多模态AI从乳腺癌活检预测新辅助治疗响应
Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies
这篇论文教AI直接从病理切片推断基因表达，再预测乳腺癌化疗是否见效，1,412人验证AUROC 0.79，不用做基因组检测了。
研究者开发了一种两阶段AI模型，从乳腺癌活检的病理切片预测新辅助治疗的病理完全缓解（pCR）。第一阶段用32种癌症、8,742名患者的数据学习从组织病理图像推断转录组表达，经病理医生复核和空间一致性验证。第二阶段基于推断的转录组和临床变量预测pCR，训练用1,080名患者，在9个队列共1,412名患者上评估，合并AUROC达0.79（95% CI 0.73-0.85）。模型表现超过组织病理学生物标志物，在分子亚型内部也能区分应答者，且避免了基因组检测的基因选择限制。
Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies
Scarcity of labeled data limits development of deep learning biomarkers in oncology. We develop a two-stage AI model predicting pathological complete response (pCR) to neoadjuvant therapy in breast cancer. The first stage learns the transcriptome from histopathology using 8,742 patients across 32 cancer types, corroborated by pathologist review and spatial agreement with measured expression. This simplifies the second stage to predicting pCR from inferred expression and clinical variables. Developed using 1,080 patients (five cohorts) and evaluated in 1,412 patients (nine cohorts), the model achieves a pooled AUROC of 0.79 (95% CI, 0.73-0.85), discriminating responders within molecular subtypes. It outperforms histopathological biomarkers, remaining stable across intratumoral sampling and with minimal biopsy tissue. Ablations show transcriptome-wide inference improves discrimination over clinical variables alone or one-stage pathology models, and robustness by avoiding genomic assays' gene selection constraints. These results indicate that biologically informed compression may generalize to data-sparse applications in precision oncology.
