Yutaro Koike1, Keiju Aokage2, Kosuke Ikeda3, Tokiko Nakai4, Kenta Tane2, Tomohiro Miyoshi2, Masato Sugano4, Motohiro Kojima3, Satoshi Fujii3, Takeshi Kuwata4, Atsushi Ochiai5, Toshiyuki Tanaka6, Kenji Suzuki7, Masahiro Tsuboi2, Genichiro Ishii8. 1. Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan; Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan; Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan. Electronic address: yukoike@east.ncc.go.jp. 2. Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan. 3. Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan. 4. Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan. 5. Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan. 6. Department of Applied Physics and Physico-Informatics, Faculty of Science and Technology, Keio University, Japan. 7. Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan. 8. Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan; Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan. Electronic address: gishii@east.ncc.go.jp.
Abstract
BACKGROUND: Cancer tissue is composed of both a cancer cell component and a stromal component. The aim of this study was to investigate if the component ratio predicts a prognosis for lung squamous cell carcinoma (SqCC) patients by using a machine learning method. METHODS: A total of 135 peripheral SqCC cases (tumor size: 3-5 cm) were enrolled in this study. The areas of the cancer cell component, the necrotic component, and the stromal component were accurately measured via a machine learning method. Each case was divided into the following three subtypes: 1) predominant cancer cell, 2) predominant necrosis, and 3) predominant stroma. The study examined if a particular subtype had prognostic significance. RESULTS: The number of cases per subtype of predominant cancer cell, predominant necrosis, and predominant stroma was 59, 6, and 70, respectively. Patients with the predominant stroma subtype had a significantly shorter recurrence free survival (RFS) than did those with the predominant cancer cell subtype (5-yr RFS: 42.3 % vs. 84.3 %,p < 0.01). Also, in pathological stage I patients, the 5-year RFS rate for the predominant stroma subtype was significantly shorter (5-yr RFS: 64.3 % vs. 88.4 %, p < 0.01). In the multivariate analysis of p-stage I patients, the predominant stroma subtype was confirmed to be an independent prognostic factor for RFS (p < 0.01). CONCLUSION: Using machine learning, the study confirmed that the predominant stroma subtype was an independent factor for RFS, suggesting that the ratio of the stromal component correlates with the malignant potential of SqCC.
BACKGROUND:Cancer tissue is composed of both a cancer cell component and a stromal component. The aim of this study was to investigate if the component ratio predicts a prognosis for lung squamous cell carcinoma (SqCC) patients by using a machine learning method. METHODS: A total of 135 peripheral SqCC cases (tumor size: 3-5 cm) were enrolled in this study. The areas of the cancer cell component, the necrotic component, and the stromal component were accurately measured via a machine learning method. Each case was divided into the following three subtypes: 1) predominant cancer cell, 2) predominant necrosis, and 3) predominant stroma. The study examined if a particular subtype had prognostic significance. RESULTS: The number of cases per subtype of predominant cancer cell, predominant necrosis, and predominant stroma was 59, 6, and 70, respectively. Patients with the predominant stroma subtype had a significantly shorter recurrence free survival (RFS) than did those with the predominant cancer cell subtype (5-yr RFS: 42.3 % vs. 84.3 %,p < 0.01). Also, in pathological stage I patients, the 5-year RFS rate for the predominant stroma subtype was significantly shorter (5-yr RFS: 64.3 % vs. 88.4 %, p < 0.01). In the multivariate analysis of p-stage I patients, the predominant stroma subtype was confirmed to be an independent prognostic factor for RFS (p < 0.01). CONCLUSION: Using machine learning, the study confirmed that the predominant stroma subtype was an independent factor for RFS, suggesting that the ratio of the stromal component correlates with the malignant potential of SqCC.