Radiomic score for lung nodules as a prognostic biomarker in locally advanced rectal cancer patients: A bi‐institutional study

Abstract

Abstract Background Undetermined lung nodules are common in locally advanced rectal cancer (LARC) and lack precise risk stratification. This study aimed to develop a radiomic‐based score (Rad‐score) to distinguish metastasis and predict overall survival (OS) in patients with LARC and lung nodules. Methods Retrospective data from two institutions (July 10, 2006—September 24, 2015) was used to develop and validate the Rad‐score for distinguishing lung nodule malignancy. The prognostic value of the Rad‐score was investigated in LARC cohorts, leading to the construction and validation of a clinical and radiomic score (Cli‐Rad‐score) that incorporates both clinical and radiomic information for the purpose of improving personalized clinical prognosis prediction. Descriptive statistics, survival analysis, and model comparison were performed to assess the results. Results The Rad‐score demonstrated great performance in distinguishing malignancy, with C‐index values of 0.793 [95% CI: 0.729–0.856] in the training set and 0.730 [95% CI: 0.666–0.874] in the validation set. In independent LARC cohorts, Rad‐score validation achieved C‐index values of 0.794 [95% CI: 0.737–0.851] and 0.747 [95% CI: 0.615–0.879]. Regarding prognostic prediction, Rad‐score effectively stratified patients. Cli‐Rad‐score outperformed the clinicopathological information alone in risk stratification, as evidenced by significantly higher C‐index values (0.735 vs. 0.695 in the internal set and 0.618 vs. 0.595 in the external set). Conclusions CT‐based radiomics could serve as a reliable and powerful tool for lung nodule malignancy distinction and prognostic prediction in LARC patients. Rad‐score predicts prognosis independently. Incorporation of Cli‐Rad‐score significantly enhances the persionalized clinical prognostic capacity in LARC patients with lung nodules.

Keywords

• locally advanced rectal cancer
• lung nodules
• prognostic prediction
• radiomics