Kong Jianqiu, Luo Mingli, Huang Yi, Lin Ying, Tan Kaiwen, Zou Yitong, Yong Juanjuan, Fu Sha, Zhang Shaoling, Fan Xinxiang, Lin Tianxin
Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, Guangdong, PR China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, Guangdong, PR China.
Eur J Endocrinol. 2025 Jan 6;192(1):61-72. doi: 10.1093/ejendo/lvae162.
Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with high recurrence rates and poor prognosis. Current prognostic models are inadequate, highlighting the need for innovative diagnostic tools. Pathomics, which utilizes computer algorithms to analyze whole-slide images, offers a promising approach to enhance prognostic models for ACC.
A retrospective cohort of 159 patients who underwent radical adrenalectomy between 2002 and 2019 was analyzed. Patients were divided into training (N = 111) and validation (N = 48) cohorts. Pathomics features were extracted using an unsupervised segmentation method. A pathomics signature (PSACC) was developed through LASSO-Cox regression, incorporating 5 specific pathomics features.
The PSACC showed a strong correlation with ACC prognosis. In the training cohort, the hazard ratio was 3.380 (95% CI, 1.687-6.772, P < .001), and in the validation cohort, it was 3.904 (95% CI, 1.039-14.669, P < .001). A comprehensive nomogram integrating PSACC and M stage significantly outperformed the conventional clinicopathological model in prediction accuracy, with concordance indexes of 0.779 versus 0.668 in the training cohort (P = .002) and 0.752 versus 0.603 in the validation cohort (P = .003).
The development of a pathomics-based nomogram for ACC presents a superior prognostic tool, enhancing personalized clinical decision making. This study highlights the potential of pathomics in refining prognostic models for complex malignancies like ACC, with implications for improving prognosis prediction and guiding treatment strategies in clinical practice.
肾上腺皮质癌（ACC）是一种罕见的侵袭性恶性肿瘤，复发率高且预后较差。目前的预后模型并不完善，凸显了对创新诊断工具的需求。病理组学利用计算机算法分析全切片图像，为改进ACC的预后模型提供了一种有前景的方法。
分析了2002年至2019年间接受根治性肾上腺切除术的159例患者的回顾性队列。患者被分为训练队列（N = 111）和验证队列（N = 48）。使用无监督分割方法提取病理组学特征。通过LASSO-Cox回归开发了一个病理组学特征（PSACC），纳入了5个特定的病理组学特征。
PSACC与ACC预后密切相关。在训练队列中，风险比为3.380（95%CI，1.687 - 6.772，P <.001），在验证队列中为3.904（95%CI，1.039 - 14.669，P <.001）。整合PSACC和M分期的综合列线图在预测准确性方面显著优于传统的临床病理模型，训练队列中的一致性指数分别为0.779和0.668（P =.002），验证队列中为0.752和0.603（P =.003）。
基于病理组学的ACC列线图的开发提供了一种优越的预后工具，增强了个性化临床决策。本研究突出了病理组学在完善ACC等复杂恶性肿瘤预后模型方面的潜力，对改善临床实践中的预后预测和指导治疗策略具有重要意义。
