Zhang Zhitao, Lan Huan, Zhao Shuai
Department of Galactophore, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, 350001, People's Republic of China.
Breast Cancer (Dove Med Press). 2024 Jun 11;16:305-318. doi: 10.2147/BCTT.S458036. eCollection 2024.
To analyze the diagnostic value of quantitative features in multimodal magnetic resonance imaging (MRI) images to construct a radio-omics model for breast cancer.
Ninety-five patients with breast-related diseases from January 2020 to January 2021 were grouped into the benign group (n=57) and malignant group (n=38) according to the pathological findings. All cases were randomized as the training group (n=66) and validation group (n=29) in a 7:3 ratio based on the examination time. All subjects were examined by T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), dynamic contrast enhancement (DCE), and apparent diffusion coefficient (ADC) multimodality MRI. The MRI findings were analyzed against pathological findings. A diagnostic breast cancer radiomics model was constructed. The diagnostic efficacy of the model in the validation group was analyzed, and the diagnostic efficacy was analyzed via the ROC curve.
Fibroadenoma accounted for 49.12% of benign breast diseases, and invasive ductal carcinoma accounted for 73.68% of malignant breast diseases. The sensitivity of T1WI, T2WI, DWI, ADC, and DCE in diagnosing breast cancer was 61.14%, 66.67%, 73.30%, 78.95%, and 85.96%, using the four-fold table method. The area under the curves (AUCs) of T1WI, T2WI, DWI, ADC, and DCE for diagnosing breast cancer were 0.715, 0.769, 0.785, 0.835, and 0.792, respectively. The AUCs of plain scan, diffuse, enhanced, plain scan + diffuse, plain scan + enhanced, enhanced + diffuse, and plain scan + enhanced + diffuse for diagnosing breast cancer were 0.746, 0.798, 0.816, 0.839, 0.890, 0.906, and 0.927, respectively.
The construction of a radio-omics model by quantitative features in multimodal MRI images was valuable in the diagnosis of breast cancer. The value of radio-omics models such as plain scan + enhanced + diffuse was higher than the other models in diagnosing breast cancer and could be widely applied in clinical practice.
分析多模态磁共振成像（MRI）图像中定量特征的诊断价值，以构建乳腺癌的放射组学模型。
将2020年1月至2021年1月的95例乳腺相关疾病患者根据病理结果分为良性组（n = 57）和恶性组（n = 38）。根据检查时间，所有病例按7:3的比例随机分为训练组（n = 66）和验证组（n = 29）。所有受试者均接受T1加权成像（T1WI）、T2加权成像（T2WI）、扩散加权成像（DWI）、动态对比增强（DCE）和表观扩散系数（ADC）多模态MRI检查。将MRI检查结果与病理结果进行对照分析。构建诊断乳腺癌的放射组学模型。分析该模型在验证组中的诊断效能，并通过ROC曲线分析诊断效能。
乳腺良性疾病中纤维腺瘤占49.12%，乳腺恶性疾病中浸润性导管癌占73.68%。采用四格表法计算，T1WI、T2WI、DWI、ADC和DCE诊断乳腺癌的灵敏度分别为为61.14%、66.67%、73.30%、78.95%和85.96%。T1WI、T2WI、DWI、ADC和DCE诊断乳腺癌的曲线下面积（AUC）分别为0.715、0.769、0.785、0.835和0.792。平扫、弥散、增强、平扫+弥散、平扫+增强、增强+弥散、平扫+增强+弥散诊断乳腺癌的AUC分别为0.746、0.798、0.816、0.839、0.890、0.906和0.927。
利用多模态MRI图像中的定量特征构建放射组学模型对乳腺癌诊断具有重要价值。平扫+增强+弥散等放射组学模型在乳腺癌诊断中的价值高于其他模型，可在临床实践中广泛应用。
