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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. |
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Image segmentation
Computed tomography
Lung
Tumors
Solids
Lung cancer
Tissues
