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IF 6.4 1区医学 Q1 ONCOLOGY

International Journal of Radiation Oncology Biology Physics Pub Date : 2025-03-13 DOI:10.1016/j.ijrobp.2025.03.012

Xuming Chen, Fanrui Meng, Ping Zhang, Lei Wang, Shengyu Yao, Chengyang An, Hui Li, Dongfeng Zhang, Hongxia Li, Jie Li, Lisheng Wang, Yong Liu

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引用次数: 0

摘要

背景：分期相同或肿瘤体积相似的患者由于个体特征不同，对放疗（RT）的反应也会有很大差异，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的计算机断层扫描（CT）来开发一种深度学习（DL）模型，以预测NSCLC患者的治疗反应：我们回顾性地收集了三家医院168名NSCLC患者的数据。A医院（35名患者）和B医院（93名患者）的数据用于模型训练和内部验证，C医院（40名患者）的数据用于外部验证。我们提取了 DL、放射组学和临床特征，建立了一个用于反应预测的不同时间间隔长短时记忆网络（VTI-LSTM）。此外，我们还为预测肿瘤总体积（GTV）回归不理想的患者推导出了一个模型教育的个性化剂量升级（DE）。接收者操作特征曲线下面积（AUC）和预测绝对误差（PAE）用于评估预测性实体瘤反应评估标准（RECIST）分类和GTV残留比例。用α/β比值为10 Gy的生物等效剂量（BED）计算DE：结果：在内部和外部验证中，仅使用治疗前 CT 的模型获得了最高的 AUC，分别为 0.762 和 0.687，而综合治疗前和治疗中 CT 的模型获得的 AUC 分别为 0.869 和 0.798，PAE 分别为 0.137 和 0.185。我们对 29 名患者进行了个性化 DE。他们的原始 BED 约为 72 Gy，范围在 71.6 Gy 到 75 Gy 之间。29名患者的DE范围在77.7到120 Gy之间，其中17名患者的DE超过了100 Gy，8名患者的DE达到了模型预设的上限120 Gy：结论：结合治疗前和治疗中的 CT 可提高 RT 反应的预测性能，为 NSCLC 的个性化 DE 提供了一种很有前景的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Establishing a Deep Learning Model That Integrates Pretreatment and Midtreatment Computed Tomography to Predict Treatment Response in Non-Small Cell Lung Cancer.

Purpose: 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.

Methods and materials: 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.

Results: 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.

Conclusions: Combining pretreatment and midtreatment CT enhances prediction performance for RT response and offers a promising approach for personalized DE in NSCLC.

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来源期刊

International Journal of Radiation Oncology Biology Physics 医学-核医学

CiteScore

11.00

自引率

7.10%

发文量

2538

审稿时长

6.6 weeks

期刊介绍： International Journal of Radiation Oncology • Biology • Physics (IJROBP), known in the field as the Red Journal, publishes original laboratory and clinical investigations related to radiation oncology, radiation biology, medical physics, and both education and health policy as it relates to the field. This journal has a particular interest in original contributions of the following types: prospective clinical trials, outcomes research, and large database interrogation. In addition, it seeks reports of high-impact innovations in single or combined modality treatment, tumor sensitization, normal tissue protection (including both precision avoidance and pharmacologic means), brachytherapy, particle irradiation, and cancer imaging. Technical advances related to dosimetry and conformal radiation treatment planning are of interest, as are basic science studies investigating tumor physiology and the molecular biology underlying cancer and normal tissue radiation response.