Shafiei Somayeh B, Shadpour Saeed, Pangburn Barbara, Bentley-McLachlan Martha, de Leon-Casasola Oscar
The Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA.
Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada.
Sci Rep. 2025 Mar 15;15(1):8954. doi: 10.1038/s41598-025-93678-y.
Objective measurements of pain and safe methods to alleviate it could revolutionize medicine. This study used functional near-infrared spectroscopy (fNIRS) and virtual reality (VR) to improve pain assessment and explore non-pharmacological pain relief in cancer patients. Using resting-state fNIRS (rs-fNIRS) data and multinomial logistic regression (MLR), we identified brain-based pain biomarkers and classified pain severity in cancer patients. Participants included healthy individuals who underwent rs-fNIRS recording without VR (Group A), cancer patients who underwent rs-fNIRS recording both before and after engaging in the Oceania relaxation program VR intervention (Group B), and cancer patients who underwent rs-fNIRS recording without VR (Group C). All participants wore a wireless fNIRS headcap for brain activity recording. Pain severity was self-reported by patients using the FACES Pain Scale-Revised (FPS-R). fNIRS data were analyzed with MLR, categorizing pain into no/mild (0-4/10), moderate (5-7/10), and severe (8-10/10) levels. The MLR model classified pain severity in an unseen test group, selected using the leave-one-participant-out technique and repeated across all participants, achieving an accuracy of 74%. VR significantly reduced pain intensity (Wilcoxon signed-rank test, P < 0.001), with significant changes in brain functional connectivity patterns (P < 0.05). Additionally, 75.61% of patients experienced pain reductions exceeding the clinically relevant threshold of 30%. These findings underscore the potential of fNIRS for pain assessment and VR as a useful non-pharmacological intervention for cancer-related pain management, with broader implications for clinical pain management.
对疼痛进行客观测量以及采用安全的方法缓解疼痛可能会给医学带来变革。本研究使用功能近红外光谱技术（fNIRS）和虚拟现实（VR）来改善疼痛评估，并探索癌症患者的非药物性疼痛缓解方法。利用静息态fNIRS（rs-fNIRS）数据和多项逻辑回归（MLR），我们识别出了基于大脑的疼痛生物标志物，并对癌症患者的疼痛严重程度进行了分类。参与者包括未使用VR进行rs-fNIRS记录的健康个体（A组）、在参与大洋洲放松计划VR干预前后均进行rs-fNIRS记录的癌症患者（B组）以及未使用VR进行rs-fNIRS记录的癌症患者（C组）。所有参与者均佩戴无线fNIRS头罩以记录大脑活动。患者使用面部疼痛量表修订版（FPS-R）自我报告疼痛严重程度。fNIRS数据采用MLR进行分析，将疼痛分为无/轻度（0-4/10）、中度（5-7/10）和重度（8-10/10）级别。MLR模型对一个使用留一参与者法选择并在所有参与者中重复的未见过的测试组中的疼痛严重程度进行了分类，准确率达到74%。VR显著降低了疼痛强度（Wilcoxon符号秩检验，P < 0.001），大脑功能连接模式也有显著变化（P < 0.05）。此外，75.61%的患者疼痛减轻超过了30%的临床相关阈值。这些发现强调了fNIRS在疼痛评估方面的潜力以及VR作为癌症相关疼痛管理有用的非药物性干预手段的潜力，对临床疼痛管理具有更广泛的意义。
