More than meets the eye: predicting adrenocortical carcinoma outcomes with pathomics.

Abstract

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.