, are feasible, even for air-dried or stained samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). These capabilities provide morphological and compositional information essential for distinguishing between malignant and benign cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe hypothesized that malignant cells with a higher nuclear deoxyribonucleic acid density than that of non-malignant cells would exhibit an increased phosphorus mass concentration, and that this difference could be detected using SEM-EDX (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Based on this hypothesis, we aimed to prospectively investigate whether malignant cells in brush cytology specimens could be identified through the quantitative analysis of phosphorus characteristic X-ray signal counts (P-counts) and nuclear areas (N-areas) using SEM-EDX.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis single-center, prospective observational study was conducted at the National Cancer Center Hospital, Tokyo, Japan. We included 49 patients who underwent bronchoscopy for the diagnosis of PPLs, from whom residual cytology specimens were obtained via bronchial brushing between April 2021 and March 2022. The study was approved by the institutional review board (No. 2020\u0026thinsp;\u0026minus;\u0026thinsp;291), and written informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBronchoscopic procedures\u003c/h3\u003e\n\u003cp\u003eAll bronchoscopic procedures were performed under moderate sedation using intravenous sedatives (midazolam) and opioids (fentanyl or pethidine). First, a flexible bronchoscope (BF-P260F or BF-P290; Olympus, Tokyo, Japan) was inserted into the bronchial tree, and bronchial brushing was performed at a bronchoscopically normal site. The bronchoscope was advanced to the target lesion, and a 1.4-mm radial endobronchial ultrasound probe (UM-S20-17S, Olympus) was used to confirm lesion localization. Subsequently, the bronchial brushing of the lesions was conducted under fluoroscopic guidance. Additional sampling, including repeated bronchial brushing, forceps biopsy, and transbronchial needle aspiration (TBNA), was performed at the discretion of the bronchoscopist.\u003c/p\u003e\n\u003ch3\u003eSample preparation\u003c/h3\u003e\n\u003cp\u003eBrush cytology specimens obtained from the bronchoscopically normal sites and target lesions were smeared onto two glass slides each. The slides were air-dried, fixed in alcohol, and subsequently stained with Diff-QUIK\u0026reg; (Sysmex, Kobe, Japan). One slide was routinely evaluated at the pathology department, whereas the other was retained as a residual specimen and sent to Hitachi for analysis.\u003c/p\u003e \u003cp\u003eBright-field images of cell-rich regions, excluding red blood cells, were acquired using an optical microscope (BX53, Olympus) equipped with a 50\u0026times; coverglass-free objective lens (MplanApo N, 50\u0026times;, Olympus) and a digital camera (DP74, Olympus). A total of 20 to 100 cells per specimen were clearly identified as normal (from the normal site) or malignant (from the target lesion) cells by two interventional pulmonologists (one with board certification in cytopathology from the Japanese Society of Clinical Cytology, and the other with over 5 years of extensive experience in ROSE), with all cell classifications confirmed through double-checking.\u003c/p\u003e\n\u003ch3\u003eSEM-EDX and data collection\u003c/h3\u003e\n\u003cp\u003eBrush cytology specimens were examined using a tabletop SEM (TM4000PlusII, Hitachi High-Tech, Tokyo, Japan; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) with a 15-kV accelerating voltage and a 10-mm working distance. Using the corresponding bright-field images as references, the nuclear regions of the selected cells were identified at 400\u0026times; magnification. Elemental analysis was performed using an EDX detector (Oxford Instruments, Abingdon, UK), and the P-counts were acquired by scanning each nucleus for 20 s. The analyzed N-area was manually traced based on contrast differences in the backscattered electron images. This scan duration was selected to ensure sufficient signal intensity for reliable quantification while maintaining practical throughput. The P-counts for each nucleus reflected the differences in phosphorus density per nucleus. For each specimen, 20\u0026ndash;100 clearly identified cells were analyzed.\u003c/p\u003e \u003cp\u003eThe N-area was measured from the same regions used for EDX acquisition using the ImageJ software (NIH, Bethesda, MD, USA). The enclosed area was calculated based on the image pixel size of 248.0469 nm and quantified in square micrometers (\u0026micro;m\u0026sup2;), representing the two-dimensional projected area of each nucleus. This measurement was a reference for correlating P-counts with the nuclear size. In addition, the distribution of normal cells was visualized in a histogram format based on intervals of 50 P counts per nucleus.\u003c/p\u003e \u003cp\u003eTo establish a reference distribution for normal cells, a scatter plot was constructed based on values representing the phosphorus density per nucleus and plotted on a scatter plot against the corresponding N-area values from all 49 cases. Subsequently, the distribution of malignant cells was plotted on the same axes to assess the deviation from this reference.\u003c/p\u003e \u003cp\u003eStatistical comparisons between the distribution of measurements from normal cells across all specimens and malignant cells in each specimen were performed using the Wilcoxon rank-sum test. This non-parametric test was selected to evaluate the differences without assuming normality in the data distributions. Based on the presence of significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the cases were classified into four distribution patterns: (I) significantly increased P-counts only, (II) significantly increased P-counts and N-areas, (III) significantly increased N-areas only, or (IV) no significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). All statistical analyses were performed using JMP (SAS Institute, Cary, NC, USA) version 18. Statistical significance was set at a two-sided \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eResidual cytology specimens were obtained for all 49 cases, and the cytological and final diagnoses are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Of these patients, 46 were definitively diagnosed with malignancy based on cytology. One additional specimen, which was not diagnosed via brushing cytology owing to mild cytological atypia, was confirmed as a well-differentiated adenocarcinoma through forceps biopsy, prompting its inclusion in the malignant group (n\u0026thinsp;=\u0026thinsp;47). The remaining two specimens were classified as benign: one was a typical carcinoid tumor diagnosed via TBNA after malignant cells could not be obtained through brushing, and the other showed granulomatous inflammation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathological diagnosis of the test specimens\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCytological diagnosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive for malignancy (N\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative for malignancy (N\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-small cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-grade neuroendocrine carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic carcinoma (colon)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarcinoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGranuloma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of P-counts and N-areas in normal cells\u003c/h2\u003e \u003cp\u003eTo establish reference values for normal cells, SEM-EDX was performed on brush cytology specimens collected from bronchoscopically normal sites in all 49 cases. The distributions of P-counts and N-areas are shown as histograms in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cb\u003eb\u003c/b\u003e, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec shows representative images of the EDX analysis regions indicated in the SEM image and the corresponding optical microscope image.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison of P-counts between malignant and normal cells\u003c/h3\u003e\n\u003cp\u003eThe P-counts measured within the nuclear regions of malignant and normal cells are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The normal cell data of all 49 cases were combined to define the reference distribution. Malignant cells from each case were arranged in ascending order of median P-counts. Of the 47 malignant cases, 40 (85.1%) showed a statistically significant increase in P-counts compared with those of normal cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas seven cases (14.9%) showed no significant differences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDistribution patterns based on P-counts and N-areas\u003c/h3\u003e\n\u003cp\u003eAll malignant cases (n\u0026thinsp;=\u0026thinsp;47) were classified into four patterns (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patterns I, II, and III were observed in 3 (6.4%), 37 (78.7%), and 7 cases (14.9%), respectively; no cases met the criteria for Pattern IV. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays representative cases for Patterns I\u0026ndash;III, showing characteristic scatter plot distributions of P-counts versus N-areas alongside the corresponding cytology images. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicts the scatter plots and corresponding cytology images of the two analyzed benign cases. In these cases, the cell distributions largely overlapped with the reference range of normal cells, with no marked increase in the P-count or N-area values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution patterns based on phosphorus characteristic X-ray signal counts and nuclear areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution pattern\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphorus characteristic X-ray signal counts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNucleus areas\u003c/p\u003e \u003cp\u003e(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI, Proliferation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII, Proliferation and enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII, Enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV, False negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e I, II, III, IV refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; An asterisk (*) indicates that a significant difference was found in the median value in the test, and a minus sign (-) indicates that no significant difference was found.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the diagnostic utility of SEM-EDX for cytology specimens obtained via the bronchial brushing of PPLs. By quantifying P-counts and N-areas, we demonstrated that malignant cells exhibited significantly higher P-count and/or N-area values than did normal cells, enabling their stratification into distinct distribution patterns. These findings suggest that SEM-EDX complements conventional cytology in differentiating malignant and non-malignant cells, particularly in challenging cases where standard cytology may be inconclusive.\u003c/p\u003e \u003cp\u003ePrevious applications of SEM and EDX in pathology have primarily focused on morphological or compositional analyses, rather than direct cytological diagnosis. For example, SEM has been used to visualize microbial organisms, including fungal hyphae and viral particles, in pathological tissues [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], whereas SEM-EDX has proven valuable in detecting metal contaminants and asbestos fibers in lung cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], as well as microcalcifications in breast cancer [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These applications illustrate the versatility of SEM-EDX in identifying exogenous and endogenous deposits; however, no study has been designed to directly apply this technique to cytology specimens from lung cancer to distinguish between malignant and benign cells. Thus, our investigation represents a novel extension of SEM-EDX to diagnostic cytopathology and provides an initial step toward evaluating its feasibility in clinical practice.\u003c/p\u003e \u003cp\u003eWhen analyzing the P-count/N-area distribution patterns, we found that malignant cells (n\u0026thinsp;=\u0026thinsp;47) were characterized by a significant increase in P-counts and/or N-areas compared with those in normal cells. Distinct distribution patterns emerged, with malignant cases predominantly classified as Pattern II (concurrent increases in P-counts and N-areas) or III (increase in the N-area only). Notably, small-cell lung cancer was expected to align predominantly with Pattern I (increase in P-counts only) owing to its small nuclear area; however, only one of the three cases conformed to this prediction. This discrepancy underscores the heterogeneity of nuclear morphology across tumor subtypes and highlights the need for a larger case series to refine the interpretive framework. Furthermore, one case of cytology-negative adenocarcinoma demonstrated Pattern II, suggesting that SEM-EDX reveals malignant features, even in instances where cytological assessment fails to detect malignancy. This suggests that SEM-EDX could serve as an adjunctive diagnostic modality in equivocal cases, providing independent confirmation of malignancy.\u003c/p\u003e \u003cp\u003eFurthermore, two benign cases were analyzed, and their distributions did not align with the predominant malignant patterns. Although both cases tended to exhibit low P-counts and relatively small N-areas, the minimal benign specimens precluded definitive conclusions. Benign and reactive conditions may encompass a broader spectrum of P-count/N-area distributions than those observed in this study. Therefore, further investigations incorporating a wider variety of inflammatory and non-neoplastic pulmonary lesions are essential to determine whether SEM-EDX reliably distinguishes between benign and malignant cells in routine cytology practice. Expanding the scope to include conditions such as granulomatous inflammation or reactive atypia is crucial to ensuring clinical applicability and minimizing the risk of false-positive interpretations.\u003c/p\u003e \u003cp\u003eThis study had some limitations. First, the number of cases was limited and derived from a single center; hence, there was a biased distribution of tumor histology. Although the observed P-count/N-area distribution patterns were consistent across most malignant cases, validation in a larger multicenter cohort that includes a broader range of benign and malignant diseases is essential. Second, the current analyses were performed offline; that is, specimen acquisition, image acquisition, and data processing were conducted sequentially after the bronchoscopic procedure. However, this approach does not reflect real-time conditions; therefore, the feasibility of integrating SEM-EDX into ROSE workflows should be validated. Applying SEM-EDX intraprocedurally requires addressing two challenges: the automation of cell selection and the acceleration of the analysis. Manual cell selection is labor-intensive and operator-dependent, whereas artificial intelligence (AI)-based methods are emerging as powerful tools for automating ROSE and can be adapted for SEM-EDX. In parallel, improvements in instrument design and analytical protocols are required to shorten the processing time and enable real-time decision-making during bronchoscopy. Recent studies demonstrating the reliability of remote and AI-assisted cytological evaluations support the feasibility of this approach and highlight the potential of integrating SEM-EDX into future workflows.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides preliminary evidence that using SEM-EDX on the cytology specimens obtained via bronchial brushing differentiates between malignant and benign cells based on P-counts and N-areas. Although the number of cases was limited, the results suggest that distinct P-count/N-area distribution patterns reflect underlying biological differences across tumor subtypes and that SEM-EDX enhances diagnosis in cases where conventional cytology is inconclusive. Further multicenter validation, the inclusion of benign and reactive conditions, and technical innovations aimed at real-time automation are critical for determining the ultimate clinical utility of SEM-EDX in cytopathological practice.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Institutional Review Board of the National Cancer Center Hospital, Tokyo, Japan (No. 2020\u0026thinsp;\u0026minus;\u0026thinsp;291), and written informed consent was obtained from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe Hitachi High-Tech Corporation has the right to obtain patents (PCT/JP2018/032823).A.H. is employed by Hitachi, Ltd., and Y.O. is employed by Hitachi High-Tech Corporation.This study was supported by collaborative research funding from Hitachi Ltd. and the Hitachi High-Tech Corporation (Project No. C2020-031, C2021-177).\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by collaborative research funding from Hitachi Ltd. and the Hitachi High-Tech Corporation (Project No. C2020-031, C2021-177).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.I. and Y. M. designed the study, coordinated clinical sample collection, and wrote the manuscript. A.H. performed the SEM-EDX imaging and elemental analysis. H.F. and T.T. assisted with bronchoscopic procedures and clinical evaluation. Y.O. supervised the instrumentation and provided technical guidance for SEM operation. T.T. contributed to the study design and manuscript review. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Dr. Erino Matsumoto, Dr. Toshihide Agemura, and Dr. Mami Konomi for their technical expertise in SEM-EDX image acquisition and analysis. We also extend our appreciation to Kimie Mase for the technical assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original contributions presented in this study are included in the article and supplementary files. 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We aimed to evaluate whether SEM-EDX provides quantitative criteria for differentiating between malignant and benign cells in cytology specimens. In this prospective observational study, 49 cytology specimens obtained via bronchial brushing of peripheral pulmonary lesions between April 2021 and March 2022 were analyzed using a tabletop SEM (TM4000PlusII, Hitachi High-Tech). For each specimen, the phosphorus characteristic X-ray signal counts (P-counts) and nuclear areas (N-areas) were quantified in malignant and normal cells. Cases were classified into four distribution patterns based on statistically significant differences in P-counts and N-areas: (I) increased P-counts only, (II) increased P-counts and N-areas, (III) increased N-areas only, or (IV) no significant differences. All 47 malignant cases demonstrated significantly higher P-counts and/or N-areas than normal cells. Patterns I, II, and III were observed in 3 (6.4%), 37 (78.7%), and 7 (14.9%) cases, respectively; no cases met the Pattern IV criteria. The two benign cases showed distributions overlapping those of normal cells. SEM-EDX distinguished between malignant and non-malignant cells by quantifying the nuclear phosphorus content and area, supporting its potential role as an adjunct diagnostic tool in cancer cytopathology.\u003c/p\u003e","manuscriptTitle":"Application of SEM-EDX for the Identification of Malignant Cells in Bronchial Brush Cytology: A Prospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:04:22","doi":"10.21203/rs.3.rs-8234993/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T13:53:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T12:14:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T14:04:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159473923440845555811975731243285276051","date":"2026-02-05T05:58:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108906973188375357426507777461049346330","date":"2026-02-04T02:03:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93075148449253187344377452099926452915","date":"2026-01-24T13:02:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10810521563141846402840076315288577684","date":"2025-12-19T12:00:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T09:30:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-17T09:24:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-04T17:58:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-02T11:04:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-02T10:20:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"721294f9-86dc-42ac-b44a-d4d8e2c7b122","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":59826539,"name":"Biological sciences/Cancer"},{"id":59826540,"name":"Health sciences/Diseases"},{"id":59826541,"name":"Health sciences/Medical research"},{"id":59826542,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-30T16:16:37+00:00","versionOfRecord":{"articleIdentity":"rs-8234993","link":"https://doi.org/10.1038/s41598-026-44891-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-26 16:08:51","publishedOnDateReadable":"March 26th, 2026"},"versionCreatedAt":"2025-12-22 10:04:22","video":"","vorDoi":"10.1038/s41598-026-44891-w","vorDoiUrl":"https://doi.org/10.1038/s41598-026-44891-w","workflowStages":[]},"version":"v1","identity":"rs-8234993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8234993","identity":"rs-8234993","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}