방사선의학 웹진

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Abstract
We investigated predictions from 18F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent 18F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians' assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (p < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians.

Figure 1. comparison of the ROC curves of the ML models according to the included PET data. It showed that the AUC of ML using PET/CT data obtained after neoadjuvant CCRT was significantly higher than that of using only baseline PET/CT data。

Affiliations

Jang Yoo  1 , Jaeho Lee  2 , Miju Cheon  1 , Sang-Keun Woo  3 , Myung-Ju Ahn  4 , Hong Ryull Pyo  5 , Yong Soo Choi  6 , Joung Ho Han  7 , Joon Young Choi  8
1 Department of Nuclear Medicine, Veterans Health Service Medical Center, Seoul 05368, Korea.
2 Department of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, Korea.
3 Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul 01812, Korea.
4 Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
5 Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
6 Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
7 Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
8 Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.