用于检测肾细胞癌术后晚期复发和随访损失的机器学习分析

IF 1.6 Q3 UROLOGY & NEPHROLOGY BJUI compass Pub Date : 2024-09-02 DOI:10.1002/bco2.425
Kodai Sato, Tomokazu Sazuka, Takayuki Arai, Hiroaki Sato, Manato Kanesaka, Keisuke Ando, Shinpei Saito, Sangjon Pae, Yasutaka Yamada, Yusuke Imamura, Shinichi Sakamoto, Tomohiko Ichikawa
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引用次数: 0

摘要

目的 肾细胞癌(RCC)有晚期复发的倾向,在治愈性手术后 5 年或更长时间才会复发。随访需要影像诊断,至于随访应该持续多久,目前还没有明确的答案。有些患者会自行决定停止随访。如何更好地预测晚期复发和随访损失(LF)仍不清楚。 患者和方法 这项研究的对象是1988年至2021年间在千叶大学医院接受根治性或部分肾切除术的非转移性RCC患者。RCC患者的随访通常是终身的。我们使用基于机器学习的生存分析方法--随机生存森林(RSFs)来预测晚期复发和LF。为了验证预测的准确性,我们采用了与时间相关的接收者操作特征曲线下面积(t-AUC)。为了分析晚期复发和LF的风险,我们使用了SurvSHAP(t)和部分依赖图。 结果 本研究共分析了 1051 个病例。中位随访时间为 58.5 个月(0-376 个月)。使用 RSF 预测术后 60、120、180 和 240 个月的复发准确率分别为 t-AUC 0.806、0.761、0.674 和 0.566。复发风险影响在术后约 50 个月内呈现出随时间而增加的趋势。50 个月后,晚期复发的风险因素特征并不明显。术后 60、120、180、240 和 300 个月时,使用 RSF 预测 LF 的准确性 t-AUC 分别为 0.542、0.699、0.685、0.628 和 0.674。LF 的风险随着 70 岁以上年龄的增长而增加。 结论 很难确定预测晚期复发的因素。在长期随访观察中,必须特别关注 70 岁及以上的 RCC 患者。有必要建立框架,促进与患者居住地附近的当地医院合作,在社区内提供护理。
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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Machine learning analysis for detecting late recurrence and loss to follow-up after renal cell carcinoma surgery

Objectives

Renal cell carcinoma (RCC) is shown to have a tendency for late recurrence, occurring 5 or more years after curative surgery. Imaging diagnosis is required for follow-up, and there is no definitive answer as to how long this should continue. Some patients discontinue follow-up visits at their own discretion. How best to predict late recurrence and loss to follow-up (LF) remains unclear.

Patients and methods

This study targeted patients diagnosed with non-metastatic RCC who underwent either radical or partial nephrectomy at Chiba University Hospital between 1988 and 2021. Follow-up for patients with RCC is typically lifelong. We used random survival forests (RSFs), a machine learning-based survival analysis method, to predict late recurrence and LF. For verification of prediction accuracy, we applied the time-dependent area under the receiver operating characteristic curve (t-AUC). To analyse the risks of late recurrence and LF, SurvSHAP(t) and partial dependence plots were used.

Results

We analysed 1051 cases in this study. Median follow-up was 58.5 (range: 0–376) months. The predictive accuracy of recurrence using RSF was t-AUC 0.806, 0.761, 0.674 and 0.566 at 60, 120, 180 and 240 months postoperatively, respectively. The recurrence risk impact showed a time-dependent increase up to approximately 50 months postoperatively. Beyond 50 months, there were no distinct risk factors characteristic of late recurrence. The predictive accuracy of LF using RSF was t-AUC 0.542, 0.699, 0.685, 0.628 and 0.674 at 60, 120, 180, 240 and 300 months postoperatively, respectively. The risk of LF increased with advancing age beyond 70 years.

Conclusion

It is difficult to identify factors that predict late recurrence. For long-term follow-up observation, it is essential to pay particular attention to patients with RCC aged 70 years and above. Establishing frameworks to facilitate collaboration with local hospitals near patients' residences and providing care within the community is necessary.

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