背景:乳腺纤维腺瘤和叶状肿瘤都是具有相当组织学特征的纤维上皮肿瘤。然而,快速、准确的鉴别诊断是临床病理学的难点。鉴于叶状肿瘤复发的趋势,纤维腺瘤的鉴别诊断困难导致这些患者的最佳治疗困难.
方法:在本研究中,我们使用拉曼光谱根据生化和代谢组成区分叶状肿瘤和乳腺纤维腺瘤,并建立分类模型。该模型在训练集中通过5倍交叉验证进行验证,并在独立测试集中进行测试。在拉曼光谱中观察到的两种类型肿瘤之间的潜在代谢差异通过使用液相色谱-串联质谱(LC-MS/MS)的靶向代谢组学分析得到证实。
结果:共204例福尔马林固定石蜡包埋(FFPE)组织样本,我们从2014年4月至2021年8月招募了100例纤维腺瘤和104例叶状肿瘤.将所有患者随机分为训练队列(n=153)和测试队列(n=51)。拉曼分类模型可以区分叶状肿瘤和纤维腺瘤,具有交叉验证的准确性,灵敏度,精度,曲线下面积(AUC)为85.58%±1.77%,83.82%±1.01%,87.65%±4.22%,和93.18%±1.98%,分别。在独立测试集中测试时,它在测试准确性方面表现良好,灵敏度,特异性,AUC为83.50%,86.54%,80.39%,90.71%。此外,拉曼模型的AUC明显高于超声(P=0.0017)和冰冻切片诊断(P<0.0001)。当在纤维腺瘤和良性或小尺寸叶状肿瘤之间进行病理检查时,诊断要困难得多。拉曼模型能够区分的AUC高达97.45%和95.61%,分别。另一方面,有针对性的代谢组学分析,基于新鲜冷冻的组织样本,确认了差异代谢物(包括胸腺嘧啶,二氢胸腺嘧啶,反式-4-羟基-1-脯氨酸,等。)从叶状肿瘤和纤维腺瘤之间的拉曼光谱中鉴定。
■在这项研究中,我们首次获得了拉曼光谱提供的乳腺叶状肿瘤的分子信息图谱。我们确定了一种新颖的拉曼指纹特征,具有精确表征和区分叶状肿瘤与纤维腺瘤的潜力,作为一种快速准确的诊断工具。拉曼光谱有望在未来进一步指导乳腺纤维上皮肿瘤的精确诊断和优化治疗。
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.
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: 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.
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.