en Radiation Biology Radiation Biology تحقيق بديع Original Research <div style="text-align: justify;"><span style="font-size:10pt"><span style="text-justify:newspaper"><span style="text-kashida-space:50%"><span style="line-height:119%"><span style="font-family:Calibri"><span style="color:black"><span lang="en-US" style="font-size:9.0pt"><span style="font-family:Calibri"><span style="color:#1f497d"><span style="font-style:italic"><span style="font-weight:bold"><span style="language:en-US">Background:</span></span></span></span></span></span> <span lang="en-US" style="font-size:9.0pt"><span style="font-family:Calibri"><span style="language:en-US">Distinguishing focal interstitial fibrosis from pulmonary adenocarcinoma based on computed tomography characteristics is challenging. We investigated the computed tomography features of part-solid lung nodules to identify characteristics useful for differentiating focal interstitial fibrosis from pre-invasive lesions or invasive pulmonary adenocarcinomas. </span></span></span><span lang="en-US" style="font-size:9.0pt"><span style="font-family:Calibri"><span style="color:#1f497d"><span style="font-style:italic"><span style="font-weight:bold"><span style="language:en-US">Materials and Methods: </span></span></span></span></span></span><span lang="en-US" style="font-size:9.0pt"><span style="font-family:Calibri"><span style="language:en-US">Our research analyzed 182 part-solid lung nodules from 177 patients, comparing the computed tomography characteristics of focal interstitial fibrosis, pre-invasive lesions, and invasive pulmonary adenocarcinomas. Predictive factors for focal interstitial fibrosis were determined via binary logistic regression analysis. Predictive capability of the logistic regression model was assessed utilizing receiver operating characteristic curves. </span></span></span><span lang="en-US" style="font-size:9.0pt"><span style="font-family:Calibri"><span style="color:#1f497d"><span style="font-style:italic"><span style="font-weight:bold"><span style="language:en-US">Results: </span></span></span></span></span></span><span lang="en-US" style="font-size:9.0pt"><span style="font-family:Calibri"><span style="language:en-US">Invasive pulmonary adenocarcinoma was seen in 124 part-solid lung nodules, while 21 nodules showed focal interstitial fibrosis. Binary logistic regression analysis between focal interstitial fibrosis and pre-invasive lesions revealed that irregular shape and concentrated distribution of the solid portion were significantly associated with focal interstitial fibrosis. Binary logistic regression analysis between focal interstitial fibrosis and invasive pulmonary adenocarcinomas revealed that smaller lesion size, ill-defined lesion borders, and solid portion&rsquo;s well-defined borders were notable independent factors linked to focal interstitial fibrosis. The model using these three predictors to distinguish focal interstitial fibrosis from invasive pulmonary adenocarcinomas achieved a high receiver operating characteristic curve area of 0.845. </span></span></span><span lang="en-US" style="font-size:9.0pt"><span style="font-family:Calibri"><span style="color:#1f497d"><span style="font-style:italic"><span style="font-weight:bold"><span style="language:en-US">Conclusion: </span></span></span></span></span></span><span lang="en-US" style="font-size:9.0pt"><span style="font-family:Calibri"><span style="language:en-US">Focal interstitial fibrosis exhibited distinct computed tomography features compared to pre-invasive lesions or invasive pulmonary adenocarcinomas; the solid portion of part-solid lung nodules might serve as a valuable distinguishing feature.</span></span></span></span></span></span></span></span></span></div> Tomography, X-Ray computed, solitary pulmonary nodule, pulmonary fibrosis, adenocarcinoma of lung. 847 853 http://ijrr.com/browse.php?a_code=A-10-1-1406&slc_lang=en&sid=1 D. An 7900319475328460032157 7900319475328460032157 No Department of Medical Imaging Center, First Hospital of Qinhuangdao, Qinhuangdao, Hebei Province, China Z. Zou 7900319475328460032158 7900319475328460032158 No Department of Interventional Treatment, First Hospital of Qinhuangdao, Qinhuangdao, Hebei Province, China J. An 7900319475328460032159 7900319475328460032159 No Department of Interventional Treatment, First Hospital of Qinhuangdao, Qinhuangdao, Hebei Province, China Y. Tian tianye0917@163.com 7900319475328460032160 7900319475328460032160 Yes Department of Interventional Treatment, First Hospital of Qinhuangdao, Qinhuangdao, Hebei Province, China