Borhani Amir A, Zhang Peng, Diergaarde Brenda, Darwiche Sophie, Chuperlovska Kalina, Wang Stewart C, Schoen Robert E, Su Grace L
Northwestern University, Evanston, USA.
University of Pittsburgh, Pittsburgh, USA.
Abdom Radiol (NY). 2025 May;50(5):1907-1915. doi: 10.1007/s00261-024-04656-3. Epub 2024 Nov 2.
Imaging biomarkers are emerging as non-invasive predictors of cancer prognosis and clinical outcome. We assessed tumor-specific ("radiomics") and body composition imaging features ("morphomics") extracted from baseline pre-treatment CT for prediction of recurrence in patients with stage III colorectal cancer (CRC).
Patients with newly diagnosed stage III CRC were enrolled in this prospective observational study. Patients with available preoperative scans were included (N = 101). The tumor, if visible, was manually segmented and first-order radiomics features were extracted with a commercially available software. The morphomics features (reflecting muscle, fat, and bone characteristics) were extracted in a standardized fashion using a proprietary software and the values were adjusted and normalized based on a reference standard. Time to recurrence was the final outcome. Correlation between demographics, clinical features, radiomics, and morphomics features and outcome were assessed using univariate and multivariate tests as well as Kaplan-Meier and log-rank tests.
Morphomic analysis was performed in all 101 patients. 60 patients had discrete tumors suitable for radiomics analysis. These patients had lower ECOG score (p < 0.05), more muscle mass (p > 0.05), and lower fat density (p > 0.05) compared to the patients in whom radiomics analysis could not be performed. Pathological stage (HR: 2.69; p = 0.03), CEA level after surgery (HR: 1.11 for 1 ng/mL; p < 0.005), bone mineral density (HR: 1.01 for 1 Hounsfield Unit; p < 0.01), and tumor skewness (HR: 0.33 for 1 unit; p < 0.05) had association with recurrence based on both univariate and multivariate analyses. A model using Cox's regression analyses was able to divide the patients into low-, medium-, and high-risk for recurrence.
Both radiomics and morphomics features were independently associated with the risk of CRC recurrence and, when combined, each contributed valuable information to explain risk of recurrence.
Clinical trial.gov NCT02842203. Patient recruitment occurred between 22/07/2016 and 18/03/2020.
影像生物标志物正逐渐成为癌症预后和临床结局的非侵入性预测指标。我们评估了从基线治疗前CT中提取的肿瘤特异性（“放射组学”）和身体成分影像特征（“形态组学”），以预测III期结直肠癌（CRC）患者的复发情况。
新诊断为III期CRC的患者被纳入这项前瞻性观察性研究。纳入有术前扫描资料的患者（N = 101）。若肿瘤可见，则进行手动分割，并使用商用软件提取一阶放射组学特征。使用专用软件以标准化方式提取形态组学特征（反映肌肉、脂肪和骨骼特征），并根据参考标准对数值进行调整和归一化。复发时间为最终结局。使用单因素和多因素检验以及Kaplan-Meier和对数秩检验评估人口统计学、临床特征、放射组学和形态组学特征与结局之间的相关性。
对所有101例患者进行了形态组学分析。60例患者有适合进行放射组学分析的离散肿瘤。与无法进行放射组学分析的患者相比，这些患者的东部肿瘤协作组（ECOG）评分较低（p < 0.05），肌肉量较多（p > 0.05），脂肪密度较低（p > 0.05）。根据单因素和多因素分析，病理分期（风险比[HR]：2.69；p = 0.03）、术后癌胚抗原（CEA）水平（每增加1 ng/mL，HR：1.11；p < 0.005）、骨密度（每增加1亨氏单位，HR：1.01；p < 0.01）和肿瘤偏度（每增加1个单位，HR：0.33；p < 0.05）与复发相关。使用Cox回归分析的模型能够将患者分为低、中、高复发风险组。
放射组学和形态组学特征均与CRC复发风险独立相关，并且在联合使用时，各自都为解释复发风险提供了有价值的信息。
Clinical trial.gov NCT02842203。患者招募时间为2016年7月22日至2020年3月18日。
