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能否利用人工智能聚类分析来识别发生脊柱转移的乳腺癌患者,以预测术后不良事件的高风险人群?

Can We Use Artificial Intelligence Cluster Analysis to Identify Patients with Metastatic Breast Cancer to the Spine at Highest Risk of Postoperative Adverse Events?

机构信息

Departments of Orthopaedic Surgery and Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Orthopaedic Surgery, Montefiore Medical Center, Bronx, New York, USA.

Departments of Orthopaedic Surgery and Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

出版信息

World Neurosurg. 2023 Jun;174:e26-e34. doi: 10.1016/j.wneu.2023.02.064. Epub 2023 Feb 18.

DOI:10.1016/j.wneu.2023.02.064
PMID:36805503
Abstract

OBJECTIVE

Group patients who required open surgery for metastatic breast cancer to the spine by functional level and metastatic disease characteristics to identify factors that predispose to poor outcomes.

METHODS

A retrospective analysis included patients managed at 2 tertiary referral centers from 2008 to 2020. The primary outcome was a 90-day adverse event. A 2-step unsupervised cluster analysis stratified patients into cohorts using function at presentation, preoperative spine radiation, structural instability, epidural spinal cord compression (ESCC), neural deficits, and tumor location/hormone status. Comparisons were performed using χ test and one-way analysis of variance.

RESULTS

Five patient "clusters" were identified. High function (HIGH) had thoracic metastases and an Eastern Cooperative Oncology Group (ECOG) score of 1.0 ± 0.8. Low function/irradiated (LOW + RADS) had preoperative radiation and the lowest Karnofsky scores (56.0 ± 10.6). Estrogen receptor or progesterone receptor (ER/PR) positive patients had >90% estrogen/progesterone positivity and moderate Karnofsky scores (74.0 ± 11.5). Lumbar/noncompressive (NON-COMP) had the fewest patients with ESCC grade 2 or 3 epidural disease (42.1%, P < 0.001). Low function/neurologic deficits (LOW + NEURO) had ESCC grade 2 or 3 disease and neurologic deficits. Adverse event rates were 25.0% in the HIGH group, 73.3% in LOW + RADS, 24.0% in ER/PR, 31.6% in NON-COMP, and 60.0% in LOW + NEURO (P = 0.003).

CONCLUSIONS

Function at presentation, tumor hormone signature, radiation history, and epidural compression delineated postoperative trajectory. We believe our results can aid in expectation management and the identification of at-risk patients who may merit closer surveillance following surgical intervention.

摘要

目的

通过功能水平和转移性疾病特征对需要接受开放性手术治疗转移性乳腺癌脊柱转移的患者进行分组,以确定导致不良预后的因素。

方法

回顾性分析纳入了 2008 年至 2020 年在 2 家三级转诊中心接受治疗的患者。主要结局是 90 天不良事件。采用无监督两步聚类分析,根据患者就诊时的功能、术前脊柱放疗、结构不稳定、硬膜外脊髓压迫(ESCC)、神经功能缺损和肿瘤位置/激素状态对患者进行分组。采用卡方检验和单因素方差分析进行比较。

结果

确定了 5 个患者“聚类”。高功能(HIGH)组患者有胸转移,东部肿瘤协作组(ECOG)评分 1.0±0.8。低功能/放疗(LOW+RADS)组患者术前有放疗,卡诺夫斯基评分最低(56.0±10.6)。雌激素受体或孕激素受体(ER/PR)阳性患者的雌激素/孕激素阳性率超过 90%,卡诺夫斯基评分中等(74.0±11.5)。腰椎/非压迫(NON-COMP)组患者 ESCC 2 级或 3 级硬膜外疾病患者最少(42.1%,P<0.001)。低功能/神经功能缺损(LOW+NEURO)组患者有 ESCC 2 级或 3 级疾病和神经功能缺损。HIGH 组不良事件发生率为 25.0%,LOW+RADS 组为 73.3%,ER/PR 组为 24.0%,NON-COMP 组为 31.6%,LOW+NEURO 组为 60.0%(P=0.003)。

结论

就诊时的功能、肿瘤激素特征、放疗史和硬膜外压迫情况描绘了术后轨迹。我们认为我们的结果可以帮助进行预期管理,并确定可能需要更密切监测的高危患者,以在手术干预后进行监测。

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