Prieto-de-la-Lastra C, Carbonell-Asins J A, Bueno A, Gómez-Alderete A, Busto M, Alcolado-Jaramillo A B, Jimenez-Pastor A, Monzonís X, Cuñat A, Montagut C, Moreno-Ruiz P, Huerta M, Roda D, Gimeno-Valiente F, Estepa-Fernández A, Bellvís-Bataller F, Fuster-Matanzo A, Gibert J, Roselló S, Martinez-Ciarpaglini C, Vidal J, Alberich-Bayarri Á, Cervantes A, Tarazona N
Quantitative Imaging Biomarkers in Medicine, Quibim S.L., Madrid, Spain; Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Madrid, Spain.
Biostatistics Unit, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain.
ESMO Open. 2025 Jul 16;10(8):105495. doi: 10.1016/j.esmoop.2025.105495.
Accurately estimating relapse risk in localized colon cancer (LCC) remains a challenge, as clinicopathological staging often fails to differentiate patients with a higher likelihood of recurrence. There is a need for novel tools to improve patient selection for post-operative chemotherapy. Radiomics has emerged as a powerful, noninvasive approach that may enhance clinical decision making.
This retrospective study selected consecutive stage II and III LCC patients operated with curative intent from 2015 to 2017 in two academic institutions. Patients were assigned to either a training cohort made up of 80% of them or a test cohort, to further validate the initial findings. Penalized Cox proportional hazards and gradient boosted algorithms were designed to estimate time to relapse following a five-fold cross-validation process. Three models were assessed: (i) based only on clinical and pathological features, (ii) on radiomic features alone, and (iii) including clinical/pathological and radiomic variables. A new 'Risk Classification' score was generated based on the best risk assessment.
A total of 278 patients were included in both cohorts. The Cox model trained with clinical and imaging variables showed the highest prognostic power, with a C-index of 0.68 and a mean cumulative dynamic area under the curve (AUC) of 0.69 on the test set. Feature screening identified 20 variables, including clinical data, radiomics features, and fractal features. SHapley Additive exPlanations (SHAP) analysis highlighted factors related to geometry, vascular invasion, and tumor stage as significant variables related to relapse. The new 'Risk Classification' score was able to identify patients with high risk of relapse both in univariable [hazard ratio (HR) 14.22, 95% confidence interval (CI) 1.91-106.08, P = 0.010] and multivariable (HR 11.74, 95% CI, 1.54-89.34, P = 0.017) models.
Risk analysis revealed the new 'Risk Classification' variable as the one with the highest prognostic power compared with the ones currently used. Our findings suggest the potential for improved time-to-relapse estimation, enabling better patient stratification.
准确估计局部结肠癌（LCC）的复发风险仍然是一项挑战，因为临床病理分期常常无法区分复发可能性较高的患者。需要新的工具来改善术后化疗患者的选择。放射组学已成为一种强大的非侵入性方法，可能会增强临床决策。
这项回顾性研究选取了2015年至2017年在两家学术机构接受根治性手术的连续II期和III期LCC患者。患者被分配到由其中80%组成的训练队列或测试队列中，以进一步验证初始结果。采用惩罚Cox比例风险模型和梯度提升算法，通过五重交叉验证过程来估计复发时间。评估了三种模型：（i）仅基于临床和病理特征，（ii）仅基于放射组学特征，以及（iii）包括临床/病理和放射组学变量。基于最佳风险评估生成了一个新的“风险分类”评分。
两个队列共纳入278例患者。用临床和影像变量训练的Cox模型显示出最高的预后能力，在测试集上的C指数为0.68，平均曲线下累积动态面积（AUC）为0.69。特征筛选确定了20个变量，包括临床数据、放射组学特征和分形特征。SHapley加性解释（SHAP）分析突出了与几何形状、血管侵犯和肿瘤分期相关的因素是与复发相关的重要变量。新的“风险分类”评分在单变量[风险比（HR）14.22，95%置信区间（CI）1.91 - 106.08，P = 0.010]和多变量（HR 11.74，95% CI，1.54 - 89.34，P = 0.017）模型中都能够识别出复发风险高的患者。
风险分析表明，与目前使用的变量相比，新的“风险分类”变量具有最高的预后能力。我们的研究结果表明，在复发时间估计方面有改进的潜力，能够实现更好的患者分层。
