Background: Non-small cell lung cancer (NSCLC) has been the most common cancer globally in the recent decade. CT is the most common imaging modality for the initial diagnosis of NSCLC. The gold standard for definitive diagnosis is the histological evaluation of a biopsy or surgical sample, which usually requires a long processing time for the confirmation of diagnosis. This study aims to develop artificial intelligence models to predict overall staging based on patient demographics and radiomics retrieved from the initial CT images, so as to prioritize later-stage patients for histology evaluation to facilitate cancer diagnosis. Method: Two cohorts of NSCLC patient datasets were utilized for this study. The NSCLC-radiomics dataset from The Cancer Imaging Archive (TCIA) was divided into 70% for the training group and 30% for the internal testing group. Another cohort from a local hospital was collected for the an external testing group. Patient demographics and 107 radiomic features were retrieved from the gross tumor volume delineated by clinical oncologists on CT images. Artificial neural networks were used to build models for NSCLC overall staging (stage I, II, or III) prediction. Four traditional classifiers were also adopted to build models for comparison. Result: The proposed feed-forward neural network (FFNN) model showed good performance in predicting overall staging with an accuracy of 88.84%, 76.67%, and 74.52% in overall accuracies in validation, internal cohort testing, and external cohort testing, respectively. The sensitivity and specificity are balanced in all the stages, with average precision and F1 score in each of the stages. Conclusion: The FFNN demonstrated good performance in overall staging prediction for NSCLC patients. It has the benefit of predicting multiple overall stages in a single model. The software required and the proposed model are simple. It can be operated on a general-purpose computer in the radiology department. The application will eventually be used as a prediction tool to prioritize the biopsy or surgery sample for histological analysis and molecular investigation, thus shortening the time for diagnosis by pathologists, which supports the triage of patients for further testing.
背景：近十年来，非小细胞肺癌（NSCLC）已成为全球最常见的癌症。CT是NSCLC初步诊断最常用的影像学检查手段。确诊的金标准是对活检或手术样本进行组织学评估，但该过程通常耗时较长。本研究旨在开发人工智能模型，基于患者人口统计学特征及从初始CT图像中提取的影像组学特征预测总体分期，从而优先安排晚期患者进行组织学评估，以促进癌症诊断。 方法：本研究采用两组NSCLC患者数据集。来自癌症影像档案库（TCIA）的NSCLC影像组学数据集按70%和30%的比例划分为训练组和内部测试组。另收集本地医院的一组数据作为外部测试组。从临床肿瘤医师在CT图像上勾画的肿瘤总体积中提取患者人口统计学特征及107个影像组学特征。采用人工神经网络构建NSCLC总体分期（I期、II期或III期）预测模型，并引入四种传统分类器构建对比模型。 结果：所提出的前馈神经网络（FFNN）模型在总体分期预测中表现良好，在验证集、内部测试集和外部测试集中的总体准确率分别为88.84%、76.67%和74.52%。该模型在各分期中的敏感性与特异性保持平衡，各分期平均精确率和F1分数均表现稳定。 结论：FFNN模型在NSCLC患者总体分期预测中展现出良好性能，其优势在于可通过单一模型预测多个总体分期。所需软件及模型结构简洁，可在放射科通用计算机上运行。该应用最终将作为预测工具，优先安排活检或手术样本进行组织学分析和分子检测，从而缩短病理医师的诊断时间，为患者进一步检查的分诊提供支持。
肿瘤(癌症)患者之家