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DISTINGUISHING MINIMALLY INVASIVE CARCINOMA AND ADENOCARCINOMA IN SITU FROM INVASIVE ADENOCARCINOMA WITH INTRATUMORAL AND PERI-TUMORAL TEXTURAL FEATURES
DISTINGUISHING MINIMALLY INVASIVE CARCINOMA AND ADENOCARCINOMA IN SITU FROM INVASIVE ADENOCARCINOMA WITH INTRATUMORAL AND PERI-TUMORAL TEXTURAL FEATURES
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机译：具有肺内和周围质地特征的浸润性腺癌鉴别原发性微小浸润性癌和腺癌。
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摘要
Embodiments include controlling a processor to access a radiological image of a region of lung tissue, where the radiological image includes a ground glass (GGO) nodule; define a tumoral region by segmenting the GGO nodule, where defining the tumoral region includes defining a tumoral boundary; define a peri-tumoral region based on the tumoral boundary; extract a set of radiomic features from the peri-tumoral region and the tumoral region; provide the set of radiomic features to a machine learning classifier trained to distinguish minimally invasive adenocarcinoma (MIA) and adenocarcinoma in situ (AIS) from invasive adenocarcinoma; receive, from the machine learning classifier, a probability that the GGO nodule is invasive adenocarcinoma, where the machine learning classifier computes the probability based on the set of radiomic features; generate a classification of the GGO nodule as MIA or AIS, or invasive adenocarcinoma, based, at least in part, on the probability; and display the classification.
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