European journal of radiology · 2025
A novel and general spatiotemporal diagnostic model: Intratumoral outflow and peritumoral inflow for the differentiation and stratification of breast tumor.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Meng Zhanao, Zhou Chenyi, Xie Hui, Chen Ting, Wu Chunhua, Li Wenxuan, Tang Wenjie, Wang Yanling
Аннотация
OBJECTIVE: A novel Intratumoral Outflow and Peritumoral Inflow (IOPI) model based on spatiotemporal information was developed to quantify differences in blood perfusion and microcirculation. Its performance was compared with conventional indicators, and its clinical application for diagnosing benign and malignant breast tumors, as well as its potential for risk stratification, was validated in a two-center study.
MATERIALS AND METHODS: A retrospective analysis of 159 patients (80 malignant and 79 benign lesions) undergoing dynamic contrast-enhanced MRI was conducted. Training set was from Center 1, testing set was from Center 2. Conventional kinetic parameters (PE and SER) and interpreted MRI features (ADC and BI-RADS) were extracted. Hemodynamics models (IOPI, IIO, PIO and IPIO) and combined models (ADC + IOPI, BIRADS + IOPI) were constructed by logistic regression. Classification performance was assessed by AUC, sensitivity, specificity and accuracy.
RESULTS: In the training cohort, the proposed IOPI model achieved an AUC of 0.911, with specificity of 95.1 %, sensitivity of 82.0 % and accuracy of 89.5 %. In external testing cohort, the AUC was 0.802, significantly outperformed conven tional indicators and others models (p < 0.05). The BIRADS combined IOPI model achieved the highest diagnostic performance in classifying malignancy of breast lesions, with the AUC values of 0.940 and sensitivity of 92.0 % in training cohort. In the testing cohort, the ADC combined IOPI model achieved the highest diagnostic performance with the AUC of 0.878, accuracy of 84.2 % and specificity of 88.9 %. The combined models also showed comparable performance in stratifying invasive grades and predicting Ki-67 expression (exceptional specificity of 90.9 % in ADC combined IOPI).
CONCLUSION: IOPI serves as a valuable tool that highlights the additional potential of spatiotemporal kinetic information for improving breast tumor diagnosis. The combined model which integrates time and spatial concepts, exhibited strong diagnostic performance for improved breast cancer diagnosis and risk stratification.
MATERIALS AND METHODS: A retrospective analysis of 159 patients (80 malignant and 79 benign lesions) undergoing dynamic contrast-enhanced MRI was conducted. Training set was from Center 1, testing set was from Center 2. Conventional kinetic parameters (PE and SER) and interpreted MRI features (ADC and BI-RADS) were extracted. Hemodynamics models (IOPI, IIO, PIO and IPIO) and combined models (ADC + IOPI, BIRADS + IOPI) were constructed by logistic regression. Classification performance was assessed by AUC, sensitivity, specificity and accuracy.
RESULTS: In the training cohort, the proposed IOPI model achieved an AUC of 0.911, with specificity of 95.1 %, sensitivity of 82.0 % and accuracy of 89.5 %. In external testing cohort, the AUC was 0.802, significantly outperformed conven tional indicators and others models (p < 0.05). The BIRADS combined IOPI model achieved the highest diagnostic performance in classifying malignancy of breast lesions, with the AUC values of 0.940 and sensitivity of 92.0 % in training cohort. In the testing cohort, the ADC combined IOPI model achieved the highest diagnostic performance with the AUC of 0.878, accuracy of 84.2 % and specificity of 88.9 %. The combined models also showed comparable performance in stratifying invasive grades and predicting Ki-67 expression (exceptional specificity of 90.9 % in ADC combined IOPI).
CONCLUSION: IOPI serves as a valuable tool that highlights the additional potential of spatiotemporal kinetic information for improving breast tumor diagnosis. The combined model which integrates time and spatial concepts, exhibited strong diagnostic performance for improved breast cancer diagnosis and risk stratification.