Precise diagnosis of breast phyllodes tumors using Raman spectroscopy: Biochemical fingerprint, tumor metabolism and possible mechanism.

Humans Female Phyllodes Tumor / diagnosis Fibroadenoma / diagnosis Spectrum Analysis, Raman Chromatography, Liquid Tandem Mass Spectrometry Breast Neoplasms / pathology Neoplasms, Fibroepithelial

Journal

Analytica chimica acta
ISSN: 1873-4324
Titre abrégé: Anal Chim Acta
Pays: Netherlands
ID NLM: 0370534

Informations de publication

Date de publication:
01 Dec 2023
Historique:
received: 22 05 2023
revised: 31 08 2023
accepted: 09 10 2023
medline: 20 11 2023
pubmed: 18 11 2023
entrez: 17 11 2023
Statut: ppublish

Résumé

Breast fibroadenomas and phyllodes tumors are both fibroepithelial tumors with comparable histological characteristics. However, rapid and precise differential diagnosis is a tough point in clinical pathology. Given the tendency of phyllodes tumors to recur, the difficulty in differential diagnosis with fibroadenomas leads to the difficulty in optimal management for these patients. In this study, we used Raman spectroscopy to differentiate phyllodes tumors from breast fibroadenomas based on the biochemical and metabolic composition and develop a classification model. The model was validated by 5-fold cross-validation in the training set and tested in an independent test set. The potential metabolic differences between the two types of tumors observed in Raman spectroscopy were confirmed by targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 204 patients with formalin-fixed paraffin-embedded (FFPE) tissue samples, including 100 fibroadenomas and 104 phyllodes tumors were recruited from April 2014 to August 2021. All patients were randomly divided into the training cohort (n = 153) and the test cohort (n = 51). The Raman classification model could differentiate phyllodes tumor versus fibroadenoma with cross-validation accuracy, sensitivity, precision, and area under curve (AUC) of 85.58 % ± 1.77 %, 83.82 % ± 1.01 %, 87.65 % ± 4.22 %, and 93.18 % ± 1.98 %, respectively. When tested in the independent test set, it performed well with the test accuracy, sensitivity, specificity, and AUC of 83.50 %, 86.54 %, 80.39 %, and 90.71 %. Furthermore, the AUC was significantly higher for the Raman model than that for ultrasound (P = 0.0017) and frozen section diagnosis (P < 0.0001). When it came to much more difficult diagnosis between fibroadenoma and benign or small-size phyllodes tumor for pathological examination, the Raman model was capable of differentiating with AUC up to 97.45 % and 95.61 %, respectively. On the other hand, targeted metabolomic analysis, based on fresh-frozen tissue samples, confirmed the differential metabolites (including thymine, dihydrothymine, trans-4-hydroxy-l-proline, etc.) identified from Raman spectra between phyllodes tumor and fibroadenoma. In this study, we obtained the molecular information map of breast phyllodes tumors provided by Raman spectroscopy for the first time. We identified a novel Raman fingerprint signature with the potential to precisely characterize and distinguish phyllodes tumors from fibroadenoma as a quick and accurate diagnostic tool. Raman spectroscopy is expected to further guide the precise diagnosis and optimal treatment of breast fibroepithelial tumors in the future.

Sections du résumé

BACKGROUND BACKGROUND
Breast fibroadenomas and phyllodes tumors are both fibroepithelial tumors with comparable histological characteristics. However, rapid and precise differential diagnosis is a tough point in clinical pathology. Given the tendency of phyllodes tumors to recur, the difficulty in differential diagnosis with fibroadenomas leads to the difficulty in optimal management for these patients.
METHOD METHODS
In this study, we used Raman spectroscopy to differentiate phyllodes tumors from breast fibroadenomas based on the biochemical and metabolic composition and develop a classification model. The model was validated by 5-fold cross-validation in the training set and tested in an independent test set. The potential metabolic differences between the two types of tumors observed in Raman spectroscopy were confirmed by targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS).
RESULTS RESULTS
A total of 204 patients with formalin-fixed paraffin-embedded (FFPE) tissue samples, including 100 fibroadenomas and 104 phyllodes tumors were recruited from April 2014 to August 2021. All patients were randomly divided into the training cohort (n = 153) and the test cohort (n = 51). The Raman classification model could differentiate phyllodes tumor versus fibroadenoma with cross-validation accuracy, sensitivity, precision, and area under curve (AUC) of 85.58 % ± 1.77 %, 83.82 % ± 1.01 %, 87.65 % ± 4.22 %, and 93.18 % ± 1.98 %, respectively. When tested in the independent test set, it performed well with the test accuracy, sensitivity, specificity, and AUC of 83.50 %, 86.54 %, 80.39 %, and 90.71 %. Furthermore, the AUC was significantly higher for the Raman model than that for ultrasound (P = 0.0017) and frozen section diagnosis (P < 0.0001). When it came to much more difficult diagnosis between fibroadenoma and benign or small-size phyllodes tumor for pathological examination, the Raman model was capable of differentiating with AUC up to 97.45 % and 95.61 %, respectively. On the other hand, targeted metabolomic analysis, based on fresh-frozen tissue samples, confirmed the differential metabolites (including thymine, dihydrothymine, trans-4-hydroxy-l-proline, etc.) identified from Raman spectra between phyllodes tumor and fibroadenoma.
SIGNIFICANCE AND NOVELTY UNASSIGNED
In this study, we obtained the molecular information map of breast phyllodes tumors provided by Raman spectroscopy for the first time. We identified a novel Raman fingerprint signature with the potential to precisely characterize and distinguish phyllodes tumors from fibroadenoma as a quick and accurate diagnostic tool. Raman spectroscopy is expected to further guide the precise diagnosis and optimal treatment of breast fibroepithelial tumors in the future.

Identifiants

DOI: 10.1016/j.aca.2023.341897 PMID: 37977771
pubmed: 37977771
pii: S0003-2670(23)01118-2
doi: 10.1016/j.aca.2023.341897
pii:
doi:

Types de publication

Randomized Controlled Trial Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

341897

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare no potential conflicts of interest.

Auteurs

Yifan Wu (Y)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.

Yaohui Wang (Y)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China. Electronic address: wangyaohui@renji.com.

Chang He (C)

School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China.

Yan Wang (Y)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.

Jiayi Ma (J)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.

Yanping Lin (Y)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.

Liheng Zhou (L)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.

Shuguang Xu (S)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.

Yumei Ye (Y)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.

Wenjin Yin (W)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China. Electronic address: yinwenjin@renji.com.

Jian Ye (J)

School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China. Electronic address: yejian78@sjtu.edu.cn.

Jingsong Lu (J)

Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China. Electronic address: lujingsong@renji.com.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Precise diagnosis of breast phyllodes tumors...
questionsmedicales.fr › Tumeurs › Tumeurs par type histologique › Tumeurs du tissu conjonctif et des tissus mous › Sarcomes › Tumeur phyllode › Precise diagnosis of breast phyllodes tumors...
questionsmedicales.fr › Tumeurs › Tumeurs par type histologique › Tumeurs épithéliales épidermoïdes et glandulaires › Tumeurs fibroépithéliales › Fibroadénome › Precise diagnosis of breast phyllodes tumors...
questionsmedicales.fr › Techniques d'investigation › Techniques de chimie analytique › Analyse spectrale › Analyse spectrale Raman › Precise diagnosis of breast phyllodes tumors...
questionsmedicales.fr › Techniques d'investigation › Techniques de chimie analytique › Chromatographie › Chromatographie en phase liquide › Precise diagnosis of breast phyllodes tumors...
questionsmedicales.fr › Techniques d'investigation › Techniques de chimie analytique › Spectrométrie de masse › Spectrométrie de masse en tandem › Precise diagnosis of breast phyllodes tumors...
questionsmedicales.fr › Maladies de la peau et du tissu conjonctif › Maladies de la peau › Maladies du sein › Tumeurs du sein › Precise diagnosis of breast phyllodes tumors...
questionsmedicales.fr › Tumeurs › Tumeurs par type histologique › Tumeurs épithéliales épidermoïdes et glandulaires › Tumeurs fibroépithéliales › Precise diagnosis of breast phyllodes tumors...