Chen Xuming, Meng Fanrui, Zhang Ping, Wang Lei, Yao Shengyu, An Chengyang, Li Hui, Zhang Dongfeng, Li Hongxia, Li Jie, Wang Lisheng, Liu Yong
Department of Radiation Oncology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
Int J Radiat Oncol Biol Phys. 2025 Aug 1;122(5):1380-1390. doi: 10.1016/j.ijrobp.2025.03.012. Epub 2025 Mar 13.
Patients with identical stages or similar tumor volumes can vary significantly in their responses to radiation therapy (RT) due to individual characteristics, making personalized RT for non-small cell lung cancer (NSCLC) challenging. This study aimed to develop a deep learning model by integrating pretreatment and midtreatment computed tomography (CT) to predict the treatment response in NSCLC patients.
We retrospectively collected data from 168 NSCLC patients across 3 hospitals. Data from Shanghai General Hospital (SGH, 35 patients) and Shanxi Cancer Hospital (SCH, 93 patients) were used for model training and internal validation, while data from Linfen Central Hospital (LCH, 40 patients) were used for external validation. Deep learning, radiomics, and clinical features were extracted to establish a varying time interval long short-term memory network for response prediction. Furthermore, we derived a model-deduced personalize dose escalation (DE) for patients predicted to have suboptimal gross tumor volume regression. The area under the receiver operating characteristic curve (AUC) and predicted absolute error were used to evaluate the predictive Response Evaluation Criteria in Solid Tumors classification and the proportion of gross tumor volume residual. DE was calculated as the biological equivalent dose using an /α/β ratio of 10 Gy.
The model using only pretreatment CT achieved the highest AUC of 0.762 and 0.687 in internal and external validation respectively, whereas the model integrating both pretreatment and midtreatment CT achieved AUC of 0.869 and 0.798, with predicted absolute error of 0.137 and 0.185, respectively. We performed personalized DE for 29 patients. Their original biological equivalent dose was approximately 72 Gy, within the range of 71.6 Gy to 75 Gy. DE ranged from 77.7 to 120 Gy for 29 patients, with 17 patients exceeding 100 Gy and 8 patients reaching the model's preset upper limit of 120 Gy.
Combining pretreatment and midtreatment CT enhances prediction performance for RT response and offers a promising approach for personalized DE in NSCLC.
由于个体特征，处于相同分期或肿瘤体积相似的患者对放射治疗（RT）的反应可能存在显著差异，这使得非小细胞肺癌（NSCLC）的个性化放疗具有挑战性。本研究旨在通过整合治疗前和治疗中期的计算机断层扫描（CT）来开发一种深度学习模型，以预测NSCLC患者的治疗反应。
我们回顾性收集了3家医院168例NSCLC患者的数据。来自上海交通大学医学院附属瑞金医院（SGH，35例患者）和山西省肿瘤医院（SCH，93例患者）的数据用于模型训练和内部验证，而来自临汾市中心医院（LCH，40例患者）的数据用于外部验证。提取深度学习、影像组学和临床特征，建立用于反应预测的可变时间间隔长短期记忆网络。此外，我们为预测肿瘤总体积退缩不理想的患者推导了模型推导的个性化剂量递增（DE）。采用受试者操作特征曲线（AUC）下面积和预测绝对误差来评估实体瘤疗效评价标准分类和肿瘤总体积残留比例。使用10 Gy的α/β比值将DE计算为生物等效剂量。
仅使用治疗前CT的模型在内部验证和外部验证中的AUC分别达到最高的0.762和0.687，而整合治疗前和治疗中期CT的模型的AUC分别为0.869和0.798，预测绝对误差分别为0.137和0.185。我们对29例患者进行了个性化DE。他们原来的生物等效剂量约为72 Gy，在71.6 Gy至75 Gy范围内。29例患者的DE范围为77.7至120 Gy，其中17例超过100 Gy，8例达到模型预设的120 Gy上限。
结合治疗前和治疗中期CT可提高放疗反应的预测性能，并为NSCLC的个性化DE提供了一种有前景的方法。
