More than meets the eye: predicting adrenocortical carcinoma outcomes with pathomics
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View abstract on PubMed
Summary
This summary is machine-generated.Pathomics analysis of adrenal cortical carcinoma (ACC) provides a powerful new prognostic tool. A novel pathomics signature (PSACC) integrated into a nomogram significantly improves prediction accuracy for ACC patient outcomes.
Area Of Science
- Oncology
- Digital Pathology
- Medical Imaging Analysis
Background
- Adrenocortical carcinoma (ACC) is a rare, aggressive cancer with high recurrence and poor prognosis.
- Existing prognostic models for ACC are insufficient, necessitating advanced diagnostic tools.
- Pathomics, analyzing whole-slide images with algorithms, shows promise for improving ACC prognostication.
Purpose Of The Study
- To develop and validate a pathomics-based signature for predicting adrenocortical carcinoma prognosis.
- To assess the performance of a pathomics signature compared to conventional models.
- To create a nomogram integrating pathomics for enhanced clinical decision-making in ACC.
Main Methods
- Retrospective analysis of 159 patients undergoing radical adrenalectomy (2002-2019).
- Development of a pathomics signature (PSACC) using LASSO-Cox regression on 5 pathomics features extracted via unsupervised segmentation.
- Creation of a nomogram integrating PSACC and M stage.
Main Results
- The PSACC demonstrated a strong correlation with ACC prognosis in both training (HR 3.380) and validation (HR 3.904) cohorts.
- The pathomics-integrated nomogram significantly outperformed the conventional clinicopathological model in prediction accuracy.
- Concordance indexes for the pathomics nomogram were 0.779 (training) and 0.752 (validation), versus 0.668 and 0.603 for the conventional model.
Conclusions
- A pathomics-based nomogram offers a superior prognostic tool for adrenocortical carcinoma.
- This approach enhances personalized clinical decision-making and treatment strategies.
- Pathomics holds significant potential for refining prognostic models in complex malignancies.
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