Uveal melanoma (UM) is the most common intraocular malignancy in adults. Plaque brachytherapy remains the dominant eyeball-conserving therapy for UM. Tumor regression in UM after plaque brachytherapy has been reported as a valuable prognostic factor. The present study aimed to develop an accurate machine-learning model to predict the 4-year risk of metastasis and death in UM based on ocular ultrasound data.

A total of 454 patients with UM were enrolled in this retrospective, single-center study. All patients were followed up for at least 4 years after plaque brachytherapy and underwent ophthalmologic evaluations before the therapy. B-scan ultrasonography was used to measure the basal diameters and thickness of tumors preoperatively and postoperatively. Random Forest (RF) algorithm was used to construct two prediction models: whether a patient will survive for more than 4 years and whether the tumor will develop metastasis within 4 years after treatment.

Our predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.708 for predicting death using only a one-time follow-up record. Including the data from two additional follow-ups increased the AUC of the model to 0.883. We attained AUCs of 0.730 and 0.846 with data from one and three-time follow-up, respectively, for predicting metastasis. The model found that the amount of postoperative follow-up data significantly improved death and metastasis prediction accuracy. Furthermore, we divided tumor treatment response into four patterns. The D(decrease)/S(stable) patterns are associated with a significantly better prognosis than the I(increase)/O(other) patterns.

The present study developed an RF model to predict the risk of metastasis and death from UM within 4 years based on ultrasound follow-up records following plaque brachytherapy. We intend to further validate our model in prospective datasets, enabling us to implement timely and efficient treatments.

葡萄膜黑色素瘤（UM）是成人最常见的眼内恶性肿瘤。斑块近距离放射治疗仍然是 UM 的主要眼球保留疗法。据报道，斑块近距离放射治疗后UM的肿瘤消退是一个有价值的预后因素。本研究旨在开发一种准确的机器学习模型，以根据眼部超声数据预测 UM 的 4 年转移和死亡风险。

共有 454 名 UM 患者参加了这项回顾性、单中心研究。所有患者在斑块近距离放射治疗后均随访至少 4 年，并在治疗前接受眼科评估。术前、术后采用B超测量肿瘤基底直径和厚度。采用随机森林（RF）算法构建两个预测模型：患者是否能存活超过4年以及治疗后4年内肿瘤是否会发生转移。

我们的预测模型仅使用一次性随访记录来预测死亡，受试者工作特征曲线下面积 (AUC) 达到 0.708。包含另外两次随访的数据后，模型的 AUC 增加至 0.883。我们通过一次和三次随访的数据分别获得了 0.730 和 0.846 的 AUC，用于预测转移。该模型发现术后随访数据量显着提高了死亡和转移预测的准确性。此外，我们将肿瘤治疗反应分为四种模式。D（减少）/S（稳定）模式与 I（增加）/O（其他）模式相比，预后明显更好。

本研究开发了一种 RF 模型，根据斑块近距离放射治疗后的超声随访记录来预测 4 年内 UM 转移和死亡的风险。我们打算在未来的数据集中进一步验证我们的模型，使我们能够实施及时有效的治疗。