First preliminary results of artificial intelligence-generated, explainable treatment recommendations for renal cell cancer based on multidisciplinary cancer conferences.

Abstract

465 Background: The expert panel in multidisciplinary cancer conferences (MCC) decides on the best available treatment for the individual cancer patient. To support these complex evidence-based decisions, an artificial intelligence (AI) was developed to generate treatment recommendations for renal cell cancer (RCC) patients to support decision-making in MCC. Methods: We have transformed comprehensive patient data (99 individual machine-readable features) of 880 MCC recommendations for RCC from the years 2015 - 2022 into representations that can be used in software development. We developed a two-step process in order to train classifiers to mimic MMC recommendations. First, we identified superordinate categories of the recommendations. Afterwards, we specified the detailed recommendation. For this purpose, we used different machine learning (CatBoost, XGBoost, Random Forest) and deep learning (SoftOrdering-1d-CNN) approaches with 787 training cases and 93 test set cases. Accuracy weights are determined by F1-Score. Results: The KITTU-AI is able to generate fully automated treatment recommendations for patients with histologically confirmed RCC in MCC. First, the AI can decide which kind of superordinate recommendation should be applied, e.g. surgery or anticancer-drugs (Table). Second, our AI system is able to suggest the specific surgical treatment as well as the correct drugs (Table). The AI-generated recommendation is presented explainable based on the clinical features and their importance score. Conclusions: To our knowledge, we present the first time data for fully automated AI-based treatment recommendations for MMC in RCC with promising accuracy rates.Our selected AI architecture is able to learn and to generate medically comprehensible and explainable treatment recommendations. Small numbers of recommended therapies hamper AI training but this will improve with increasing numbers over time. Next, clinical trial data will be implemented to enable a higher level of explainability. Meanwhile, the first prospective validation is ongoing. Accuracy rates for AI-generated treatment recommendations of renal cell cancer based on F1-scores (test set of 93 cases). Task (number) F1-Score ↑ #Classes Class (number) F1-Score High Level Prediction (93) 0.76 (CatBoost) 5 Surgery (10)Medication (45)Aftercare (22)Best supportive care (4)Radiotherapy (12) 0.450.920.880.000.47 Low Level Surgical Prediction (10) 0.81(Soft Ordering) 3 Primary tumor resection (4)Resection of recurrent tumor (1)Metastases resection (5) 0.670.000.73 Low LevelDrug Prediction (45) 0.73 (XGBoost) 8 Sunitinib (8)Nivolumab (7)Cabozantinib (6)Pembrolizumab/Axitinib (5)Nivolumab/Ipilimumab (4)Pazopanib (3)Pembrolizumab (3)Other (9) 0.520.780.400.890.601.001.000.67

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