Surgery today · 2024
Preoperative evaluation of visceral pleural invasion in peripheral lung cancer utilizing deep learning technology.
СтатьяНазвание и аннотация: Перевод в очереди
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Kudo Yujin, Saito Akira, Horiuchi Tomoaki, Murakami Kotaro, Kobayashi Masaharu, Matsubayashi Jun, Nagao Toshitaka, Ohira Tatsuo, Kuroda Masahiko, Ikeda Norihiko
Аннотация
PURPOSE: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction.
METHODS: This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.
RESULTS: Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.
CONCLUSION: AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.
METHODS: This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.
RESULTS: Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.
CONCLUSION: AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.