Liu Yuwei, Zhao Litao, Liu Jiangang, Wang Liang
Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China.
School of Engineering Medicine, Beihang University, Beijing, People's Republic of China.
Oncologist. 2024 Dec 6;29(12):e1692-e1700. doi: 10.1093/oncolo/oyae184.
Surgery and radiotherapy are primary nonconservative treatments for prostate cancer (PCa). However, personalizing treatment options between these treatment modalities is challenging due to unclear criteria. We developed an artificial intelligence (AI)-based model that can identify patients with localized PCa who would benefit more from either radiotherapy or surgery, thereby providing personalized clinical decision-making.
Data from consecutive patients with localized PCa who received radiotherapy or surgery with complete records of clinicopathological variables and follow-up results in 12 registries of the Surveillance, Epidemiology, and End Results database were analyzed. Patients from 7 registries were randomly assigned to training (TD) and internal validation datasets (IVD) at a 9:1 ratio. The remaining 5 registries constituted the external validation dataset (EVD). TD was divided into training-radiotherapy (TRD) and training-surgery (TSD) datasets, and IVD was divided into internal-radiotherapy (IRD) and internal-surgery (ISD) datasets. Six models for radiotherapy and surgery were trained using TRD and TSD to predict radiotherapy survival probability (RSP) and surgery survival probability (SSP), respectively. The models with the highest concordance index (C-index) on IRD and ISD were chosen to form the final treatment recommendation model (FTR). FTR recommendations were based on the higher value between RSP and SSP. Kaplan-Meier curves were generated for patients receiving recommended (consistent group) and nonrecommended treatments (inconsistent group), which were compared using the log-rank test.
The study included 118 236 patients, categorized into TD (TRD: 44 621; TSD: 41 500), IVD (IRD: 4949; ISD: 4621), and EVD (22 545). Both radiotherapy and surgery models accurately predicted RSP and SSP (C-index: 0.735-0.787 and 0.769-0.797, respectively). The consistent group exhibited higher survival rates than the inconsistent group, particularly among patients not suitable for active surveillance (P < .001).
FTR accurately identifies patients with localized PCa who would benefit more from either radiotherapy or surgery, offering clinicians an effective AI tool to make informed choices between these 2 treatments.
手术和放射治疗是前列腺癌（PCa）的主要非保守治疗方法。然而，由于标准不明确，在这些治疗方式之间进行个性化治疗选择具有挑战性。我们开发了一种基于人工智能（AI）的模型，该模型可以识别出局部PCa患者中从放射治疗或手术中获益更多的患者，从而提供个性化的临床决策。
分析了来自监测、流行病学和最终结果数据库的12个登记处中接受放射治疗或手术且具有完整临床病理变量记录和随访结果的连续局部PCa患者的数据。将来自7个登记处的患者以9:1的比例随机分配到训练数据集（TD）和内部验证数据集（IVD）。其余5个登记处构成外部验证数据集（EVD）。TD分为训练-放射治疗（TRD）和训练-手术（TSD）数据集，IVD分为内部-放射治疗（IRD）和内部-手术（ISD）数据集。使用TRD和TSD分别训练6个放射治疗和手术模型，以预测放射治疗生存概率（RSP）和手术生存概率（SSP）。选择在IRD和ISD上一致性指数（C指数）最高的模型，形成最终治疗推荐模型（FTR）。FTR推荐基于RSP和SSP中的较高值。为接受推荐治疗（一致组）和未接受推荐治疗（不一致组）的患者生成Kaplan-Meier曲线，并使用对数秩检验进行比较。
该研究纳入了118236例患者，分为TD（TRD：44621例；TSD：41500例）、IVD（IRD：4949例；ISD：4621例）和EVD（22545例）。放射治疗和手术模型均准确预测了RSP和SSP（C指数分别为0.735 - 0.787和0.769 - 0.797）。一致组的生存率高于不一致组，特别是在不适合主动监测的患者中（P <.001）。
FTR准确识别出局部PCa患者中从放射治疗或手术中获益更多的患者，为临床医生提供了一种有效的AI工具，以便在这两种治疗方法之间做出明智的选择。
