通过瘤内和瘤周 PET 放射组学分析预测接受新辅助化疗的乳腺癌患者的病理反应。

Ayşegül Aksu , Zeynep Gülsüm Güç , Kadir Alper Küçüker , Ahmet Alacacıoğlu , Bülent Turgut
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引用次数: 0

摘要

研究目的我们的研究旨在评估从肿瘤和瘤周区域获得的 18F-FDG PET 放射影像学数据对预测接受新辅助化疗(NAC)的局部晚期乳腺癌患者病理完全反应(pCR)的贡献:对确诊为浸润性导管癌并接受新辅助化疗的女性患者进行回顾性评估。人工分割原发肿瘤的感兴趣体积(VOI)(VOI-T),然后在VOI-T周围添加一个体素厚的VOI来定义瘤周区域(VOI-PT)。根据 VOI 获取形态、基于强度、直方图和纹理参数。患者被分为两组,即 pCR 和非完全病理反应(npCR)。仅利用放射学特征创建 "放射学模型",利用放射学特征和免疫组化数据创建 "病理放射学模型":结果:在纳入研究的 66 例患者中,有 21 例属于 pCR 组。在pCR和npCR患者中,唯一具有统计学意义的原发肿瘤特征是Morphological_Compacity-T(AUC:0.666)。在反应组之间,VOI-PT 的 2 个形态特征、1 个强度特征和 4 个纹理特征存在显著差异;Morphological_Compacity-PT 与 NGTDM_contrast-PT 之间没有相关性。计算得出的放射学模型的灵敏度和准确度值分别为 61.9% 和 75.8%(AUC:0.786)。当加入 HER2 状态时,病理放射学模型的灵敏度和准确度值分别增加到 85.7% 和 81.8%(AUC:0.903):结论:与原发肿瘤相比,同时评估 PET 周围肿瘤放射学特征能更好地预测乳腺癌患者的 NAC pCR。
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Intra and peritumoral PET radiomics analysis to predict the pathological response in breast cancer patients receiving neoadjuvant chemotherapy

Objective

The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC).

Methods

Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-T) was manually segmented, then a voxel-thick VOI was added around VOI-T to define the peritumoral area (VOI-PT). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A “radiomic model” was created with only radiomic features, and a “patho-radiomic model” was created using radiomic features and immunohistochemical data.

Results

Of the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-T (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-PT; no correlation was found between Morphological_Compacity-PT and NGTDM_contrast-PT. The obtained radiomic model’s sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903).

Conclusions

Evaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.

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