Dihge Looket, Bendahl Pär-Ola, Skarping Ida, Hjärtström Malin, Ohlsson Mattias, Rydén Lisa
Department of Clinical Sciences Lund, Division of Surgery, Lund University, Lund, Sweden.
Department of Plastic and Reconstructive Surgery, Skåne University Hospital, Malmö, Sweden.
Front Oncol. 2023 Mar 1;13:1102254. doi: 10.3389/fonc.2023.1102254. eCollection 2023.
To implement artificial neural network (ANN) algorithms for noninvasive lymph node staging (NILS) to a decision support tool and facilitate the option to omit surgical axillary staging in breast cancer patients with low-risk of nodal metastasis.
The NILS tool is a further development of an ANN prototype for the prediction of nodal status. Training and internal validation of the original algorithm included 15 clinical and tumor-related variables from a consecutive cohort of 800 breast cancer cases. The updated NILS tool included 10 top-ranked input variables from the original prototype. A workflow with four ANN pathways was additionally developed to allow different combinations of missing preoperative input values. Predictive performances were assessed by area under the receiver operating characteristics curves (AUC) and sensitivity/specificity values at defined cut-points. Clinical utility was presented by estimating possible sentinel lymph node biopsy (SLNB) reduction rates. The principles of user-centered design were applied to develop an interactive web-interface to predict the patient's probability of healthy lymph nodes. A technical validation of the interface was performed using data from 100 test patients selected to cover all combinations of missing histopathological input values.
ANN algorithms for the prediction of nodal status have been implemented into the web-based NILS tool for personalized, noninvasive nodal staging in breast cancer. The estimated probability of healthy lymph nodes using the interface showed a complete concordance with estimations from the reference algorithm except in two cases that had been wrongly included (ineligible for the technical validation). NILS predictive performance to distinguish node-negative from node-positive disease, also with missing values, displayed AUC ranged from 0.718 (95% CI, 0.687-0.748) to 0.735 (95% CI, 0.704-0.764), with good calibration. Sensitivity 90% and specificity 34% were demonstrated. The potential to abstain from axillary surgery was observed in 26% of patients using the NILS tool, acknowledging a false negative rate of 10%, which is clinically accepted for the standard SLNB technique.
The implementation of NILS into a web-interface are expected to provide the health care with decision support and facilitate preoperative identification of patients who could be good candidates to avoid unnecessary surgical axillary staging.
将用于无创淋巴结分期（NILS）的人工神经网络（ANN）算法应用于决策支持工具，并为低淋巴结转移风险的乳腺癌患者提供省略手术腋窝分期的选择。
NILS工具是用于预测淋巴结状态的ANN原型的进一步发展。原始算法的训练和内部验证包括来自连续800例乳腺癌病例队列的15个临床和肿瘤相关变量。更新后的NILS工具包括来自原始原型的10个排名靠前的输入变量。还开发了一个具有四条ANN路径的工作流程，以允许术前输入值缺失的不同组合。通过受试者操作特征曲线（AUC）下的面积以及在定义的切点处的敏感性/特异性值来评估预测性能。通过估计可能的前哨淋巴结活检（SLNB）减少率来展示临床实用性。应用以用户为中心的设计原则开发了一个交互式网络界面，以预测患者淋巴结健康的概率。使用从100名测试患者中选择的数据进行界面的技术验证，这些患者涵盖了组织病理学输入值缺失的所有组合。
用于预测淋巴结状态的ANN算法已被应用于基于网络的NILS工具中，用于乳腺癌的个性化无创淋巴结分期。使用该界面估计的淋巴结健康概率与参考算法的估计完全一致，但有两例被错误纳入（不符合技术验证条件）的情况除外。NILS区分淋巴结阴性和阳性疾病的预测性能，包括存在缺失值的情况，显示AUC范围为0.718（95%CI，0.687 - 0.748）至0.735（95%CI，0.704 - 0.764），具有良好的校准。敏感性为90%且特异性为34%。使用NILS工具的患者中有26%观察到有避免腋窝手术的可能性，同时承认假阴性率为10%，这在标准SLNB技术中是临床上可接受的。
将NILS应用于网络界面有望为医疗保健提供决策支持，并有助于术前识别那些可能是避免不必要手术腋窝分期的合适候选者的患者。
