通过基于集成视觉变换器的模型对乳腺癌患者新辅助化疗的病理完全缓解进行长期监测。

Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.

作者信息

Comes Maria Colomba, Fanizzi Annarita, Bove Samantha, Boldrini Luca, Latorre Agnese, Guven Deniz Can, Iacovelli Serena, Talienti Tiziana, Rizzo Alessandro, Zito Francesco Alfredo, Massafra Raffaella

机构信息

Laboratorio di Biostatistica e Bioinformatica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy.

Unità Operativa Complessa di Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli I.R.C.C.S, Rome, Italy.

出版信息

Cancer Med. 2024 Dec;13(24):e70482. doi: 10.1002/cam4.70482.

DOI:10.1002/cam4.70482

PMID:39692281

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11653217/

Abstract

BACKGROUND

Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-acquired pre- and mid-treatment quantitatively capture information about tumor heterogeneity as potential earlier indicators of pathological complete response (pCR) to NAC in breast cancer.

AIMS

This study aimed to develop an ensemble deep learning-based model, exploiting a Vision Transformer (ViT) architecture, which merges features automatically extracted from five segmented slices of both pre- and mid-treatment exams containing the maximum tumor area, to predict and monitor pCR to NAC.

MATERIALS AND METHODS

Imaging data analyzed in this study referred to a cohort of 86 breast cancer patients, randomly split into training and test sets at a ratio of 8:2, who underwent NAC and for which information regarding the pCR status was available (37.2% of patients achieved pCR). We further validated our model using a subset of 20 patients selected from the publicly available I-SPY2 trial dataset (independent test).

RESULTS

The performances of the proposed model were assessed using standard evaluation metrics, and promising results were achieved: area under the curve (AUC) value of 91.4%, accuracy value of 82.4%, a specificity value of 80.0%, a sensitivity value of 85.7%, precision value of 75.0%, F-score value of 80.0%, and G-mean value of 82.8%. The results obtained from the independent test show an AUC of 81.3%, an accuracy of 80.0%, a specificity value of 76.9%, a sensitivity of 85.0%, a precision of 66.7%, an F-score of 75.0%, and a G-mean of 81.2%.

DISCUSSION

As far as we know, our research is the first proposal using ViTs on DCE-MRI exams to monitor pCR over time during NAC.

CONCLUSION

Finally, the changes in DCE-MRI at pre- and mid-treatment could affect the accuracy of pCR prediction to NAC.

摘要

背景

乳腺癌的形态学和血管特征在新辅助化疗（NAC）期间可能会发生变化。动态对比增强磁共振成像（DCE-MRI）获取的治疗前和治疗中期数据可定量捕捉肿瘤异质性信息，作为乳腺癌对NAC病理完全缓解（pCR）的潜在早期指标。

目的

本研究旨在开发一种基于深度学习集成模型，利用视觉Transformer（ViT）架构，融合从治疗前和治疗中期包含最大肿瘤区域的五个分割切片中自动提取的特征，以预测和监测乳腺癌对NAC的pCR。

材料和方法

本研究分析的影像数据来自86例乳腺癌患者队列，以8:2的比例随机分为训练集和测试集，这些患者接受了NAC治疗，且有关于pCR状态的信息（37.2%的患者达到pCR）。我们使用从公开可用的I-SPY2试验数据集中选取的20例患者子集进一步验证了我们的模型（独立测试）。

结果

使用标准评估指标评估所提出模型的性能，取得了有前景的结果：曲线下面积（AUC）值为91.4%，准确率值为82.4%，特异性值为80.0%，灵敏度值为85.7%，精确率值为75.0%，F值为80.0%，G均值为82.8%。独立测试获得的结果显示AUC为81.3%，准确率为80.0%，特异性值为76.9%，灵敏度为85.0%，精确率为66.7%，F值为75.0%，G均值为81.2%。