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A competing round-robin prediction model for histologic subtype prediction of lung adenocarcinomas based on thoracic computed tomography

机译：基于胸部计算机断层摄影术的肺腺癌组织学亚型预测的竞争循环预测模型

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Adenocarcinomas (ADC) is the major subtype of non-small cell lung cancers. Currently, surgery is used as the main approach for the treatment of the early-stage ADCs. However, different histological subtypes of ADC classified by the IASLC/ATS/ERS system may potentially impact on the surgical management, which subsequently influence the prognosis of the surgery. Thus, preoperative determination of ADC subtypes is essential and highly desirable. Nevertheless, the histological subtypes of ADCs may be either unknown or incompletely determined by biopsy before the surgery. Alternatively, the histological subtypes of ADCs may be predicted from the pulmonary computed tomographic (CT) images. However, previous studies showed limitations on the prediction results due to the complex composition of ADC subtypes. One possible reason is the radiomic descriptors used to differentiate different subtypes could be very different. The conventional approaches based on the same set of descriptors to distinguish all subtypes are inherently infeasible. Another possible reason is the complex composition of multiple subtypes in a lung nodule may hinder the extraction of effective radiomic descriptors to characterize each subtype. To overcome these challenges, a competing round-robin prediction model was proposed to predict the histological subtypes of ADCs, which was composed of three key ideas, namely, pair-specific radiomic descriptors for differentiation of every pair of subtypes, inter-regional descriptors for characterization of complex composition of subtypes in a nodule, and a multi-level round-robin classifier. Based on 70 ADCs patients, the proposed model achieved an accuracy of 86.3% in predicting five histological subtypes of adenocarcinomas.
机译：腺癌（ADC）是非小细胞肺癌的主要亚型。当前，手术被用作治疗早期ADC的主要方法。但是，由IASLC / ATS / ERS系统分类的ADC的不同组织学亚型可能会影响手术管理，从而影响手术的预后。因此，术前确定ADC亚型是必不可少的，也是非常需要的。然而，ADC的组织学亚型可能是未知的，或者在手术前通过活检不能完全确定。备选地，可以根据肺部计算机断层摄影（CT）图像来预测ADC的组织学亚型。但是，由于ADC亚型的复杂组成，以前的研究显示出对预测结果的局限性。一个可能的原因是，用来区分不同亚型的放射描述符可能非常不同。基于同一组描述符来区分所有子类型的常规方法本质上是不可行的。另一个可能的原因是肺结节中多种亚型的复杂组成可能会阻碍有效放射学描述子的提取，以表征每种亚型。为了克服这些挑战，提出了一种竞争性轮循预测模型来预测ADC的组织学亚型，该模型由三个关键思想组成，即用于区分每对亚型的对特异性放射学描述符，区域间描述符。结节中亚型复杂组成的表征，以及多层次轮循分类器。基于70位ADC患者，该模型在预测5种腺癌组织学亚型中的准确性达到86.3％。

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