Salmanpour Mohammad R, Amiri Sajad, Gharibi Sara, Shariftabrizi Ahmad, Xu Yixi, Weeks William B, Rahmim Arman, Hacihaliloglu Ilker
Department of Radiology, University of British Columbia, Vancouver, BC, Canada.
Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
J Imaging Inform Med. 2025 Jul 3. doi: 10.1007/s10278-025-01585-5.
Artificial intelligence (AI) can advance medical diagnostics, but interpretability limits its clinical use. This work links standardized quantitative Radiomics features (RF) extracted from medical images with clinical frameworks like PI-RADS, ensuring AI models are understandable and aligned with clinical practice. We investigate the connection between visual semantic features defined in PI-RADS and associated risk factors, moving beyond abnormal imaging findings, and establishing a shared framework between medical and AI professionals by creating a standardized radiological/biological RF dictionary. Six interpretable and seven complex classifiers, combined with nine interpretable feature selection algorithms (FSA), were applied to RFs extracted from segmented lesions in T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) multiparametric MRI sequences to predict TCIA-UCLA scores, grouped as low-risk (scores 1-3) and high-risk (scores 4-5). We then utilized the created dictionary to interpret the best predictive models. Combining sequences with FSAs including ANOVA F-test, Correlation Coefficient, and Fisher Score, and utilizing logistic regression, identified key features: The 90th percentile from T2WI, (reflecting hypo-intensity related to prostate cancer risk; Variance from T2WI (lesion heterogeneity; shape metrics including Least Axis Length and Surface Area to Volume ratio from ADC, describing lesion shape and compactness; and Run Entropy from ADC (texture consistency). This approach achieved the highest average accuracy of 0.78 ± 0.01, significantly outperforming single-sequence methods (p-value < 0.05). The developed dictionary for Prostate-MRI (PM1.0) serves as a common language and fosters collaboration between clinical professionals and AI developers to advance trustworthy AI solutions that support reliable/interpretable clinical decisions.
人工智能（AI）可推动医学诊断发展，但其可解释性限制了其临床应用。这项工作将从医学图像中提取的标准化定量放射组学特征（RF）与诸如PI-RADS等临床框架相联系，确保人工智能模型易于理解并与临床实践保持一致。我们研究了PI-RADS中定义的视觉语义特征与相关风险因素之间的联系，超越了异常影像学表现，并通过创建标准化的放射学/生物学RF词典，在医学专业人员和人工智能专业人员之间建立了一个共享框架。将六个可解释的和七个复杂的分类器，与九种可解释的特征选择算法（FSA）相结合，应用于从T2加权成像（T2WI）、扩散加权成像（DWI）和表观扩散系数（ADC）多参数MRI序列中的分割病变提取的RF，以预测TCIA-UCLA评分，分为低风险（评分1-3）和高风险（评分4-5）。然后，我们利用创建的词典来解释最佳预测模型。将序列与包括方差分析F检验、相关系数和Fisher评分在内的FSA相结合，并使用逻辑回归，确定了关键特征：T2WI的第90百分位数（反映与前列腺癌风险相关的低信号强度）；T2WI的方差（病变异质性）；形状指标，包括ADC的最小轴长和表面积与体积比，描述病变形状和紧凑性；以及ADC的游程熵（纹理一致性）。这种方法实现了最高平均准确率0.78±0.01，显著优于单序列方法（p值<0.05）。所开发的前列腺MRI词典（PM1.0）作为一种通用语言，促进了临床专业人员和人工智能开发者之间的合作，以推进支持可靠/可解释临床决策的可信人工智能解决方案。
