Application of machine learning methodology for PET-based definition of lung cancer

作者：Kerhet A^*; Small C; Quon H; Riauka T; Schrader L; Greiner R; Yee D; McEwan A; Roa W

来源：Current Oncology, 2010, 17(1): 41-47.

DOI：10.3747/co.v17i1.394

• Positron-emission tomography
• PET
• radiation treatment
• lung cancer
• gross tumour volume
• GTV
• artificial intelligence
• machine learning
• support vector machine
• SVM

We applied a learning methodology framework to assist in the threshold-based segmentation of non-small-cell lung cancer (NSCLC) tumours in positron-emission tomography-computed tomography (PET-CT) imaging for use in radiotherapy planning. Gated and standard free-breathing studies of two patients were independently analysed (four studies in total). Each study had a PET-CT and a treatment-planning CT image. The reference gross tumour volume (GTV) was identified by two experienced radiation oncologists who also determined reference standardized uptake value (SUV) thresholds that most closely approximated the GTV contour on each slice. A set of uptake distribution-related attributes was calculated for each pet slice. A machine learning algorithm was trained on a subset of the pet slices to cope with slice-to-slice variation in the optimal SUV threshold: that is, to predict the most appropriate SUV threshold from the calculated attributes for each slice. The algorithm's performance was evaluated using the remainder of the pet slices. A high degree of geometric similarity was achieved between the areas outlined by the predicted and the reference SUV thresholds (Jac-card index exceeding 0.82). No significant difference was found between the gated and the free-breathing results in the same patient. In this preliminary work, we demonstrated the potential applicability of a machine learning methodology as an auxiliary tool for radiation treatment planning in NSCLC.

• 出版日期2010