Kiyonori Ebara1, Shodayu Takashima2, Binghu Jiang2, Hodaka Numasaki2, Mai Fujino2, Yasuhiko Tomita3, Katsuyuki Nakanishi3, Masahiko Higashiyama3. 1. Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka 565-0871, Japan. Electronic address: kiyoharu0825@yahoo.co.jp. 2. Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka 565-0871, Japan. 3. Osaka Medical Center for Cancer and Cardiovascular Diseases, Higashinari-ku, Osaka, Japan.
Abstract
RATIONALE AND OBJECTIVES: To evaluate the clinical utility of three-dimensional (3D) computed tomography (CT) for predicting pleural invasion by peripheral lung cancer. MATERIALS AND METHODS: CT findings (tumor size, vertical diameter, length and area of the interface between tumor and the pleura, ratios of length and area [Rarea] of interface between tumor and the pleura to tumor size, angle between the tumor and adjacent pleura, presence or absence of pleural thickening, and originally developed 3D pleural patterns) in 201 consecutive patients with lung cancer of ≤3 cm in contact with pleural surface were correlated with pathologic findings. Logistic modeling was used for determining the significant factors for prediction of pleural invasion, and receiver operating characteristic (ROC) curves were used for investigating diagnostic capability of significant factors, resulting in a recommendation to the optimal criteria for predicting pleural invasion and to the optimal threshold for differentiating parietal from visceral invasion. RESULTS: Sixty-one (30%) of the 201 patients had pathologically verified pleural invasion. Logistic modeling revealed that the 3D pleural pattern was the only significant factor (P < .001; relative risk of 7.34). Among every combination of the 3D patterns, skirt-like pattern showed the highest accuracy of 77% for predicting pleural invasion. In differentiating parietal from visceral pleural invasion, ROC analysis revealed that Rarea was optimal for differentiating parietal from visceral pleural invasion, and the highest accuracy of 77% was obtained with a cut-off value of 13.4 for this criterion. CONCLUSIONS: Computer-aided 3D CT analysis of the pleura was useful for predicting pleural invasion.
RATIONALE AND OBJECTIVES: To evaluate the clinical utility of three-dimensional (3D) computed tomography (CT) for predicting pleural invasion by peripheral lung cancer. MATERIALS AND METHODS: CT findings (tumor size, vertical diameter, length and area of the interface between tumor and the pleura, ratios of length and area [Rarea] of interface between tumor and the pleura to tumor size, angle between the tumor and adjacent pleura, presence or absence of pleural thickening, and originally developed 3D pleural patterns) in 201 consecutive patients with lung cancer of ≤3 cm in contact with pleural surface were correlated with pathologic findings. Logistic modeling was used for determining the significant factors for prediction of pleural invasion, and receiver operating characteristic (ROC) curves were used for investigating diagnostic capability of significant factors, resulting in a recommendation to the optimal criteria for predicting pleural invasion and to the optimal threshold for differentiating parietal from visceral invasion. RESULTS: Sixty-one (30%) of the 201 patients had pathologically verified pleural invasion. Logistic modeling revealed that the 3D pleural pattern was the only significant factor (P < .001; relative risk of 7.34). Among every combination of the 3D patterns, skirt-like pattern showed the highest accuracy of 77% for predicting pleural invasion. In differentiating parietal from visceral pleural invasion, ROC analysis revealed that Rarea was optimal for differentiating parietal from visceral pleural invasion, and the highest accuracy of 77% was obtained with a cut-off value of 13.4 for this criterion. CONCLUSIONS: Computer-aided 3D CT analysis of the pleura was useful for predicting pleural invasion.
Authors: Juheon Lee; Yi Cui; Xiaoli Sun; Bailiang Li; Jia Wu; Dengwang Li; Michael F Gensheimer; Billy W Loo; Maximilian Diehn; Ruijiang Li Journal: Eur Radiol Date: 2017-08-07 Impact factor: 5.315
Authors: Benedikt H Heidinger; Ursula Schwarz-Nemec; Kevin R Anderson; Constance de Margerie-Mellon; Antonio C Monteiro Filho; Yigu Chen; Marius E Mayerhoefer; Paul A VanderLaan; Alexander A Bankier Journal: Radiol Cardiothorac Imaging Date: 2019-08-29