Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.

IF 1.6 4区医学 Q2 SURGERY

Frontiers in Surgery Pub Date : 2024-10-22 eCollection Date: 2024-01-01 DOI:10.3389/fsurg.2024.1489040

Min Liang, Peimiao Li, Shangyu Xie, Xiaoying Huang, Xiaocai Li, Shifan Tan

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引用次数: 0

Abstract

Introduction: The prognostic landscape of stage III Lung Adenosquamous Carcinoma (ASC) following primary tumor resection remains underexplored. A thoughtfully developed prognostic model has the potential to guide clinicians in patient counseling and the formulation of effective therapeutic strategies.

Methods: Utilizing data from the Surveillance, Epidemiology, and End Results database spanning 2000 to 2018, this study identified independent prognostic factors influencing Overall Survival (OS) in ASC using Boruta analysis. Employing Gradient Boosting, Random Forest, and Neural Network algorithms, predictive models were constructed. Model performance was assessed through key metrics, including Area Under the Receiver Operating Characteristic Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA).

Results: Among 241 eligible patients, seven clinical parameters-age, sex, primary tumor size, N stage, primary tumor site, chemotherapy, and systemic therapy-were identified as significant predictors of OS. Advanced age, male gender, larger tumor size, absence of chemotherapy, and lack of systemic therapy were associated with poorer survival. The Random Forest model outperformed others, achieving 3- and 5-year AUCs of 0.80/0.79 (training) and 0.74/0.65 (validation). It also demonstrated better calibration, lower Brier scores (training: 0.189/0.171; validation: 0.207/0.199), and more favorable DCA. SHAP values enhanced model interpretability by highlighting the impact of each parameter on survival predictions. To facilitate clinical application, the Random Forest model was deployed on a web-based server for accessible prognostic assessments.

Conclusions: This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.

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Ⅲ期肺腺鳞癌肿瘤切除术后的综合预后建模：机器学习见解和基于网络的实施。

导言：原发肿瘤切除术后III期肺腺鳞癌（ASC）的预后情况仍未得到充分探索。一个经过深思熟虑开发的预后模型有可能指导临床医生为患者提供咨询并制定有效的治疗策略：本研究利用从 2000 年到 2018 年的监测、流行病学和最终结果数据库中的数据，采用 Boruta 分析方法确定了影响 ASC 总生存期（OS）的独立预后因素。采用梯度提升、随机森林和神经网络算法，构建了预测模型。通过接收者工作特征曲线下面积（AUC）、校准图、布赖尔评分和决策曲线分析（DCA）等关键指标对模型性能进行评估：在241名符合条件的患者中，年龄、性别、原发肿瘤大小、N分期、原发肿瘤部位、化疗和全身治疗等7项临床参数被确定为OS的重要预测因素。高龄、男性、肿瘤较大、未接受化疗和未接受系统治疗与较差的生存率有关。随机森林模型的表现优于其他模型，3年和5年的AUC分别为0.80/0.79（训练）和0.74/0.65（验证）。它还表现出更好的校准性、更低的 Brier 评分（训练：0.189/0.171；验证：0.207/0.199）和更有利的 DCA。SHAP 值突出了每个参数对生存预测的影响，从而提高了模型的可解释性。为便于临床应用，随机森林模型被部署在一个基于网络的服务器上，以便进行可访问的预后评估：本研究提出了一个强大的机器学习模型和一个基于网络的工具，可帮助医疗从业人员为原发性肿瘤切除术后的 ASC 患者做出个性化的临床决策和治疗优化。

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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来源期刊

Frontiers in Surgery Medicine-Surgery

CiteScore

1.90

自引率

11.10%

发文量

1872

审稿时长

12 weeks

期刊介绍： Evidence of surgical interventions go back to prehistoric times. Since then, the field of surgery has developed into a complex array of specialties and procedures, particularly with the advent of microsurgery, lasers and minimally invasive techniques. The advanced skills now required from surgeons has led to ever increasing specialization, though these still share important fundamental principles. Frontiers in Surgery is the umbrella journal representing the publication interests of all surgical specialties. It is divided into several “Specialty Sections” listed below. All these sections have their own Specialty Chief Editor, Editorial Board and homepage, but all articles carry the citation Frontiers in Surgery. Frontiers in Surgery calls upon medical professionals and scientists from all surgical specialties to publish their experimental and clinical studies in this journal. By assembling all surgical specialties, which nonetheless retain their independence, under the common umbrella of Frontiers in Surgery, a powerful publication venue is created. Since there is often overlap and common ground between the different surgical specialties, assembly of all surgical disciplines into a single journal will foster a collaborative dialogue amongst the surgical community. This means that publications, which are also of interest to other surgical specialties, will reach a wider audience and have greater impact. The aim of this multidisciplinary journal is to create a discussion and knowledge platform of advances and research findings in surgical practice today to continuously improve clinical management of patients and foster innovation in this field.