Purpose

Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data.

Methods

This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC).

Results

The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22).

Conclusion

A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.

中文翻译：

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开发基于图像的随机森林分类器，用于预测腹腔镜乙状结肠切除术的手术时间

目的

乙状结肠憩室炎是一种具有较高社会经济负担的疾病，导致全世界左侧结肠切除术较多。现代手术安排依赖于手术时间的准确预测，以加强患者护理并优化医疗资源。本研究旨在根据术前 CT 生物识别和人口统计患者数据，开发腹腔镜乙状结肠切除术手术持续时间的预测模型。

方法

这项回顾性单中心队列研究纳入了 85 名因憩室病接受腹腔镜乙状结肠切除术的患者。在术前 CT 成像中测量了外科专家推荐的潜在相关手术特异性解剖参数。随机分为训练集和测试集 (75% / 25%) 后，进行多类逻辑回归，并根据训练队列中的 CT 成像参数、患者年龄和性别对随机森林分类器进行训练，以预测分类手术持续时间。使用既定的性能指标（包括接收者操作特征曲线下面积（AUROC））在测试队列中对模型进行评估。

结果

随机森林模型取得了 0.78 的良好平均 AUROC。它可以很好地预测长（AUROC = 0.89；特异性 0.71；敏感性 1.0）和短（AUROC = 0.81；特异性 0.77；敏感性 0.56）程序。它明显优于多类逻辑回归模型（AUROC：平均值 = 0.33；短 = 0.31；长 = 0.22）。

结论

根据人口统计和 CT 成像生物识别患者数据训练的随机森林分类器可以预测腹腔镜乙状结肠切除术的手术持续时间异常值。这种方法有待多中心研究的验证，有可能改善内脏手术的手术安排，并扩展到其他手术。