Tendero Raquel, Larroza Andrés, Pérez-Benito Francisco Javier, Perez-Cortes Juan Carlos, Román Marta, Llobet Rafael
Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera s/n, 46022, València, Spain.
Universitat Politècnica de València, Camino de Vera s/n, 46022, València, Spain.
Eur Radiol. 2025 Sep 11. doi: 10.1007/s00330-025-11980-9.
This study evaluates whether integrating clinical data with mammographic features using artificial intelligence (AI) improves 2-year breast cancer risk prediction compared to using either data type alone.
This retrospective nested case-control study included 2193 women (mean age, 59 ± 5 years) screened at Hospital del Mar, Spain (2013-2020), with 418 cases (mammograms taken 2 years before diagnosis) and 1775 controls (cancer-free for ≥ 2 years). Three models were evaluated: (1) ERTpd + im, based on Extremely Randomized Trees (ERT), split into sub-models for personal data (ERTpd) and image features (ERTim); (2) an image-only model (CNN); and (3) a hybrid model (ERTpd + im + CNN). Five-fold cross-validation, area under the receiver operating characteristic curve (AUC), bootstrapping for confidence intervals, and DeLong tests for paired data assessed performance. Robustness was evaluated across breast density quartiles and detection type (screen-detected vs. interval cancers).
The hybrid model achieved an AUC of 0.75 (95% CI: 0.71-0.76), significantly outperforming the CNN model (AUC, 0.74; 95% CI: 0.70-0.75; p < 0.05) and slightly surpassing ERTpd + im (AUC, 0.74; 95% CI: 0.70-0.76). Sub-models ERTpd and ERTim had AUCs of 0.59 and 0.73, respectively. The hybrid model performed consistently across breast density quartiles (p > 0.05) and better for screen-detected (AUC, 0.79) than interval cancers (AUC, 0.59; p < 0.001).
This study shows that integrating clinical and mammographic data with AI improves 2-year breast cancer risk prediction, outperforming single-source models. The hybrid model demonstrated higher accuracy and robustness across breast density quartiles, with better performance for screen-detected cancers.
Question Current breast cancer risk models have limitations in accuracy. Can integrating clinical and mammographic data using artificial intelligence (AI) improve short-term risk prediction? Findings A hybrid model combining clinical and imaging data achieved the highest accuracy in predicting 2-year breast cancer risk, outperforming models using either data type alone. Clinical relevance Integrating clinical and mammographic data with AI improves breast cancer risk prediction. This approach enables personalized screening strategies and supports early detection. It helps identify high-risk women and optimizes the use of additional assessments within screening programs.
本研究评估与单独使用临床数据或乳房X线特征数据相比，利用人工智能（AI）将临床数据与乳房X线特征相结合是否能改善2年乳腺癌风险预测。
这项回顾性巢式病例对照研究纳入了在西班牙巴塞罗那海洋医院（2013 - 2020年）接受筛查的2193名女性（平均年龄59±5岁），其中418例为病例组（诊断前2年的乳房X线照片），1775例为对照组（无癌≥2年）。评估了三种模型：（1）ERTpd + im，基于极端随机树（ERT），分为个人数据子模型（ERTpd）和图像特征子模型（ERTim）；（2）仅图像模型（CNN）；（3）混合模型（ERTpd + im + CNN）。采用五折交叉验证、受试者操作特征曲线下面积（AUC）、用于置信区间的自助法以及用于配对数据的德龙检验来评估性能。在不同乳房密度四分位数和检测类型（筛查发现的癌症与间期癌）中评估稳健性。
混合模型的AUC为0.75（95%CI：0.71 - 0.76），显著优于CNN模型（AUC，0.74；95%CI：0.70 - 0.75；p < 0.05），并略优于ERTpd + im（AUC，0.74；95%CI：0.70 - 0.76）。子模型ERTpd和ERTim的AUC分别为0.59和0.73。混合模型在不同乳房密度四分位数中表现一致（p > 0.05），对于筛查发现的癌症（AUC，0.79）比间期癌（AUC，0.59；p < 0.001）表现更好。
本研究表明，将临床和乳房X线数据与AI相结合可改善2年乳腺癌风险预测，优于单源模型。混合模型在不同乳房密度四分位数中表现出更高的准确性和稳健性，对筛查发现的癌症表现更佳。
问题 当前乳腺癌风险模型在准确性方面存在局限性。利用人工智能（AI）整合临床和乳房X线数据能否改善短期风险预测？发现 结合临床和影像数据的混合模型在预测2年乳腺癌风险方面准确性最高，优于单独使用任何一种数据类型的模型。临床意义 将临床和乳房X线数据与AI相结合可改善乳腺癌风险预测。这种方法能够实现个性化筛查策略并支持早期检测。有助于识别高危女性并优化筛查计划中额外评估的使用。
