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

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.